AI-generated summaries
Today's ML research,
without the noise.
Daily summaries of the latest machine learning papers from arXiv, processed every 8 hours.
24
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Reinforcement Learning for Syndrome Extraction
Reinforcement Learning
Optimization
Theory
- Introduces FastSched, a reinforcement learning-based tool for syndrome extraction in quantum error correction.
- Achieves significant reductions in logical error rates compared to state-of-the-art tools.
- Combines quality and scalability in schedule synthesis, addressing limitations of previous methods.
- Utilizes importance sampling for efficient evaluation of schedules.
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Reinforcement Learning for Syndrome Extraction
Summary
This paper addresses the challenge of syndrome extraction in quantum error correction (QEC), which is crucial for maintaining the reliability of quantum computations. The authors highlight that the number of possible syndrome extraction implementations grows exponentially with the syndrome size, leading to a search problem for finding low logical error rate (LER) implementations. Previous methods have either compromised on solution quality or scalability. The authors propose a novel approach called FastSched, which utilizes reinforcement learning (RL) combined with importance sampling to efficiently search for optimal schedules without sacrificing quality. FastSched significantly outperforms existing tools, reducing LER by 25.9% compared to AlphaSyndrome and 71.7% compared to PropHunt, achieving a remarkable 97.8% reduction for a surface code with distance 15. The methodology involves training an RL agent to learn effective gate orders and evaluate schedules using rare-event methods, which contrasts with the Monte Carlo methods used by prior tools. This work not only enhances the performance of syndrome extraction but also provides a scalable solution applicable to larger quantum codes.
Methodology
The authors developed FastSched, which employs reinforcement learning to search for optimal schedules for syndrome extraction. The RL agent learns from logical error rate estimates and selects CNOT orders based on previous choices. Importance sampling is used to evaluate schedules efficiently, focusing on a few promising candidates to reduce computational costs.
Results
FastSched outperforms existing scheduling tools, achieving a 25.9% reduction in logical error rate compared to AlphaSyndrome and a 71.7% reduction compared to PropHunt. For a surface code with distance 15, it culminates in a 97.8% reduction in logical error rate.
Implications
The findings suggest that reinforcement learning can effectively optimize quantum error correction processes, potentially leading to more reliable quantum computations. This approach could be extended to other areas of quantum computing and error correction, enhancing the scalability and efficiency of quantum algorithms.
Reinforcement Learning over Patient Trajectories for Clinical Reasoning in EHR Foundation Models
Reinforcement Learning
Generative Models
Time Series
- Proposes RL fine-tuning for EHR foundation models to enhance clinical reasoning.
- Introduces time-aware, rollout-sensitive rewards for optimizing patient trajectory generation.
- Demonstrates that smaller models can outperform larger pre-trained models in data-limited settings.
- Shows positive transfer across multiple clinical reasoning tasks through multi-task RL.
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Reinforcement Learning over Patient Trajectories for Clinical Reasoning in EHR Foundation Models
Summary
This paper addresses the limitations of Electronic Health Record (EHR) foundation models in clinical reasoning by proposing a reinforcement learning (RL) fine-tuning framework. Traditional EHR models, while effective in clinical prediction tasks, often rely on next-token prediction, which can be insufficient for long-horizon reasoning due to incomplete patient data. The authors introduce a novel approach that treats EHR models as generative policies over patient trajectories and formulates clinical prediction problems as event-conditioned, time-windowed reasoning tasks. They design time-aware, rollout-sensitive rewards to optimize the model's performance while considering finite rollout lengths and temporally inconclusive outcomes. The results demonstrate that RL fine-tuning significantly enhances the clinical reasoning capabilities of EHR models, allowing smaller models to outperform larger ones in data-limited scenarios and facilitating positive transfer across multiple clinical tasks. Additionally, the RL fine-tuned models generate patient trajectories that align more closely with ground truth, improving downstream predictive utility.
Methodology
The authors developed a reinforcement learning fine-tuning framework that treats pre-trained EHR foundation models as generative policies. They formulated clinical prediction tasks as event-conditioned, time-windowed reasoning problems and designed a reward system that accounts for the temporal dynamics of patient trajectories. The framework filters training data based on outcome relevance and rollout length, ensuring that the RL optimization process remains clinically meaningful.
Results
The RL fine-tuning approach consistently outperformed pre-trained models and strong baselines in both single-task and multi-task settings. Smaller models achieved superior performance compared to larger models in scenarios with limited data. Additionally, the generated patient trajectories exhibited stronger alignment with ground truth data and provided enhanced predictive utility when used in downstream classifiers.
Implications
This work has significant implications for improving clinical decision-making processes using EHR data. By enhancing the reasoning capabilities of EHR foundation models, the proposed framework can lead to better patient outcomes through more accurate predictions of clinical events. It also opens avenues for further research in optimizing clinical reasoning tasks using reinforcement learning techniques.
FINESSE: An Agent-Based Simulator and Benchmark Dataset for Multimodal Financial Event Sequences
Multimodal
Time Series
- FINESSE provides a novel agent-based model for generating synthetic, structured financial event sequences.
- The benchmark dataset, FINESSE-Bench, supports four representative financial tasks.
- Baseline results are reported using state-of-the-art methods, highlighting the effectiveness of the framework.
- The simulator addresses privacy concerns by generating data independent of sensitive information.
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FINESSE: An Agent-Based Simulator and Benchmark Dataset for Multimodal Financial Event Sequences
Summary
The paper introduces FINESSE, a Financial Event Sequence Simulation Environment designed to address the limitations of existing financial datasets, which are often monomodal and narrow in focus. FINESSE employs an agent-based simulation framework to generate synthetic datasets that reflect the complex, interdependent nature of financial behaviors, such as transactions, payments, and account status changes. The authors present FINESSE-Bench, a benchmark dataset that supports four key tasks: balance forecasting, transaction fraud detection, missed payment prediction, and next event prediction. By utilizing methods from time series forecasting, event sequence modeling, temporal graphs, and temporal point processes, the paper reports baseline results for these tasks. The framework and dataset are made publicly available to facilitate further research in structured, multimodal event sequence modeling within the financial services sector.
