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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Haiku to Opus in Just 10 bits: LLMs Unlock Massive Compression Gains
NLP
Large Language Models
Efficient ML
- Domain-adapted LoRA adapters improve lossless compression by 2Γ over baseline models.
- Lossy compression through succinct rewrites achieves a 2Γ improvement over original responses.
- Question-Asking compression (QA) allows small models to recover significant performance gaps using interactive questioning.
- Compression ratios achieved are significantly smaller than prior state-of-the-art methods.
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Haiku to Opus in Just 10 bits: LLMs Unlock Massive Compression Gains
Summary
This paper investigates the compression of text generated by Large Language Models (LLMs) in both lossless and lossy contexts, establishing a compression-compute frontier where increased compression necessitates greater computational resources. For lossless compression, the authors demonstrate that domain-adapted LoRA (Low-Rank Adaptation) adapters can enhance LLM-based arithmetic coding, achieving a 2Γ improvement over baseline methods. In the lossy compression domain, they propose a novel approach where the model generates a succinct rewrite of the original text before applying arithmetic coding, resulting in a compression ratio of approximately 0.03, which is a 2Γ improvement over compressing the original output. The paper introduces an interactive lossy protocol called Question-Asking compression (QA), inspired by the game 'Twenty Questions', where a smaller model refines its output by asking yes/no questions to a more capable model, effectively transferring one bit of information per question. Evaluating this method across eight benchmarks in math, science, and coding, the authors find that 10 binary questions can recover 23% to 72% of the capability gap between small and large models on standard benchmarks, achieving compression ratios between 0.0006 and 0.004, which is over 100Γ smaller than previous LLM-based compression methods. This work highlights the potential of interactive protocols to facilitate efficient knowledge transfer in text compression.
Methodology
The authors employ domain-adapted LoRA adapters for lossless compression, utilize response rewriting techniques for lossy compression, and introduce an interactive protocol (QA-compression) where a smaller model iteratively refines its output by querying a larger model.
Results
The study demonstrates that domain-adapted LoRA adapters provide a 2Γ improvement in lossless compression. For lossy compression, succinct rewrites yield a compression ratio of approximately 0.03, while QA-compression achieves ratios of 0.0006 to 0.004, recovering 23% to 72% of the performance gap in easier benchmarks and 7% to 38% in harder benchmarks.
Implications
The findings suggest that interactive protocols can significantly enhance the efficiency of knowledge transfer in text compression, potentially leading to more effective applications in data storage and transmission, particularly in scenarios where exact text reproduction is not critical.
A Numerical Method for Coupling Parameterized Physics-Informed Neural Networks and FDM for Advanced Thermal-Hydraulic System Simulation
Theory
Efficient ML
- Development of a hybrid framework (P2F) combining Parameterized PINNs and FDM for thermal-hydraulic simulations.
- NA-PINN allows for data-free training and avoids retraining for different problem parameters.
- The method ensures exact mass conservation and simplifies momentum solving in simulations.
- Demonstrated high accuracy in a six-tank draining scenario with minimal error across various initial conditions.
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A Numerical Method for Coupling Parameterized Physics-Informed Neural Networks and FDM for Advanced Thermal-Hydraulic System Simulation
Summary
This paper presents a novel numerical method called Parameterized PINNs coupled with Finite Difference Method (P2F) aimed at enhancing the simulation of advanced thermal-hydraulic systems, particularly in the context of nuclear safety assessments. The authors address the limitations of existing surrogate models that require extensive simulation data and the retraining needs of physics-informed neural networks (PINNs) when problem parameters change. The P2F method integrates a Node-Assigned PINN (NA-PINN) that learns a solution manifold based on inputs such as water-level difference and initial velocity, allowing it to serve as a data-free surrogate for momentum conservation across multiple flow paths without retraining. Coupled with a finite difference solver, the P2F method ensures exact mass conservation while simplifying the momentum solving process. The framework was verified through a six-tank gravity-driven draining scenario, demonstrating high accuracy in predicting water levels and velocities across various initial conditions without the need for retraining or additional simulation data. This work represents a significant advancement in the coupling of machine learning techniques with traditional numerical solvers for complex thermal-hydraulic simulations.
Methodology
The study introduces the P2F method, which combines a parameterized Node-Assigned PINN that learns a solution manifold for momentum conservation with a finite difference solver for mass conservation. This hybrid approach allows for simultaneous advancement of both methods within a shared time-marching loop, thus avoiding error accumulation over long simulation horizons.
Results
The P2F method achieved a mean absolute error of 7.85 Γ 10^-5 m for water levels and 3.21 Γ 10^-3 m/s for velocities under nominal conditions, maintaining consistent accuracy across time steps from 0.2 to 1.0 seconds and generalizing effectively to five distinct initial conditions without retraining.
Implications
The proposed P2F method has significant implications for improving the efficiency and accuracy of thermal-hydraulic system simulations in nuclear engineering, potentially enhancing safety assessments and enabling more effective parametric studies and uncertainty quantification.
Feature Weighting Improves Pool-Based Sequential Active Learning for Regression
Optimization
Theory
Efficient ML
- Introduces feature weighting in distance computation for active learning in regression.
- Proposes five new active learning approaches that incorporate feature weighting.
- Demonstrates improved performance of feature-weighted methods over traditional unweighted methods.
- Extends the applicability of feature weighting to both single-task and multi-task regression problems.
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Feature Weighting Improves Pool-Based Sequential Active Learning for Regression
Summary
This paper addresses the challenge of pool-based sequential active learning for regression (ALR), which aims to select a small number of unlabeled samples to label in order to build a more accurate regression model within a limited labeling budget. The author identifies that existing ALR methods fail to account for the varying importance of different features when calculating distances between samples, leading to sub-optimal sample selection. To remedy this, the paper proposes three feature-weighted single-task ALR approaches (FW-RD, FW-GSx, FW-iGS) and two multi-task approaches (FW-MT-GSx, FW-MT-iGS) that utilize ridge regression coefficients from previously labeled samples to weight features in distance computations. Extensive experiments demonstrate that these feature-weighted approaches consistently outperform their unweighted counterparts across both single-task and multi-task regression scenarios, indicating that feature weighting can enhance the performance of various regression models.
Methodology
The paper develops feature-weighted versions of existing active learning approaches by integrating ridge regression coefficients to adjust the importance of features in distance calculations. The proposed methods include FW-RD, FW-GSx, FW-iGS for single-task learning, and FW-MT-GSx, FW-MT-iGS for multi-task learning. The performance of these methods is evaluated through extensive experiments comparing them against their unweighted versions.
Results
The experimental results show that all five proposed feature-weighted ALR approaches significantly outperform their corresponding unweighted versions. This improvement is consistent across both linear and nonlinear regression models, indicating the robustness and effectiveness of the feature weighting strategy.
Implications
The findings suggest that incorporating feature weighting can lead to more efficient sample selection in active learning scenarios, potentially reducing labeling costs and improving model accuracy. The proposed methods can be easily adapted for use in other domains such as stream-based active learning and classification tasks.
Reflective Context Learning: Studying the Optimization Primitives of Context Space
Optimization
Reinforcement Learning
Theory
- Introduction of Reflective Context Learning (RCL) as a unified framework for context optimization.
- Emphasis on reflection and iterative updates to context instead of traditional gradient-based methods.
- Integration of classical optimization techniques to enhance context learning.
- Demonstrated improvements in performance across multiple benchmarks.
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Reflective Context Learning: Studying the Optimization Primitives of Context Space
Summary
The paper introduces Reflective Context Learning (RCL), a novel framework designed to address the challenges of learning in context space, which include credit assignment, overfitting, and local optima. RCL emphasizes the importance of reflection on agent behavior and iterative updates to context rather than traditional gradient-based optimization. The authors argue that context optimization should be treated as an optimization problem, allowing for systematic study and improvement through classical optimization techniques. They recast existing context-optimization methods within this framework and enhance them using techniques such as batching, improved credit assignment, auxiliary losses, and failure replay. The framework is evaluated on various benchmarks, demonstrating that these optimization primitives significantly improve performance over strong baselines, with their effectiveness varying across different task regimes. The findings suggest that context updates can lead to more robust and generalizable agent behaviors, highlighting the need for a unified approach to context-space learning.
Methodology
The authors developed RCL, which utilizes reflection on execution trajectories to generate directional update signals for context optimization. They systematically integrated classical optimization primitives such as batching, auxiliary losses, and failure replay into the context learning process. The framework was tested on several benchmarks, including AppWorld, BrowseComp+, and RewardBench2, to evaluate its effectiveness.
Results
The application of optimization primitives within the RCL framework led to significant performance improvements over existing strong baselines across various tasks. The study also explored factors such as initialization robustness, batch size effects, and the allocation of model strengths to different optimization components, revealing that the relative importance of these primitives shifts depending on the task regime.
Implications
The findings suggest that optimizing context rather than model parameters can lead to more adaptable and robust AI agents. This approach may facilitate continuous learning and adaptation in real-world applications, making it easier to implement and debug agent behaviors without the need for extensive retraining.
Coupled Query-Key Dynamics for Attention
NLP
Large Language Models
Efficient ML
- Introduces Coupled QK Dynamics, enhancing attention mechanisms by evolving queries and keys jointly.
- Achieves significant improvements in language modeling perplexity with minimal additional parameters.
- Structural ablation studies confirm that coupling is the key factor for performance gains.
- Effectiveness varies by corpus, with benefits observed in domain-coherent texts but not in heterogeneous datasets.
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Coupled Query-Key Dynamics for Attention
Summary
This paper introduces a novel framework for attention mechanisms in neural networks, termed Coupled Query-Key (QK) Dynamics. Unlike standard attention, which computes scores from static and independent projections of the input, the proposed method evolves queries and keys jointly through shared learned dynamics prior to scoring. This coupling enhances language modeling performance and training stability, as evidenced by significant reductions in perplexity on the WikiText-103 dataset. The authors demonstrate that coupled dynamics achieves a perplexity of 22.55β22.62 at 60M parameters, outperforming standard attention's 24.22 with only a marginal increase in parameters. Through structural ablation studies, they isolate the benefits of coupling from other factors, revealing that the coupling itself, rather than the specific integrator used (Hamiltonian or Euler), is crucial for performance improvements. The paper also characterizes the conditions under which coupling is beneficial, noting its effectiveness on domain-coherent text while showing degradation on heterogeneous datasets. The findings suggest that coupled dynamics can serve as a sample-efficiency mechanism, requiring fewer tokens for similar performance compared to standard attention when trained for longer durations.
Methodology
The authors propose a framework for evolving queries and keys through shared learned dynamics before scoring, utilizing both Hamiltonian and Euler integrators. They conduct structural ablation studies to isolate the effects of coupling and evaluate performance across various datasets and model sizes.
Results
Coupled QK Dynamics achieves a perplexity of 22.55β22.62 on WikiText-103 at 60M parameters, a 6.6-6.9% improvement over standard attention. The method shows consistent benefits on domain-coherent datasets like WikiText-103 and PubMed, while performance degrades on heterogeneous web text. At larger model sizes (350M), the advantage narrows, with Differential Attention surpassing coupled dynamics.
Implications
The findings suggest that incorporating coupled dynamics into attention mechanisms can lead to more stable training and improved performance in language modeling tasks. This approach may also inform future developments in transformer architectures and other applications requiring efficient attention mechanisms.
DISCO-TAB: A Hierarchical Reinforcement Learning Framework for Privacy-Preserving Synthesis of Complex Clinical Data
Reinforcement Learning
Generative Models
Large Language Models
- DISCO-TAB synthesizes clinical data while preserving privacy and ensuring clinical validity.
- The framework uses a hierarchical reinforcement learning approach to evaluate data quality at multiple granularities.
- It incorporates techniques to preserve medical logic and address class imbalances in synthetic data.
- DISCO-TAB shows significant improvements in clinical classifier utility and statistical fidelity compared to existing methods.
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DISCO-TAB: A Hierarchical Reinforcement Learning Framework for Privacy-Preserving Synthesis of Complex Clinical Data
Summary
The paper presents DISCO-TAB, a novel framework designed to synthesize complex clinical data while preserving privacy and ensuring clinical validity. Traditional methods for generating synthetic data from Electronic Health Records (EHR) often fail to capture the intricate dependencies and class imbalances present in biomedical datasets. DISCO-TAB addresses these challenges by integrating a fine-tuned Large Language Model (LLM) with a multi-objective discriminator system, optimized through a hierarchical reinforcement learning approach. This framework evaluates the quality of synthetic data at multiple levelsβtoken, sentence, feature, and rowβallowing for a more nuanced assessment of data validity. The authors introduce techniques such as Automated Constraint Discovery and Inverse-Frequency Reward Shaping to maintain medical logic and mitigate issues related to minority class representation. The framework is validated on various benchmarks, including datasets related to heart failure and Parkinson's disease, demonstrating significant improvements in downstream clinical classifier utility and statistical fidelity. The results indicate that DISCO-TAB outperforms existing methods, achieving up to a 38.2% enhancement in utility while maintaining robust defenses against membership inference attacks. This work sets a new benchmark for generating trustworthy synthetic tabular data in healthcare applications.
