AI-generated summaries
Today's ML research,
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Daily summaries of the latest machine learning papers from arXiv, processed every 8 hours.
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The Significance of Style Diversity in Annotation-Free Synthetic Data Generation
NLP
Large Language Models
Generative Models
- Introduces an annotation-free framework for synthetic dialogue generation using intent definitions.
- Demonstrates that style diversity is more crucial than topic diversity for the utility of synthetic data.
- Presents two novel stylization models (Univ and Exam) for enhancing the linguistic style of generated dialogues.
- Achieves up to 93.3% accuracy compared to human-annotated data, showcasing the effectiveness of the proposed methods.
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The Significance of Style Diversity in Annotation-Free Synthetic Data Generation
Summary
This paper presents a novel framework for generating synthetic dialogue data for intent classification without relying on human-annotated seed data. The authors emphasize the importance of style diversity over topic diversity in enhancing the utility of synthetic data. The proposed framework utilizes intent definitions to generate dialogues and incorporates two types of attributes—topic and style—to improve data diversity. Additionally, two post-hoc stylization models, Univ and Exam, are introduced to transform generated utterances into varied, human-like styles. An LLM-as-a-judge filtering process is employed to ensure data quality. Experimental results indicate that the proposed approach achieves up to 93.3% of the performance of models trained on human-annotated data, highlighting the critical role of style diversity in preventing spurious correlations in model training. The findings suggest that incorporating style attributes during the generation process is more effective than adapting styles post-hoc.
Methodology
The authors developed a framework that generates synthetic dialogues based solely on intent definitions, categorizing attributes into topic and style. They implemented two stylization models to adapt the generated dialogues to human-like styles and utilized an LLM to filter out low-quality outputs, ensuring high data quality.
Results
The experimental results showed that the proposed framework achieved 90.7% and 93.3% accuracy on industrial and public datasets, respectively, compared to models trained on human-annotated data. The study confirmed that style diversity significantly enhances the utility of synthetic data, preventing models from learning spurious correlations.
Implications
This research has significant implications for industries requiring rapid adaptation of dialogue systems in new domains without the availability of human-annotated data. It suggests that focusing on style diversity can lead to more effective synthetic data generation, improving model performance in intent classification tasks.
Insulin4RL: Real-Time Insulin Management in the Intensive Care Unit for Offline Reinforcement Learning
Reinforcement Learning
- Insulin4RL dataset features real clinical trajectories with irregular inputs and actions.
- The dataset is derived from MIMIC-IV and includes over 375,000 labeled decisions.
- Traditional discretization of EHR data can lead to biased evaluations and maladaptive policies.
- The paper provides baseline performance metrics and a standardized evaluation protocol for ORL models.
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Insulin4RL: Real-Time Insulin Management in the Intensive Care Unit for Offline Reinforcement Learning
Summary
The paper introduces Insulin4RL, a novel offline reinforcement learning (ORL) dataset designed to enhance clinical decision-making in insulin management within Intensive Care Units (ICUs). Traditional ORL practices often rely on temporally discretized electronic health record (EHR) data, which can misrepresent complex clinical scenarios and lead to biased evaluations. Insulin4RL addresses this issue by providing a dataset derived from the MIMIC-IV database, featuring over 375,000 labeled decisions from 12,209 patients requiring insulin infusion titration. This dataset maintains the natural irregularity of clinical data, allowing for more realistic training and evaluation of ORL models. The authors describe the dataset's structure, present baseline performance metrics using model-free ORL techniques, and propose a standardized evaluation protocol. They also highlight the importance of evaluating model performance under realistic clinical conditions to avoid maladaptive policies that may arise from traditional discretization methods. The paper concludes with suggestions for future research directions that could leverage this dataset to improve ORL applications in healthcare.
Methodology
The authors developed the Insulin4RL dataset from the MIMIC-IV database, focusing on insulin titration in ICU patients. They employed model-free offline reinforcement learning techniques, including behavioral cloning and Q-learning methods, to establish baseline performance metrics. A standardized evaluation protocol using fitted Q-evaluation was also introduced to assess model robustness under realistic clinical conditions.
Results
Baseline experiments demonstrated that different temporal assumptions in the dataset could lead to divergent policies. The results indicated that models trained on the Insulin4RL dataset could better reflect the complexities of real-world clinical decision-making compared to those trained on traditionally discretized datasets.
Implications
The Insulin4RL dataset has the potential to significantly improve the training and evaluation of reinforcement learning models in healthcare, particularly for insulin management in critically ill patients. By providing a more realistic framework for model evaluation, it can lead to safer and more effective clinical decision-making tools.
Compositionality Emerges in a Narrow Depth-Connectivity Regime: Architecture Constraints and Solution Manifolds
Theory
Optimization
Generative Models
- Compositionality in neural networks emerges in a specific depth-connectivity regime.
- Sparse networks exhibit compositionality based on retained connections rather than just weight sparsity.
- The introduction of Similarity-based Pruning (SP) enhances compositional connectivity.
- A heuristic depth predictor identifies optimal depths for achieving compositionality.
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Compositionality Emerges in a Narrow Depth-Connectivity Regime: Architecture Constraints and Solution Manifolds
Summary
This paper investigates the emergence of compositionality in neural networks, which is crucial for generalization and robust performance. The authors identify a narrow regime of depth and connectivity where compositionality can be effectively realized. They demonstrate that compositionality is not merely a function of network sparsity but is significantly influenced by the specific connections retained in the network. The study introduces a new pruning algorithm, Similarity-based Pruning (SP), to enhance compositional connectivity and a heuristic depth predictor to identify optimal depths for compositionality. The findings are supported by a theoretical framework that explains the conditions under which compositional solutions are reachable, emphasizing the importance of both depth and connectivity in network architecture. The paper also presents EMC2-Bench, a standardized evaluation suite for measuring compositionality across different networks, highlighting the need for targeted interventions to achieve compositional representations in gradient-based training.
Methodology
The authors conducted empirical studies to map the dependence of compositionality on depth and connectivity. They introduced Similarity-based Pruning (SP) to recover compositional connectivity and developed a heuristic depth predictor. Additionally, they created EMC2-Bench, an evaluation suite for consistent measurement of compositionality across networks.
Results
The study found that compositionality peaks at intermediate depths and is sensitive to the specific connections retained in the network. Networks that violated the depth or connectivity conditions tended to converge to fractured solutions rather than compositional ones. The theoretical framework provided insights into the structural reachability of compositional solutions.
Implications
The findings suggest that careful architectural design and targeted interventions can significantly improve the compositional capabilities of neural networks, which is essential for enhancing generalization and robustness in various applications, including language models and vision-language models.
SL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models
Time Series
- SL-S4Wave combines contrastive learning with structured state space models for improved modeling of physiological waveforms.
- The framework demonstrates strong label efficiency, requiring fewer labeled examples for high performance.
- It effectively captures long-range dependencies and noise robustness in multichannel physiological signals.
- SL-S4Wave outperforms existing state-of-the-art methods in arrhythmia detection and generalizes well to EEG tasks.
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SL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models
Summary
The paper introduces SL-S4Wave, a self-supervised learning framework designed to model long-sequence physiological waveforms, such as ECG and EEG, using structured state space models (S4). Traditional self-supervised learning methods struggle with high-resolution, multichannel signals due to their inability to capture long-range dependencies and noise robustness. SL-S4Wave addresses these challenges by integrating contrastive learning with a novel encoder architecture that employs multi-layer global convolution with multiscale subkernels. This design allows the model to effectively capture both local patterns and long-range temporal dependencies in noisy data. The authors conduct extensive experiments on real-world datasets, demonstrating that SL-S4Wave outperforms state-of-the-art supervised and self-supervised methods in arrhythmia detection tasks, achieves high performance with fewer labeled examples, and maintains robust performance on long waveform segments. Additionally, the model shows effective transferability to unseen arrhythmia types and generalizability to EEG tasks, indicating its potential for broader applications in clinical settings.
Methodology
The authors propose a two-component approach: (1) S4Wave, a structured state space deep-learning model that enhances global convolution architectures with multiscale kernels, residual connections, and cross-channel modeling, and (2) a self-supervised pretraining framework that utilizes contrastive objectives to learn robust representations from unlabeled physiological waveforms.
Results
SL-S4Wave consistently outperformed state-of-the-art supervised and self-supervised baselines in arrhythmia detection tasks, showcasing high label efficiency and robust performance on long waveform segments. The model also demonstrated effective transferability to unseen arrhythmia types and superior performance on EEG tasks.
Implications
The findings suggest that SL-S4Wave could significantly enhance the automatic analysis of physiological signals in clinical settings, potentially improving patient monitoring and diagnosis by reducing reliance on labeled data and increasing the robustness of models to noise.
Compute Efficiency and Serial Runtime Tradeoffs for Stochastic Momentum Methods
Optimization
Theory
Efficient ML
- Stochastic momentum methods like HB and ASGD have distinct impacts on compute efficiency and serial runtime.
- HB maintains SGD-level compute efficiency over a larger batch-size window, allowing for reduced serial runtime.
- ASGD shows improved compute efficiency for small batches but trades this off for better serial runtime at larger batch sizes.
- The study provides theoretical lower bounds for the performance of HB and ASGD under various spectral conditions.
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Compute Efficiency and Serial Runtime Tradeoffs for Stochastic Momentum Methods
Summary
This paper investigates the tradeoffs between compute efficiency (CE) and serial runtime in stochastic momentum methods, specifically focusing on heavy ball (HB) and accelerated stochastic gradient descent (ASGD) for consistent linear regression with Gaussian covariates. The authors establish lower bounds on batch-size tradeoffs, revealing that while HB does not enhance the CE frontier over standard SGD, it allows for larger batch sizes to reduce serial runtime until it reaches a deterministic accelerated scale. The study shows that for rapidly decaying power-law spectra, ASGD can improve CE over HB/SGD at small batch sizes, but this advantage diminishes as batch size increases, leading to a tradeoff between CE and serial runtime. The findings are supported by synthetic linear regression experiments that validate the theoretical predictions regarding the performance of these methods across different batch sizes and spectra.
Methodology
The authors analyze the performance of stochastic momentum methods through theoretical lower bounds and synthetic experiments. They focus on the implications of batch size on compute efficiency and serial runtime, particularly under different spectral conditions. The analysis includes a comparison of HB and ASGD in the context of linear regression with Gaussian covariates.
Results
The results indicate that HB does not improve the compute efficiency frontier over SGD but extends the batch-size window for reduced serial runtime. For ASGD, the performance varies with the spectral decay rate: it outperforms HB/SGD in small-batch scenarios but loses this advantage as batch size increases. The experiments corroborate the theoretical findings, demonstrating the predicted tradeoffs in performance.
Implications
The findings have significant implications for the optimization of training deep neural networks, particularly in large-scale settings where batch size is critical. Understanding the tradeoffs between compute efficiency and serial runtime can inform the choice of optimization algorithms in practical applications, potentially leading to more efficient training processes.
Bayesian Anytime Pareto Set Identification for Multi-Objective Multi-Armed Bandits
Optimization
Theory
Efficient ML
- Introduction of TTPFTS, the first Bayesian anytime algorithm for MOMAB PSI.
- Demonstrated efficiency gains in molecular discovery applications compared to traditional methods.
- Development of a new uncertainty quantification metric for Bayesian MOMAB PSI algorithms.
- Empirical validation against state-of-the-art algorithms on synthetic benchmarks.
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Bayesian Anytime Pareto Set Identification for Multi-Objective Multi-Armed Bandits
Summary
This paper presents the Top-Two Pareto Front Thompson Sampling (TTPFTS), the first anytime algorithm for the Multi-Objective Multi-Armed Bandit (MOMAB) Pareto Set Identification (PSI) problem, utilizing a Bayesian framework. The authors benchmark TTPFTS against leading fixed-budget algorithms on synthetic datasets and demonstrate its effectiveness in a real-world application involving molecular discovery. The algorithm not only identifies Pareto optimal solutions efficiently but also introduces a novel uncertainty quantification metric that assesses the algorithm's confidence in its predictions. The empirical results show that TTPFTS significantly outperforms traditional exhaustive virtual screening methods and state-of-the-art active learning techniques. Additionally, the paper provides a theoretical proof of the algorithm's asymptotic correctness, establishing a solid foundation for its application in complex decision-making scenarios.
Methodology
The authors developed the TTPFTS algorithm, which employs Thompson Sampling to explore the Pareto front by focusing on the top two arms. The algorithm is benchmarked against existing fixed-budget methods and validated in a practical setting involving molecular libraries. A novel uncertainty quantification metric is introduced to gauge the algorithm's confidence in its predictions without requiring ground truth.
Results
TTPFTS outperformed existing fixed-budget algorithms in synthetic environments and showed significant efficiency improvements in real-world molecular discovery tasks. The uncertainty quantification metric effectively predicted the algorithm's performance, providing a reliable means of monitoring learning progress.
Implications
The findings suggest that TTPFTS can be effectively utilized in various multi-objective optimization scenarios, such as materials discovery, clinical trials, and drug development, where efficient exploration of large solution spaces is critical.
SAGE: Retain-Aware Post-Hoc Sanitization of Final Unlearning Vector
Large Language Models
Optimization
Efficient ML
- SAGE provides a post-hoc solution for improving retention in unlearning processes without rerunning original pipelines.