Methodology
The authors developed an agent-based simulation framework (FINESSE-ABM) that generates synthetic datasets capturing interdependent event sequences in financial services. The simulation incorporates latent state dynamics to model the evolving behaviors of agents in response to various financial events. The generated datasets are then used to benchmark four tasks, employing various machine learning methodologies including time series forecasting and event sequence modeling.
Results
The paper provides initial baseline results for the four tasks supported by FINESSE-Bench, demonstrating the potential of the synthetic datasets for evaluating modeling approaches in financial services. The results indicate that the framework can effectively simulate complex financial interactions and provide valuable insights for research.
Implications
FINESSE has the potential to significantly enhance research in financial services by providing a rich, structured dataset that reflects real-world complexities. It can facilitate the development of more robust models for predicting financial behaviors and detecting fraudulent activities, ultimately contributing to improved decision-making in the industry.
Quantile-based Loss Filtering for Outlier-Robust Stochastic Gradient Descent
Optimization
Theory
- Introduction of Quantile-k-Loss SGD (QkL-SGD) for robust optimization against outliers.
- Linear convergence guarantees under convexity assumptions with sample size scaling based on corruptions.
- Probabilistic analysis for small sample sizes, linking convergence to outlier selection probability.
- Experimental results demonstrate superior performance of QkL-SGD compared to standard SGD and min-k-loss SGD.
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Quantile-based Loss Filtering for Outlier-Robust Stochastic Gradient Descent
Summary
This paper introduces Quantile-k-Loss SGD (QkL-SGD), a novel framework for loss-based filtering in stochastic gradient descent (SGD) aimed at enhancing robustness against outliers in finite-sum optimization problems. The authors address the challenge posed by corrupted component functions whose gradients can significantly distort the optimization trajectory. QkL-SGD operates by sampling k component losses at each iteration and selecting an update index from the lower empirical q-quantile of these losses. The paper establishes linear convergence under standard convexity assumptions, with sample size requirements linked to the number of corruptions and a strong-convexity threshold. Additionally, a probabilistic analysis is provided for scenarios where large sampling is impractical, demonstrating that the convergence behavior is influenced by the likelihood of selecting outliers and the curvature of the selected good step. Experimental results on polynomial regression, logistic regression, and hinge loss indicate that QkL-SGD, particularly with intermediate quantiles, outperforms both standard SGD and min-k-loss SGD, highlighting its robustness and efficiency in producing informative updates.
Methodology
The methodology involves a two-parameter family of stochastic sampling rules where at each iteration, k component losses are sampled, and an update index is chosen uniformly from those losses that fall below the empirical q-quantile. The framework is analyzed for both deterministic and probabilistic convergence guarantees.
Results
The authors prove linear convergence of QkL-SGD under standard assumptions, with sample size requirements that depend on the number of corruptions rather than the total number of components. The experimental results show that QkL-SGD outperforms standard SGD and min-k-loss SGD, particularly in retaining robustness and providing informative updates.
Implications
The proposed QkL-SGD framework has potential applications in various fields where robust optimization is crucial, such as machine learning tasks involving noisy or corrupted data. It can enhance the reliability of SGD in real-world scenarios where data integrity cannot be guaranteed.
GUIDE: Generative Utility Inference and Decision Engine
NLP
Large Language Models
Optimization
- GUIDE integrates Bayesian adaptive sampling with symbolic representation learning for effective preference elicitation.
- The architecture allows for diverse question types and produces interpretable domain-specific preference models.
- In silico experiments show GUIDE outperforms existing methods in cold-start scenarios and minimizes recommendation regret.
- The framework enhances transparency and expert oversight in the elicitation process.
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GUIDE: Generative Utility Inference and Decision Engine
Summary
The paper introduces GUIDE, a novel elicitation architecture designed to measure human user preferences effectively, addressing challenges in AI alignment. Existing methods struggle with multidimensional preferences and grounding in domain knowledge. GUIDE combines Bayesian adaptive sampling for question selection with symbolic representation learning to initialize domain-specific preference models. It features an extensible type system for diverse elicitation questions and produces interpretable preference representations through symbolic rule-based learning. The architecture enhances observability and steerability, facilitating deployment and analysis of elicitation processes. Evaluated in investment portfolio optimization, GUIDE demonstrates improved cold-start performance and reduced recommendation regret compared to prior methods and LLM-only baselines, showcasing its effectiveness in early elicitation interactions across various user personas.
Methodology
GUIDE employs a Bayesian elicitation framework that utilizes dimension discovery and preference calibration processes. It incorporates an extensible set of question types for effective querying and a symbolic domain initialization pipeline to create interpretable rules and population priors, ensuring empirical grounding. The architecture is designed for transparency, enabling analysis and expert steering throughout the elicitation process.
Results
The evaluation of GUIDE in financial portfolio optimization indicates its ability to recover ground-truth preferences across diverse investor profiles. It consistently improves recommendation quality during cold-start and early-turn interactions compared to ablated versions, LLM-only approaches, and prior works such as OPEN and PEBOL.
Implications
GUIDE has significant implications for AI alignment and decision-making processes, particularly in finance. Its ability to accurately elicit and represent user preferences can enhance personalized decision support systems and reduce biases in recommendations. The framework also provides a foundation for further research into elicitation strategies and the interplay between statistical and generative AI methods.
Information-Induced Training Geometry: Exact Reduction, Canonical Completion, and Structured Expressivity
Optimization
Theory
Efficient ML
- Establishes a framework for understanding how training data constrains optimizer geometry.
- Introduces a unique completion for full-column-rank SPD channels under Riemannian geometry.
- Derives a closed-form pair metric that separates visible-metric motion from subspace rotation.
- Characterizes exact reduction and structured expressivity in the context of finite-dimensional AIRM models.