Methodology
DISCO-TAB combines a fine-tuned Large Language Model with a hierarchical reinforcement learning optimization strategy. It evaluates synthetic data quality at four levels: token, sentence, feature, and row, using multi-objective feedback to ensure compliance with clinical constraints. The framework employs Automated Constraint Discovery and Inverse-Frequency Reward Shaping to maintain medical logic and address minority class collapse.
Results
The framework achieved up to a 38.2% improvement in downstream clinical classifier utility compared to baseline methods such as GANs and diffusion models. It also demonstrated exceptional statistical fidelity with Jensen-Shannon Divergence (JSD) values below 0.01 and strong resistance to membership inference attacks.
Implications
DISCO-TAB has significant implications for the development of reliable clinical decision support systems, enabling the generation of synthetic data that is both useful for training AI models and compliant with privacy regulations. This could facilitate advancements in precision medicine and improve patient care by providing high-quality, explainable data for AI applications.
FedSQ: Optimized Weight Averaging via Fixed Gating
Federated Learning
- FedSQ decouples structural and quantitative knowledge in federated learning.
- The method stabilizes aggregation under heterogeneous client data by fixing gating masks.
- Empirical results show improved convergence efficiency compared to standard federated averaging.
- FedSQ is particularly effective in cross-silo federated learning settings.
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FedSQ: Optimized Weight Averaging via Fixed Gating
Summary
The paper introduces FedSQ, a novel federated learning (FL) approach designed to address challenges posed by statistical heterogeneity and client drift in non-i.i.d. data environments. FedSQ leverages a transfer-initialized neural network framework that separates structural knowledge (gating behavior) from quantitative knowledge (weight values) during federated fine-tuning. By freezing a structural copy of a pretrained model, FedSQ maintains fixed binary gating masks while optimizing only the quantitative parameters across clients. This method stabilizes the learning process and enhances convergence efficiency, particularly in cross-silo settings where clients have diverse data distributions. The authors empirically validate FedSQ against standard federated learning baselines, demonstrating improved robustness and reduced communication rounds to achieve optimal validation performance while maintaining accuracy in transfer learning scenarios.
Methodology
FedSQ employs a two-stage federated learning protocol where all clients start from a shared pretrained model. The structural component of the model is frozen to create fixed gating masks, while only the quantitative parameters are updated and aggregated across clients. This approach reduces the complexity of learning to within-regime affine refinements, improving stability and efficiency during federated fine-tuning.
Results
The experiments conducted on two convolutional neural network backbones under both i.i.d. and Dirichlet data splits indicate that FedSQ significantly enhances robustness and reduces the number of communication rounds required to achieve optimal validation performance, compared to traditional federated learning methods.
Implications
FedSQ has the potential to improve federated learning applications in environments with heterogeneous data distributions, such as healthcare and finance, where data privacy is crucial. The method can lead to more efficient model training and deployment in real-world scenarios, enhancing the practicality of federated learning.
Conditional Sampling via Wasserstein Autoencoders and Triangular Transport
Generative Models
Theory
Efficient ML
- Introduction of Conditional Wasserstein Autoencoders (CWAEs) for conditional sampling.
- Utilization of block-triangular decoders to exploit low-dimensional structures in data.
- Demonstration of substantial error reductions in approximation compared to traditional methods.
- Theoretical exploration of connections between CWAEs and conditional optimal transport.
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Conditional Sampling via Wasserstein Autoencoders and Triangular Transport
Summary
This paper introduces Conditional Wasserstein Autoencoders (CWAEs), a novel framework for conditional sampling that leverages low-dimensional structures in both conditioned and conditioning variables. The authors modify the traditional Wasserstein autoencoder by employing a block-triangular decoder and imposing independence assumptions on latent variables. This approach allows for effective conditional simulation while exploiting low-dimensional structures inherent in high-dimensional data. The paper explores the theoretical foundations of CWAEs, particularly their relationship to conditional optimal transport problems, and presents three architectural variants of the model. Through numerical experiments, the authors demonstrate that CWAEs significantly reduce approximation errors compared to the low-rank ensemble Kalman filter (LREnKF), especially in scenarios where the conditional measures exhibit low-dimensional characteristics. The proposed framework offers a scalable, data-driven alternative to existing methods, facilitating efficient sampling from conditional distributions in complex, high-dimensional settings.
Methodology
The authors propose a framework that combines the Wasserstein autoencoder with block-triangular transport maps. They introduce a low-dimensional latent variable to capture essential data structures and train the encoder-decoder pair by minimizing the Wasserstein distance between generated and true distributions. The model is designed to learn transport maps for generating samples from conditional distributions directly from data.
Results
Numerical experiments show that the different variants of CWAEs achieve significant reductions in approximation errors compared to the low-rank ensemble Kalman filter, particularly in cases where the support of conditional measures is low-dimensional. The results validate the effectiveness of the proposed framework in high-dimensional conditional sampling tasks.
Implications
The CWAEs framework has potential applications in various fields requiring conditional sampling, such as Bayesian inference, nonlinear filtering, and machine learning. Its ability to automatically discover and exploit low-dimensional structures from data could lead to advancements in efficient sampling techniques in complex systems.
Random Is Hard to Beat: Active Selection in online DPO with Modern LLMs
NLP
Large Language Models
Optimization
- Active Preference Learning (APL) shows minimal advantage over RANDOM sampling in online DPO.
- Improvements in proxy win rates can occur alongside declines in general model capabilities.
- The study highlights the inefficiency of active selection strategies in the presence of strong pre-trained priors.
- The findings raise questions about the practical benefits of computationally intensive active selection methods.
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Random Is Hard to Beat: Active Selection in online DPO with Modern LLMs
Summary
This paper investigates the effectiveness of Active Preference Learning (APL) in the context of online Direct Preference Optimization (DPO) using modern large language models (LLMs). The authors argue that due to the strong priors inherited from web-scale pretraining, the potential benefits of active selection strategies are limited. They compare APL, which aims to optimize query efficiency by selecting informative pairs from an on-policy candidate pool, against a simple RANDOM sampling approach. The study evaluates these methods across various settings, including harmlessness, helpfulness, and instruction-following, using both reward models and LLM-as-a-judge proxies. The findings reveal that APL provides negligible improvements in performance metrics compared to RANDOM sampling. Notably, the study uncovers a dissociation where improvements in proxy win rates do not correlate with general capability, indicating that while APL may yield higher proxy scores, it can lead to a degradation in overall model performance. The authors conclude that in scenarios dominated by strong pre-trained priors, the computational costs associated with active selection may not be justified when simple random sampling offers comparable diversity at minimal cost.
Methodology
The authors conducted a controlled empirical study comparing APL and RANDOM sampling for pair selection in online DPO. They maintained a fixed training and labeling budget while varying the proxy judge and pair-selection strategy. The evaluation involved measuring proxy preference metrics and general capabilities using standard benchmarks.
Results
The results indicated that APL did not consistently outperform RANDOM sampling, with proxy win rates showing improvements that did not align with general capability metrics. This suggests that the benefits of APL may be superficial and not indicative of true model enhancement.
Implications
The findings suggest that researchers and practitioners may need to reconsider the reliance on active selection methods in scenarios where LLMs exhibit strong pre-trained capabilities. The study emphasizes the importance of evaluating both proxy metrics and actual performance to avoid misleading conclusions about model improvements.
Bridging Deep Learning and Integer Linear Programming: A Predictive-to-Prescriptive Framework for Supply Chain Analytics
Time Series
Optimization
- The study systematically compares deep learning models with traditional statistical methods for demand forecasting.
- N-BEATS outperforms MSTL in forecasting accuracy, making it the most optimized model for this dataset.
- The proposed framework integrates forecasting with operational decision-making through integer linear programming.
- The research demonstrates the practical application of improved forecasting in logistics planning.
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Bridging Deep Learning and Integer Linear Programming: A Predictive-to-Prescriptive Framework for Supply Chain Analytics
Summary
This paper addresses the challenges of demand forecasting in supply chain management, particularly the difficulties posed by seasonality, irregular spikes, and noise in retail data. The authors propose a three-step analytical framework that integrates forecasting with operational analytics. The first step involves exploratory data analysis of 180,519 transactions to identify trends and seasonal patterns. The second step compares the forecasting performance of the N-BEATS and N-HiTS deep learning models against the MSTL statistical model. Results indicate that both deep learning models significantly outperform MSTL, with N-BEATS achieving the lowest forecasting error. In the final step, the forecasts are utilized in an integer linear programming (ILP) model to optimize delivery plans, minimizing total delivery time while adhering to budget and capacity constraints. The study highlights the practical impact of accurate forecasting and interpretable model optimization in logistics, demonstrating a cohesive workflow from predictive analytics to prescriptive decision-making.
Methodology
The methodology consists of three stages: (1) exploratory data analysis to identify trends and seasonal components in the dataset, (2) comparative analysis of forecasting models (N-BEATS, N-HiTS, and MSTL) to determine the most accurate model, and (3) application of the selected forecasting model in an integer linear programming framework to optimize delivery plans.
Results
The results show that both N-BEATS and N-HiTS significantly outperform the MSTL model in forecasting accuracy, with N-BEATS being the most effective. The optimized delivery plan generated through the ILP model resulted in a feasible and cost-effective shipping strategy, minimizing delivery time under budget and capacity constraints.
Implications
The findings suggest that integrating advanced forecasting techniques with optimization models can enhance decision-making in supply chain management. This approach can lead to more efficient logistics operations and reduced costs, making it valuable for businesses facing complex demand patterns.
Universal Hypernetworks for Arbitrary Models
Computer Vision
Graph Learning
NLP
- UHN is a fixed-architecture generator that can produce weights for various models without redesigning the hypernetwork.
- It supports multi-model generalization and multi-task learning across different architectures.
- UHN allows for stable recursive generation of hypernetworks, enhancing flexibility in model creation.
- Empirical results show UHN's competitive performance across diverse benchmarks.
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Universal Hypernetworks for Arbitrary Models
Summary
The paper introduces the Universal Hypernetwork (UHN), a novel approach that addresses the limitations of conventional hypernetworks, which are typically tied to specific model architectures. UHN is a fixed-architecture generator that predicts neural network weights based on deterministic descriptors related to parameters, architecture, and tasks. This decoupling allows UHN to generate diverse models across various architectures and tasks without the need for redesign or retraining. The authors present three main empirical claims: (1) UHN maintains competitive performance with direct training across multiple benchmarks in vision, graph, text, and formula-regression; (2) it supports both multi-model generalization within a family and multi-task learning across heterogeneous models; and (3) UHN enables stable recursive generation, allowing for the creation of intermediate hypernetworks before producing the final model. The findings suggest that UHN can effectively scale to larger and more diverse target networks while remaining efficient and versatile.
Methodology
The UHN predicts each scalar parameter from deterministic descriptors that encode parameter indices, architecture information, and task details. This method utilizes Gaussian Fourier features to model complex weight fields, allowing a single hypernetwork to generate parameters for various target models efficiently.
Results
The empirical evaluations demonstrate that UHN is competitive with direct training methods across multiple benchmarks, including CIFAR-10, Cora, and AG News. It effectively generalizes across model families and tasks while maintaining performance stability during recursive generation.
Implications
The UHN framework has significant implications for model design in machine learning, particularly in scenarios requiring flexibility across different architectures and tasks. It can streamline the process of model adaptation and deployment, making it easier to leverage hypernetworks in diverse applications.
HyperFitS -- Hypernetwork Fitting Spectra for metabolic quantification of ${}^1$H MR spectroscopic imaging
Optimization
Efficient ML
- HyperFitS significantly reduces spectral fitting times from hours to seconds.
- The method allows for flexible baseline corrections and water suppression adjustments.
- Metabolite maps generated by HyperFitS show strong agreement with conventional fitting methods.
- Baseline parametrization can substantially impact metabolic quantification results.
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HyperFitS -- Hypernetwork Fitting Spectra for metabolic quantification of ${}^1$H MR spectroscopic imaging
Summary
The paper introduces HyperFitS, a novel hypernetwork designed for the rapid and flexible fitting of spectra in proton magnetic resonance spectroscopic imaging (1H MRSI) to quantify metabolites in the human brain. Traditional methods, such as LCModel, while accurate, are time-consuming and often struggle with baseline corrections and water suppression factors, which can significantly affect quantification accuracy. HyperFitS addresses these limitations by allowing for a wide range of baseline adjustments and water suppression corrections without the need for retraining. The authors demonstrate that HyperFitS can produce metabolite maps from 3T and 7T MRSI data with isotropic resolutions of 10 mm, 3.4 mm, and 2 mm, achieving results comparable to conventional methods but with processing times reduced from hours to mere seconds. The study highlights the importance of baseline parametrization, showing that it can influence quantification results by up to 30%. Overall, HyperFitS represents a significant advancement in the field of metabolic imaging, combining the speed of deep learning with the configurability needed for clinical applications.
Methodology
HyperFitS utilizes a hypernetwork architecture that takes baseline and water suppression parameters as input to predict weights for a quantification network. This network is based on a physics-informed model that combines metabolite spectra, macromolecule basis sets, and baseline correction functions, enabling it to adapt to various spectral data qualities.