- The method quantifies retention damage using retention activation bias and applies spectral sanitization to the final update vector.
- Empirical results show a consistent improvement in the retain-forget trade-off across multiple unlearning methods and model sizes.
- SAGE achieves an average retention capability increase of 26.3% while maintaining effective unlearning.
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SAGE: Retain-Aware Post-Hoc Sanitization of Final Unlearning Vector
Summary
The paper addresses the challenge of unlearning in Large Language Models (LLMs), where the goal is to remove undesirable knowledge while preserving retained capabilities. Existing unlearning methods often struggle with a trade-off between forgetting and retention. The authors introduce SAGE (Spectral Activation-GEometry Sanitization), a post-hoc method that sanitizes the final update vector of any unlearning process without needing to rerun the original unlearning pipeline. SAGE leverages retention activation bias to quantify the damage inflicted on retention by unlearning methods and aims to restore retention performance. The method involves collecting real module inputs from a small retain proxy, extracting their dominant activation geometry, and applying a closed-form optimization to suppress update components that align with high-energy retained directions while preserving the forgetting carrier. The empirical results demonstrate that SAGE significantly improves the retention capability across various unlearning methods and model scales, achieving an average increase of 26.3% in retention capability while enhancing unlearning performance. This highlights the potential of post-hoc sanitization as a viable approach in machine unlearning.
Methodology
SAGE collects module-level input activations from a small retain proxy and uses truncated Singular Value Decomposition (SVD) to identify a stable low-rank subspace. It then applies a closed-form optimization to suppress components that negatively impact retention while preserving the original forgetting signal.
Results
SAGE consistently improves retention capability by an average of 26.3% across various unlearning methods and model scales, demonstrating enhanced performance in balancing retention and forgetting.
Implications
The findings suggest that post-hoc sanitization can be a practical approach to improve machine unlearning processes, potentially enhancing safety, privacy, and compliance in applications involving LLMs.
Constrained hybrid modelling to predict microbial dynamics and organic matter turnover in soil systems
Theory
Optimization
- Introduction of HySoMi, a hybrid modeling framework for soil carbon cycling predictions using genomic data.
- Integration of ecological constraints into the model to ensure realistic predictions of microbial dynamics.
- Demonstrated improved performance over traditional models, even with small training datasets.
- Effective learning of dynamics for unmeasurable components of the soil model.
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Constrained hybrid modelling to predict microbial dynamics and organic matter turnover in soil systems
Summary
This paper presents a novel hybrid modeling framework, HySoMi, designed to predict microbial dynamics and organic matter turnover in soil systems by integrating genomic data with process-based soil models. The authors highlight the critical role of soil microorganisms in carbon cycling and the challenges associated with parameterizing complex soil models due to the unavailability of direct measurements for many microbial processes. HySoMi employs a neural network to derive biokinetic parameters from metagenome-inferred functional traits, while also incorporating constraints from ecological theory to ensure realistic model behavior. The evaluation of HySoMi on both synthetic and real datasets demonstrates its superior performance compared to traditional models, particularly in scenarios with limited data. The framework effectively learns the dynamics of unmeasurable components, showcasing its potential for advancing soil carbon cycling predictions and enhancing our understanding of microbial contributions to soil health and climate change mitigation.
Methodology
The HySoMi framework combines a process-based soil model with a neural network to learn the mapping from genomic data to biokinetic parameters. It incorporates ecological constraints into the loss function to ensure realistic behavior of non-observed state variables, addressing the challenges of parameter estimation in soil models.
Results
HySoMi outperformed both unconstrained and non-hybrid approaches across various experiments, including evaluations on synthetic datasets of varying complexity and real data. The model effectively learned the dynamics of unmeasurable components, demonstrating its robustness even with small training datasets.
Implications
The HySoMi framework has significant implications for improving predictions of soil carbon dynamics, which are crucial for understanding climate change impacts. It provides a new approach to integrate genomic data into soil modeling, potentially enhancing soil management practices and informing climate mitigation strategies.
Neural Additive and Basis Models with Feature Selection and Interactions
Interpretability
Efficient ML
Theory
- Introduction of a feature selection mechanism in NAM and NBM to enhance computational efficiency.
- Ability to handle high-dimensional datasets and capture feature interactions effectively.
- Demonstrated better or comparable performance against existing GAMs and other models.
- Maintains high interpretability while improving throughput over traditional NAM and NBM.
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Neural Additive and Basis Models with Feature Selection and Interactions
Summary
This paper addresses the challenges of interpretability and computational efficiency in deep neural networks (DNNs) by enhancing neural additive models (NAM) and neural basis models (NBM) with a feature selection mechanism. While NAM and NBM are known for their interpretability and flexibility in modeling, they struggle with high-dimensional datasets and feature interactions due to increased computational demands. The authors propose incorporating a feature selection layer that updates selection weights during training, allowing for reduced computational costs and model sizes. This innovation enables the effective use of two-input neural networks for capturing feature interactions even in high-dimensional contexts. The experimental results demonstrate that the proposed models, termed NAM-FS and NBM-FS, outperform or match the performance of existing generalized additive models (GAMs) while maintaining interpretability. The study highlights the importance of feature selection in enhancing model efficiency and accuracy, particularly in applications requiring high reliability.
Methodology
The authors incorporated a feature selection layer into NAM and NBM, allowing for dynamic selection of features during training. This approach reduces the number of shape functions, thereby alleviating computational bottlenecks associated with high-dimensional datasets. The models were evaluated against existing GAMs and other interpretable models using high-dimensional classification datasets.
Results
The proposed models, NAM-FS and NBM-FS, demonstrated improved computational efficiency and maintained or exceeded the predictive performance of traditional NAM and NBM, as well as other state-of-the-art GAMs. The feature selection mechanism proved effective in enhancing model throughput and accuracy.
Implications
The findings suggest that integrating feature selection into interpretable models like NAM and NBM can significantly enhance their applicability in high-dimensional settings, making them more suitable for critical domains such as healthcare and finance where interpretability and reliability are paramount.
RouteJudge: An Open Platform for Reproducible and Preference-Aware LLM Routing
Large Language Models
NLP
Efficient ML
- RouteJudge shifts the evaluation focus from model-level response quality to router-level decision quality.
- The platform allows for preference-aware evaluations through anonymous pairwise comparisons of model responses.
- ORBIT provides a standardized workflow for developing and assessing LLM routing algorithms.
- The framework supports continuous expansion of routing methods and encourages reproducibility in evaluations.
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RouteJudge: An Open Platform for Reproducible and Preference-Aware LLM Routing
Summary
RouteJudge introduces a novel online framework for evaluating large language model (LLM) routing systems, addressing the limitations of existing evaluation methods that rely on static benchmarks and fixed notions of response quality. The platform emphasizes router-level decision quality by allowing multiple routing strategies to recommend candidate models for user queries, which are then assessed through anonymous pairwise comparisons. This approach captures user preferences, enabling a more nuanced evaluation of routing effectiveness that considers cost, latency, and task-specific factors. Additionally, the paper presents ORBIT, a modular toolbox that standardizes the workflow for LLM routing, facilitating the development and evaluation of routing algorithms under consistent protocols. Together, RouteJudge and ORBIT create an open ecosystem for researchers to validate and test routing methods in both offline and online settings, ultimately enhancing the deployment of LLMs in real-world applications.
Methodology
RouteJudge employs an online pairwise preference evaluation framework where multiple routing strategies recommend models for user queries. Users compare the outputs of selected models anonymously, and preferences are recorded alongside metadata such as costs and latencies. ORBIT serves as a modular toolbox that standardizes the end-to-end workflow for LLM routing, including benchmark loading and budget-aware evaluation.
Results
The implementation of RouteJudge and ORBIT allows for a more comprehensive evaluation of LLM routing systems, capturing user preferences and enabling cost-aware analysis. The framework facilitates the development of new routing methods and provides a platform for their online evaluation against real user preferences.
Implications
RouteJudge and ORBIT can significantly enhance the deployment of LLMs in practical applications by ensuring that routing decisions align with user preferences. This can lead to improved user satisfaction and more efficient use of computational resources in LLM applications.
A Hybrid LSTM--Vision Transformer Architecture for Predicting HRRR Forecast Errors
Time Series
Multimodal
- The hybrid LSTM-ViT framework improves forecast-error prediction skill compared to baseline LSTM models.
- Incorporating vertically resolved atmospheric profiles enhances the model's ability to capture complex PBL processes.
- The model achieves significant improvements in predicting precipitation forecast errors, with up to a twofold increase in predictive skill.
- The approach is applicable across diverse forecasting environments due to the availability of dense surface observations.
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A Hybrid LSTM--Vision Transformer Architecture for Predicting HRRR Forecast Errors
Summary
This paper presents a novel hybrid architecture combining Long Short-Term Memory (LSTM) networks and Vision Transformers (ViT) to enhance the prediction of forecast errors in the High-Resolution Rapid Refresh (HRRR) numerical weather prediction (NWP) model. The authors identify that forecast errors are often linked to unresolved processes in the planetary boundary layer (PBL) and propose that incorporating vertically resolved atmospheric profiles can improve prediction accuracy. The LSTM-ViT framework integrates temporal sequence learning from surface observations with atmospheric profiles from the New York State Mesonet profiler network. The model is trained to predict hourly errors in precipitation, wind speed, and temperature forecasts. Results indicate that the hybrid architecture significantly outperforms the baseline LSTM model, particularly in capturing convective error evolution and reducing degradation during complex PBL conditions. The findings suggest that combining temporal and vertical information can provide a more robust approach to forecasting errors in operational NWP systems, thereby offering improved guidance for forecasters regarding model biases and forecast confidence.
Methodology
The authors developed a hybrid LSTM-ViT framework that combines LSTM's temporal sequence learning capabilities with ViT's attention mechanisms to process vertically resolved atmospheric data. The model was trained using surface observations and atmospheric profiles from the New York State Mesonet to predict forecast errors in precipitation, wind speed, and temperature.
Results
The LSTM-ViT framework demonstrated improved predictive skill across all three forecast error types, with the most significant gains observed at shorter lead times and during periods of enhanced PBL activity. Specifically, the model achieved approximately a twofold increase in predictive skill for precipitation forecast errors compared to the baseline LSTM.
Implications
The findings suggest that integrating vertically informed attention mechanisms into forecasting models can lead to more accurate predictions of weather forecast errors. This has practical implications for operational NWP systems, providing forecasters with better tools for assessing model biases and improving forecast confidence.
Connect the Dots: Training LLMs for Long-Lifecycle Agents with Cross-Domain Generalization Via Reinforcement Learning
Large Language Models
Reinforcement Learning
- Introduction of the CoD framework for training LLMs as long-lifecycle agents.
- Emphasis on the meta-capability of continuous learning and adaptation in dynamic environments.
- Development of a specialized RL algorithm for effective credit assignment.
- Demonstrated improvements in task-solving performance through empirical results.
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Connect the Dots: Training LLMs for Long-Lifecycle Agents with Cross-Domain Generalization Via Reinforcement Learning
Summary
This paper introduces a novel framework called 'Connect the Dots' (CoD) aimed at training large language models (LLMs) to function as long-lifecycle agents capable of continuous learning and adaptation in dynamic environments. The CoD framework emphasizes the meta-capability of LLMs to solve a sequence of related tasks while exploring their environment and updating their contextual understanding. The authors propose an end-to-end reinforcement learning (RL) approach that interleaves task-solving and context-updating episodes, facilitating the development of this meta-capability. Key components of the framework include a specially designed RL algorithm for credit assignment across episodes and tailored tasks and environments that promote the desired learning outcomes. Empirical results demonstrate the effectiveness of the CoD framework, showing significant improvements in task-solving performance and the potential for cross-domain generalization. The authors also provide implementations to support further research and applications in this area.
Methodology
The authors designed a general framework for CoD that includes an RL algorithm tailored for long rollout sequences, interleaving episodes for solving tasks and updating context. They created specific tasks and environments to incentivize the targeted meta-capability during training and to measure progress during evaluation. The empirical validation involved applying the CoD-Train process to a specific LLM on tailored environments.
Results
The empirical results showed that the success rate of the LLM in solving tasks improved significantly when conditioned on self-updated context, with rates increasing from 28% to 76% for sequential tasks. The framework also demonstrated potential for out-of-distribution generalization across different domains.
Implications
The CoD framework opens new avenues for enhancing LLM capabilities in real-world applications, particularly in scenarios requiring long-term deployment and continual learning. It suggests that LLMs can be trained to adapt and improve over time, potentially reducing the need for human intervention in dynamic environments.
Evolutionary Two-Stage Hyperparameter Optimization Strategies for Physics-Informed Neural Networks
Optimization
- Introduces a two-stage evolutionary optimization strategy for hyperparameter tuning in PINNs.
- Demonstrates that evolutionary algorithms outperform classical methods like Bayesian optimization and grid search.
- Establishes guidelines for optimal budget distribution between exploration and exploitation phases.
- Achieves significant improvements in solution accuracy with constrained computational resources.