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Information-Induced Training Geometry: Exact Reduction, Canonical Completion, and Structured Expressivity
Summary
This paper investigates the geometric implications of training data on optimizer geometry through the lens of information channels. The author establishes how partial information can determine a full positive cometric relative to a reference, revealing degrees of freedom that remain unidentified. The central result is the resolution of the full-column-rank SPD channel under affine-invariant Riemannian geometry, leading to a split-Hadamard metric submetry and a unique completion that realizes a visible target. The paper elucidates how the geometry evolves as the channel moves, forming a gauge-invariant rank stratification of the SPD cone. The derived metrics separate visible-metric motion from subspace rotation, providing a framework for understanding singularities in reference-valued modes. The results also include a characterization of exact reduction, reference-dependent completion, and structured expressivity within a finite-dimensional AIRM model, contributing to the understanding of adaptive optimization methods and their geometric foundations.
Methodology
The paper employs Riemannian geometry to analyze the relationship between training data and optimizer geometry. It utilizes concepts such as metric submetries, Hadamard manifolds, and affine-invariant Riemannian metrics to derive results related to visible decision problems and their geometric properties. The author also develops closed-form expressions for metrics and completions, facilitating the analysis of rank stratifications and the behavior of optimization algorithms.
Results
The main results include the establishment of a split-Hadamard submetry for full-column-rank SPD channels, the derivation of a unique canonical completion for visible targets, and the formulation of a closed-form mismatch-weighted pair metric. These results provide insights into the geometric structure of optimization problems and the conditions for lossless visible reduction. Additionally, the paper quantifies the remaining invisible ambiguity and characterizes the structured expressivity of various optimizer families.
Implications
The findings have significant implications for the design and analysis of adaptive optimization algorithms in machine learning. By understanding the geometric constraints imposed by training data, researchers can develop more effective optimization strategies that leverage the underlying structure of the problem. This work may also inform future research on the interplay between geometry and optimization in high-dimensional spaces.
Correlation-Guided Fast Machine Unlearning via Hessian Analysis
Efficient ML
Theory
- Introduces a computationally efficient unlearning framework using Pearson correlation.
- Derives a closed-form parameter update rule that eliminates costly Hessian computations.
- Establishes theoretical guarantees and error bounds for the proposed method.
- Demonstrates significant speedup and improved accuracy compared to existing unlearning techniques.
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Correlation-Guided Fast Machine Unlearning via Hessian Analysis
Summary
This paper addresses the pressing need for efficient machine unlearning mechanisms in the context of network security and privacy regulations like GDPR. The authors propose a novel framework that leverages Pearson correlation to identify correlated data points in the training set, allowing for a computationally efficient unlearning process. Traditional unlearning methods often rely on expensive Hessian-inverse-vector computations, which become prohibitive when multiple data points need to be removed. The proposed method introduces a closed-form parameter update rule derived from the Sherman-Morrison formula, significantly reducing computational overhead while maintaining model performance. The authors establish a theoretical relationship between high Pearson correlation and gradient proportionality for quadratic loss, ensuring that their method is robust and effective. Comprehensive evaluations across seven diverse datasets demonstrate that their approach achieves an 82Γ speedup over standard methods while improving accuracy. The results indicate superior forgetting effectiveness, making this method particularly suitable for real-world applications in security systems where rapid response to data removal requests is essential.
Methodology
The authors utilize Pearson correlation to identify correlated data points and derive a closed-form parameter update rule using the Sherman-Morrison formula. This approach avoids the need for repeated Hessian-inverse-vector computations, thus enhancing computational efficiency. The method is evaluated across multiple datasets and architectures to assess its effectiveness in forgetting specific training data.
Results
The proposed framework achieves an 82Γ speedup in wall-clock time compared to standard influence function unlearning methods while preserving model utility with a 10β2 improvement in accuracy. The evaluation shows superior forgetting effectiveness, with membership inference attack success rates of 0.660 and tug-of-war scores of 0.950 across seven diverse datasets.
Implications
This research has significant implications for the development of ethical AI systems, particularly in sectors requiring compliance with privacy regulations and the ability to efficiently manage data removal requests. The proposed method can enhance the security and privacy of machine learning models in real-world applications, such as intrusion detection systems and spam filters.
ProactiveBench: Can Streaming Video Models Really Interact Like Humans?
Multimodal
- ProactiveBench evaluates streaming video models at one-second intervals without explicit response cues.
- The framework includes six subtasks that assess different aspects of response timing and trigger clarity.
- Premature responses are identified as the predominant error in the evaluated systems.
- The study reveals a substantial gap in the temporal decision-making required for human-like interaction.
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ProactiveBench: Can Streaming Video Models Really Interact Like Humans?
Summary
The paper introduces ProactiveBench, a novel evaluation framework for streaming video understanding models that emphasizes proactive interaction. Unlike traditional benchmarks that assess models at predetermined timestamps, ProactiveBench evaluates models continuously at one-second intervals without explicit response cues. This approach allows for a more realistic assessment of a model's ability to monitor ongoing requests and respond appropriately based on temporal context. The framework includes six subtasks that vary in trigger ambiguity and timing tolerance, enabling a comprehensive evaluation of response behaviors. Key findings indicate that premature responses are more common than missed responses across multiple evaluated systems, highlighting a significant gap in the temporal decision-making capabilities of these models compared to human-like interaction. The study emphasizes the need for improved response timing and decision-making in streaming video models to enhance their interactive capabilities.
Methodology
The authors developed ProactiveBench to evaluate models on their ability to respond to standing requests in streaming video contexts. The evaluation protocol involves continuous monitoring of video streams at one-second intervals, where models must decide whether to respond or remain silent based on the evidence available. The six subtasks are designed to vary in terms of response timing tolerance and semantic clarity, allowing for a nuanced assessment of model performance.
Results
The evaluation of six different systems using ProactiveBench revealed that premature responses were more frequent than missed responses in four out of six cases. This finding indicates that while models can respond, they often do so too early, lacking the necessary temporal decision-making to mimic human interaction effectively. The results also highlighted the importance of paired scoring to accurately assess model performance.
Implications
The findings suggest that current streaming video models require significant improvements in their temporal decision-making capabilities to achieve human-like interaction. ProactiveBench provides a framework for future research to develop models that can better manage response timing and silence, potentially leading to more effective applications in real-time video processing and interactive systems.