Results
The results indicate that HyperFitS provides metabolite maps that align closely with those generated by the gold-standard LCModel fitting, while achieving significantly faster processing times. The study also emphasizes the critical role of baseline parametrization in metabolic quantification, revealing potential discrepancies of up to 30% in results based on different baseline configurations.
Implications
HyperFitS has the potential to enhance the clinical applicability of 1H MRSI by providing rapid and accurate metabolic quantification, which could facilitate the diagnosis and monitoring of metabolic disorders in patients. Its flexibility and configurability may also allow for broader use across different imaging protocols and field strengths.
Causal-Audit: A Framework for Risk Assessment of Assumption Violations in Time-Series Causal Discovery
Time Series
- Causal-Audit formalizes assumption validation as calibrated risk assessment.
- The framework computes risk scores based on five assumption families and provides uncertainty intervals.
- An abstention-aware decision policy recommends methods only when reliable inference is possible.
- Evaluation shows high calibration accuracy (AUROC > 0.95) and significant false positive reduction.
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Causal-Audit: A Framework for Risk Assessment of Assumption Violations in Time-Series Causal Discovery
Summary
The paper presents Causal-Audit, a novel framework designed to assess the risks associated with assumption violations in time-series causal discovery. Time-series causal discovery methods depend on certain assumptions, such as stationarity and regular sampling, which, if violated, can lead to misleading causal graphs without any indication of unreliability. Causal-Audit formalizes the validation of these assumptions through calibrated risk assessment, computing effect-size diagnostics across five families of assumptions and aggregating them into four calibrated risk scores with uncertainty intervals. The framework employs an abstention-aware decision policy that recommends specific causal discovery methods only when reliable inference is supported by evidence. The authors demonstrate the effectiveness of Causal-Audit through evaluations on a synthetic dataset of 500 data-generating processes, achieving high calibration accuracy and significant reductions in false positives. The framework's open-source implementation aims to facilitate structured assumption auditing in various research domains, including climate science and epidemiology.
Methodology
Causal-Audit employs a three-stage pipeline: Stage I involves automatic diagnostics for assumption violations, Stage II calibrates risk scores based on these diagnostics, and Stage III implements a decision policy to recommend or abstain from using specific causal discovery methods. The framework is designed to be method-agnostic in the diagnostic stage while providing method-specific risk calibration and decision thresholds.
Results
The evaluation of Causal-Audit on a synthetic atlas of 500 data-generating processes demonstrated well-calibrated risk scores with AUROC exceeding 0.95, a 62% reduction in false positives among recommended datasets, and a 78% abstention rate in cases of severe violations. External evaluations confirmed the consistency of recommend-or-abstain decisions with benchmark specifications.
Implications
Causal-Audit has significant implications for researchers in fields reliant on time-series causal discovery, such as climate science, neuroscience, and economics. By providing a systematic approach to assess the reliability of causal inference, it enhances the transparency and validity of research findings, potentially reducing the publication of spurious causal claims.
Robust Graph Representation Learning via Adaptive Spectral Contrast
Graph Learning
Theory
- Identifies a spectral dilemma in graph contrastive learning regarding the trade-off between high-frequency signal utility and noise sensitivity.
- Introduces ASPECT, a framework that utilizes a reliability-aware spectral gating mechanism to improve robustness in graph representation learning.
- Demonstrates that existing global spectral fusion strategies are suboptimal for mixed graphs with varying node-wise frequency preferences.
- Achieves state-of-the-art performance on 8 out of 9 benchmarks, particularly on heterophilic graphs.
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Robust Graph Representation Learning via Adaptive Spectral Contrast
Summary
This paper addresses the challenges of spectral graph contrastive learning, particularly the vulnerability of high-frequency signals to noise, which is critical for encoding heterophilic structures. The authors identify a spectral dilemma where high-frequency components, while essential for capturing heterophily, exhibit higher variance under perturbations. They propose ASPECT, a novel framework that employs a reliability-aware spectral gating mechanism to dynamically adjust the reliance on frequency channels based on their stability against adversarial perturbations. This approach is formulated as a minimax game, optimizing a node-wise gate against a spectral adversary targeting energy distributions. Empirical evaluations demonstrate that ASPECT achieves state-of-the-art performance on 8 out of 9 benchmarks, effectively distinguishing meaningful structural heterophily from incidental noise, thereby enhancing robustness in graph representation learning.
Methodology
The authors develop ASPECT, which formulates a minimax game to optimize a node-wise gate that adjusts the reliance on frequency channels based on their stability against perturbations. This is achieved through a Rayleigh quotient penalty targeting spectral energy distributions, allowing the encoder to learn robust representations while filtering out unreliable high-frequency noise.
Results
ASPECT outperforms existing methods on 8 out of 9 benchmarks, particularly excelling in scenarios involving heterophilic graphs. The analysis of the learned gate values indicates a strong correlation with local homophily, confirming the framework's effectiveness in disentangling structural signals from noise.
Implications
The findings suggest that enhancing robustness in spectral graph learning is crucial for developing models that generalize well under mixed structural conditions. This work could inform future research in graph representation learning, particularly in applications involving complex graph structures with varying node characteristics.
Batched Contextual Reinforcement: A Task-Scaling Law for Efficient Reasoning
NLP
Large Language Models
Reinforcement Learning
Efficient ML
- Introduction of Batched Contextual Reinforcement (BCR) for efficient reasoning in LLMs.
- Discovery of a task-scaling law where increasing concurrent problems reduces token usage while maintaining accuracy.
- Demonstration of a 'free lunch' phenomenon where accuracy improves despite reduced verbosity.
- Emergence of self-regulated efficiency in models, eliminating redundant reasoning loops.
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Batched Contextual Reinforcement: A Task-Scaling Law for Efficient Reasoning
Summary
This paper introduces Batched Contextual Reinforcement (BCR), a novel training paradigm aimed at enhancing the efficiency of reasoning in Large Language Models (LLMs) while maintaining or improving accuracy. Traditional methods for improving efficiency often lead to degraded reasoning quality or require complex training processes. BCR simplifies this by allowing models to solve multiple problems simultaneously within a shared context window, rewarding them based solely on per-instance accuracy. The authors identify a new task-scaling law, showing that as the number of concurrent problems increases, per-problem token usage decreases while accuracy remains relatively stable. This challenges the conventional accuracy-efficiency trade-off, revealing a 'free lunch' phenomenon where models can achieve better accuracy with reduced verbosity. The study demonstrates that BCR can reduce token usage by 15.8% to 62.6% across different model sizes while improving performance on major mathematical benchmarks. Furthermore, qualitative analyses indicate that models trained with BCR develop self-regulated efficiency, autonomously eliminating redundant reasoning processes. The findings suggest that BCR provides a stable, constraint-based alternative for length control in LLMs, unlocking latent high-density reasoning capabilities without explicit supervision.
Methodology
The authors propose BCR, which involves training models to solve N problems simultaneously within a shared context window, rewarded by per-instance accuracy. This method creates an implicit token budget that encourages efficient reasoning without the need for explicit length penalties or complex training structures.
Results
BCR achieves a reduction in token usage by 15.8% to 62.6% across model sizes (1.5B and 4B) while consistently maintaining or improving accuracy on five major mathematical benchmarks. The method reveals a task-scaling law that allows for controllable throughput and accuracy trade-offs.
Implications
The findings suggest that BCR can significantly enhance the efficiency of reasoning in LLMs, making it a valuable framework for practical applications in areas requiring complex reasoning, such as mathematical problem-solving and other cognitive tasks. This could lead to more efficient deployment of LLMs in real-world applications, reducing computational costs while improving performance.
LiME: Lightweight Mixture of Experts for Efficient Multimodal Multi-task Learning
Multimodal
Efficient ML
Theory
- LiME reduces the number of trainable parameters significantly compared to traditional MoE-PEFT methods.
- The approach allows for expert specialization without the need for separate adapters for each expert.
- Zero-parameter routing is achieved by utilizing existing representations, eliminating the overhead of learned routers.
- LiME is compatible with various PEFT methods, enhancing its versatility.
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LiME: Lightweight Mixture of Experts for Efficient Multimodal Multi-task Learning
Summary
The paper introduces LiME (Lightweight Mixture of Experts), a novel approach that enhances the efficiency of multimodal multi-task learning by combining Mixture of Experts (MoE) with parameter-efficient fine-tuning (PEFT). Traditional MoE-PEFT methods require separate adapters for each expert, leading to a significant increase in trainable parameters and limiting their applicability. LiME addresses these issues by utilizing a single shared PEFT module, modulated by lightweight expert vectors, which reduces the number of trainable parameters while allowing for expert specialization. Additionally, LiME eliminates the need for learned routing parameters by leveraging existing frozen representations from the model's forward pass, thus achieving zero-parameter routing. Theoretical proofs demonstrate that increasing the number of experts retains more task-relevant information and that the modulation approach approximates expert-specific PEFT with bounded error. The methodology includes n-gram windowed routing and adaptive expert selection based on routing confidence. Experiments conducted on the MMT-47 benchmark, which encompasses 47 tasks across text, image, and video modalities, show that LiME achieves competitive or superior performance while using up to 4 times fewer trainable parameters and up to 29% faster training compared to existing MoE-PEFT baselines.
Methodology
LiME employs a single shared PEFT module with lightweight expert vectors for modulation, allowing for expert specialization without replicating adapters. It utilizes zero-parameter routing by leveraging frozen representations from the model's forward pass, and incorporates n-gram windowed routing and adaptive expert selection based on confidence levels.
Results
LiME demonstrated competitive or superior performance on the MMT-47 benchmark, achieving up to 4 times fewer trainable parameters and up to 29% faster training compared to existing MoE-PEFT methods.
Implications
LiME's approach can significantly reduce the computational and memory requirements of multimodal multi-task learning, making it more accessible for applications in various domains such as natural language processing, computer vision, and beyond. Its compatibility with multiple PEFT methods also opens avenues for further research and application in efficient model adaptation.
PRISM: LLM-Guided Semantic Clustering for High-Precision Topics
NLP
Large Language Models
Interpretability
- PRISM combines LLM capabilities with efficient topic modeling techniques.
- The framework utilizes a student-teacher model to distill LLM supervision into a lightweight encoder.
- Thresholded clustering allows for precise topic separation without over-partitioning.
- PRISM shows improved performance over existing topic modeling methods across multiple corpora.
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PRISM: LLM-Guided Semantic Clustering for High-Precision Topics
Summary
The paper introduces Precision-Informed Semantic Modeling (PRISM), a novel framework for topic modeling that leverages the strengths of large language models (LLMs) while maintaining low computational costs and high interpretability. PRISM fine-tunes a sentence encoding model using a sparse set of labels provided by LLMs on samples from a target corpus. By employing thresholded clustering techniques, PRISM effectively separates closely related topics within specific domains. The authors demonstrate that PRISM outperforms state-of-the-art local topic models and large embedding models in terms of topic separability, requiring significantly fewer LLM queries during training. The framework contributes to the field by establishing a student-teacher pipeline for distilling LLM supervision into a lightweight model, analyzing sampling strategies for improved cluster separability, and providing an effective tool for web-scale text analysis, particularly useful for tracking nuanced claims and subtopics in sensitive areas such as public security.
Methodology
PRISM fine-tunes a pre-trained sentence encoding model using a dataset generated from LLM-provided labels on text samples. The model is then used to create embeddings for the corpus, which are clustered using a thresholded clustering algorithm. The fine-tuning process adapts the model to the specific domain of interest, enhancing its ability to discern subtle topics.
Results
The results indicate that PRISM significantly improves topic separability compared to traditional local topic models and large embedding models. The framework requires only a small number of LLM queries for training, demonstrating its efficiency and scalability.
Implications
PRISM has potential applications in various fields, including social media analysis, public security, and any domain requiring nuanced topic tracking. Its interpretability and efficiency make it a valuable tool for researchers and practitioners looking to analyze complex narratives.
UQ-SHRED: uncertainty quantification of shallow recurrent decoder networks for sparse sensing via engression
Time Series
Theory
Efficient ML
- UQ-SHRED provides a distributional learning framework for valid uncertainty quantification in sparse sensing.
- The method combines noise injection with energy score minimization, maintaining computational efficiency.
- Theoretical guarantees are established for the learned conditional distribution, supporting its use in uncertainty-aware applications.
- UQ-SHRED is validated across multiple scientific datasets, showcasing its effectiveness in various domains.
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UQ-SHRED: uncertainty quantification of shallow recurrent decoder networks for sparse sensing via engression
Summary
The paper introduces UQ-SHRED, a novel framework for uncertainty quantification in the context of reconstructing high-dimensional spatiotemporal fields from sparse sensor measurements. Building on the SHallow REcurrent Decoder (SHRED) architecture, UQ-SHRED addresses the critical limitation of uncertainty estimation in complex and data-scarce environments. The framework employs a distributional learning approach through a method called engression, which allows for the modeling of predictive distributions conditioned on sensor history. By injecting stochastic noise into sensor inputs and utilizing an energy score loss for training, UQ-SHRED efficiently generates well-calibrated predictive distributions without the need for extensive computational resources or multiple network architectures. The authors validate UQ-SHRED on various real-world datasets, including turbulent flow and atmospheric dynamics, demonstrating its robustness and effectiveness across diverse scientific applications. The paper also includes ablation studies to analyze the impact of different model settings on performance, confirming the framework's capability for valid uncertainty quantification in sparse sensing scenarios.