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Evolutionary Two-Stage Hyperparameter Optimization Strategies for Physics-Informed Neural Networks
Summary
This paper addresses the challenges of training Physics-Informed Neural Networks (PINNs), which are used to solve Partial Differential Equations (PDEs) by incorporating physical laws into their training. The authors highlight the issues of unstable convergence and sensitivity to hyperparameters that hinder the performance of PINNs. To tackle these issues, they propose a two-stage hyperparameter optimization strategy utilizing evolutionary algorithms. The first stage involves low-fidelity training runs to quickly screen candidate hyperparameter configurations, treating the selection process as a black-box optimization problem. In the second stage, the most promising configurations are fully trained using standard gradient-based optimizers. The proposed method is evaluated on three benchmark PDE problems: Advection, Klein–Gordon, and Helmholtz equations, demonstrating that it consistently outperforms traditional training methods and achieves lower mean errors within fixed computational budgets. The findings suggest that the two-stage approach can significantly enhance the robustness and accuracy of PINNs, providing practical guidelines for hyperparameter tuning in complex physical systems.
Methodology
The authors propose a two-phase optimization strategy that integrates evolutionary algorithms into the training of PINNs. The first phase involves conducting low-fidelity training runs to evaluate various hyperparameter configurations quickly. The second phase focuses on fully training the most promising configurations using gradient-based optimizers. The study compares different evolutionary algorithms, including JADE, LSHADE, Grey Wolf, and Whales, against classical hyperparameter tuning methods.
Results
The proposed two-stage optimization strategy consistently outperformed standard training methods across the evaluated PDE problems, achieving significantly lower mean errors. The results indicate that an exploration budget of approximately 10% of standard training can lead to an average improvement of about 40% in baseline error values under fixed computational budgets.
Implications
The findings provide a systematic approach to hyperparameter tuning in PINNs, reducing reliance on manual tuning and enhancing the robustness of solutions in complex physical systems. This methodology could be applied to various fields where PINNs are utilized, such as fluid dynamics, heat transfer, and quantum mechanics.
Algebraic Dead Directions in LayerNorm Transformers: A Forward-Pass-Only Diagnostic at LLM Scale
Theory
Large Language Models
Optimization
- Introduces a forward-pass-only method to identify dead directions in LayerNorm transformers.
- Derives a closed-form expression for the dead direction based on the LayerNorm scale parameter.
- Validates the method across 14 pretrained transformers, achieving high accuracy in predictions.
- Demonstrates that training increases the depth of dead directions significantly.
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Algebraic Dead Directions in LayerNorm Transformers: A Forward-Pass-Only Diagnostic at LLM Scale
Summary
This paper introduces a novel diagnostic method for identifying dead directions in LayerNorm transformers without requiring a forward or backward pass. Dead directions are parameter space directions where the Fisher information metric degenerates, indicating flatness in the loss landscape. The authors derive a closed-form expression for the dead direction in LayerNorm transformers, specifically the inverse-scale direction of the LayerNorm affine parameter, which serves as an algebraic kernel for the activation covariance. This method allows for the identification of dead directions using only the LayerNorm scale parameter, validated across 14 pretrained transformers, including both LayerNorm and RMSNorm architectures. The findings demonstrate that at random initialization, the predicted dead direction aligns closely with the measured bottom singular direction in LayerNorm models, while RMSNorm models do not exhibit such a direction. Furthermore, the study reveals that training significantly deepens the covariance eigenvalue along this direction, indicating the opening of additional dead directions. The results suggest that the presence of the kernel direction can classify a transformer's normalization type based solely on its parameters.
Methodology
The authors utilize a theoretical framework to derive the dead direction in LayerNorm transformers, focusing on the inverse-scale direction of the LayerNorm affine parameter. They validate their findings through empirical testing on 14 pretrained models, comparing predicted dead directions with measured singular directions using cosine similarity metrics.
Results
The predicted dead direction matches the measured bottom singular direction to four decimal places in all LayerNorm models tested. In RMSNorm models, the predicted direction is absent, confirming the theoretical predictions. Additionally, the covariance eigenvalue along the dead direction deepens by approximately 1000 times after training, indicating the emergence of further dead directions.
Implications
This work provides a new diagnostic tool for understanding the loss landscape of transformers, which could enhance model training strategies and architecture design. It also offers insights into the behavior of different normalization techniques in neural networks.
Artemis: Anatomy-Resolved inTervention for Eliminating Multimodal NeuroImage confounderS
Graph Learning
Multimodal
- Artemis addresses region-specific demographic confounding in multimodal neuroimaging.
- The framework provides a lightweight, plug-in intervention module for existing GNN architectures.
- Significant improvements in predictive accuracy and AUC metrics were observed across multiple clinical benchmarks.
- The approach enhances the interpretability of GNN models in clinical neuroscience.
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Artemis: Anatomy-Resolved inTervention for Eliminating Multimodal NeuroImage confounderS
Summary
The paper introduces Artemis, a novel framework designed to address demographic confounding in multimodal neuroimaging data, particularly when using graph neural networks (GNNs) for clinical predictions. Traditional GNN approaches often overlook the influence of demographic factors such as age and sex, which can lead to biased predictions and misinterpretations in clinical settings. Artemis tackles this issue by implementing region-level causal interventions that adjust for demographic confounders specific to each brain region. This is achieved through a lightweight module that integrates with existing GNN architectures, allowing for region-specific confounder representations. The framework employs a shared multilayer perceptron to map demographic data to per-region confounder embeddings and utilizes an exponential-moving-average memory bank to maintain a running estimate of the confounder distribution. The authors validate Artemis across three clinical benchmarks—ADNI, OASIS, and HCP—demonstrating significant improvements in prediction accuracy and area under the curve (AUC) metrics compared to standard GNN baselines. The results highlight the importance of accounting for demographic factors in neuroimaging analyses and suggest that Artemis can enhance the robustness and interpretability of GNN-based models in clinical applications.
Methodology
Artemis employs a region-level causal intervention framework that utilizes a shared multilayer perceptron to create region-specific confounder embeddings from demographic data. It incorporates an exponential-moving-average memory bank to estimate the population confounder distribution for each region, allowing for effective backdoor adjustment. This framework is designed to be compatible with various GNN architectures, making it a flexible addition to existing models.
Results
Artemis outperformed ten representative GNN-based baselines across three clinical benchmarks: achieving accuracy improvements of +20.9%, +27.9%, and +7.8%, and AUC improvements of +26.2%, +34.2%, and +8.0% on the ADNI, HCP, and OASIS datasets, respectively.
Implications
The findings suggest that incorporating demographic adjustments in neuroimaging analyses can lead to more accurate and interpretable models, potentially improving clinical decision-making and understanding of brain disorders. Artemis could serve as a valuable tool for researchers and clinicians working with multimodal neuroimaging data.
Bounded Context Management for Tabular Foundation Models on Stream Learning
Theory
Efficient ML
Time Series
- Introduces a future-information view for context management in tabular stream learning.
- Proposes CURE, a context management policy that enhances prediction accuracy.
- Demonstrates up to 27.0% relative improvement over classical stream learners.
- CURE maintains robustness across multiple TFM architectures.
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Bounded Context Management for Tabular Foundation Models on Stream Learning
Summary
This paper addresses the challenges of tabular stream learning, where predictions must be made on sequentially arriving examples under distribution shifts. Traditional methods focus on model state updates, while the authors propose using tabular foundation models (TFMs) that condition predictions on a labeled context. The core challenge shifts to managing this context effectively. The authors introduce a future-information view that outlines three key requirements for context management: preserving recent examples, retaining uncertain examples, and removing redundant examples. They develop CURE (Context management via Uncertainty-aware admission and Redundancy-aware Eviction), a policy that implements these requirements through entropy-gated admission and redundancy-aware eviction. The effectiveness of CURE is demonstrated across seven different data streams, showing significant improvements over classical stream learners and robustness across various TFM backbones.
Methodology
The authors propose a context management policy called CURE, which employs an entropy-gated admission mechanism to retain examples that provide high future information and a redundancy-aware eviction strategy to remove less informative examples. The policy is evaluated using a prequential protocol on seven different data streams, focusing on the balance between recent, uncertain, and redundant examples.
Results
CURE achieves the best prequential accuracy across seven streams, with improvements of up to +19.59 points compared to classical stream-learning baselines. It consistently outperforms other policy variants and demonstrates robustness across multiple TFM backbones.
Implications
The findings suggest that effective context management can significantly enhance the performance of tabular foundation models in stream learning scenarios. This approach could be applied in various real-time prediction tasks where data arrives sequentially and distribution shifts occur, such as financial forecasting, online recommendation systems, and adaptive learning systems.
Manifold Bandits: Bayesian Curriculum Learning over the Latent Geometry of Large Language Models
Large Language Models
Reinforcement Learning
Optimization
- Introduces Bayesian Manifold Curriculum (BMC) for structured problem sampling in RL for LLMs.
- Frames problem sampling as a manifold-structured bandit problem, capturing the relationships between tasks.
- Demonstrates the importance of balancing productivity, diversity, and utility in training strategies.
- Develops latent task trees to represent the hierarchical structure of task relationships.
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Manifold Bandits: Bayesian Curriculum Learning over the Latent Geometry of Large Language Models
Summary
This paper addresses the challenge of efficient training in reinforcement learning (RL) for large language models (LLMs) by proposing a novel framework called Bayesian Manifold Curriculum (BMC). Traditional adaptive curriculum learning methods often focus on selecting prompts of intermediate difficulty, treating problem selection as a standard bandit problem. However, this approach neglects the structured nature of the task space and the relationships between problems. The authors frame problem sampling as a manifold-structured bandit problem, where the model's latent representation space influences the sampling decisions. BMC organizes problems into a hierarchical task tree and employs Bayesian learning to guide the sampling process. The authors conduct empirical analyses to demonstrate that different sampling strategies yield trade-offs between productivity, diversity, and utility, emphasizing that merely prioritizing difficulty is insufficient for optimal performance. The paper introduces three key contributions: the development of latent task trees, the BMC framework, and an analysis of the productivity-diversity-utility trade-offs in problem selection.
Methodology
The authors propose a framework that organizes problems into a hierarchical task tree based on model embeddings, allowing for a multi-scale representation of task relationships. BMC employs Bayesian decision-making to guide the sampling of problems, taking into account the non-stationary dynamics induced by the model's learning process. The framework is evaluated through empirical analyses that assess the trade-offs between productivity, diversity, and utility in problem selection.
Results
The empirical results indicate that different sampling strategies lead to significant trade-offs among productivity (the learning signal), diversity (coverage of the task manifold), and utility (evaluation relevance). The findings suggest that a focus solely on difficulty does not guarantee strong downstream performance, underscoring the necessity of incorporating structural and type-aware considerations into problem sampling.
Implications
The proposed BMC framework has the potential to enhance the training efficiency of large language models by improving the selection of training problems. This could lead to better generalization and performance in various applications of LLMs, particularly in complex reasoning tasks. Additionally, the insights on productivity-diversity-utility trade-offs can inform future research on adaptive curriculum learning strategies.
AGDN: Learning to Solve Traveling Salesman Problem with Anisotropic Graph Diffusion Network
Graph Learning
Optimization
- AGDN addresses critical limitations in existing GNN approaches for TSP, particularly regarding graph sparsification and multi-hop information propagation.
- The MixScore transition matrix enhances the model's ability to capture informative topological priors.
- Anisotropic graph diffusion allows for improved information exchange between nodes, addressing the challenges of disconnected optimal node pairs.
- AGDN outperforms existing methods in various experimental settings while ensuring efficient computation.
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AGDN: Learning to Solve Traveling Salesman Problem with Anisotropic Graph Diffusion Network
Summary
The paper introduces the Anisotropic Graph Diffusion Network (AGDN), a novel framework designed to address the Traveling Salesman Problem (TSP), a significant challenge in combinatorial optimization. The authors identify two main issues with existing graph-based approaches: the lack of informative topological priors in fully connected TSP graphs and the loss of connected nodes in optimal solutions due to graph sparsification techniques. To tackle these challenges, AGDN employs a MixScore transition matrix that combines node similarity with pairwise distance, providing a richer topological context. Additionally, the framework incorporates an anisotropic graph diffusion strategy that facilitates efficient multi-hop information exchange. The authors conduct extensive experiments across various instance sizes and node distributions, demonstrating that AGDN consistently outperforms existing methods while maintaining competitive computation times. Notably, AGDN shows strong generalization capabilities to problem sizes and distributions not encountered during training, indicating its robustness and potential for broader applications in solving TSP.
Methodology
The AGDN framework utilizes a MixScore transition matrix that integrates node similarity and pairwise distance to provide a more informative topological prior. It employs a graph diffusion mechanism with multi-hop attention, allowing for the control of information flow directionality and enhancing the model's ability to capture higher-order neighbor information within a single convolution layer.
Results
AGDN consistently outperformed existing methods in solving TSP across diverse instances, showcasing competitive computation times. The framework also demonstrated robust generalization capabilities, effectively handling problem sizes and distributions beyond those seen during training.
Implications
The findings suggest that AGDN can be a powerful tool for solving TSP and potentially other combinatorial optimization problems, with applications in areas such as logistics, circuit design, and vehicle routing. The approach may also inspire further research into graph learning methodologies that effectively leverage topological information.
Concept Modulation Models: A Unified Framework for Identifiability and Extrapolation
Generative Models
Theory
- Introduction of Concept Modulation Models (CMMs) as a unified framework for identifiability and extrapolation.
- CMMs separate attribute-specific indexing from shared modulation mechanisms, enhancing understanding of latent-variable settings.