Performance, Efficiency and Collapse -- Advantages and Challenges in Offline Post-training of Code LLMs
Large Language Models
Reinforcement Learning
Efficient ML
- Offline RL can significantly improve the performance of code-generating LLMs without online sampling.
- The study reveals that model performance is sensitive to learning rates and training epochs.
- Prolonged training can lead to model collapse, necessitating early stopping strategies.
- Logit variance is identified as a key source of instability in offline RL training.
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Performance, Efficiency and Collapse -- Advantages and Challenges in Offline Post-training of Code LLMs
Summary
This paper investigates the post-training phase of code-generating large language models (LLMs) using offline reinforcement learning (RL). The authors highlight the computational challenges associated with traditional online post-training, which requires generating new code samples and incurs significant GPU-CPU communication overhead. They propose a novel approach that leverages existing datasets for offline RL, demonstrating that substantial improvements in zero-shot code generation performance can be achieved without the need for online sampling. The study encompasses various model families and sizes, revealing that offline RL can enhance performance across models ranging from 0.5B to 7B parameters, although the degree of improvement varies. The authors also explore the sensitivity of model performance to learning rates and training epochs, noting that while multiple epochs can enhance performance, excessive training may lead to model collapse. They identify logit variance as a primary source of instability in offline RL training and propose diagnostics for early stopping to mitigate this issue. This work represents a significant contribution to the understanding of offline RL in the context of code LLMs, paving the way for more efficient post-training methodologies.
Methodology
The authors conducted experiments using offline RL on various model families (Qwen Coder, DeepSeek Coder, CodeLlama) and sizes, utilizing the CodeNet dataset. They analyzed the impact of learning rates and training epochs on model performance and investigated the causes of instability during offline RL training.
Results
The results demonstrated that offline RL can lead to significant performance improvements in code generation tasks across different model families and sizes. However, the performance gains were sensitive to the choice of learning rates and the number of training epochs, with excessive training leading to model collapse. Early stopping based on logit variance was proposed as a solution to mitigate this issue.
Implications
The findings suggest that offline RL can be a viable alternative to traditional online post-training methods, potentially reducing computational costs and improving efficiency in the development of code-generating LLMs. This approach may also facilitate the exploration of additional feedback types beyond functional correctness in future research.
Robust Policy Optimization via Adversarial Importance Sampling
Reinforcement Learning
Optimization
Robotics
- Introduction of Adversarial Importance Sampling (Advis) for robust policy optimization.
- Development of advrl, a modular library for implementing and evaluating robustness methods.
- Emphasis on the need for diverse adversarial configurations in robustness evaluation.
- Demonstration of the effectiveness of the proposed method in continuous control environments.
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Robust Policy Optimization via Adversarial Importance Sampling
Summary
This paper addresses the vulnerabilities of deep reinforcement learning (DRL) policies to adversarial perturbations by proposing a novel method called Adversarial Importance Sampling (Advis). The authors identify three main limitations in existing robust DRL approaches: the need for additional environment interactions, reliance on auxiliary networks, and a focus on short-term robustness. Advis overcomes these issues by utilizing importance sampling over trajectories from standard training to estimate and optimize worst-case returns without requiring extra samples or networks, thus capturing long-term robustness. Additionally, the authors introduce advrl, a modular PyTorch library designed for rapid prototyping and reproducible evaluations of robustness methods and adversarial attacks. They also highlight the importance of evaluating policies against a diverse set of adversarial configurations, demonstrating that optimal hyperparameters do not transfer across agents, which can lead to misleading assessments of robustness. The proposed method is evaluated in continuous control environments, showing consistent improvement over existing baselines.
Methodology
The authors combine convex relaxation of neural networks with importance sampling to compute a worst-case policy distribution. This allows them to estimate the discounted return under this worst-case policy using previously collected trajectories, thus optimizing for long-term robustness without additional samples or networks. The advrl library is designed to facilitate the implementation and evaluation of robustness methods in a clean and modular manner.
Results
The proposed approach consistently outperforms existing robust DRL methods in continuous control environments, demonstrating its effectiveness in enhancing policy robustness against adversarial perturbations. The evaluation involved a significantly larger set of adversarial configurations compared to prior work, leading to more reliable assessments of robustness.
Implications
The findings suggest that robust DRL can be significantly improved by addressing the limitations of existing methods, particularly in terms of evaluation practices. The advrl library can serve as a valuable tool for researchers and practitioners in the field, promoting reproducibility and ease of experimentation in developing robust reinforcement learning algorithms.
Observation-Anchored Selective Assimilation for Longitudinal Tumor-State Proxy Forecasting in Post-Treatment Glioma
Time Series
Computer Vision
- Introduction of Observation-Anchored Selective Assimilation (OASA) for tumor-state proxy forecasting.
- Utilization of longitudinal MRI data to enhance patient-specific tumor-state estimates.
- Comparison of OASA with multiple forecasting methods, showing competitive performance.
- Demonstration of improved calibration metrics alongside high Dice scores.
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Observation-Anchored Selective Assimilation for Longitudinal Tumor-State Proxy Forecasting in Post-Treatment Glioma
Summary
This paper addresses the challenge of forecasting tumor-state proxies in post-treatment glioma patients using longitudinal MRI data. The authors propose a novel framework called Observation-Anchored Selective Assimilation (OASA), which integrates new follow-up observations to enhance patient-specific state estimates. The study analyzes data from 203 patients across 594 follow-up time points, applying a no-new-treatment criterion to retain relevant triplet data for training, validation, and testing. Each time point is represented by a voxel-wise tumor-state proxy map derived from MRI lesion labels. The OASA framework anchors the observed intermediate proxy and selectively applies model-predicted updates through a tiered case-level gating rule and voxel-wise soft gates. The performance of OASA is compared against various forecasting methods, demonstrating its effectiveness in maintaining high Dice scores while also improving calibration metrics. The results indicate that OASA can provide a more accurate and biologically relevant forecast of tumor-state proxies, highlighting the importance of incorporating intermediate observations in longitudinal forecasting tasks.
Methodology
The methodology involves a shared single-step forecaster that predicts updates from multimodal source-state tensors. The OASA framework anchors the observed intermediate proxy and applies selective updates based on a validation-selected tiered case-level rule and voxel-wise soft gates. The model uses a SegMamba-based encoder-decoder architecture to generate tumor-state proxy maps from MRI data.