Methodology
UQ-SHRED utilizes a distributional learning framework that incorporates noise injection into the input of the SHRED architecture. The model is trained using an energy score loss to optimize the predictive distribution of spatial states based on sensor measurements. This approach allows for uncertainty to be modeled throughout the network without requiring additional architectural modifications. At inference, the model generates samples from the conditional predictive distribution by propagating input noise through the trained network.
Results
The UQ-SHRED framework demonstrated effective uncertainty quantification across five complex real-world datasets, including sea-surface temperature, turbulent flows, neural activity, solar activity, and propulsion physics. The results indicated that UQ-SHRED produced well-calibrated confidence intervals and maintained robustness across diverse applications. The ablation studies provided insights into how various hyperparameters affected the quality of uncertainty estimates.
Implications
The development of UQ-SHRED has significant implications for scientific applications that require reliable uncertainty quantification, such as risk assessment, anomaly detection, and decision-making under uncertainty. The framework's ability to provide valid uncertainty estimates can enhance the safety and reliability of systems in fields like fluid dynamics, neuroscience, and atmospheric sciences.
Complex-Valued GNNs for Distributed Basis-Invariant Control of Planar Systems
Graph Learning
Robotics
Theory
- Introduces a complex-valued GNN architecture that is invariant to local basis choices.
- Enhances data efficiency and tracking performance in distributed control tasks.
- Demonstrates improved generalization over traditional real-valued GNNs.
- Addresses limitations of existing GNNs in GPS-denied and compass-denied environments.
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Complex-Valued GNNs for Distributed Basis-Invariant Control of Planar Systems
Summary
This paper introduces a novel architecture for Graph Neural Networks (GNNs) that enables distributed control of planar systems without reliance on a global reference frame. Traditional GNNs require compatible geometric observations across nodes, which limits their application in environments lacking GPS or compass data. The proposed architecture utilizes complex-valued representations to express 2D geometric features and transformations, allowing for a globally invariant control policy. By employing complex-valued linear layers with phase-equivariant activation functions, the model enhances data efficiency, tracking performance, and generalization capabilities compared to a real-valued baseline in an imitation learning flocking task. This advancement addresses the limitations of existing GNN architectures in multi-robot control scenarios, particularly in dynamic and uncertain environments.
Methodology
The authors developed a complex-valued parameterization for GNNs that allows for the transformation of latent space encodings between different local frames. The architecture incorporates complex-valued linear layers and phase-equivariant activation functions, enabling the GNN to learn control policies that are invariant to the choice of local reference frames. The methodology is evaluated through an imitation learning flocking task, comparing the performance of the proposed architecture against a real-valued baseline.
Results
The complex-valued GNN architecture demonstrated significant improvements in data efficiency, tracking performance, and generalization capabilities compared to the real-valued baseline. The experiments showed that the proposed model effectively learned control policies that maintained performance across varying local frames, thereby validating its effectiveness in distributed control scenarios.
Implications
The findings suggest that complex-valued GNNs can be effectively utilized in multi-robot control applications, particularly in environments where traditional reference frames are unavailable. This could lead to advancements in autonomous systems, such as drone swarms or robotic fleets, operating in challenging conditions. The architecture's ability to generalize across different tasks and environments may also facilitate broader applications in robotics and control theory.
Toward an Operational GNN-Based Multimesh Surrogate for Fast Flood Forecasting
Graph Learning
Time Series
Efficient ML
- Development of a GNN-based surrogate model for flood forecasting.
- Utilization of a projected-mesh strategy to enhance training efficiency.
- Incorporation of multimesh connectivity to improve spatial reception.
- Significant reduction in prediction time from 180 minutes to 0.4 seconds.
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Toward an Operational GNN-Based Multimesh Surrogate for Fast Flood Forecasting
Summary
This paper addresses the challenge of operational flood forecasting, which traditionally relies on high-fidelity hydraulic solvers that are computationally expensive. The authors propose a graph-neural network (GNN)-based surrogate model designed to accelerate flood predictions on the lower TΓͺt River in France. They create a learning-ready database of synthetic flood events based on a high-resolution Telemac2D model, which features over 400,000 nodes. The GNN surrogate utilizes a projected-mesh strategy to maintain high-fidelity supervision while ensuring training efficiency. Additionally, the multimesh connectivity enhances the model's spatial receptive field without increasing its depth. The study investigates the impact of incorporating an explicit discharge feature and employing pushforward training for improved autoregressive rollouts. Experimental results demonstrate that conditioning on discharge is crucial, and the combination of discharge conditioning, multimesh connectivity, and pushforward training yields the best performance. The learned surrogate achieves rapid predictions, generating 6-hour forecasts in approximately 0.4 seconds on a single NVIDIA A100 GPU, compared to 180 minutes required by the traditional solver. These findings suggest that GNN-based surrogates can effectively complement existing hydraulic models for real-time flood mapping.
Methodology
The authors constructed a synthetic database of flood events using a high-resolution Telemac2D model. They developed a GNN surrogate that employs a projected-mesh strategy and multimesh connectivity. The model was trained with an explicit discharge feature and pushforward training techniques to enhance prediction stability and accuracy.
Results
The GNN surrogate model produced 6-hour flood predictions in about 0.4 seconds on a single NVIDIA A100 GPU, significantly faster than the 180 minutes required by the traditional Telemac2D simulation. The combination of discharge conditioning, multimesh connectivity, and pushforward training led to the best performance in terms of accuracy and stability.
Implications
The proposed GNN-based surrogate model can serve as a practical tool for operational flood forecasting, enabling rapid decision-making in emergency situations. Its ability to generate inundation maps in near real-time could significantly enhance flood management and response strategies.
Beyond Semantic Manipulation: Token-Space Attacks on Reward Models
Reinforcement Learning
Large Language Models
Optimization
- Introduction of TOMPA, a framework for adversarial optimization in token space.
- Demonstration of TOMPA's ability to exploit vulnerabilities in state-of-the-art reward models.
- Significant performance improvement over GPT-5 reference answers, achieving high rewards with nonsensical outputs.
- Identification of a length-dependent effect in adversarial token patterns.
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Beyond Semantic Manipulation: Token-Space Attacks on Reward Models
Summary
This paper introduces a novel attack framework called Token Mapping Perturbation Attack (TOMPA) that targets reward models (RMs) used in reinforcement learning from human feedback (RLHF). Unlike previous methods that manipulate semantic outputs to exploit RM biases, TOMPA operates directly in the token space, allowing for the discovery of adversarial patterns that do not conform to coherent natural language. By bypassing the decode-re-tokenize interface, TOMPA enables the optimization of raw token sequences based on black-box scalar feedback. The authors demonstrate that TOMPA can significantly outperform GPT-5 reference answers on the Skywork-Reward-V2-Llama-3.1-8B model, achieving nearly double the reward score while generating nonsensical outputs. This reveals a critical vulnerability in current RLHF systems, as the high rewards are derived from non-linguistic token patterns rather than meaningful content, highlighting the limitations of reward models in their ability to discern true task performance from adversarial manipulations.
Methodology
The authors developed TOMPA, which applies a perturbation mapping to directly feed transformed token sequences into the reward model, bypassing traditional semantic constraints. The attack policy is trained using reinforcement learning with black-box feedback to discover non-linguistic token patterns that yield high rewards.
Results
TOMPA achieved a mean reward of +33.64 on the Skywork-Reward-V2-Llama-3.1-8B model, nearly doubling the GPT-5 reference score of +17.48. The attack outperformed GPT-5 on 98.0% of prompts, despite generating outputs that were nonsensical and devoid of coherent language.
Implications
The findings suggest that current RLHF systems are vulnerable to exploitation through non-semantic token manipulations, necessitating a reevaluation of reward model robustness and the development of more resilient frameworks to prevent reward hacking.
On the Geometric Structure of Layer Updates in Deep Language Models
NLP
Large Language Models
Interpretability
- Introduces a functional decomposition of layer updates into a dominant tokenwise component and a residual.
- Demonstrates a strong geometric separation between the full update and the tokenwise component.
- Finds a significant correlation between approximation error and output perturbation, indicating the importance of the residual.
- Validates findings across multiple architectures, offering a broad perspective on layerwise dynamics.
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On the Geometric Structure of Layer Updates in Deep Language Models
Summary
This paper investigates the geometric structure of layer updates in deep language models, focusing on how representations change from one layer to the next rather than what information is encoded in them. The author introduces a decomposition of layer updates into two components: a dominant tokenwise transformation that operates independently on each token, and a residual component that captures the remaining transformation not explained by tokenwise functions. The study finds that the full layer update is closely aligned with the tokenwise component, while the residual shows weaker alignment and is geometrically distinct. This separation has functional implications, as the approximation error associated with the tokenwise model correlates strongly with output perturbation, indicating that significant computation occurs in the residual component. The findings are validated across various architectures, including Transformers and state-space models, providing an architecture-agnostic framework for analyzing layer updates in modern language models.
Methodology
The author operationalizes the decomposition of layer updates by approximating each layer transition using input-conditioned tokenwise maps and analyzing the residual component. This approach is applied across various deep language model architectures to assess the geometric structure of updates.
Results
The study reveals that the full layer update aligns almost perfectly with the tokenwise component, while the residual exhibits weaker alignment and larger angular deviation. The correlation between approximation error and output perturbation is strong, with Spearman correlations often exceeding 0.7 and reaching up to 0.95 in larger models.
Implications
The findings suggest a structured view of layerwise dynamics in deep language models, indicating that significant computation occurs in a geometrically distinct residual component. This framework can enhance our understanding of how computations are organized across layers in modern sequence models and may inform future model design and interpretability efforts.
Towards Intrinsically Calibrated Uncertainty Quantification in Industrial Data-Driven Models via Diffusion Sampler
Theory
Optimization
- Introduces a diffusion-based framework for uncertainty quantification in industrial models.
- Eliminates the need for post-hoc calibration by providing intrinsically calibrated predictive uncertainty.
- Demonstrates significant improvements in uncertainty calibration and predictive accuracy over existing methods.
- Evaluated on synthetic datasets and real-world industrial case studies.
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Towards Intrinsically Calibrated Uncertainty Quantification in Industrial Data-Driven Models via Diffusion Sampler
Summary
This paper addresses the critical challenge of uncertainty quantification (UQ) in industrial data-driven models, which are essential for real-time monitoring of performance indicators that are difficult to measure directly. The authors propose a novel diffusion-based posterior sampling framework that inherently generates well-calibrated predictive uncertainty, thus eliminating the need for post-hoc calibration. The method is evaluated extensively on synthetic distributions, a Raman-based phenylacetic acid soft sensor benchmark, and a real ammonia synthesis case study. The results demonstrate significant improvements in both uncertainty calibration and predictive accuracy compared to existing UQ techniques. This work highlights the potential of diffusion samplers as a principled and scalable approach for enhancing uncertainty-aware modeling in industrial applications, ultimately fostering greater trust and reliability in data-driven decision-making processes.
Methodology
The authors developed a diffusion-based posterior sampling framework that utilizes Bayesian inference principles to produce calibrated predictive distributions. This approach focuses on faithful posterior sampling to accurately represent uncertainty without requiring additional calibration steps.
Results
The proposed method achieved practical improvements in uncertainty calibration and predictive accuracy across various evaluations, including synthetic distributions and real-world industrial applications. The results indicate that the diffusion sampler effectively captures the true posterior distribution, leading to more reliable uncertainty estimates.
Implications
The findings suggest that the diffusion-based UQ framework can enhance the deployment of data-driven models in safety-critical industrial settings, enabling better decision-making and risk management. This approach may lead to broader acceptance and trust in data-driven technologies within process industries.
MATA-Former & SIICU: Semantic Aware Temporal Alignment for High-Fidelity ICU Risk Prediction
Time Series
Multimodal
- Introduction of MATA-Former, a transformer architecture that aligns clinical semantics with temporal dynamics.
- Development of Plateau-Gaussian Soft Labeling (PSL) for continuous risk modeling instead of binary classification.
- Creation of the SIICU dataset with over 506,000 expert-annotated clinical events to enhance evaluation of ICU risk prediction models.
- Demonstration of superior performance in risk prediction from text-intensive, irregular clinical time series.
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MATA-Former & SIICU: Semantic Aware Temporal Alignment for High-Fidelity ICU Risk Prediction
Summary
This paper addresses the challenge of predicting clinical risks in Intensive Care Units (ICUs) by proposing a novel framework called the Medical-semantics Aware Time-ALiBi Transformer (MATA-Former). The authors argue that traditional methods fail to capture the complex relationships between clinical events due to their reliance on chronological proximity rather than intrinsic pathological dependencies. MATA-Former utilizes event semantics to dynamically adjust attention weights, allowing the model to prioritize causal relevance over mere time lags. Additionally, the authors introduce Plateau-Gaussian Soft Labeling (PSL), which reformulates binary classification into a continuous multi-horizon regression framework, enabling a more nuanced understanding of risk evolution over time. The framework is evaluated on a newly constructed dataset, the Semantic-Integrated Intensive Care Unit (SIICU), which includes over 506,000 expert-annotated clinical events. The results demonstrate that MATA-Former outperforms existing methods in capturing risks from both structured and unstructured clinical data, showcasing robust generalization capabilities across different datasets.