- Establishment of algebraic criteria for extrapolation based on attribute potentials.
- Recovery of existing identifiability and extrapolation results while providing new guarantees for structured attribute spaces.
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Concept Modulation Models: A Unified Framework for Identifiability and Extrapolation
Summary
This paper introduces Concept Modulation Models (CMMs), a new framework for understanding identifiability and extrapolation in conditional latent variable models. The authors argue that reliable generalization in these models requires a clear understanding of how observed variations across attributes influence latent structures and how these structures can predict distributions at unseen attributes. Existing approaches to identifiability and extrapolation are often model-specific, leading to fragmented insights. CMMs provide a unified structure represented as A → Λ → C → X, where attributes modulate latent concepts that generate observed features. The paper establishes that feature agreement on observed attributes induces a latent concept transition constrained by the CMM class, thereby lifting transition-based identifiability to conditional settings. The authors derive algebraic criteria for extrapolation based on attribute potentials, which are log-density ratios between attribute-conditioned concept laws. This framework not only recovers existing results in causal representation learning and perturbation modeling but also introduces new guarantees for extrapolation in structured attribute spaces. Overall, CMMs bridge the gap between identifiability and extrapolation, offering a comprehensive approach to understanding conditional generative models.
Methodology
The authors develop a theoretical framework for CMMs that combines transition-based identifiability with conditional generative modeling. They analyze the relationships between observed attributes and latent concepts through log-density ratios, termed attribute potentials, to derive conditions for both identifiability and extrapolation.
Results
The paper demonstrates that feature equivalence on observed attributes leads to a latent concept transition compatible with the model class. It provides algebraic criteria for extrapolation, showing that agreement on observed attributes can extend to unseen attributes under specific conditions. The results unify and extend previous findings in the literature on causal representation learning and perturbation modeling.
Implications
The findings have significant implications for the design of generative models in machine learning, particularly in scenarios where reliable generalization to unseen conditions is critical. The CMM framework can be applied in various domains, including causal inference, representation learning, and any field requiring robust extrapolation from observed data.
Towards Modality-imbalanced Federated Graph Learning: A Data Synthesis-based Approach
Federated Learning
Graph Learning
Multimodal
- Introduces a novel framework, FedMGS, for addressing modality imbalance in federated graph learning.
- Identifies and characterizes two types of modality imbalance: client-level and node-level.
- Employs a graph-aware approach to recover missing modalities without compromising data privacy.
- Demonstrates significant performance improvements over existing methods through extensive experiments.
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Towards Modality-imbalanced Federated Graph Learning: A Data Synthesis-based Approach
Summary
This paper addresses the challenges of modality imbalance in MultiModal Federated Graph Learning (MM-FGL), where multimodal graph data is distributed across different clients. The authors identify two types of modality imbalance: client-level, where certain clients lack entire modalities, and node-level, where individual nodes miss specific attributes. Existing methods primarily focus on centralized or graph-agnostic scenarios, making them unsuitable for MM-FGL. To tackle these issues, the authors propose FedMGS (Federated Modality-aware Graph Synthesis), which formulates the problem as an implicit graph-aware latent semantic representation synthesis task. FedMGS includes three key components: an availability-aware graph encoder to prevent contamination from missing modalities, a prototype-guided latent semantic synthesizer to create cross-client semantic anchors, and a reliability-calibrated semantic fusion mechanism to manage the impact of synthesized representations. Experimental results demonstrate that FedMGS outperforms competitive baselines, achieving up to a 17.41% improvement in performance while maintaining efficiency.
Methodology
The authors propose FedMGS, which consists of three main components: an availability-aware graph encoder to filter out missing modalities during local training, a prototype-guided latent semantic synthesizer that uses federated prototypes to synthesize missing representations, and a reliability-calibrated semantic fusion mechanism that adjusts the contribution of synthesized semantics based on their reliability.
Results
FedMGS consistently outperforms competitive baselines across four tasks, achieving performance gains of up to 17.41%. The framework demonstrates a strong efficiency-performance tradeoff, validating its effectiveness in modality-imbalanced settings.
Implications
The proposed approach has significant implications for federated learning in multimodal contexts, particularly in fields where data privacy is crucial, such as healthcare and finance. It enables more effective collaboration across institutions while addressing the challenges posed by incomplete data.
The Token Is a Group Element: On Lie-Algebra Attention over Matrix Lie Groups
Robotics
Computer Vision
Theory
- Introduces the concept of a token as a bare group element in matrix Lie groups.
- Develops a closed-form attention score based on the negative squared algebra norm of the relative pose.
- Demonstrates the method's applicability to various matrix Lie groups, including non-compact and non-abelian cases.
- Shows significant performance improvements over traditional vector-token attention methods.
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The Token Is a Group Element: On Lie-Algebra Attention over Matrix Lie Groups
Summary
This paper introduces a novel attention mechanism termed Lie-Algebra Attention, which innovatively positions the attention token as an element of a matrix Lie group, G. Unlike traditional approaches where tokens are feature vectors acted upon by a group, this method treats tokens as bare transformations without any feature payload. The attention score is derived from the closed-form algebra norm of the relative pose, allowing the method to encompass affine full-frame groups that are typically excluded by existing methods. The paper demonstrates that the relative geometry of token pairs is canonical and intrinsic, leading to automatic equivariance and cocycle consistency without the need for complex representation-theoretic machinery. The author provides closed-form instantiations for several groups, including SO(2), SE(2), SO(3), SE(3), Aff(2), and Aff(3), and validates the approach through sequence-completion experiments. The results show that the proposed method outperforms traditional learned kernel approaches while using significantly fewer parameters, highlighting the efficiency and effectiveness of the Lie-Algebra Attention mechanism.
Methodology
The methodology involves defining the attention token as an element of a matrix Lie group, which allows for the derivation of an intrinsic pairwise invariant and a closed-form attention score. The paper employs mathematical constructs from Lie algebra to establish the properties of the attention mechanism, ensuring automatic equivariance and cocycle consistency.
Results
The experiments conducted on SE(2), SO(3), and Aff(2) demonstrate that the Lie-Algebra Attention mechanism matches the performance of a learned MLP kernel while utilizing 50 to 80 times fewer parameters. The vector-token baseline significantly breaks invariance, underscoring the advantages of the proposed approach.
Implications
The findings suggest that Lie-Algebra Attention could be beneficial for applications in spatial reasoning tasks such as robotics, computer vision, and molecular modeling, where transformations are fundamental. The method's efficiency and intrinsic properties may lead to advancements in equivariant models and improve performance in various machine learning tasks.
Robust and Interpretable Adaptation of Equivariant Materials Foundation Models via Sparsity-promoting Fine-tuning
Graph Learning
Interpretability
Efficient ML
- Introduction of a sparsity-promoting fine-tuning method for equivariant MLIPs.
- Achieves high accuracy with minimal parameter updates (0.5% to 3%).
- Demonstrates versatility across various material property predictions.
- Provides physically interpretable insights into model representations.
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Robust and Interpretable Adaptation of Equivariant Materials Foundation Models via Sparsity-promoting Fine-tuning
Summary
This paper presents a novel sparsity-promoting fine-tuning method for adapting equivariant materials foundation models, which are machine learning interatomic potentials (MLIPs) that approximate potential energy surfaces. The authors highlight the challenges faced by pre-trained MLIPs, particularly the need for domain-specific calibration due to the diversity of material systems and discrepancies between training and practical conditions. The proposed method selectively updates model parameters while preserving the structural properties of equivariant models, achieving significant efficiency by modifying only 0.5% to 3% of parameters. The authors demonstrate the effectiveness of their approach on energy and force prediction tasks across various benchmarks, achieving comparable or superior performance to full fine-tuning and other parameter-efficient methods. Additionally, the method is shown to generalize to other tasks, such as magnetic moment prediction, and the analysis of sparsity patterns provides insights into the physical representations learned by the models. Overall, this work establishes a flexible and interpretable approach for the domain specialization of equivariant MLIPs.
Methodology
The authors propose a sparsity-promoting fine-tuning method that selectively updates parameters of equivariant materials foundation models. This approach maintains the equivariance properties while allowing for a significant reduction in the number of parameters that need to be updated, thus enhancing interpretability and reducing the risk of overfitting.
Results
The proposed method matches or exceeds the performance of full fine-tuning and other parameter-efficient methods on energy and force prediction tasks across molecular and crystalline benchmarks. It also extends effectively to magnetic moment prediction and magnetism-aware total energy modeling, showcasing its generalizability.
Implications
This work has significant implications for the field of materials science and computational chemistry, as it provides a robust and interpretable method for adapting machine learning models to diverse material systems. The ability to fine-tune models with minimal updates can lead to more efficient computational practices and better insights into the physical properties of materials.
When, Where, and How: Adaptive Binning for Tabular Self-Supervised Learning
Theory
Efficient ML
Optimization
- Introduction of Adaptive Binning, a training-adaptive discretization method for tabular SSL.
- Feature-wise coarse-to-fine curriculum that refines discretization based on learning dynamics.
- Integration of categorical reconstruction with ordinal supervision for mixed feature types.
- Demonstrated consistent performance improvements across various medical tabular datasets.
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When, Where, and How: Adaptive Binning for Tabular Self-Supervised Learning
Summary
This paper addresses the challenges of applying deep learning to medical tabular data, which is often underutilized due to the lack of reliable labels that require expert adjudication. The authors propose a novel self-supervised learning (SSL) approach called Adaptive Binning, which introduces a training-adaptive discretization pretext for tabular data. Unlike existing methods that use a fixed global quantile discretization, Adaptive Binning employs a feature-wise coarse-to-fine curriculum that refines discretization based on the learning process. This method is motivated by the spectral bias of neural networks and principles of curriculum learning, allowing for progressive refinement of discretization as features reach saturation. The approach integrates a heterogeneity-aware objective that combines categorical reconstruction with ordinal supervision for numerical features. The authors validate their method on public medical tabular datasets, demonstrating consistent improvements in performance for linear probing and fine-tuning without the need for dataset-specific tuning. Additionally, they establish a benchmark for medical tabular SSL with standardized evaluation protocols to foster reproducible research in this area.
Methodology
The authors developed an autoencoding-based framework for tabular SSL that employs Adaptive Binning to refine discretization during pretraining. This method uses feature-wise saturation triggers to determine when to refine discretization and incorporates representation-aware split selection to enhance learning. The training-adaptive approach allows for a dynamic curriculum that evolves based on the model's performance and the characteristics of the data.
Results
The experiments conducted on public medical tabular datasets showed that Adaptive Binning significantly improved performance metrics for both linear probing and fine-tuning tasks. The results indicated that the method outperformed traditional fixed-binning approaches without requiring specific tuning for each dataset, showcasing its robustness and adaptability.
Implications
The proposed Adaptive Binning method has the potential to enhance self-supervised learning in clinical settings, where labeled data is scarce. By effectively utilizing unlabeled tabular data, this approach could lead to better predictive models in healthcare applications, ultimately improving patient outcomes and decision-making processes.
Optimal Deterministic Multicalibration and Omniprediction
Theory
- Introduces a minimax-optimal multicalibration algorithm that outputs deterministic predictors.
- Demonstrates that deterministic predictors can achieve the same sample complexity as randomized ones.
- Extends the algorithm to ensure outcome indistinguishability for finite test collections.
- Provides deterministic omnipredictors and panpredictors, resolving open problems in the field.
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Optimal Deterministic Multicalibration and Omniprediction
Summary
This paper addresses the problem of multicalibration in machine learning, which ensures that a model is unbiased not only overall but also when reweighted by various group weights. Prior to this work, existing algorithms achieving the optimal sample complexity for multicalibration produced randomized predictors, while deterministic predictors had significantly worse performance. The authors resolve the open question of whether randomization is necessary for optimal sample complexity by presenting a minimax-optimal multicalibration algorithm that outputs deterministic predictors. Furthermore, they extend this algorithm to achieve outcome indistinguishability concerning finite collections of tests, leading to the development of deterministic omnipredictors and panpredictors with optimal sample complexity. This work not only answers previous inquiries regarding the necessity of randomization in multicalibration but also provides a framework for achieving trustworthy machine learning models that are easier to audit and reproduce.
Methodology
The authors develop a deterministic learning algorithm that achieves multicalibration by leveraging a series of steps including learning randomized predictors from valid interval hints, constructing a finite list of rounding cells, and rounding the randomized predictor to obtain a deterministic output. They also explore the concept of outcome indistinguishability to ensure that the predictions remain consistent across different contexts.
Results
The proposed algorithm achieves minimax-optimal sample complexity for multicalibration with deterministic predictors, matching the performance of previously known randomized algorithms. Additionally, the work successfully constructs deterministic omnipredictors and panpredictors, providing a significant advancement in the field of multicalibration.
Implications
This research has significant implications for the development of trustworthy machine learning systems, as it allows for the creation of models that are both efficient and deterministic. This enhances the ability to audit and reproduce machine learning predictions, which is crucial for applications in sensitive areas such as healthcare, finance, and legal systems.
Signature filtering: a lightweight enhancement for statistical watermark detection in large language models
Large Language Models
Optimization
NLP
- Signature filtering improves watermark detection rates significantly, especially in challenging scenarios.
- The method does not require changes to watermark embedding or text generation processes.