Results
OASA maintained a Dice score comparable to persistence at Ο = 0.2 (0.6071 Β± 0.0025 vs. 0.6070) and achieved a higher Dice score at Ο = 0.5 (0.4269 Β± 0.0079 vs. 0.3981). Calibration improved Dice at Ο = 0.2 to 0.6178 Β± 0.0025, increased false-positive support, and reduced false-negative support, indicating enhanced near-threshold support calibration.
Implications
The findings suggest that incorporating intermediate observations in forecasting models can significantly improve the accuracy of tumor-state predictions in glioma patients. This approach may be applicable in other areas of medical imaging and longitudinal data analysis, potentially leading to better patient management and treatment planning.
DCRA: Diffusion-Conditioned Representation Alignment for Robust Time-Series Learning
Time Series
- Introduction of DCRA framework leveraging forward diffusion for structured corruption in time-series learning.
- Feature-level consistency mechanism enhances representation alignment between clean and corrupted signals.
- DCRA shows improved robustness and sensitivity in seizure detection tasks on the CHB-MIT EEG dataset.
- Framework is encoder-agnostic, compatible with various model architectures.
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DCRA: Diffusion-Conditioned Representation Alignment for Robust Time-Series Learning
Summary
The paper presents a novel framework called Diffusion-Conditioned Representation Alignment (DCRA) aimed at improving the robustness of time-series representation learning, particularly in clinical applications like EEG and ECG analysis. The authors address the challenges posed by noise and distribution shifts in time-series data, which often lead to unstable feature representations. DCRA utilizes the forward diffusion process as a structured corruption scheduler, allowing for a controlled evolution of representations across varying noise levels. This approach contrasts with traditional augmentation methods that treat noise as independent perturbations. The framework introduces a feature-level consistency objective that aligns representations from clean and corrupted signals, ensuring that the latent features remain semantically coherent and class-discriminative. The encoder-agnostic nature of DCRA allows it to be integrated with various architectures, including state space models and Transformers. Experimental results on the CHB-MIT EEG dataset demonstrate that DCRA significantly enhances performance under multiple noise conditions, achieving higher sensitivity at lower false-positive rates. The findings suggest that structured corruption combined with representation alignment is effective for robust time-series learning.
Methodology
The DCRA framework employs the forward diffusion process to create a structured corruption scheduler for time-series data. It introduces a feature-level consistency objective that aligns representations from clean and corrupted signals, promoting robust and semantically coherent latent features. The framework is designed to be compatible with various encoder architectures, including state space models and Transformers.
Results
The experimental evaluation on the CHB-MIT EEG dataset reveals that DCRA consistently outperforms baseline models under various noise conditions, achieving higher sensitivity while maintaining low false-positive rates. The analysis indicates that DCRA produces more balanced and structured representations compared to traditional and diffusion-only models.
Implications
The DCRA framework has significant implications for clinical applications where robust time-series analysis is critical, such as in seizure detection and other medical signal processing tasks. Its ability to maintain performance under noise and distribution shifts could enhance the reliability of automated diagnostic systems.
Rank-Efficient LoRA via Joint Tangent-Space Optimization under Isotropic Curvature
NLP
Large Language Models
Optimization
- Effective rank utilization is crucial for optimizing LoRA adaptations.
- Different optimizers can significantly impact the effective rank achieved during training.
- Iso-LoRA optimizes LoRA updates by focusing on tangent-space updates, improving rank utilization.
- The proposed method shows substantial performance gains across various language models.
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Rank-Efficient LoRA via Joint Tangent-Space Optimization under Isotropic Curvature
Summary
This paper addresses the optimization of Low-Rank Adaptation (LoRA) for large pretrained models, emphasizing the importance of effective rank utilization during the adaptation process. While LoRA traditionally uses a nominal rank to control the parameter budget and representational capacity, the authors argue that the optimizer plays a crucial role in determining how effectively this capacity is utilized. Through a case study on GPT-2 adaptation, they observe that different optimizers (AdamW vs. Muon) exhibit varying effects on rank utilization, with AdamW leading to concentrated singular spectra and low effective rank. To overcome this limitation, the authors propose Iso-LoRA, a geometry-aware optimizer that couples LoRA factor updates through spectral descent in the induced tangent space. Iso-LoRA encourages a more even distribution of energy across singular directions, enhancing effective rank and downstream performance. The paper provides theoretical guarantees for Iso-LoRA's effectiveness and validates its performance across various language models, demonstrating significant improvements, particularly at moderate-to-large LoRA ranks. This work highlights the critical interaction between optimizer design and rank utilization in parameter-efficient adaptation.
Methodology
The authors introduce Iso-LoRA, an optimizer that modifies the update process of LoRA factors by targeting the induced tangent update in weight space. It employs a spectral descent approach to ensure that updates distribute energy evenly across singular directions, rather than concentrating it in a few modes. The methodology includes a theoretical analysis of the optimizer's effectiveness and empirical validation through experiments on language model adaptations.
Results
Iso-LoRA was shown to improve effective rank and downstream performance across language models ranging from 0.1B to 7B parameters. The strongest performance gains were observed at moderate-to-large LoRA ranks, indicating that the proposed optimizer effectively utilizes the rank budget more efficiently than standard optimizers.
Implications
The findings suggest that optimizing the design of training algorithms can lead to better performance in parameter-efficient adaptations of large language models. This has potential applications in various NLP tasks where computational resources are limited, enabling more effective use of pretrained models.
Groupoid-Based Internal State Representations for Reinforcement Learning with Local Symmetries
Reinforcement Learning
- Introduction of a groupoid-based framework for reinforcement learning that captures local symmetries.
- Dynamic discovery of equivalence structures allows for more efficient learning in complex environments.
- Empirical results show improved sample efficiency and convergence compared to traditional Q-learning.
- The framework supports local generalization without requiring global symmetry, enhancing robustness.