Methodology
The authors propose MATA-Former, which integrates unified clinical embeddings with a semantic-guided temporal attention mechanism to dynamically generate query-specific focus windows. This allows the model to prioritize historical events based on their pathological relevance rather than their physical proximity. PSL is introduced to transform binary classification into a continuous regression framework, enabling the capture of dynamic risk trajectories throughout the ICU stay.
Results
The evaluation of MATA-Former on the SIICU dataset and the MIMIC-IV dataset shows that it significantly outperforms existing methods in terms of predictive accuracy and generalization. The framework effectively captures the complexities of clinical risk evolution, demonstrating its capability to utilize both structured and unstructured data.
Implications
The proposed framework has the potential to improve Clinical Decision Support Systems (CDSS) in ICUs by providing more accurate risk predictions, ultimately leading to better patient outcomes. The SIICU dataset can serve as a valuable resource for future research in clinical risk modeling.
Koopman-Based Nonlinear Identification and Adaptive Control of a Turbofan Engine
Optimization
Theory
Robotics
- Development of a physics-based component-level model for turbofan engine control.
- Introduction of a meta-heuristic extended dynamic mode decomposition for accurate dynamic modeling.
- Creation of two controllers: AKMPC and K-FBLC, with AKMPC showing superior robustness.
- Demonstration of the Koopman model's flexibility across different control objectives.
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Koopman-Based Nonlinear Identification and Adaptive Control of a Turbofan Engine
Summary
This paper explores the application of Koopman operator-based methods for the multivariable control of a two-spool turbofan engine. A physics-based component-level model is developed to generate training data and validate the controllers. The author introduces a meta-heuristic extended dynamic mode decomposition, which utilizes a cost function to effectively capture spool-speed dynamics and engine pressure ratio (EPR). This allows for the creation of a single Koopman model that can be adapted for various control objectives. Two controllers are developed based on the identified time-varying Koopman model: an adaptive Koopman-based model predictive controller (AKMPC) with a disturbance observer and a Koopman-based feedback linearization controller (K-FBLC) as a benchmark. The performance of these controllers is evaluated across two control strategiesβspool speeds and EPRβunder both sea-level and varying flight conditions. The findings indicate that the identification approach provides accurate predictions for spool speeds and EPR, facilitating the flexible reuse of the Koopman model across different control formulations. While both control strategies yield similar performance in steady conditions, the AKMPC demonstrates enhanced robustness compared to the K-FBLC under varying flight conditions, effectively compensating for model mismatches. Additionally, the EPR control strategy is shown to improve thrust response, underscoring the potential of the Koopman-based control framework for robust turbofan engine management.
Methodology
The study employs a physics-based component-level model to generate training data and validate control strategies. A meta-heuristic extended dynamic mode decomposition is developed to create a single Koopman model. Two control strategies are implemented: an adaptive Koopman-based model predictive controller (AKMPC) and a feedback linearization controller (K-FBLC). The performance of these controllers is assessed under different flight conditions.
Results
The proposed identification approach successfully predicts spool speeds and EPR, allowing for flexible application of the Koopman model. The AKMPC outperforms the K-FBLC in terms of robustness under varying flight conditions, while both controllers achieve comparable performance in steady conditions. The EPR control strategy is found to improve thrust response.
Implications
The findings suggest that Koopman-based control methodologies can significantly enhance the robustness and adaptability of turbofan engine management systems, potentially leading to improved fuel efficiency and operational flexibility in aviation.
Re-analysis of the Human Transcription Factor Atlas Recovers TF-Specific Signatures from Pooled Single-Cell Screens with Missing Controls
Theory
- Developed a reproducible pipeline for analyzing pooled single-cell TF screens.
- Successfully assigned TF identities to 79.2% of cells in the dataset.
- Recovered TF-specific signatures for 59 out of 61 testable TFs, significantly improving upon previous analyses.
- Identified key transcriptional remodelers and linked them to specific biological pathways.
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Re-analysis of the Human Transcription Factor Atlas Recovers TF-Specific Signatures from Pooled Single-Cell Screens with Missing Controls
Summary
This paper presents a re-analysis of the Human Transcription Factor (TF) Atlas dataset, focusing on recovering TF-specific signatures from pooled single-cell perturbation screens that lack internal controls. The authors utilized a comprehensive pipeline for quality control, barcode demultiplexing, differential expression analysis, and functional enrichment, applied to a dataset comprising 3,550 TF open reading frames and over 254,000 cells. They successfully assigned TF identities to 60,997 cells and identified significant transcriptional signatures for 59 out of 61 testable TFs, demonstrating that robust TF-level signals can be extracted despite the absence of intra-pool controls. The study highlights key transcriptional remodelers and links specific TFs to various biological pathways, revealing convergent signatures across conditions. The findings underscore the potential of the TF Atlas data for validated transcriptional and pathway analyses when combined with external controls and systematic artifact removal.
Methodology
The authors re-analyzed the GSE216481 dataset using a fully automated pipeline that included quality control, normalization, dimensionality reduction, and differential expression analysis. They employed embryoid body cells as external controls to mitigate batch effects and performed functional enrichment analysis to link TFs to biological pathways.
Results
The analysis revealed significant transcriptional signatures for 59 TFs, with HOPX, MAZ, PAX6, FOS, and FEZF2 identified as the strongest remodelers. The study found that condition-level analyses indicated convergent Wnt, neurogenic, EMT, and Hippo signatures, and the per-TF effect sizes correlated with previously published rankings.
Implications
The findings suggest that the Human TF Atlas can be effectively utilized for detailed transcriptional and pathway analyses, even in the absence of internal controls. This approach can enhance our understanding of TF functions and their roles in cellular processes, potentially informing therapeutic strategies in regenerative medicine and cancer biology.
Understanding Latent Diffusability via Fisher Geometry
Generative Models
Theory
Efficient ML
- Introduces a theoretical framework linking latent diffusability to Fisher Information Geometry.
- Identifies and decouples three penalties of latent geometric distortion affecting diffusion performance.
- Derives conditions for preserving Fisher Information Rate (FIR) to ensure stable diffusability.
- Empirical validation shows the effectiveness of FI and FIR metrics in predicting latent diffusion performance.
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Understanding Latent Diffusability via Fisher Geometry
Summary
This paper addresses the degradation of diffusion models when applied to latent spaces, particularly in Variational Autoencoders (VAEs). The authors introduce a framework to quantify latent-space diffusability by analyzing the rate of change of the Minimum Mean Squared Error (MMSE) along diffusion trajectories. They decompose this rate into contributions from Fisher Information (FI) and Fisher Information Rate (FIR), revealing that while global isometry ensures FI alignment, FIR is influenced by local geometric properties of the encoder. The analysis identifies three measurable penalties of latent geometric distortion: dimensional compression, tangential distortion, and curvature injection. The authors derive theoretical conditions for FIR preservation, which are crucial for maintaining diffusability across spaces. Through experiments on various autoencoding architectures, they validate their framework and demonstrate that the proposed FI and FIR metrics serve as effective diagnostics for identifying and mitigating latent diffusion failures.
Methodology
The authors utilize a theoretical approach based on Fisher geometry to analyze latent diffusability. They quantify the denoising complexity through the MMSE rate of change along diffusion trajectories, decomposing it into Fisher Information and Fisher Information Rate. They derive theoretical conditions for FIR preservation and conduct extensive experiments on various autoencoding architectures to validate their findings.
Results
The study finds that maintaining both Fisher Information and Fisher Information Rate is essential for stable latent diffusability. The derived conditions for FIR preservation are shown to be critical, and experiments confirm that standard VAEs exhibit significant FIR deviations, correlating with generation failures. In contrast, geometry-preserving architectures demonstrate improved performance in latent diffusion tasks.
Implications
The findings have significant implications for the design of autoencoders and diffusion models, suggesting that careful consideration of latent space geometry can enhance generative modeling capabilities. The proposed metrics can serve as diagnostic tools for researchers and practitioners to evaluate and improve the performance of latent diffusion models.
Generalization Limits of Reinforcement Learning Alignment
NLP
Large Language Models
Reinforcement Learning
- RLHF primarily redistributes existing capabilities rather than acquiring new ones.
- The introduction of 'compound jailbreaks' demonstrates significant vulnerabilities in LLM safety mechanisms.
- Attack success rates increased from 14.3% with individual methods to 71.4% with combined approaches.
- Safety mechanisms may fail against unknown attack patterns due to limited training data.
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Generalization Limits of Reinforcement Learning Alignment
Summary
This paper investigates the limitations of reinforcement learning from human feedback (RLHF) in ensuring the safety of large language models (LLMs), specifically focusing on the OpenAI gpt-oss-20b model. The authors argue that RLHF does not facilitate the acquisition of new capabilities but rather redistributes the utilization probabilities of existing ones, which raises concerns about the generalization of safety mechanisms to unknown attack patterns. To empirically demonstrate these limitations, the authors introduce 'compound jailbreaks,' which combine multiple attack techniques that are individually defended against, leading to a significant increase in attack success rates. The study reveals that while individual defenses may be effective, they can be breached when combined, highlighting the need for multifaceted safety evaluations in LLMs. The findings suggest that safety training may not generalize well and could leave models vulnerable to sophisticated attack strategies.
Methodology
The authors conducted a theoretical analysis of RLHF limitations and performed empirical evaluations using compound jailbreaks on the gpt-oss-20b model. They combined various existing attack techniques to test the robustness of the model's safety mechanisms, measuring the attack success rates before and after combining the techniques.
Results
The study found that the attack success rate (ASR) significantly increased from 14.3% when using individual attack methods to 71.4% when employing the compound jailbreak approach. This empirical evidence supports the hypothesis that safety training does not generalize effectively to new attack patterns, revealing structural vulnerabilities in the instruction hierarchy of the model.
Implications
The findings suggest that LLMs may remain vulnerable to sophisticated attacks despite safety training. This highlights the need for ongoing research into more robust safety mechanisms and the importance of evaluating models against a broader range of attack scenarios to ensure their reliability in real-world applications.
Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation
Graph Learning
- Introduction of ExSTraQt, a supervised learning framework for detecting money laundering transactions.
- Utilization of graph-based features tailored for AML detection.
- Demonstrated significant improvements in detection accuracy over existing models.
- Framework designed for scalability and simplicity in implementation.
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Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation
Summary
This paper addresses the challenge of detecting money laundering transactions, which is a significant issue for financial institutions due to the evolving tactics of criminal organizations. Traditional anti-money laundering (AML) methods often rely on predefined risk-based rules, leading to high false positive rates and resource-intensive investigations. To improve detection efficiency, the authors propose ExSTraQt (Extract Suspicious Transactions from Quasi-temporal Graph Representation), a supervised learning framework designed to identify suspicious transactions in financial datasets. The framework utilizes a rich set of graph-based transaction features and is noted for its simplicity, scalability, and performance compared to existing AML detection models. The authors conducted evaluations on both real and synthetic datasets, achieving notable improvements in detection accuracy, with F1 score increases of up to 1% on real datasets and over 8% on synthetic datasets. The framework is designed to complement existing AML systems, potentially reducing operational costs and enhancing the detection of money laundering activities.
Methodology
The authors developed a supervised machine learning framework that leverages a rich set of graph-based features to detect suspicious transactions. The implementation is massively parallelizable, allowing for efficient computation of complex subgraph metrics and quantification of flow-based money laundering activities.
Results
The ExSTraQt framework consistently outperformed state-of-the-art AML detection models, achieving an uplift in the F1 score of up to 1% for real datasets and more than 8% for certain synthetic datasets. This indicates a significant enhancement in transaction-level detection accuracy.
Implications
The proposed framework could significantly improve the efficiency of AML detection systems in banks, reducing the number of false positive alerts and operational costs associated with manual investigations. It also offers a scalable solution that can adapt to the increasing volume and complexity of financial transactions.
Residuals-based Offline Reinforcement Learning
Reinforcement Learning
Optimization
Theory
- Introduces a residuals-based framework for offline reinforcement learning that addresses data coverage limitations.
- Defines a residuals-based Bellman optimality operator that incorporates estimation errors into policy optimization.
- Develops a residuals-based offline deep Q-learning algorithm and demonstrates its effectiveness in a stochastic environment.
- Provides finite-sample guarantees and conditions for asymptotic optimality of the proposed methods.
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Residuals-based Offline Reinforcement Learning
Summary
This paper addresses the challenges of offline reinforcement learning (RL), which relies on previously collected data without real-time interaction with the environment. The authors propose a novel residuals-based offline RL framework that incorporates estimation errors in transition dynamics into policy optimization. By defining a residuals-based Bellman optimality operator, the framework allows for learning policies without the stringent requirement of data coverage across all state-action pairs. The authors develop a residuals-based offline deep Q-learning (DQN) algorithm and demonstrate its effectiveness in a stochastic CartPole environment. The proposed method not only mitigates issues related to distribution shift but also enables the generation of unseen states through empirical residuals, thereby enhancing the learning process in high-stakes applications where traditional online RL methods are impractical.