- It utilizes a mixed-integer linear program to identify disruptive tokens for removal.
- Empirical results show that detection rates can increase from 8-31% to 78-99% with filtering.
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Signature filtering: a lightweight enhancement for statistical watermark detection in large language models
Summary
This paper introduces 'signature filtering', a novel detection-time module designed to enhance the effectiveness of statistical watermark detection in large language models (LLMs). Traditional watermark detectors often face challenges when watermark signals are weak, texts are repetitive, or watermarks are edited. Signature filtering addresses these issues by removing a small set of 'signature' tokens that disrupt watermark detection, thereby improving the reliability of detection without altering the watermark embedding or text generation process. The signatures are derived through a mixed-integer linear programming approach, maximizing the true positive rate while maintaining low false positive rates. The authors validate their method across four watermark families, four benchmark corpora, and six different LLMs, demonstrating significant improvements in detection rates, particularly in weak-signal and low-entropy scenarios. The results indicate that signature filtering can effectively enhance watermark detection, making it a scalable and model-agnostic solution for ensuring the provenance of LLM-generated text.
Methodology
The authors developed signature filtering by identifying and removing a set of statistically disruptive tokens from the text before watermark detection. This was achieved through a mixed-integer linear programming approach that maximizes the true positive rate while ensuring the validity of the underlying statistical tests. The method was empirically tested across multiple watermark families and LLMs, demonstrating its effectiveness in various scenarios.
Results
The implementation of signature filtering led to a dramatic increase in detection rates for weak-signal and low-entropy texts, achieving rates between 78-99% compared to 8-31% without filtering. In stress tests involving significant text perturbations, the method preserved detection gains and often outperformed existing advanced watermark detectors.
Implications
Signature filtering has the potential to enhance the reliability of watermark detection in LLMs, which is crucial for organizations needing to attribute AI-generated content. This method can be integrated into existing workflows to improve compliance and trust in information services that utilize LLM outputs.
Seed-Guided Semi-Supervised Clustering by A-Contrario Anomaly Detection
Theory
Efficient ML
- Introduces a statistical duality framework for clustering and anomaly detection.
- Develops a robust Perception algorithm that eliminates the need for manual parameter tuning.
- Implements a seed-guided expansion process that integrates expert intent while being resilient to noise.
- Achieves competitive performance on various datasets with minimal user input.
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Seed-Guided Semi-Supervised Clustering by A-Contrario Anomaly Detection
Summary
This paper presents a novel semi-supervised clustering framework that leverages the statistical duality between clustering and anomaly detection. The proposed method addresses the challenges of defining robust clusters in noisy environments, where traditional algorithms often misclassify outliers. By employing a-contrario statistical reasoning and Gestalt principles, the framework defines clusters as maximal subsets of data points devoid of anomalies, relative to a null hypothesis of uniform randomness. Central to this approach is the Perception algorithm, which utilizes an expectation-based threshold to autonomously identify outliers without manual parameter tuning. The algorithm is seed-guided, requiring minimal user-provided labels to initialize robust cluster medians, which are then expanded by including non-anomalous points. This iterative 'clustering-by-exclusion' mechanism effectively isolates fringe points and emerging clusters. The method is evaluated on both synthetic and real-world datasets, demonstrating competitive performance with as few as 10-30 seeds per cluster, while maintaining linear scalability concerning observations and dimensionality.
Methodology
The methodology involves a seed-guided clustering approach that utilizes a-contrario statistical reasoning to define clusters based on the absence of anomalies. The Perception algorithm autonomously detects outliers using an expectation-based threshold, and an iterative clustering-by-exclusion mechanism is employed to expand clusters while rejecting inconsistent points.
Results
The proposed method shows strong performance on synthetic and real-world benchmarks, including image and text datasets, achieving competitive results with only 10-30 seeds per cluster. The algorithm maintains linear scalability with respect to both the number of observations and the dimensionality of the data.
Implications
This framework has potential applications in various fields such as cybersecurity, document processing, and molecular biology, where robust clustering in the presence of noise and outliers is critical. The seed-guided approach allows for effective integration of expert knowledge while minimizing the need for extensive parameter tuning.
Be Your Own Teacher: Steering Protein Language Models via Unsupervised Reward Optimization
Generative Models
Reinforcement Learning
Optimization
- Introduces unsupervised reward optimization for fine-tuning protein language models.
- Proposes two algorithms, SRO and BRO, that enhance controllability without ground-truth labels.
- Demonstrates significant performance improvements over competitive baselines in various tasks.
- Provides new datasets and benchmark tasks for evaluating PLM controllability.
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Be Your Own Teacher: Steering Protein Language Models via Unsupervised Reward Optimization
Summary
This paper addresses the challenges of adapting protein language models (PLMs) for controllable biomolecular design without relying on costly wet-lab validation or curated datasets. The authors propose a novel framework for unsupervised reward optimization of PLMs, which leverages task-agnostic rewards that combine intrinsic model uncertainty with extrinsic semantic consistency derived from protein representation models. The study introduces two offline algorithms, Soft Reward Optimization (SRO) and Binarized Reward Optimization (BRO), designed to maximize the classical reinforcement learning from human feedback (RLHF) objective using these proxy rewards. Extensive experiments demonstrate that both methods significantly outperform existing baselines (DPO, KTO) across various out-of-distribution prompts, model scales, and sampling temperatures, achieving performance close to oracle models fine-tuned with ground-truth labels. The findings suggest that PLMs fine-tuned with unsupervised rewards can enhance their coverage in pass@k evaluations, providing a scalable approach for controllable biomolecular design in scenarios with limited labeled data.
Methodology
The authors conducted a comprehensive study on unsupervised post-training of PLMs, focusing on reward design, data sampling, and optimization algorithms. They identified effective task-agnostic metrics as proxy rewards and developed SRO and BRO algorithms to optimize the RLHF objective using continuous and discrete reward signals, respectively.
Results
The proposed methods, SRO and BRO, significantly enhanced the steerability of PLMs, achieving performance comparable to oracle models in certain conditions. The empirical evaluations showed that both methods outperformed competitive baselines across various out-of-distribution generation tasks, sampling temperatures, and model scales.
Implications
This framework enables the self-improvement of PLMs through their own generated experiences, which is particularly beneficial for biomedical applications where labeled data is scarce. It opens new avenues for controllable biomolecular design and could lead to advancements in generalist biological artificial intelligence.
Does VLA Even Know the Basics? Measuring Commonsense and World Knowledge Retention in Vision-Language-Action Models
Multimodal
Robotics
- Introduction of Act2Answer, a protocol for evaluating VLA models through action-based answer selection.
- Creation of a diverse benchmark suite with 1,720 questions across various commonsense and world knowledge categories.
- Empirical analysis shows VLA models excel at simple tasks but have significant gaps in complex semantic understanding.
- Co-training with VQA tasks correlates with improved knowledge retention in VLA models.
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Does VLA Even Know the Basics? Measuring Commonsense and World Knowledge Retention in Vision-Language-Action Models
Summary
This paper investigates the retention of commonsense and factual knowledge in Vision-Language-Action (VLA) models after they are fine-tuned on robotics data. The authors introduce a novel evaluation protocol called Act2Answer, which adapts existing Vision-Language Model (VLM) benchmarks for VLA evaluation by requiring agents to answer questions through action rather than text generation. This approach reduces confounding factors related to low-level control and allows for a more accurate assessment of knowledge retention. The study includes a curated test suite with 1,720 unique binary questions across 12 categories, enabling systematic evaluation of VLA models. The authors conduct a large-scale empirical analysis of 7 VLA models and 9 VLM baselines, revealing that while VLA models perform well on simple concepts, they struggle with more complex semantic categories. Additionally, the study finds that co-training with Visual Question Answering (VQA) tasks enhances knowledge retention, and that relevant information is often retained in the middle layers of the models but diminishes in the upper layers.
Methodology
The authors developed Act2Answer, an embodied evaluation protocol that transforms VLM knowledge benchmarks into action-based tasks. They curated a test suite with diverse questions and conducted a large-scale empirical study to assess the performance of multiple VLA and VLM models. Layerwise intent probing was employed to analyze the distribution of answer-relevant information across the model layers.
Results
The study found that VLA models performed well on basic perceptual tasks but exhibited larger performance gaps on richer semantic categories compared to their source VLMs. Co-training with VQA tasks was associated with better knowledge retention, and the analysis showed that relevant information peaked in the middle layers of the models but diminished in the upper layers.
Implications
The findings suggest that while VLA models can handle basic tasks, their ability to retain and utilize complex commonsense knowledge is limited. This has implications for the design of future VLA systems, particularly in applications requiring nuanced understanding of the environment, such as robotics in everyday settings.
Semantic Robustness Certification for Vision-Language Models
Multimodal
- Introduces a framework for certifying VLM robustness under semantic-level transformations.
- Uses text prompts as semantic proxies to formalize transformations without needing additional data.
- Characterizes VLM decision boundaries to determine prediction-invariant intervals.
- Demonstrates effectiveness through experiments on synthetic and real-world data.
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Semantic Robustness Certification for Vision-Language Models
Summary
This paper addresses the robustness of Vision-Language Models (VLMs) against semantic variations that can occur in real-world applications. The authors propose a novel framework for certifying the robustness of VLMs under semantic-level transformations, which differ from traditional pixel-level or geometric transformations. By utilizing the open-vocabulary capabilities of VLMs, the framework employs text prompts as semantic proxies to define transformations that quantify the extent of semantic variation. The authors characterize the decision boundary of VLM classifiers in closed form, allowing them to analytically determine prediction changes under these transformations. The framework is unique in that it does not require additional data for each variation, making it practical for real-world applications. Experimental results demonstrate that the proposed framework effectively certifies robustness across various semantic variations, providing a means to monitor semantic drift and diagnose potential failure modes in VLMs.
Methodology
The authors reformulate robustness certification for VLMs by defining semantic transformations in the embedding space, using text prompts to specify source and target semantics. They project image embeddings onto a two-dimensional subspace spanned by the corresponding textual embeddings to quantify the strength of semantic variations. The decision boundary of VLM classifiers is characterized in closed form, allowing for the identification of prediction-invariant intervals based on the extent of semantic transformations.
Results
The framework successfully certifies prediction-invariant intervals for various semantic transformations, showing that the predictions remain stable within certain ranges of semantic extent. Experiments indicate that the transformations align with intended semantic variations and accurately capture prediction changes, validating the effectiveness of the proposed certification method.
Implications
This work has significant implications for the deployment of VLMs in high-stakes applications, as it provides a practical approach to ensure model robustness against semantic variations. It can be used to monitor model performance over time, diagnose issues related to semantic drift, and enhance the reliability of VLMs in real-world scenarios.
An Information Theoretic Framework for Graph Novelty Generation via Latent Mixture Modeling
Generative Models
Graph Learning
Theory
- Introduces a novel framework for graph novelty generation using latent mixture modeling.
- Imposes novelty and reliability conditions based on the Minimum Description Length principle.
- Theoretical guarantees on misclassification probabilities for novelty and reliability.
- Empirical results demonstrate superior control over novelty generation compared to existing methods.
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An Information Theoretic Framework for Graph Novelty Generation via Latent Mixture Modeling
Summary
This paper introduces an information-theoretic framework for generating novel graph data that diverges from existing patterns while maintaining structural integrity. The authors propose a method that embeds data into a latent space and employs finite mixture models to characterize the latent distribution. Novelty is enforced by ensuring that generated samples are poorly represented by existing mixture components, while reliability is maintained through the Minimum Description Length (MDL) principle. The framework includes a theoretical analysis demonstrating that with appropriate threshold settings, the probabilities of misclassifying non-novel or unreliable samples converge to zero. Empirical evaluations on synthetic and benchmark datasets show that the proposed method effectively generates novelty with quantifiable risk, outperforming existing approaches in controlling novelty and reliability through threshold variations.
Methodology
The proposed methodology involves embedding graph data into a latent space and modeling the latent distribution using finite mixture models. Novel samples are generated by ensuring they are poorly explained by existing components (novelty condition) while not significantly altering the overall mixture structure (reliability condition). The authors utilize an MDL-guided sampling scheme to enforce these conditions, with a theoretical analysis to support the effectiveness of their approach.
Results
The experiments conducted on both synthetic and real-world datasets indicate that the proposed framework successfully generates novel graph data while adhering to the defined novelty and reliability conditions. The results show that the method can effectively manage the trade-off between introducing new data and maintaining the integrity of the existing data structure, with quantifiable risk metrics that validate its performance.
Implications
This framework has significant implications for applications requiring the generation of creative and novel data instances, such as community formation in social networks, material design, and other domains where traditional data augmentation or extrapolation methods fall short. It provides a mathematically rigorous approach to novelty generation, paving the way for further research and development in generative models.
ThousandWorlds: A benchmark for climate emulation of potentially habitable exoplanets
Theory
Efficient ML
- ThousandWorlds is a curated benchmark dataset for exoclimate emulation, containing 1800 simulations from five GCMs.
- The dataset supports three levels of complexity in regression tasks, catering to both single and multi-simulator scenarios.
- Evaluation protocols are introduced to measure emulator performance against GCM variability, enhancing scientific utility assessment.
- Gaussian process methods show superior performance compared to deep learning techniques in this context.