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Groupoid-Based Internal State Representations for Reinforcement Learning with Local Symmetries
Summary
This paper introduces a novel framework for reinforcement learning (RL) that utilizes groupoids to capture local, state-dependent symmetries, enhancing the agent's ability to learn in complex environments. Traditional RL methods often rely on fixed group actions and predefined state abstractions, which limit their effectiveness in environments with modular and context-dependent regularities. The proposed framework allows for dynamic discovery of equivalence structures during agent-environment interactions, enabling the agent to maintain orbit representatives and transporters that map raw states to canonical forms. This approach facilitates learning and decision-making in a symmetry-reduced space while preserving local distinctions. Empirical evaluations demonstrate that the groupoid-based method significantly improves sample efficiency and convergence rates in dense and large-scale environments exhibiting partial symmetries, outperforming standard Q-learning. The findings suggest that leveraging local symmetries provides a mathematically grounded and practical pathway to scalable and generalizable reinforcement learning.
Methodology
The authors developed a reinforcement learning framework that employs groupoids to represent local symmetries. The methodology includes maintaining orbit representatives and transporters to facilitate value propagation across equivalence-related states. This enables the agent to learn in a dynamically growing canonical space, allowing for efficient experience sharing while preserving necessary distinctions.
Results
The empirical results indicate that the groupoid-based approach leads to faster convergence and lower policy variance in environments characterized by repeated structural motifs. The method demonstrates substantial performance gains over standard Q-learning, particularly in dense and large-scale settings where classical methods struggle.
Implications
The findings suggest that incorporating groupoid-based representations in reinforcement learning can significantly enhance the efficiency and robustness of learning algorithms. This approach has potential applications in various fields such as robotics, healthcare, and any domain where local symmetries can be exploited to improve decision-making processes.
GSF-Ο: Global Stereochemical Fields for Chiral Graph Transformers
Graph Learning
- GSF-Ο introduces a global stereochemical field that modulates pairwise interactions among all atoms.
- The Chiral-RoPE operator allows for reflection-invariant attention mechanisms that respect molecular chirality.
- The model achieves significant improvements in accuracy for chirality classification and ECD predictions.
- Extensive validation through property tests confirms the model's robustness and adherence to chirality principles.
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GSF-Ο: Global Stereochemical Fields for Chiral Graph Transformers
Summary
The paper introduces GSF-Ο, a novel graph transformer designed to effectively handle molecular representations of chiral compounds. Traditional molecular encoders often struggle with enantiomers, which share identical atomic structures but exhibit different behaviors in chiral environments. GSF-Ο addresses this by implementing a global stereochemical field that modulates interactions among all atoms based on stereogenic units, ensuring that the encoder respects atom relabelings and proper rotations while avoiding reflection biases. The core innovation is the Chiral-RoPE (Rotary Positional Encoding), which utilizes a pseudoscalar to direct relative rotations in the attention mechanism. This allows for a reflection-even phase field that maintains the architectural roles of all atoms. The authors validate their approach through extensive property tests and demonstrate that GSF-Ο achieves state-of-the-art performance on chirality classification benchmarks, outperforming existing models in both central and axial electronic circular dichroism (ECD) predictions.
Methodology
The authors developed GSF-Ο by integrating a Chiral-RoPE operator into a graph transformer architecture. This operator utilizes a pseudoscalar to dictate the direction of relative rotations among atoms, ensuring that the attention mechanism is invariant to reflections. The model employs a parity projection to separate even and odd outputs, allowing for consistent predictions across enantiomer pairs. The methodology includes rigorous property tests and a coordinate-reflection audit to validate the model's theoretical foundations.
Results
GSF-Ο achieved state-of-the-art results on standard chirality classification benchmarks, improving axial rotation and symbol predictions by 12.6% and 7.9%, respectively, compared to the strongest baseline. The model demonstrated exact enantiomer-pair consistency with minimal accuracy trade-offs under complete supervision, and it maintained predictive capabilities even with limited mirror supervision.
Implications
The advancements presented in GSF-Ο have significant implications for drug discovery and molecular design, particularly in fields where chirality plays a critical role in pharmacological efficacy and safety. The model's ability to accurately predict chiral properties could enhance the development of chiral drugs and materials, leading to more effective therapeutic options.
Quality-Constrained Routing over a Fixed Pool of Quantized Mixture-of-Experts Instances
Optimization
Large Language Models
Efficient ML
- Introduces a fixed-pool routing problem for quantized MoE instances with a focus on quality constraints.
- Develops Fragility-Weighted Perplexity (FWP) as a predictive metric for request-specific risk.
- Demonstrates that FWP allocation significantly improves throughput compared to static and request-agnostic methods.
- Establishes a two-timescale framework separating provisioning decisions from routing actions.
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Quality-Constrained Routing over a Fixed Pool of Quantized Mixture-of-Experts Instances
Summary
This paper addresses the challenge of routing requests to a fixed pool of quantized Mixture-of-Experts (MoE) instances, aiming to maximize throughput while adhering to quality-degradation budgets. The authors introduce a novel metric, Fragility-Weighted Perplexity (FWP), which quantifies the request-specific risk associated with instance degradation due to quantization. The methodology involves a two-timescale approach where provisioning decisions regarding instance configurations are made separately from the routing decisions. The routing problem is formalized as a linear programming (LP) problem that optimizes token serving under quality constraints. Empirical evaluations on 88 extended Qwen prompts demonstrate that the proposed FWP allocation method outperforms both static and request-agnostic mixing strategies, achieving a 1.284Γ offline model-based multiplier compared to 1.253Γ for request-agnostic mixing and 1.000Γ for static W4 instances. The findings highlight the importance of adaptive routing strategies in optimizing resource allocation in MoE systems.
Methodology
The authors employ a two-timescale approach to separate provisioning decisions from routing actions. They introduce Fragility-Weighted Perplexity (FWP) to assess request-specific risks based on expert fragility and affinity. A window-level linear programming model is formulated to maximize token serving while adhering to quality-degradation constraints, with the dual formulation ensuring KKT-consistency with optimal routing.