Methodology
The authors construct an estimated transition model from static offline data using supervised learning. They compute empirical residuals to capture discrepancies between the learned model and true dynamics. By sampling these residuals, they generate trajectories for training policies, allowing for on-policy training and addressing distribution shift.
Results
The proposed residuals-based offline DQN algorithm was tested in a stochastic CartPole environment, demonstrating improved performance over traditional offline RL methods. The framework showed that it could effectively generate unseen states and mitigate the impact of distribution shift, leading to more reliable policy evaluations.
Implications
This work has significant implications for high-stakes applications in fields such as healthcare, transportation, and energy, where offline RL can be safely deployed without the risks associated with online trial-and-error learning. The framework can enhance decision-making processes in environments where real-time interaction is not feasible.
Crystalite: A Lightweight Transformer for Efficient Crystal Modeling
Generative Models
Graph Learning
Efficient ML
- Introduction of the Geometric Enhancement Module (GEM) for direct geometric biasing in Transformers.
- Replacement of one-hot atom representations with a compact chemically informed tokenization.
- Crystalite achieves state-of-the-art results in crystal structure prediction and generation.
- Significantly faster sampling compared to traditional geometry-heavy models.
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Crystalite: A Lightweight Transformer for Efficient Crystal Modeling
Summary
The paper introduces Crystalite, a lightweight diffusion Transformer designed for efficient modeling of crystalline materials. Traditional generative models for crystals often utilize equivariant graph neural networks (GNNs), which, while effective, are computationally expensive and slow. Crystalite addresses these challenges by incorporating two novel components: Subatomic Tokenization, which replaces high-dimensional one-hot atom representations with a more compact and chemically structured format, and the Geometry Enhancement Module (GEM), which integrates periodic geometric information directly into the attention mechanism of the Transformer. This approach maintains the simplicity and efficiency of standard Transformers while enhancing their capability to model crystal structures. The authors demonstrate that Crystalite achieves state-of-the-art performance on crystal structure prediction benchmarks and excels in de novo generation tasks, outperforming existing geometry-heavy alternatives in terms of sampling speed.
Methodology
Crystalite employs a lightweight diffusion Transformer architecture that integrates the GEM to inject periodic geometric information into the attention mechanism. The model uses Subatomic Tokenization for atom representation, enhancing the efficiency of the diffusion process. The architecture preserves the standard multi-head attention framework while incorporating additive geometric biases to improve performance on crystal modeling tasks.
Results
Crystalite demonstrates superior performance on crystal structure prediction benchmarks, achieving the best S.U.N. discovery score among evaluated models. It also shows enhanced de novo generation capabilities while significantly reducing sampling time compared to more complex, geometry-heavy alternatives.
Implications
The development of Crystalite has significant implications for materials science, particularly in the discovery and design of novel crystalline materials with desired properties. Its efficiency and performance could facilitate faster exploration of the vast compositional space in materials research, potentially accelerating advancements in various applications such as electronics, photonics, and catalysis.
Malliavin Calculus for Counterfactual Gradient Estimation in Adaptive Inverse Reinforcement Learning
Reinforcement Learning
Theory
Optimization
- Introduces a novel passive Langevin-based algorithm for adaptive inverse reinforcement learning.
- Utilizes Malliavin calculus to efficiently estimate counterfactual gradients conditioned on measure-zero events.
- Achieves optimal convergence rates independent of trajectory resampling or kernel smoothing.
- Provides a comprehensive algorithmic framework for counterfactual gradient estimation.
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Malliavin Calculus for Counterfactual Gradient Estimation in Adaptive Inverse Reinforcement Learning
Summary
This paper addresses the challenge of adaptive inverse reinforcement learning (IRL), which aims to reconstruct the loss function of a forward learner by passively observing its gradient dynamics during reinforcement learning (RL). The authors propose a novel Langevin-based algorithm that utilizes Malliavin calculus to efficiently estimate counterfactual gradients, which are essential for adaptive IRL but are conditioned on events of probability zero under the forward learner's trajectory. Traditional Monte Carlo methods are inefficient for this purpose, and kernel smoothing techniques suffer from slow convergence. By reformulating the counterfactual conditioning as a ratio of unconditioned expectations involving Malliavin derivatives, the authors achieve standard estimation rates. The paper details the derivation of necessary Malliavin derivatives and their adjoint Skorohod integral formulations, leading to a concrete algorithmic approach for counterfactual gradient estimation. The proposed method overcomes limitations of existing kernel-based Langevin algorithms and demonstrates improved convergence rates without the need for resampling or kernel smoothing. Numerical implementations validate the effectiveness of the proposed algorithm in recovering the forward learner's loss function in real time.
Methodology
The authors employ Malliavin calculus to reformulate counterfactual gradient estimation as a ratio of unconditioned expectations. They derive necessary derivatives and integral formulations to create an efficient algorithm for adaptive IRL, which replaces traditional kernel-based methods.
Results
The proposed Malliavin-based gradient estimator yields unbiased Monte Carlo estimators for counterfactual conditional expectations, achieving optimal convergence rates. Numerical experiments demonstrate effective recovery of the forward learner's loss function.
Implications
This work has significant implications for real-time adaptive IRL applications, particularly in scenarios where observing the complete trajectory of the forward learner is impractical. The methodology could enhance the efficiency and accuracy of learning algorithms in various domains, including robotics and automated decision-making systems.
Auction-Based Online Policy Adaptation for Evolving Objectives
Reinforcement Learning
Robotics
Optimization
- Introduces a modular framework for multi-objective reinforcement learning using auction-based policy adaptation.
- Local policies compete through bids reflecting urgency, allowing for dynamic prioritization of objectives.
- Demonstrates superior performance compared to monolithic policies in dynamic environments.
- Enhances interpretability by allowing clear identification of active policies and objectives.
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Auction-Based Online Policy Adaptation for Evolving Objectives
Summary
This paper addresses the challenge of multi-objective reinforcement learning (MORL) in dynamic environments where objectives can appear or disappear at runtime. The authors propose a modular framework that utilizes an auction-based mechanism for policy adaptation. Each objective is managed by a selfish local policy that bids for the right to execute actions based on the urgency of its corresponding state. This auction system allows for a dynamic trade-off among competing objectives, enabling the system to adapt quickly when objectives change. The framework is designed to be modular, allowing for easy addition or removal of policies as objectives evolve. The authors demonstrate that this approach outperforms traditional monolithic policies trained with proximal policy optimization (PPO) in complex environments, such as Atari Assault and a gridworld path-planning task. The modular design not only enhances performance but also improves interpretability, as it allows for clear identification of the active policy at any moment.
Methodology
The authors implemented a compositional reinforcement learning framework where each objective is managed by a local policy. These policies engage in a general-sum game, competing for action execution rights through an auction mechanism. Policies are trained concurrently using proximal policy optimization (PPO), with penalties imposed for dishonest bidding to ensure truthful urgency estimation.
Results
The proposed auction-based framework significantly outperformed monolithic policies in both Atari Assault and a gridworld path-planning task, achieving higher payoffs and demonstrating effective adaptation to changing objectives. The modular approach also facilitated faster adaptation and clearer interpretability of policy actions.
Implications
This work has potential applications in robotics, particularly in environments where tasks and objectives are dynamic and uncertain, such as autonomous navigation and resource allocation. The framework can be adapted to various multi-objective scenarios, enhancing decision-making in real-time systems.
Self-Distilled RLVR
Reinforcement Learning
Large Language Models
Theory
- RLSD combines the advantages of OPSD and RLVR, addressing the limitations of each.
- The paper identifies severe information leakage in OPSD, leading to unstable training.
- RLSD decouples update direction from update magnitude, enhancing training stability.
- Empirical results show RLSD achieves faster convergence and better performance than GRPO.
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Self-Distilled RLVR
Summary
The paper introduces RLSD (Reinforcement Learning with Self-Distillation), a novel training paradigm that combines the strengths of on-policy self-distillation (OPSD) and reinforcement learning with verifiable rewards (RLVR). The authors highlight the limitations of OPSD, particularly its tendency for information leakage and instability during long-term training. They propose a method where the teacher model provides fine-grained token-level updates while the environmental feedback dictates the direction of updates. This approach mitigates the issues of OPSD by decoupling the reliable direction signal from the dense magnitude signal, leading to improved training stability and faster convergence. The paper provides theoretical insights into the structural differences between OPSD and OPD, demonstrating that the asymmetry in information access leads to performance degradation in OPSD. The proposed RLSD framework achieves a higher convergence ceiling and superior training stability compared to existing methods, as evidenced by empirical results on reasoning tasks.
Methodology
The authors propose RLSD, where the teacher model provides token-level policy differences for update magnitudes, while the environmental feedback determines the update directions. This decoupling allows for reliable gradient directions based on environmental rewards and dense credit assignment from self-distillation.
Results
RLSD demonstrates improved performance on reasoning tasks, surpassing GRPO in fewer training steps. The empirical results indicate that RLSD maintains training stability and achieves a higher convergence ceiling compared to OPSD and traditional RLVR methods.
Implications
The findings suggest that RLSD can be effectively applied to training large reasoning models, potentially leading to more efficient and stable training processes in various applications, particularly in natural language processing and reinforcement learning.
FourierMoE: Fourier Mixture-of-Experts Adaptation of Large Language Models
NLP
Large Language Models
Efficient ML
- FourierMoE integrates MoE architecture with inverse discrete Fourier transform (IDFT) for frequency-aware adaptation.
- The method addresses task interference and representation deficiency in multi-task fine-tuning settings.
- FourierMoE employs a frequency-adaptive router and learns complex coefficients to capture both phase and amplitude information.
- Extensive evaluations show superior performance across various benchmarks with fewer trainable parameters compared to existing methods.
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FourierMoE: Fourier Mixture-of-Experts Adaptation of Large Language Models
Summary
The paper introduces FourierMoE, a novel adaptation method for large language models (LLMs) that leverages the mixture-of-experts (MoE) architecture in the spectral domain. Traditional parameter-efficient fine-tuning (PEFT) methods face challenges in multi-task settings due to task interference and representational limitations. FourierMoE addresses these issues by reformulating adaptation through spectral analysis, revealing that different tasks exhibit unique frequency energy distributions and that LLM layers have varying frequency sensitivities. The proposed method employs a frequency-adaptive router to allocate tokens to experts that specialize in distinct frequency bands, allowing for more effective adaptation. Each expert learns conjugate-symmetric complex coefficients, ensuring lossless reconstruction into real-valued spatial weights. Extensive evaluations across 28 benchmarks demonstrate that FourierMoE consistently outperforms existing methods in both single-task and multi-task scenarios while utilizing significantly fewer trainable parameters, showcasing the potential of spectral-domain adaptation for efficient LLM fine-tuning.
Methodology
FourierMoE reformulates the adaptation of LLMs in the spectral domain, utilizing a frequency-adaptive router to direct tokens to specialized experts based on distinct frequency bands. Each expert learns conjugate-symmetric complex coefficients, allowing for a comprehensive representation of spectral information while ensuring lossless reconstruction into real-valued weights.
Results
The results indicate that FourierMoE outperforms competitive baselines across 28 benchmarks, demonstrating enhanced performance in both single-task and multi-task settings while significantly reducing the number of trainable parameters required for adaptation.
Implications
The findings suggest that spectral-domain expert adaptation can serve as an effective and parameter-efficient approach for fine-tuning large language models, potentially leading to advancements in multi-task learning and applications in natural language processing.
Neural network methods for two-dimensional finite-source reflector design
Optimization
- Introduces a neural network parameterization for reflector design that addresses finite-source light distribution.
- Develops two differentiable objective functions for optimizing reflector height.
- Demonstrates superior performance of the neural network approach over traditional deconvolution methods.
- Provides a comprehensive evaluation across multiple benchmarks, including height constraints.
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Neural network methods for two-dimensional finite-source reflector design
Summary
This paper addresses the inverse design problem of creating two-dimensional reflectors that can transform light from a finite, extended source into a desired far-field distribution. The authors propose a novel approach using neural network parameterization to model the reflector height, coupled with two differentiable objective functions. The first function is a direct change-of-variables loss that facilitates the mapping of the source distribution through the learned inverse function. The second is a mesh-based loss that allows for continuous mapping back to the source, even in cases of discontinuous sources. The gradients for optimization are computed using automatic differentiation and a robust quasi-Newton method. The authors also establish a baseline comparison with a deconvolution method based on a simplified finite-source approximation. Through four benchmark tests, including scenarios with continuous and discontinuous sources, the neural network approach demonstrates faster convergence and lower normalized mean absolute error (NMAE) compared to the deconvolution method, while naturally accommodating height constraints. The paper concludes with a discussion on extending the method to three-dimensional designs using iterative correction schemes.
Methodology
The authors utilize a neural network to parameterize the reflector height and develop two differentiable objective functions: a direct change-of-variables loss and a mesh-based loss. They employ automatic differentiation for gradient computation and optimize using a quasi-Newton method. A baseline deconvolution method is also formulated for comparison, based on a simplified finite-source approximation.
Results
The neural network approach converges more rapidly and achieves consistently lower NMAE across all benchmarks compared to the deconvolution method. It effectively handles height constraints and demonstrates robustness in both continuous and discontinuous source scenarios.