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ThousandWorlds: A benchmark for climate emulation of potentially habitable exoplanets
Summary
The paper introduces ThousandWorlds, a benchmark dataset designed for machine learning emulation of exoplanet climates, which is crucial for interpreting biosignatures in the search for extraterrestrial life. The dataset comprises approximately 1800 simulations from five global climate models (GCMs), mapping eight planetary parameters to 3D atmospheric fields such as temperature, humidity, winds, clouds, and radiation. The authors highlight the challenges of using GCMs due to their high computational cost and the need for domain expertise, which limits the ability to conduct extensive studies. ThousandWorlds addresses this by providing a curated, ML-ready dataset that facilitates parameter-to-field regression in a low-data environment. The dataset is structured into three nested subsets that increase in complexity, allowing for single-simulator and multi-simulator regression tasks. The authors propose two evaluation protocols to assess emulator performance, one for general method ranking and another that compares emulator error against the variability among GCMs. The evaluation of seven baseline methods, including Gaussian processes and deep learning techniques, reveals that GP-based methods outperform others, indicating that standard deep learning approaches may struggle in this specific regime.
Methodology
The authors developed ThousandWorlds by compiling simulations from various GCMs, structuring the dataset into three nested subsets for increasing complexity in regression tasks. They proposed two evaluation protocols to assess the performance of different emulation methods, including Gaussian processes and deep learning models, against the variability observed in GCM outputs.
Results
The evaluation of seven baseline methods demonstrated that Gaussian process-based approaches achieved the best performance, suggesting that the challenges posed by the ThousandWorlds dataset may not be well-suited for standard deep learning methods.
Implications
ThousandWorlds has the potential to significantly advance the field of exoplanet climate modeling and the search for extraterrestrial life by enabling faster and more efficient climate predictions. It can facilitate large parameter sweeps and improve the integration of observational data with climate models, ultimately aiding in the interpretation of biosignatures in exoplanet atmospheres.
Sensorimotor World Models: Perception for Action via Inverse Dynamics
Robotics
Reinforcement Learning
Theory
- Introduction of Sensorimotor World Model (SMWM) that integrates perception and action.
- Utilization of inverse dynamics regularization to prevent representation collapse.
- Training from offline, reward-free trajectories without complex regularizers.
- Empirical evidence of learned representations tracking controllable dynamics.
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Sensorimotor World Models: Perception for Action via Inverse Dynamics
Summary
The paper introduces a novel approach to world modeling in the context of reinforcement learning and robotics, termed the Sensorimotor World Model (SMWM). This model emphasizes the importance of shaping representations not solely based on visual fidelity but on their relevance to actions. The authors address the challenge of representation collapse in latent world models, particularly those based on Joint Embedding Predictive Architectures (JEPA), by employing inverse dynamics regularization. This technique ensures that the latent states retain information about the actions that lead to transitions, thus promoting the learning of compact and interpretable latent spaces. The SMWM is trained end-to-end from offline, reward-free trajectories, allowing it to effectively filter out irrelevant information while focusing on controllable dynamics. Empirical results demonstrate that the SMWM achieves competitive planning performance in both 2D and 3D control tasks, showcasing its potential for practical applications in intelligent agents.
Methodology
The SMWM is trained using a joint architecture that includes an encoder, a forward dynamics model, and an inverse dynamics model. The encoder maps observations to latent embeddings, while the forward model predicts future embeddings based on current embeddings and actions. The inverse model predicts the action taken between two consecutive observations, and gradients are backpropagated through the inverse dynamics loss to ensure that action-relevant information is preserved in the embeddings.
Results
The SMWM successfully learns compact and interpretable latent representations that maintain spatial geometry and filter out irrelevant information. It demonstrates competitive planning performance in both simple 2D and 3D control tasks, indicating its effectiveness in real-world applications.
Implications
The findings suggest that the SMWM could enhance the development of intelligent agents capable of more effective perception and action coordination, particularly in environments where traditional reward-based learning is not feasible. This approach may also inform future research in cognitive robotics and autonomous systems.
Domain-Shift Aware Neural Networks for Unbalance Characterization in Rotating Systems
Theory
- Introduces a domain-shift aware neural network for estimating unbalance in rotating systems.
- Utilizes a maximum mean discrepancy strategy for feature alignment across different operational conditions.
- Demonstrates improved prediction accuracy in the presence of domain shifts.
- Highlights the challenges of data scarcity and domain discrepancies in SHM.
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Domain-Shift Aware Neural Networks for Unbalance Characterization in Rotating Systems
Summary
This paper presents a novel approach using domain-shift aware neural networks to estimate unbalance masses in rotating shafts under varying operational conditions. The authors collected experimental data from a test rig with a primary shaft carrying unbalanced masses and a secondary shaft that could introduce domain discrepancies. The study formulates the mass estimation as an inverse problem within a domain adaptation framework, employing a maximum mean discrepancy strategy to align feature representations across different data distributions. The results indicate that explicitly addressing domain shifts significantly enhances prediction accuracy, particularly when the physical behavior of the system and sources of domain discrepancies are not fully understood. This work underscores the potential of domain-shift aware models in improving regression tasks within Structural Health Monitoring (SHM), especially in scenarios where labeled data is scarce or unavailable.
Methodology
The authors employed a domain adaptation framework to tackle the inverse problem of mass estimation. They trained a neural network using a maximum mean discrepancy strategy to align feature representations from source and target distributions, addressing the challenges posed by domain shifts in the data.
Results
The study found that the domain-shift aware neural network significantly improved the accuracy of unbalance mass predictions compared to traditional methods, particularly under conditions where the system's behavior deviated from the training data. This improvement was particularly notable when the operational conditions were not fully known.
Implications
The findings suggest that domain-shift aware models can enhance the reliability of predictive modeling in Structural Health Monitoring, potentially leading to better diagnostics in engineering infrastructures. This approach could be particularly beneficial in safety-critical applications where accurate damage assessment is essential.
Kolmogorov-Arnold Reservoir Computing
Theory
Efficient ML
Time Series
- KARC improves upon traditional reservoir computing by using explicit basis-function expansions.
- The framework allows for efficient closed-form training while preserving expressive capacity.
- KARC outperforms existing methods on challenging benchmarks, including chaotic systems and PDEs.
- The approach can be integrated with generative models, enhancing applications in areas like text-to-image generation.
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Kolmogorov-Arnold Reservoir Computing
Summary
This paper introduces Kolmogorov-Arnold Reservoir Computing (KARC), a novel framework for forecasting dynamical systems that addresses the limitations of traditional reservoir computing (RC) methods. Traditional RC struggles with long-range dependencies and hyperparameter sensitivity, while next-generation reservoir computing (NG-RC) faces issues with rapidly increasing feature dimensions. KARC leverages the Kolmogorov-Arnold representation theorem to replace conventional reservoirs with explicit basis-function expansions, allowing for efficient closed-form training. The authors demonstrate that KARC maintains the expressive capacity of Kolmogorov-Arnold networks (KANs) while providing a lightweight design that avoids the pitfalls of recurrent architectures. Experimental results show that KARC outperforms existing RC methods on complex benchmarks, including chaotic ordinary differential equations and partial differential equations, achieving more accurate long-horizon predictions. Additionally, KARC can be integrated with generative diffusion models for applications such as text-to-image generation, establishing a significant connection between reservoir computing and KANs.
Methodology
KARC employs explicit univariate basis-function expansions inspired by the Kolmogorov-Arnold representation theorem. It projects time-delay coordinates onto basis functions (e.g., Fourier, B-spline, Chebyshev) and combines this representation with a linear readout, which is trained efficiently using ridge regression. This method avoids the recurrent structure of traditional RC while maintaining the advantages of closed-form training.
Results
KARC demonstrated superior performance in long-horizon predictions compared to existing reservoir computing methods across various benchmarks, including the double-scroll system and the Kuramoto-Sivashinsky equation. The framework also supports multiple basis functions, providing flexibility and robustness in feature forecasting.
Implications
KARC's efficient and expressive framework for modeling complex dynamics has potential applications in scientific forecasting, generative modeling, and other areas requiring accurate predictions of dynamical systems. Its integration with generative diffusion models opens new avenues for research in multimodal applications.
From Sparse Features to Trustworthy Proxies: Certifying SAE-Based Interpretability
NLP
Large Language Models
Interpretability
- Introduces a certification framework for assessing the interpretability of frozen language models using sparse autoencoders.
- Derives a risk bound that decomposes into four measurable terms, providing a clear criterion for trustworthiness.
- Empirical validation shows non-vacuous bounds for multiple language models at practical sample sizes.
- Layerwise analysis indicates that later layers are easier to certify, highlighting depth-dependent behavior in model interpretability.
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From Sparse Features to Trustworthy Proxies: Certifying SAE-Based Interpretability
Summary
This paper investigates the interpretability of language models (LMs) through sparse autoencoders (SAEs) and proposes a post-hoc certification framework to assess when SAE-based explanations can be trusted as faithful representations of the underlying frozen LMs. The authors introduce a method to replace the native hidden activations of a frozen LM with their SAE reconstructions, creating a sparse proxy. They derive an upper bound on the expected risk of the original model based on four measurable quantities: proxy risk, SAE reconstruction gap, concept-pool mismatch, and sparse complexity. A non-vacuous bound indicates that the sparse features extracted retain meaningful predictive information and that the proxy is behaviorally close to the original model. Empirical results demonstrate that this bound becomes non-vacuous for models like GPT-2 Small, Gemma-2B, and Llama-3-8B at practical sample sizes. A detailed analysis reveals that later layers of the Llama-3-8B model are easier to certify, showing stronger local fidelity and reduced downstream error amplification. The study also distinguishes genuine semantic alignment from mere statistical sparsity, providing insights into the reliability of SAE-based explanations.
Methodology
The authors replace the hidden activations of a frozen language model with reconstructions from a pretrained sparse autoencoder to create a sparse proxy. They derive an upper bound on the expected risk of the original model based on four measurable quantities, which allows them to certify the faithfulness of the SAE-based explanations. Empirical validation is conducted on several language models, with a focus on layerwise analysis of Llama-3-8B.
Results
The certification framework successfully identifies non-vacuous bounds for GPT-2 Small, Gemma-2B, and Llama-3-8B, indicating that the extracted sparse features are informative. The layerwise analysis of Llama-3-8B shows that later layers are easier to certify, with stronger local fidelity and less error amplification compared to earlier layers. The results also demonstrate that the depth effect is model-specific.
Implications
The findings suggest that the proposed certification framework can enhance the reliability of interpretability methods for language models, guiding practitioners in understanding when SAE-based explanations can be trusted. This has implications for the development of more interpretable AI systems and for ensuring that model explanations align with human understanding.
P$^2$CE: Model-Agnostic Plausible Pareto-Optimal Counterfactual Explanations
Interpretability
- P2CE is a model-agnostic algorithm that generates optimal counterfactual explanations.
- The algorithm ensures that explanations are plausible and within the data distribution.
- P2CE leverages outlier detection and SHAP values to enhance computational efficiency.
- Empirical evaluations show superior performance compared to existing counterfactual explanation methods.
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P$^2$CE: Model-Agnostic Plausible Pareto-Optimal Counterfactual Explanations
Summary
The paper introduces P2CE, a novel algorithm designed to generate plausible Pareto-optimal counterfactual explanations in machine learning. As machine learning algorithms increasingly influence significant decisions in areas like credit scoring and job selection, the need for transparent and fair explanations has become critical. Counterfactual explanations help individuals understand what changes could lead to a more favorable outcome. However, existing methods often struggle to balance feasibility, plausibility, and computational efficiency. P2CE addresses these challenges by providing a diverse set of optimal trade-offs between different notions of feasibility. The algorithm employs an auxiliary isolation forest outlier detector to ensure that the generated explanations align with the data distribution and utilizes SHAP values to optimize results efficiently, regardless of the underlying model. The authors conducted empirical evaluations on three datasets, demonstrating that P2CE outperforms existing techniques in both solution quality and computational efficiency, thus making it a valuable tool for generating actionable insights in various applications.
Methodology
P2CE employs a branch-and-bound search strategy in a grid of potential counterfactual explanations, integrating an isolation forest outlier detector to maintain plausibility. It utilizes SHAP values to identify feasible variable changes, allowing for the generation of counterfactuals that meet multiple objectives.
Results
The empirical evaluation of P2CE on three datasets demonstrated that it provides higher quality solutions and improved computational efficiency compared to related techniques. The algorithm successfully generates a diverse set of counterfactual explanations that are both feasible and plausible.
Implications
P2CE can be applied in various domains where machine learning decisions impact individuals, such as finance, healthcare, and employment. By providing actionable insights, it enhances transparency and fairness in automated decision-making processes.
Latent Confounded Causal Discovery via Lie Bracket Geometry
Graph Learning
Theory
Optimization
- Introduces BRIDGE and SKFM algorithms for causal discovery under latent confounding.
- Establishes that latent confounding obstructs coherent causal information transport.
- Demonstrates high performance on synthetic data while exposing limitations on real datasets.
- Combines geometric insights with causal inference to enhance discovery methods.