Results
The empirical results indicate that the FWP allocation method achieves a 1.284Γ offline model-based multiplier on the Qwen prompts, outperforming request-agnostic mixing (1.253Γ) and static W4 instances (1.000Γ). The FWP method contributes an additional 2.5% relative gain over request-agnostic mixing, demonstrating its effectiveness in optimizing resource allocation.
Implications
The findings suggest that adaptive routing strategies based on request-specific risk can significantly enhance the performance of quantized MoE systems. This approach can be applied in various applications requiring efficient resource management and quality control in machine learning models.
Attention Quantization for Tabular Foundation Models
Efficient ML
- Focus on attention calculation quantization rather than weight quantization for tabular models.
- Development of a quantization strategy for queries, keys, and values to FP8 format.
- Achieved up to 1.7x speedup in attention calculations with minimal accuracy loss.
- Importance of aligning quantization errors between training and testing datasets.
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Attention Quantization for Tabular Foundation Models
Summary
This paper addresses the optimization of inference performance in tabular foundation models, which have gained traction similar to transformer-based large language models (LLMs). The authors propose a novel quantization strategy focused on the attention calculation rather than weight or KV cache quantization, which are more common in LLMs. They introduce a method to quantize queries, keys, and values to FP8 format, leveraging explicit FP8 matrix multiplication instructions to enhance the speed of attention calculations. A critical finding is that aligning the quantization errors between training and testing rows is essential to maintain accuracy. The proposed Triton kernel demonstrates a speedup of up to 1.7x compared to standard 16-bit kernels, with no significant accuracy loss observed across various datasets, including TabPFN-v3 and TabICLv2. This work represents the first exploration of FP8 attention in tabular foundation models, suggesting that such quantization can be effectively integrated into existing models post-training.
Methodology
The authors implemented a quantization strategy for the attention mechanism in tabular foundation models, specifically targeting the matrix multiplications involved in the attention calculation. They utilized the e4m3fn FP8 format for quantization and dynamically computed scales for each tensor to minimize quantization error. The approach was evaluated using a Triton kernel designed for efficient FP8 matrix multiplications, focusing on the in-context learning (ICL) stage of the transformer architecture.
Results
The proposed method achieved a speedup of up to 1.7x over traditional 16-bit kernels, particularly when the number of training rows exceeded 8192. The accuracy remained stable across different datasets, with no relevant loss observed in performance metrics on TabArena and BeyondArena benchmarks. The results indicate that the quantization strategy is effective for maintaining model performance while significantly enhancing computational efficiency.
Implications
This research has significant implications for the deployment of tabular foundation models in resource-constrained environments, where inference speed and efficiency are critical. The findings suggest that FP8 quantization can be a viable approach for improving the performance of machine learning models on modern hardware, potentially leading to broader adoption of tabular models in practical applications.
Toward Reliable Railway-Bogie Response Prediction Using Multifidelity TDNN and Physics-Informed Residual Learning
Time Series
- Introduces a multifidelity approach for railway-bogie response prediction.
- Combines low-fidelity simulation data with high-fidelity experimental measurements.
- Utilizes a time-delay neural network (TDNN) and a residual-correction network.
- Achieves significant accuracy in response predictions with a mean coefficient of determination of 0.8197.
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Toward Reliable Railway-Bogie Response Prediction Using Multifidelity TDNN and Physics-Informed Residual Learning
Summary
This paper addresses the challenge of accurately predicting railway-bogie responses under various operating conditions, which are often impractical to test exhaustively. The authors propose a multifidelity approach that integrates low-fidelity multibody simulation data with high-fidelity roller-rig measurements. The methodology employs a time-delay neural network (TDNN) to capture the condition-dependent simulation trends, while a residual-correction network is used to model discrepancies between the simulated and physical responses. The approach is anchored in physics-informed learning, which constrains the model based on dynamic balance equations that account for differences in inertia, damping, stiffness, and external forces. The training objective combines several loss functions to ensure accurate response predictions. The results demonstrate a mean coefficient of determination of 0.8197 and a mean normalized root-mean-square error of 4.6055%, indicating a promising level of accuracy for the railway-bogie response predictions, particularly at high speeds.
Methodology
The methodology involves treating multibody simulation histories as low-fidelity data and roller-rig measurements as high-fidelity evidence. A TDNN is employed to model the baseline simulation trends, while a residual-correction network captures the discrepancies. The training incorporates physics-informed constraints based on dynamic balance equations, along with various loss functions to ensure smoothness and consistency in the predictions.
Results
The proposed method yields a mean coefficient of determination of 0.8197, a mean normalized root-mean-square error (NRMSE) of 4.6055%, and a mean normalized mean absolute error (NMAE) of 1.9297%. These results indicate a high level of accuracy in predicting railway-bogie responses, particularly validated at a speed of 385 km/h.
Implications
The findings suggest that the multifidelity approach can enhance the reliability of railway-bogie response predictions, which is crucial for the design and safety of railway systems. This methodology could be applied to other engineering domains where simulation and experimental data need to be integrated for accurate predictions.
Theoretical Guarantees for One-Shot Magnitude Pruning and Compute-Adaptive Early Exit
Theory
Efficient ML
- Establishes a concentration theorem for one-shot magnitude pruning in single neurons.
- Introduces the conditional perceptron model for adaptive early exit with proven generalization error decay.
- Characterizes the accumulation of pruning distortions in deep networks and derives compute-accuracy tradeoffs.
- Provides numerical simulations that support theoretical predictions regarding compute reduction mechanisms.
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Theoretical Guarantees for One-Shot Magnitude Pruning and Compute-Adaptive Early Exit
Summary
This paper investigates compute reduction in neural networks through a unified perspective on partial versus full computation, focusing on one-shot magnitude pruning in static settings and compute-adaptive early exit in dynamic scenarios. The author establishes a concentration theorem for one-shot magnitude pruning in a single-neuron model, demonstrating that the cosine similarity between full and pruned weight vectors converges to a constant dependent on the pruning rate. Additionally, the paper introduces the conditional perceptron model for early exit, revealing that the excess generalization error decreases as a power of the compute gap, with the exponent increasing as the alignment between partial and full computations improves. The analysis extends to deep networks, where the accumulation of pruning-induced distortions is characterized, leading to a compute-accuracy tradeoff for frozen-backbone early exits modeled as a neural network Gaussian process. Numerical simulations validate the theoretical predictions, providing insights into the mechanisms governing compute reduction in neural networks from single neurons to deep architectures.