Implications
The proposed method has significant implications for optical design, particularly in applications requiring precise control of light propagation, such as advanced illumination systems, solar concentrators, and optical communications. The ability to extend the method to three-dimensional designs opens up further possibilities in complex beam shaping and freeform optics.
Sven: Singular Value Descent as a Computationally Efficient Natural Gradient Method
Optimization
Efficient ML
Theory
- Sven optimizes neural networks by treating each data point's residual as a separate condition.
- The algorithm approximates the Moore-Penrose pseudoinverse using truncated SVD, leading to lower computational costs.
- Sven significantly outperforms Adam and other first-order methods in regression tasks.
- The method is particularly suited for over-parameterized models and can be applied in scientific computing.
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Sven: Singular Value Descent as a Computationally Efficient Natural Gradient Method
Summary
This paper introduces Sven, a novel optimization algorithm for neural networks that leverages the natural decomposition of loss functions into individual data point contributions. Unlike traditional methods that reduce the entire loss to a single scalar, Sven treats each data point's residual as a separate condition to be satisfied simultaneously. The algorithm employs the Moore-Penrose pseudoinverse of the loss Jacobian to compute a minimum-norm parameter update that addresses all conditions at once. To enhance computational efficiency, Sven approximates this pseudoinverse using a truncated singular value decomposition (SVD), retaining only the k most significant directions, which results in a computational overhead proportional to k, significantly lower than the square of the number of parameters typical in natural gradient methods. The authors demonstrate that Sven outperforms standard first-order optimization methods like Adam in terms of convergence speed and final loss on regression tasks, while also being competitive with LBFGS at a reduced computational cost. The paper discusses challenges related to memory overhead and proposes strategies for mitigation, highlighting Sven's potential applications in scientific computing where custom loss functions can be decomposed into multiple conditions.
Methodology
Sven employs a linear algebra approach to optimization by using the Moore-Penrose pseudoinverse of the loss Jacobian, approximated through truncated singular value decomposition (SVD). This allows for simultaneous updates to model parameters based on individual data point conditions, rather than aggregating them into a single loss value.
Results
The experimental results show that Sven converges faster and achieves lower final loss compared to standard optimization methods like Adam on various regression tasks, while also being competitive with LBFGS at a fraction of the computational cost.
Implications
Sven's methodology has significant implications for optimizing neural networks, particularly in scenarios where loss functions can be decomposed into multiple conditions. Its efficiency and performance suggest potential applications in scientific computing and other fields requiring complex loss structures.
UI-Oceanus: Scaling GUI Agents with Synthetic Environmental Dynamics
Robotics
Generative Models
Reinforcement Learning
- UI-Oceanus shifts the learning paradigm from high-level trajectory imitation to mastering interaction physics.
- Forward dynamics is identified as the primary driver for scalability, outperforming traditional methods.
- The framework enables low-cost autonomous exploration to yield high-density supervision for training.
- Experimental results show significant performance improvements in both offline and real-world scenarios.
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UI-Oceanus: Scaling GUI Agents with Synthetic Environmental Dynamics
Summary
The paper presents UI-Oceanus, a novel framework designed to enhance the scalability of generalist GUI agents by addressing the limitations posed by expensive human demonstrations and the 'distillation ceiling' of synthetic teacher supervision. The authors propose a shift in focus from mimicking high-level trajectories to mastering interaction physics through ground-truth environmental feedback. By investigating self-supervised objectives, they identify forward dynamics, which involves predicting future interface states, as a key driver for scalability. UI-Oceanus utilizes low-cost autonomous exploration to generate high-density generative supervision, enabling the construction of a robust internal world model. Experimental results indicate that models employing Continual Pre-Training (CPT) on synthetic dynamics achieve a 7% improvement in success rates on offline benchmarks and a 16.8% gain in real-world online navigation compared to non-CPT baselines. The findings suggest that grounding agents in forward predictive modeling significantly enhances scalability, adaptability, and generalization capabilities in GUI automation.
Methodology
The methodology involves a two-stage training strategy where the first stage focuses on Continual Pre-Training (CPT) using a mixture of GUI dynamics and general data to establish a robust world model. The second stage, Agentic Post-Training, aligns the learned physical intuition with complex instruction following. The framework employs a scalable data engine to convert autonomous exploration into generative supervision, emphasizing forward dynamics as the key objective.
Results
The experimental evaluations demonstrate that models utilizing Continual Pre-Training on synthetic dynamics outperform non-CPT baselines by an average of 7% on offline benchmarks and achieve a 16.8% improvement in real-world online navigation. Additionally, navigation performance scales positively with the volume of synthetic data.
Implications
The findings imply that grounding GUI agents in forward predictive modeling can lead to more scalable and adaptable automation solutions. This approach may have applications in various domains requiring robust interaction with graphical user interfaces, enhancing the efficiency and effectiveness of automated systems.
Modeling and Controlling Deployment Reliability under Temporal Distribution Shift
Optimization
Time Series
Theory
- Introduces a dynamic state model for deployment reliability that separates average performance from temporal stability.
- Formulates deployment adaptation as a multi-objective control problem with constraints on intervention costs.
- Defines a class of drift-triggered intervention policies for managing reliability state and drift signals.
- Demonstrates that selective interventions can reduce operational costs by approximately 73% while maintaining model performance.
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Modeling and Controlling Deployment Reliability under Temporal Distribution Shift
Summary
This paper addresses the challenges faced by machine learning systems deployed in non-stationary environments, particularly focusing on temporal distribution shifts that affect the relationship between inputs and outcomes. Traditional mitigation strategies, such as retraining and drift detection, often overlook the temporal stability of model reliability. The authors propose a novel deployment framework that models reliability as a dynamic state, incorporating components of discrimination, calibration, and stability. They formulate deployment adaptation as a multi-objective control problem aimed at minimizing reliability volatility while considering intervention costs. The paper introduces a class of intervention policies, including a Drift-Triggered Reliability Control (DTRC) policy, and empirically constructs the cost-volatility Pareto frontier using a large-scale credit-risk dataset. The findings indicate that selective, state-dependent interventions can significantly reduce reliability volatility and operational costs compared to continuous retraining strategies, thus providing a principled approach for managing deployment reliability in high-stakes applications.
Methodology
The authors develop a formal framework to model deployment reliability as a dynamic state indexed by time, incorporating discrimination and calibration components. They analyze various intervention policies, including static deployment, periodic retraining, and drift-triggered policies, and construct the empirical Pareto frontier in the cost-volatility space using a large-scale credit-risk dataset.
Results
Experiments reveal that selective, state-dependent interventions outperform continuous retraining strategies, achieving lower reliability volatility and reducing operational costs by about 73% with only a modest loss in discrimination performance.
Implications
The proposed framework and intervention policies can enhance the reliability of machine learning systems in high-stakes applications, such as credit risk assessment and healthcare, by providing a structured approach to managing temporal distribution shifts.
Test-Time Scaling Makes Overtraining Compute-Optimal
Large Language Models
Optimization
Theory
- Introduces Train-to-Test (T2) scaling laws that optimize pretraining and test-time decisions jointly.
- Demonstrates that optimal pretraining strategies shift towards overtraining when factoring in inference costs.
- Validates the T2 scaling approach by showing improved performance of overtrained models across various tasks.
- Findings remain relevant even after post-training, suggesting practical implications for model deployment.
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Test-Time Scaling Makes Overtraining Compute-Optimal
Summary
This paper addresses the gap between pretraining scaling laws and test-time scaling strategies for large language models (LLMs). The authors introduce Train-to-Test (T2) scaling laws that optimize model size, training tokens, and inference samples under a fixed compute budget, thereby modernizing existing pretraining scaling laws. The study reveals that optimal pretraining decisions shift towards overtraining when considering inference costs, which is a departure from traditional scaling recommendations like those from Chinchilla. Through extensive evaluations across eight downstream tasks, the authors demonstrate that heavily overtrained models, when pre-trained according to T2 scaling forecasts, significantly outperform those trained under standard pretraining scaling laws. Furthermore, the findings persist even after post-training, indicating the robustness of T2 scaling in practical deployments. The paper emphasizes the need for a unified approach to pretraining and inference scaling, highlighting the nonlinear relationship between model size, training duration, and inference quality.
Methodology
The authors propose a joint optimization framework that incorporates model size, dataset size, and inference compute under a total budget. They evaluate two approaches: one based on loss and another on accuracy (pass@k). The methodology includes extensive experiments with over 100 models across different compute levels to validate the T2 scaling laws.
Results
The results indicate that the optimal pretraining decisions, when considering test-time compute, favor smaller and more overtrained models compared to traditional scaling laws. The T2 scaling laws consistently predict improved performance across eight tasks, confirming the advantages of overtraining in the context of inference costs. Additionally, the benefits of T2 scaling persist after post-training adjustments.
Implications
The findings suggest that practitioners should reconsider their pretraining strategies based on expected test-time usage, potentially leading to more efficient and effective model deployments. The T2 scaling laws could guide future research in optimizing LLMs for various applications, particularly in scenarios requiring repeated sampling.
Graph Neural Operator Towards Edge Deployability and Portability for Sparse-to-Dense, Real-Time Virtual Sensing on Irregular Grids
Graph Learning
Efficient ML
- VIRSO provides accurate sparse-to-dense reconstruction for irregular geometries.
- The method integrates spectral and spatial analysis for improved performance.
- Achieves mean relative L2 errors below 1% while reducing energy-delay product significantly.
- Demonstrates edge-deployability with low power consumption and latency.
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Graph Neural Operator Towards Edge Deployability and Portability for Sparse-to-Dense, Real-Time Virtual Sensing on Irregular Grids
Summary
The paper presents VIRSO (Virtual Irregular Real-Time Sparse Operator), a novel graph-based neural operator designed for sparse-to-dense reconstruction on irregular geometries, addressing the challenges of real-time virtual sensing in resource-constrained environments. Traditional physics-based solvers are often too slow and power-intensive for real-time applications, particularly in fields like nuclear thermal-hydraulics where accurate sensing is critical but instrumentation is limited. VIRSO integrates both spectral and spatial analysis to enhance reconstruction accuracy while minimizing latency and power consumption. The authors introduce a variable-connectivity algorithm, Variable KNN (V-KNN), for efficient graph construction tailored to mesh geometries. Evaluations on three nuclear thermal-hydraulic benchmarks demonstrate that VIRSO achieves mean relative L2 errors below 1% across various reconstruction ratios, outperforming existing operators with fewer parameters. The implementation on an NVIDIA Jetson Orin Nano shows sub-10 W power consumption and sub-second latency, highlighting its suitability for edge deployment. This work establishes a new paradigm for compute-aware operator learning, emphasizing the importance of hardware constraints in the design of virtual sensing instruments.
Methodology
The authors developed VIRSO, a graph-based neural operator that utilizes a variable-connectivity algorithm (V-KNN) for mesh-informed graph construction. The approach combines spectral and spatial analysis to enhance reconstruction accuracy from sparse boundary measurements, focusing on hardware constraints for edge deployment.
Results
VIRSO was evaluated on three nuclear thermal-hydraulic benchmarks, achieving mean relative L2 errors below 1% and demonstrating significant improvements in energy-delay product (EDP), reducing it from approximately 206 JΒ·ms to 10.1 JΒ·ms on an NVIDIA H200. The implementation on an NVIDIA Jetson Orin Nano maintained sub-10 W power consumption and sub-second latency across all configurations.
Implications
The findings suggest that VIRSO can serve as a viable solution for real-time virtual sensing in environments where traditional instrumentation is impractical, such as in advanced nuclear energy systems. This work paves the way for more efficient and deployable sensing technologies in various fields requiring real-time monitoring and control.
Enhancing the Reliability of Medical AI through Expert-guided Uncertainty Modeling
Computer Vision
Interpretability
Theory
- Expert evaluations significantly enhance the quality of uncertainty estimates in medical AI.
- The proposed two-ensemble method effectively separates epistemic and aleatoric uncertainty.
- The framework shows substantial improvements in various medical tasks, outperforming state-of-the-art methods.
- A simplified one-ensemble method offers comparable performance with greater efficiency.
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Enhancing the Reliability of Medical AI through Expert-guided Uncertainty Modeling
Summary
This paper addresses the critical challenge of AI reliability in healthcare by proposing a novel framework that integrates expert knowledge into uncertainty estimation. The authors highlight the importance of uncertainty estimation in medical AI systems, particularly in quantifying aleatoric uncertainty, which is often overlooked. They introduce a two-ensemble approach that utilizes expert disagreement to generate soft labels for training machine learning models, allowing for separate estimation of epistemic and aleatoric uncertainties. The method is validated across various medical tasks, including binary image classification and multiple-choice question answering, demonstrating significant improvements in uncertainty estimation quality. The authors also present a simplified one-ensemble variant that maintains performance while enhancing efficiency. Overall, the study emphasizes the value of expert input in developing risk-aware AI systems for healthcare applications.
Methodology
The authors propose a two-ensemble approach where one ensemble predicts hard labels for epistemic uncertainty, while a second ensemble, trained on expert-generated soft labels, estimates aleatoric uncertainty. This method leverages the law of total variance to decompose uncertainty into its components. A simplified one-ensemble alternative is also introduced for improved efficiency.