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Latent Confounded Causal Discovery via Lie Bracket Geometry
Summary
This paper presents two novel algorithms for causal discovery in the presence of latent confounding, leveraging the geometric and categorical insights from Kan-Do-Calculus (KDC). The author argues that latent confounding is not merely an omitted variable issue but an obstruction to coherent causal information transport between observational and interventional measures. The first algorithm, BRIDGE, utilizes Radon–Nikodym derivatives to identify non-closing visible pairs as candidates for latent obstruction and proposes a reduced family of admissible arrows for further analysis. The second algorithm, Spectral Kan-Do Flow Matching (SKFM), learns intervention fields and factors latent curvature, enhancing the geometric candidate screening process. Experimental results demonstrate that the SKFM/BRIDGE combination achieves a mean directed F1 score of approximately 0.86 on nonlinear random directed acyclic graphs (DAGs) while effectively recovering the visible graph structure in controlled motifs. However, the approach faces challenges in real data scenarios, such as the Sachs protein signaling dataset, highlighting the need for careful calibration in practical applications. Overall, the paper contributes a geometric-screening pipeline and insights into when direct graph extraction is feasible versus when additional scoring methods are necessary.
Methodology
The paper employs a combination of information-geometric techniques and categorical frameworks from Kan-Do-Calculus to develop two algorithms. BRIDGE focuses on identifying latent obstructions through Radon–Nikodym derivatives and geometric screening, while SKFM learns intervention fields and factors latent curvature spectrally.
Results
The algorithms were tested on ten-node nonlinear random DAGs, achieving a mean directed F1 score of approximately 0.86. In controlled motifs, SKFM successfully recovered the visible graph structure. However, in the Sachs protein signaling dataset, the results indicated a calibration frontier, suggesting that real data may not align with synthetic assumptions.
Implications
The findings suggest that the proposed geometric-screening pipeline can enhance causal discovery methods, particularly in settings with latent confounding. The insights into when direct extraction is feasible can guide future research and applications in causal inference.
Alzheimer's Disease Diagnosis using a Multimodal Approach with 3D MRI and PET
Multimodal
- Introduces a novel multimodal approach for Alzheimer's diagnosis using 3D MRI and PET.
- Utilizes three fusion strategies and a Mixture-of-Experts classifier for improved adaptability and performance.
- Achieves high classification accuracies across multiple diagnostic tasks.
- Employs Grad-CAM for model interpretability, enhancing trust in clinical applications.
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Alzheimer's Disease Diagnosis using a Multimodal Approach with 3D MRI and PET
Summary
This paper addresses the early diagnosis of Alzheimer's Disease (AD) by leveraging a multimodal approach that integrates 3D MRI and PET neuroimaging data. The authors highlight the limitations of existing multimodal models that typically use static concatenation methods and apply uniform computation across diverse patient data, which can hinder robustness and efficiency. To overcome these challenges, the study introduces a novel framework that combines 3D convolutional feature extractors with three fusion strategies: concatenation, Gated Multimodal Unit (GMU), and gated self-attention. Additionally, a sparsely gated Mixture-of-Experts (MoE) classifier is employed to facilitate input-adaptive routing, activating only the most relevant experts for each case. The model's interpretability is enhanced through the use of Grad-CAM, allowing visualization of the brain regions influencing the diagnosis. The experiments conducted involve three binary classification tasks: Normal Cognition (NC) vs. Mild Cognitive Impairment (MCI), MCI vs. AD, and NC vs. AD. The results demonstrate that the GMU achieves accuracies of 80.46% for NC vs. MCI and 95.47% for NC vs. AD, while gated self-attention reaches 82.08% for MCI vs. AD. Ablation studies confirm that the removal of the MoE layer consistently degrades performance across all tasks, underscoring the importance of input-adaptive multimodal modeling for AD diagnosis.
Methodology
The methodology involves preprocessing neuroimaging data (MRI and PET), extracting features using 3D convolutional neural networks, and applying three fusion techniques (concatenation, GMU, gated self-attention). A sparsely gated Mixture-of-Experts classifier is integrated to dynamically select relevant subnetworks for classification tasks, followed by the application of Grad-CAM for interpretability.
Results
The proposed model achieved accuracies of 80.46% for NC vs. MCI, 95.47% for NC vs. AD, and 82.08% for MCI vs. AD. Ablation studies indicated that the absence of the MoE layer led to a consistent decrease in accuracy across all classification tasks.
Implications
The findings suggest that the proposed multimodal approach can significantly enhance early diagnosis of Alzheimer's Disease, potentially leading to timely interventions. The model's interpretability may also facilitate its adoption in clinical settings, where understanding the decision-making process is crucial for healthcare professionals.
Tracking Representation Dynamics in Large Language Models with Persistent Homology
NLP
Large Language Models
Interpretability
- Persistent homology reveals significant topological changes in LLM representations during early training stages.
- Different alignment objectives produce distinguishable topological trajectories despite similar behavioral outcomes.
- Instruction-tuned and pretrained models show qualitatively different evolution patterns in their representations.
- The study emphasizes the importance of understanding internal representation dynamics beyond behavioral metrics.
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Tracking Representation Dynamics in Large Language Models with Persistent Homology
Summary
This paper investigates the internal representation dynamics of large language models (LLMs) during alignment fine-tuning using persistent homology (PH), a method from topological data analysis. The authors analyze four transformer models with parameter sizes ranging from 1B to 7B, focusing on three alignment objectives: helpful, harmless, and mixed training data. The study reveals that significant topological reorganization occurs primarily in the early stages of training, followed by rapid stabilization. The findings indicate that different alignment objectives lead to distinct topological trajectories, and that instruction-tuned and pretrained models exhibit qualitatively different patterns of evolution. The authors argue that persistent homology offers a complementary perspective on alignment, uncovering representation-level changes that behavioral metrics alone may not capture. Additionally, they provide a fully reproducible implementation of their analysis pipeline to facilitate further research in this area.
Methodology
The authors employed persistent homology to analyze the topology of activation spaces in LLMs during alignment fine-tuning. They extracted activation point clouds from various training checkpoints and computed persistent homology to summarize the topological features. The analysis was conducted across four transformer models with varying sizes and under different alignment objectives derived from a standard dataset.
Results
The results indicate that a transient peak in topological activity occurs during the initial phases of training, followed by rapid stabilization. The study found that different alignment objectives lead to unique topological trajectories, and the dynamics of representation evolution are heavily influenced by the model's initial state, with distinct patterns observed between instruction-tuned and pretrained models.
Implications
The findings suggest that persistent homology can serve as a valuable tool for understanding the internal workings of LLMs during alignment processes. This approach may lead to improved methods for fine-tuning models and enhancing their performance in various applications, as it provides insights into representation dynamics that are not captured by traditional behavioral metrics.
Why SWAVE May Not Be All You Need:A Concept-Evolution Retrospective on Complex-Valued Recurrent Language Models
NLP
Large Language Models
Theory
- SWAVE is a complex-valued recurrent language model that aims to retain information over long contexts without decay.
- The model underwent three development phases, addressing structural issues and refining its architecture.
- Key components like ComplexNorm and Wave Propagation Scan were retained, while ineffective concepts were discarded.
- The paper introduces a formal characterization of 'cos-domination collapse' and provides engineering principles for complex-valued training.
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Why SWAVE May Not Be All You Need:A Concept-Evolution Retrospective on Complex-Valued Recurrent Language Models
Summary
The paper presents a retrospective analysis of SWAVE, a complex-valued recurrent language model designed to overcome limitations of traditional transformer-based models. SWAVE operates with 169.26M parameters and is trained on FineWeb-Edu. It was built on three foundational premises: complex wave representation for richer information encoding, a Cayley-parameterized unitary transition to prevent state decay, and a hidden state that rotates to preserve signal integrity over long contexts. The development of SWAVE went through three phases, revealing issues such as 'cos-domination collapse' in the original architecture, which was addressed by adopting an untied head from the Phase-Associative Memory (PAM) architecture. The final architecture retained key components like ComplexNorm and Wave Propagation Scan while discarding ineffective multi-scale retention concepts. The paper also introduces a formal characterization of cos-domination collapse and outlines six transferable engineering principles for complex-valued recurrent training. The findings provide insights into the evolution of complex-valued models and offer a reference for future designs.
Methodology
The methodology involved a phased development approach, where design concepts were tested and evaluated based on their performance and structural integrity. Each phase focused on resolving specific issues, such as the adoption of PAM primitives to address collapse phenomena and the integration of successful components into a cohesive architecture. The evaluation included quantitative criteria to classify design concepts based on their effectiveness.
Results
The final architecture achieved stable training over 200,000 steps with a best-step perplexity of 22.0. The untied head resolved the initial collapse issue, and the model demonstrated the ability to retain information over long sequences without decay. However, several design concepts were found to be non-load-bearing and did not contribute to performance improvements.
Implications
The findings suggest that complex-valued recurrent models can provide advantages in long-context language modeling. The documented evolution of SWAVE offers valuable insights for future research and development in complex-valued architectures, potentially influencing the design of more efficient and robust language models.
GateMem: Benchmarking Memory Governance in Multi-Principal Shared-Memory Agents
Large Language Models
NLP
- GATEMEM addresses the lack of benchmarks for multi-principal shared-memory environments.
- The benchmark evaluates memory agents on utility, access control, and active forgetting.
- No existing methods achieve optimal performance across all governance dimensions.
- Long-context prompting provides the best governance score but at a high token cost.
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GateMem: Benchmarking Memory Governance in Multi-Principal Shared-Memory Agents
Summary
The paper introduces GATEMEM, a benchmark designed to evaluate memory governance in multi-principal shared-memory agents, addressing a critical gap in existing benchmarks that primarily focus on single-user settings. In environments such as hospitals, workplaces, and households, multiple users interact with a shared memory pool, necessitating not only high recall but also effective governance of memory access and deletion. GATEMEM evaluates agents based on three key dimensions: utility for legitimate requests, access control to prevent unauthorized disclosures, and active forgetting to ensure compliance with deletion requests. The benchmark encompasses four domains—medical, office, education, and household—comprising 91 long-form multi-party episodes and 2,218 hidden checkpoints. The findings reveal that no existing method simultaneously excels in all three governance aspects, with long-context prompting yielding the best trade-off at a high token cost, while retrieval-based methods often fail to prevent unauthorized access or recover deleted information. This indicates that current memory agents are not yet reliable for institutional deployment, highlighting the need for improved memory governance mechanisms.
Methodology
The authors developed GATEMEM as a benchmark consisting of multi-party episodes and hidden checkpoints to evaluate memory governance in shared environments. The evaluation process involves assessing agents based on their ability to manage utility, enforce access control, and comply with deletion requests, using a diverse set of baseline models and backbone architectures.
Results
The experiments demonstrated a consistent trade-off among utility, access control, and active forgetting, with no method achieving strong performance across all three dimensions. Long-context prompting was found to be the most effective approach, albeit at a significant token cost, while retrieval-based methods exhibited vulnerabilities in unauthorized disclosures and recovery of deleted information.
Implications
The findings suggest that improvements are needed in memory governance mechanisms for shared-memory agents, particularly in institutional contexts. This research could inform the development of more secure and reliable memory systems for applications in healthcare, education, and collaborative work environments.
Thermodynamic Signatures of Reasoning: Free-Energy and Spectral-Form-Factor Diagnostics for Hallucination Detection in Large Language Models
NLP
Large Language Models
Theory
- Introduction of Free-Energy Signatures (FES) for hallucination detection in LLMs.
- FES utilizes thermodynamic potentials and random-matrix theory to analyze attention Laplacians.
- Theoretical results demonstrate stability, expressiveness, and PAC bounds for FES.
- Empirical results show FES significantly outperforms existing spectral diagnostics in detecting hallucinations.
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Thermodynamic Signatures of Reasoning: Free-Energy and Spectral-Form-Factor Diagnostics for Hallucination Detection in Large Language Models
Summary
This paper addresses the critical issue of hallucination detection in large language models (LLMs) by introducing a novel approach called Free-Energy Signatures (FES). The authors argue that existing spectral diagnostics, which summarize the spectrum of attention-derived graph Laplacians using a limited set of eigenvalues, fail to leverage the full potential of the spectral information. FES treats each layer's attention Laplacian as a Hamiltonian and extracts various thermodynamic potentials, including the partition function, free energy, spectral entropy, and heat capacity, alongside the random-matrix-theory (RMT) spectral form factor. The paper presents three key theoretical results: (i) FES demonstrates Lipschitz stability under attention perturbation; (ii) it enriches finite spectral summaries and approximates moment-derived spectral functionals; and (iii) it establishes a finite-sample PAC bound on the AUROC of a training-free detector based on FES. Empirical evaluations across six open-weight LLMs and benchmarks show that FES outperforms existing attention-spectral baselines, achieving a significant improvement in AUROC scores without requiring updates to the underlying models. Additionally, the analysis reveals distinct spectral statistics for correct generations versus hallucinations, providing insights into the reasoning quality of LLMs.
Methodology
The methodology involves treating the attention Laplacian of LLMs as a Hamiltonian system, from which thermodynamic properties are extracted. FES is computed from the attention spectra across layers, and a lightweight supervised probe or an unsupervised RMT-deviation score is used for hallucination detection. The authors also provide theoretical proofs regarding the stability and expressiveness of FES.
Results
FES achieves the highest aggregate AUROC across six LLMs and benchmarks, improving mean AUROC by 6.5 points over the strongest existing spectral baseline. In a fully unsupervised setting, an RMT-deviation score yields a mean AUROC of 0.71. The analysis shows that correct generations exhibit Wigner-Dyson-like spectral statistics, while hallucinations show Poisson-like statistics.
Implications
The findings suggest that FES can serve as a robust tool for real-time hallucination detection in LLMs, enhancing the reliability of these models in practical applications. The insights into spectral statistics may also inform future research on model interpretability and reasoning quality.
Trainable Photonic Measurement for Physics-Informed PDE Learning
Theory
Optimization
Efficient ML
- Introduction of photonic quantum neural fields for physics-informed learning.
- Demonstrated significant performance improvements in solving PDEs compared to classical methods.
- Lower error rates achieved with fewer parameters in challenging regimes.
- Stability of Fock-probability measurements under noise conditions.
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Trainable Photonic Measurement for Physics-Informed PDE Learning
Summary
This paper introduces a novel approach to scientific machine learning using photonic quantum neural fields, specifically designed for solving partial differential equations (PDEs). The authors propose a photonic quantum neural network (PI-HPQNN) that utilizes trainable optical phases as coordinates, which are mixed through multi-photon Fock-space interference and decoded via photon-number measurements. This method allows for a more accurate representation of the underlying physics in PDEs, particularly in oscillatory and high-frequency regimes where traditional neural networks struggle due to spectral bias. The study demonstrates that the photonic approach significantly outperforms classical coordinate and Fourier-feature networks in challenging scenarios, achieving lower errors with fewer trainable parameters. Additionally, the research highlights the stability of Fock-probability readouts under noise compared to qubit-style quantum systems, suggesting that photonic quantum measurement can serve as a powerful representation-learning principle in scientific machine learning.
Methodology
The authors developed a hybrid photonic quantum neural network (PI-HPQNN) that replaces traditional coordinate networks in physics-informed neural networks (PINNs) with a photonic spectral generator. This involves encoding physical coordinates as optical phases, which are processed through a multi-mode Fock-space circuit. The resulting photon-number probabilities are then used to minimize the PDE residuals, allowing for a more accurate representation of the underlying physics.
Results
The PI-HPQNN was tested across seven different PDE benchmarks, including elliptic, wave, nonlinear dispersive, and inverse problems. The results showed that while classical networks performed adequately in smooth regimes, the photonic approach excelled in more complex scenarios, yielding lower errors by up to an order of magnitude and using only a quarter of the parameters compared to classical baselines. Additionally, the study found that the performance gain was linked to learned photonic interference patterns.
Implications
The findings suggest that photonic quantum measurement can enhance scientific machine learning by providing a more physically informed representation of complex systems governed by PDEs. This approach could lead to advancements in various fields such as physics, engineering, and applied mathematics, where accurate modeling of dynamic systems is crucial.
Do Time Series Foundation Model Benchmarks Hide Regime-Dependent Failures? Evidence from Traffic Speed Forecasting
Time Series
- Aggregate metrics can mask severe regime-dependent failures in TSFMs.
- Transition-regime MAE is significantly higher than overall MAE, indicating critical performance issues.
- A historical conditional baseline outperforms TSFMs in transition coverage but not in overall accuracy.
- Bimodal mixture augmentation (BMA) improves transition coverage while preserving TSFM accuracy.
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Do Time Series Foundation Model Benchmarks Hide Regime-Dependent Failures? Evidence from Traffic Speed Forecasting
Summary
This paper investigates the limitations of standard benchmarks for time series foundation models (TSFMs) in traffic speed forecasting, particularly during regime transitions between free-flow and congested states. The authors argue that aggregate metrics can obscure significant failures in model performance during these critical transitions. They introduce a regime-stratified evaluation approach, demonstrating that both accuracy and prediction-interval coverage significantly decline during transitions, with mean absolute error (MAE) reaching 11 mph compared to 3 mph overall. The empirical coverage of 90% prediction intervals drops to as low as 55% during these transitions. A simple historical conditional baseline outperforms TSFMs in transition coverage but lacks overall accuracy. To address this, the authors propose a novel method called bimodal mixture augmentation (BMA), which combines TSFM forecasts with historical distributional knowledge, achieving improved transition coverage while maintaining the accuracy of TSFMs. The findings suggest that TSFM benchmarks should adopt regime-aware evaluations to reveal hidden failures that aggregate metrics fail to capture.
Methodology
The authors conducted a regime-stratified evaluation of three TSFMs on two standard traffic speed benchmarks (METR-LA and PEMS-BAY). They analyzed model performance during traffic regime transitions and compared it against a historical conditional baseline. The proposed BMA method was implemented to enhance transition coverage by integrating TSFM forecasts with historical data.
Results
The study found that TSFMs exhibited a significant drop in accuracy and prediction-interval coverage during traffic transitions, with MAE reaching 11 mph. The historical conditional baseline provided better transition coverage than any TSFM, while BMA improved transition coverage by 3-19 percentage points without retraining.
Implications
The findings highlight the need for more nuanced evaluation metrics in time series forecasting, particularly in applications where regime transitions are critical. This could lead to improved forecasting models that better handle real-world scenarios in traffic management and other domains.
Execution-State Capsules: Graph-Bound Execution-State Checkpoint and Restore for Low-Latency, Small-Batch, On-Device Physical-AI Serving
NLP
Reinforcement Learning
Robotics
- FlashRT provides a low-latency execution environment for on-device AI applications.
- The execution-state capsule enables efficient checkpointing and restoring of execution states.
- The proposed system achieves significant speedups in time-to-first-token compared to existing methods.
- The design focuses on single-stream, low-latency interactions, making it suitable for real-time applications.
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Execution-State Capsules: Graph-Bound Execution-State Checkpoint and Restore for Low-Latency, Small-Batch, On-Device Physical-AI Serving
Summary
This paper introduces Execution-State Capsules, a novel approach to enhance low-latency, small-batch serving of physical-AI applications on-device. The proposed system, FlashRT, is a backend-facing kernel runtime that utilizes captured CUDA graphs over contiguous static buffers, eliminating block-table indirection. This design choice significantly reduces the time-to-first-token (TTFT) by 2.6–2.8 times compared to existing systems like vLLM. The execution-state capsule allows for efficient checkpointing and restoring of the execution state, enabling quick recovery of valid continuation states across various interactive scenarios. The system is particularly beneficial for applications requiring real-time responsiveness, such as language models, voice synthesis, and robotic control. The results demonstrate that the capsule mechanism not only improves latency but also effectively manages execution state, providing a new paradigm for serving low-latency AI applications.
Methodology
The methodology involves the development of FlashRT, a kernel-level runtime that captures CUDA graphs over static buffers without indirection. The execution-state capsule is designed to checkpoint and restore the execution state, allowing for quick recovery and reuse of computation across different interactive sessions. Performance evaluations were conducted on NVIDIA GPUs to measure latency improvements and correctness of state restoration.
Results
The results indicate that FlashRT achieves a time-to-first-token speedup of 2.6–2.8 times compared to vLLM, with further improvements observed as prefix length increases (up to 27 times). The capsule mechanism ensures byte-identical state restoration and token-identical outputs for various tasks, confirming its effectiveness in managing execution states.
Implications
The implications of this work extend to various real-time AI applications, including interactive language models, voice synthesis, and robotics, where low-latency performance is critical. The approach provides a new framework for optimizing AI serving systems, particularly in resource-constrained environments.
Emyx: Fast and efficient all-atom protein generation
Generative Models
Efficient ML
- Emyx simplifies the architecture for all-atom protein generation, reducing training costs and improving diversity.
- The model outperforms existing state-of-the-art methods in enzyme design benchmarks.
- Emyx achieves high accuracy in global fold recovery and catalytic geometry while being computationally efficient.
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Emyx: Fast and efficient all-atom protein generation
Summary
The paper introduces Emyx, a novel generative model designed for efficient all-atom protein generation, addressing the limitations of existing models that inherit complex architectures from structure prediction. Emyx employs a 140M-parameter conditional flow matching model that simplifies the architecture by concentrating capacity within standard transformer blocks and utilizing lightweight conditional representations. This approach allows Emyx to condition on sparse geometric constraints rather than rich co-evolutionary signals, leading to reduced training costs and improved sample diversity. The authors derive an exact reparametrization of the flow matching interpolant into the EDM noise-level framework, enhancing training efficiency and enabling compatibility with advanced sampling methods used in diffusion models. Emyx demonstrates superior performance compared to existing models like ProteÃna-Complexa and RFdiffusion3 on the AME enzyme design benchmark, achieving higher success rates in generating proteins with accurate global folds and catalytic geometries while requiring significantly less training time. The results indicate that Emyx not only streamlines the protein generation process but also expands the potential for novel enzyme designs in computational biocatalysis.
Methodology
Emyx utilizes a conditional flow matching model with a focus on standard transformer blocks, replacing complex embedding stacks with lightweight representations. It conditions on geometric constraints and employs a novel reparametrization into the EDM noise-level framework to enhance training efficiency.
Results
Emyx outperformed ProteÃna-Complexa and RFdiffusion3 in the AME enzyme design benchmark, achieving higher success rates in generating proteins with accurate structures and requiring only 682 GPU-hours for training, which is approximately four times less than RFdiffusion3.
Implications
The development of Emyx has significant implications for computational enzyme design, enabling the generation of novel proteins with desired catalytic properties more efficiently, which could advance applications in industrial and medical biocatalysis.
Zero-Shot Active Feature Acquisition via LLM-Elicitation
Large Language Models
Optimization
Theory
- Introduces a framework for Zero-Shot Active Feature Acquisition using LLMs.
- Focuses on eliciting unary deviations and pairwise co-variations as sufficient statistics.
- Demonstrates effectiveness in binary classification and top-k identification tasks.
- Outperforms traditional AFA methods in challenging medical scenarios.
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Zero-Shot Active Feature Acquisition via LLM-Elicitation
Summary
This paper presents a novel framework for Zero-Shot Active Feature Acquisition (AFA) using Large Language Models (LLMs) to address the challenge of acquiring features for classification or ranking decisions without relying on extensive labeled datasets. Traditional AFA methods depend heavily on labeled data, which is often scarce, particularly in medical settings. The authors propose a disciplined elicitation approach that separates the roles of LLMs and classical algorithms. Instead of asking LLMs to provide full distributions, they focus on extracting unary deviations and pairwise co-variations, which serve as sufficient statistics for a Markov random field (MRF). The framework is evaluated in two contexts: binary classification and top-k identification, particularly in the context of Inflammatory Bowel Disease (IBD) patients. The results demonstrate that the proposed method outperforms existing AFA techniques, especially in challenging cases, by effectively utilizing LLMs to inform the acquisition process without requiring task-specific labeled data.
Methodology
The authors develop a framework that utilizes LLMs to extract discriminative statistics (unary and pairwise) from domain knowledge without requiring labeled data. They implement a maximum-entropy closure to resolve identification issues arising from the elicited statistics and apply their method to both binary classification and top-k identification tasks.
Results
The proposed framework shows significant improvements over existing AFA methods, particularly in the context of IBD patients, where it effectively identifies the most relevant features for classification. The top-k acquisition policy developed in this work outperforms all existing methods, especially in the most complex patient cases.
Implications
This work has significant implications for medical diagnostics and other fields where labeled data is scarce. By enabling zero-shot feature acquisition, it opens up new avenues for applying machine learning in real-world scenarios where traditional methods are infeasible.
Recurrent neural networks approximate continuous functions
Theory
- Introduces the TMNU model to facilitate the approximation of continuous functions using RNNs.
- Proves that a single ReLU RNN can uniformly approximate any continuous function on [-1, 1] with fixed weights.
- Establishes convergence rates that align with polynomial approximation rates.
- Demonstrates that runtime is a necessary resource in the fixed-network approximation paradigm.
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Recurrent neural networks approximate continuous functions
Summary
This paper investigates the potential of recurrent neural networks (RNNs) to approximate continuous functions on the interval [-1, 1] using a fixed architecture and weights, allowing for improved accuracy through extended runtime. The authors introduce a novel intermediate model, the Turing machine with neural units (TMNU), which retains the necessary algorithmic flexibility for polynomial approximation while being compatible with RNNs. The study demonstrates that every continuous function can be uniformly approximated by the time evolution of a single ReLU RNN with fixed parameters. The convergence rates achieved reflect the polynomial approximation rates, and the authors provide minimax lower bounds to show that the required runtime is an essential resource in this fixed-network approximation paradigm. This work generalizes previous results that were limited to polynomial functions, establishing a broader framework for function approximation using RNNs.
Methodology
The authors develop a new computational model, the TMNU, which combines the capabilities of Turing machines with neural units to simplify the approximation of continuous functions. They leverage the properties of RNNs being Turing-complete to show that continuous functions can be approximated without needing to change the network architecture or weights, focusing instead on the iterative application of a single RNN.
Results
The main result shows that for every continuous function on [-1, 1], there exists a ReLU RNN with fixed weights and hidden dimensions that can approximate the function uniformly. The paper also provides minimax lower bounds indicating that the required runtime for achieving desired accuracy is not merely an artifact but a fundamental aspect of the approximation process.
Implications
This research has significant implications for the design of neural networks, suggesting that fixed architectures can be effectively utilized for continuous function approximation, potentially leading to more efficient training and deployment of RNNs in various applications. It opens avenues for further exploration in the efficiency of neural network architectures and their application in real-time systems where runtime is a critical factor.