Results
The proposed method achieved a 9% improvement in multiple-choice question answering, a 50% improvement in image classification, a 7% improvement in binary image segmentation, and a 49% improvement in multiclass image segmentation compared to the second-best solution across various datasets.
Implications
The findings suggest that integrating expert knowledge into AI systems can significantly enhance their reliability and effectiveness in medical applications, potentially leading to better patient outcomes and more efficient healthcare workflows.
Physics Informed Reinforcement Learning with Gibbs Priors for Topology Control in Power Grids
Reinforcement Learning
Graph Learning
Optimization
- Introduces a physics-informed RL methodology for topology control in power grids.
- Utilizes a Gibbs prior to select a small, state-dependent set of feasible actions.
- Employs a graph neural network to predict overload risks for action evaluation.
- Achieves significant improvements in reward and decision time compared to existing methods.
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Physics Informed Reinforcement Learning with Gibbs Priors for Topology Control in Power Grids
Summary
This paper addresses the complex problem of topology control in power grids, which involves sequential decision-making with a combinatorial action space that grows with grid size. The authors propose a physics-informed Reinforcement Learning (RL) framework that integrates semi-Markov control with a Gibbs prior to encode the system's physical dynamics. The decision-making process is triggered only when the grid enters hazardous conditions, while a graph neural network (GNN) surrogate predicts the overload risk of feasible topology actions. This approach reduces exploration difficulties and online simulation costs, maintaining the flexibility of learned policies. The method is evaluated across three benchmark environments, demonstrating strong performance: achieving oracle-level results while being significantly faster and more efficient than existing baselines. The proposed framework effectively balances control quality and computational efficiency, making it a promising solution for real-world power grid operations.
Methodology
The proposed method formulates the topology control problem as a semi-Markov decision process, intervening only during hazardous conditions. It constructs a time-dependent candidate action set using a graph-based policy and a physics-informed prior that ranks actions based on predicted overload risks. The prior is learned from simulator rollouts and is used to reweight action scores before selection.
Results
The method achieves oracle-level performance while being approximately 6Γ faster on the first benchmark, reaches 94.6% of oracle reward with about 200Γ lower decision time on the second benchmark, and improves over a PPO baseline by up to 255% in reward and 284% in survived steps on the most challenging benchmark, while remaining about 2.5Γ faster than a specialized engineering baseline.
Implications
The findings suggest that the proposed physics-informed RL framework can significantly enhance decision-making processes in power grid operations, potentially leading to safer and more efficient management of electrical networks under varying operational conditions.
SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization
Reinforcement Learning
Large Language Models
Robotics
- SKILL0 is the first RL framework explicitly designed for skill internalization, enabling zero-shot autonomous behavior.
- In-context reinforcement learning (ICRL) is introduced to transition from context-dependent execution to intrinsic competence.
- Dynamic Curriculum adaptsively withdraws skills based on their on-policy helpfulness, optimizing the learning process.
- SKILL0 achieves substantial performance improvements over traditional RL baselines while maintaining a low token context size.
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SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization
Summary
The paper introduces SKILL0, a novel framework for skill internalization in reinforcement learning (RL) that allows agents to autonomously perform tasks without relying on inference-time skill retrieval. Traditional methods of skill augmentation involve injecting skills into the model's context during inference, which can lead to retrieval noise, token overhead, and a lack of true knowledge acquisition. SKILL0 addresses these limitations by implementing an in-context reinforcement learning (ICRL) approach, where skills are initially provided as guidance during training but are completely removed during inference. This transition is facilitated through a Dynamic Curriculum that evaluates the helpfulness of each skill, retaining only those that contribute to the agent's performance. The framework demonstrates significant improvements over standard RL baselines, achieving better performance while maintaining a compact context size, thus reducing inference overhead. The results indicate that SKILL0 effectively enables zero-shot autonomous behavior, marking a significant advancement in the field of agent-based learning.
Methodology
The methodology involves a training regime that starts with full skill context and progressively removes it, utilizing in-context reinforcement learning (ICRL) to optimize the transition from context-dependent execution to autonomous behavior. Skills are grouped and rendered with interaction history into a compact visual context, and a Dynamic Curriculum evaluates the on-policy helpfulness of skills to determine their retention during training.
Results
SKILL0 shows substantial improvements over standard RL baselines, achieving a +9.7% increase for ALFWorld and a +6.6% increase for Search-QA. The framework maintains an efficient context of fewer than 0.5k tokens per step, significantly reducing inference overhead while enhancing task performance.
Implications
The implications of this research suggest that skill internalization can lead to more efficient and capable autonomous agents, reducing reliance on external skill retrieval and enhancing the scalability of agent-based systems in complex environments.
Low-Rank Compression of Pretrained Models via Randomized Subspace Iteration
Efficient ML
Theory
Computer Vision
- Establishes a theoretical link between low-rank approximation error and predictive performance.
- Proposes randomized subspace iteration (RSI) as a superior alternative to RSVD for model compression.
- Demonstrates that RSI improves approximation quality in scenarios with slow-decaying singular value spectra.
- Evaluates the effectiveness of RSI on both convolutional and transformer-based architectures.
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Low-Rank Compression of Pretrained Models via Randomized Subspace Iteration
Summary
This paper addresses the challenge of efficiently compressing large pretrained models, which are essential for practical deployment in resource-constrained environments. The authors highlight the limitations of existing low-rank decomposition methods, particularly the randomized singular value decomposition (RSVD), which can struggle with slow-decaying singular value spectra commonly found in modern pretrained models. They establish a theoretical connection between low-rank approximation error and predictive performance by analyzing softmax perturbations, demonstrating that deviations in class probabilities are influenced by the spectral error of compressed weights. To overcome the inadequacies of RSVD, the authors propose a new method called randomized subspace iteration (RSI), which incorporates multiple power iterations to enhance spectral separation and improve approximation quality. The paper evaluates RSI on convolutional networks and transformer architectures, showing that it achieves near-optimal approximation quality and outperforms RSVD in predictive accuracy, even under aggressive compression. This work contributes to the development of more effective compression techniques for large-scale models, facilitating their deployment in various applications.
Methodology
The authors analyze softmax perturbations to connect low-rank approximation error with predictive performance. They propose randomized subspace iteration (RSI) that utilizes multiple power iterations to enhance spectral separation and improve the quality of low-rank approximations compared to RSVD.
Results
The results indicate that RSI achieves near-optimal approximation quality and significantly outperforms RSVD in terms of predictive accuracy, even when aggressive compression is applied. This demonstrates the effectiveness of RSI in compressing pretrained models without compromising performance.
Implications
The findings suggest that RSI can be a valuable tool for efficiently compressing large pretrained models, making them more accessible for deployment in environments with limited computational resources. This has potential applications in various domains such as mobile computing, edge devices, and other resource-constrained settings.
Application of parametric Shallow Recurrent Decoder Network to magnetohydrodynamic flows in liquid metal blankets of fusion reactors
Time Series
- SHRED effectively reconstructs MHD states from sparse measurements.
- The integration of SVD with SHRED enhances computational efficiency.
- The framework generalizes well to unseen magnetic field configurations.
- SHRED can infer magnetic field dynamics from temperature data alone.
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Application of parametric Shallow Recurrent Decoder Network to magnetohydrodynamic flows in liquid metal blankets of fusion reactors
Summary
This paper presents a novel data-driven framework for reconstructing magnetohydrodynamic (MHD) states in liquid metal blankets of fusion reactors using a parametric Shallow Recurrent Decoder Network (SHRED). MHD phenomena are critical in nuclear fusion systems where electrically conducting fluids interact with magnetic fields, influencing flow dynamics. Traditional numerical solutions for MHD models are computationally intensive, especially in real-time or multi-query contexts. The authors propose integrating dimensionality reduction via Singular Value Decomposition (SVD) with SHRED to reconstruct full spatio-temporal states from sparse measurements. The methodology is applied to a three-dimensional model of a water-cooled tube surrounded by lead-lithium flows, examining various magnetic field configurations. Results demonstrate that SHRED achieves high accuracy and robustness in reconstructing MHD states, even under previously unseen conditions, including time-varying magnetic fields. Notably, the framework can infer the evolution of the magnetic field using only temperature measurements. The findings highlight SHRED's potential as a computationally efficient tool for real-time monitoring and control in fusion reactor blanket systems.
Methodology
The study employs a combination of Singular Value Decomposition (SVD) for dimensionality reduction and the SHallow REcurrent Decoder (SHRED) neural network architecture to reconstruct MHD states from sparse time-series measurements. The methodology is tested on a three-dimensional model representing a portion of a water-cooled blanket cell.
Results
SHRED demonstrated high reconstruction accuracy and robustness across various magnetic field configurations, including constant and time-dependent fields. The model effectively generalized to conditions not encountered during training, accurately inferring the temporal evolution of magnetic fields using temperature measurements.
Implications
The findings suggest that SHRED can serve as a powerful tool for real-time monitoring, diagnostics, and control in fusion reactor blanket systems, potentially improving the design and operation of nuclear fusion reactors.
Mitigating Reward Hacking in RLHF via Advantage Sign Robustness
Reinforcement Learning
Large Language Models
Optimization
- Introduces Sign-Certified Policy Optimization (SignCert-PO) to mitigate reward hacking in RLHF.
- Focuses on the concept of advantage sign robustness to improve policy updates.
- Operates without the need for multiple reward models or extensive training data.
- Achieves superior performance on benchmark tasks compared to existing methods.
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Mitigating Reward Hacking in RLHF via Advantage Sign Robustness
Summary
This paper addresses the issue of reward hacking in reinforcement learning from human feedback (RLHF), where the optimization of a reward model (RM) can lead to a degradation of true quality as the policy exploits inaccuracies in the RM. The authors propose that reward hacking often arises from flipped advantage signs, which can cause the policy to increase the likelihood of undesirable responses. To combat this, they introduce a method called Sign-Certified Policy Optimization (SignCert-PO), which focuses on down-weighting non-robust completions during policy updates based on a certified sign-preservation radius. This radius quantifies the smallest perturbation in RM parameters that could flip the advantage sign for a given completion. Unlike previous methods that require multiple RMs or extensive data, SignCert-PO operates solely at the policy optimization stage, making it lightweight and efficient. The authors evaluate their method on TL;DR summarization and AlpacaFarm benchmarks, demonstrating that SignCert-PO consistently outperforms baseline methods in terms of win rates and reduces instances of reward hacking.
Methodology
The authors derive a certified sign-preservation radius that quantifies the robustness of the RM's advantage sign predictions. They propose SignCert-PO, which down-weights completions whose advantage signs are easily flipped, thus preventing policy updates from being dominated by unreliable completions. This method is implemented during the policy optimization stage using only the current RM parameters and on-policy completions.
Results
SignCert-PO consistently achieves higher win rates on TL;DR summarization and AlpacaFarm benchmarks compared to baseline methods, including Dr.GRPO and uncertainty-weighted optimization approaches. The method also improves RM accuracy during policy optimization, effectively reducing reward hacking.
Implications
The findings suggest that SignCert-PO can enhance the alignment of large language models with human intent by improving the robustness of reward models. This has potential applications in various RLHF scenarios, particularly in ensuring that AI systems do not exploit weaknesses in reward structures.
Hierarchical Planning with Latent World Models
Reinforcement Learning
Robotics
Optimization
- Introduces a hierarchical planning framework that operates on multiple temporal scales.
- Achieves a 70% success rate in real-world robotic tasks with zero-shot control.
- Reduces planning time complexity by up to three times compared to flat models.
- Eliminates the need for inverse models or skill learning by using latent state matching.
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Hierarchical Planning with Latent World Models
Summary
This paper presents a novel framework for hierarchical planning using latent world models, addressing the challenges of long-horizon control in robotic tasks. Traditional model predictive control (MPC) struggles with prediction errors and computational complexity as the planning horizon increases. The proposed Hierarchical Planning with Latent World Models (HWM) introduces a multi-scale approach, allowing agents to plan at different temporal resolutions. By learning latent world models that operate at various time scales, the framework enables long-horizon reasoning while significantly reducing planning complexity. HWM functions as a plug-in abstraction applicable across diverse architectures and domains, demonstrating its effectiveness in zero-shot control scenarios. The authors report a 70% success rate in pick-and-place tasks using only final goal specifications, a significant improvement over single-level models. Additionally, in simulated environments, HWM achieves higher success rates with up to three times less planning time compared to traditional methods.
Methodology
The HWM framework employs a hierarchical model predictive control approach, utilizing learned latent world models that operate at different temporal resolutions. High-level planning generates subgoals for low-level planning, which optimizes primitive actions. A learned action encoder compresses sequences of actions into latent macro-actions, facilitating efficient planning.
Results
HWM achieved a 70% success rate in pick-and-place tasks and improved performance in simulated environments like push manipulation and maze navigation, with success rates increasing by up to 44% compared to flat world models. The hierarchical approach also resulted in a reduction of planning time by up to three times.
Implications
The proposed framework has significant implications for robotic control, enabling more efficient and effective decision-making in complex environments. Its ability to generalize to new tasks without retraining opens avenues for real-world applications in robotics and automation.