Methodology
The paper employs theoretical analysis starting from a single-neuron model to derive results on one-shot magnitude pruning and adaptive early exit. It uses concentration theorems and Gaussian process modeling to extend findings to deep networks, analyzing how pruning and early exit strategies affect compute and accuracy.
Results
The main results include a closed-form characterization of pruning distortion in single neurons, a power-law decay of excess generalization error in the conditional perceptron model, and a detailed analysis of how pruning distortions accumulate in deep networks, leading to a quantifiable compute-accuracy tradeoff.
Implications
The findings suggest that effective compute reduction strategies can be developed for neural networks, potentially leading to more efficient architectures that maintain performance while reducing computational costs. This has implications for deploying AI models in resource-constrained environments.
A Unified and Constrained View of Regularization-Based Robust Reinforcement Learning
Reinforcement Learning
Optimization
Theory
- Unification of regularization-based methods for robust DRL through new performance gap bounds.
- Introduction of a constrained optimization framework for robust training that adapts regularization weight.
- Empirical validation of theoretical analysis through extensive adversarial evaluations.
- Demonstration of improved robustness when combining different regularization techniques.
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A Unified and Constrained View of Regularization-Based Robust Reinforcement Learning
Summary
This paper addresses the challenge of training robust Deep Reinforcement Learning (DRL) policies against adversarial input perturbations by unifying various regularization-based methods. The authors derive new upper bounds on the performance gap between nominal and worst-case policies, demonstrating that these bounds can be expressed as a combination of existing regularization objectives and a KL-divergence penalty. This formulation elucidates the empirical effectiveness of adding a KL penalty for robustness. The authors propose a novel approach to robust training as a constrained optimization problem, allowing for the dynamic adjustment of the regularization weight during training. Extensive evaluations on continuous control tasks validate the theoretical findings, showing that the proposed method improves robustness compared to existing approaches.
Methodology
The authors derive upper bounds on the performance gap between nominal and worst-case policies, reformulating robust training as a constrained optimization problem. They utilize existing regularization methods and introduce a dynamic Lagrange multiplier that adjusts the regularization weight based on the current robustness of the policy.
Results
The proposed method shows significant improvements in robustness across various continuous control tasks when compared to traditional fixed regularization approaches. The theoretical analysis is supported by empirical results demonstrating the effectiveness of the unified framework.
Implications
This work has implications for the development of more robust DRL systems, particularly in environments where adversarial perturbations are a concern. The dynamic adjustment of regularization weights could lead to more efficient training processes and better performance in real-world applications.
Estimating Pedestrian Volumes from GIS-Derived Built-Environment Features: A Machine Learning Framework
Optimization
- Developed a machine learning framework for estimating pedestrian volumes from GIS data.
- Improved upon traditional Negative Binomial GLM by incorporating feature selection and gradient boosting.
- Achieved significant reductions in RMSE compared to the baseline model.
- Released code on GitHub for public access and further research.
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Estimating Pedestrian Volumes from GIS-Derived Built-Environment Features: A Machine Learning Framework
Summary
This paper addresses the challenge of estimating pedestrian volumes across urban environments, which is crucial for transportation agencies to prioritize safety investments. Traditional manual counting methods are costly and limited in coverage, prompting the need for automated estimation techniques. The authors propose a machine learning framework that predicts 2-hour PM peak pedestrian volumes at 101 urban intersections in Portland, Oregon, using built-environment, land-use, and street-network features derived from open GIS data. The study builds upon a baseline Negative Binomial Generalized Linear Model (GLM) by incorporating advanced techniques such as feature selection, count-aware gradient boosting, and repeated cross-validation. The authors benchmark seven models and select the best-performing configuration based on a combined ranking of RMSE, MAPE, and SMAPE metrics. The final model, a histogram-based gradient boosting model with Poisson loss and L1 Lasso feature selection, significantly outperforms the GLM baseline, achieving a 12% reduction in cross-validated RMSE and a 19% reduction in holdout RMSE. The code for the proposed framework is made publicly available on GitHub, promoting further research and application in pedestrian volume estimation.
Methodology
The authors utilized a machine learning pipeline that includes feature selection, count-aware gradient boosting, and repeated cross-validation. They benchmarked various models and selected the best configuration based on multiple performance metrics, focusing on a dataset of 101 urban intersections with 79 candidate predictors.
Results
The selected histogram-based gradient boosting model with Poisson loss and L1 Lasso feature selection reduced the cross-validated RMSE from 89.8 to 78.7 and the holdout RMSE from 108.0 to 87.9, demonstrating a significant improvement over the baseline GLM.
Implications
The findings suggest that machine learning approaches can effectively estimate pedestrian volumes using readily available GIS data, which can aid transportation agencies in making informed decisions about infrastructure investments and safety improvements. This framework can be adapted for other urban areas, enhancing pedestrian safety and urban planning.
MAxBench: A Multinomial Concept Recovery Benchmark
NLP
Large Language Models
Interpretability
- Introduces MAxBench, a framework for evaluating multinomial concept representations.
- Demonstrates that affine subspaces outperform other geometries in terms of reliability and recall.
- Finds that better non-zero offsets contribute significantly to the advantages of affine subspaces.
- Shows that manifold steering can be competitive but does not consistently outperform prompting.
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MAxBench: A Multinomial Concept Recovery Benchmark
Summary
The paper introduces MAxBench, a geometry-agnostic evaluation framework designed for multinomial concept representations in language models. Unlike binary concepts that can be represented by a single direction in activation space, multinomial concepts (e.g., ANIMALS, COUNTRIES) require more complex representations due to their inherent subcategories. The authors explore various localization methods across different geometries and models to assess their effectiveness in recovering these representations. The study reveals that affine subspaces provide more reliable steering and higher recall compared to rank-one or linear subspaces, with much of the advantage attributed to better non-zero offsets. Additionally, manifold steering shows competitive performance when applicable, but no method consistently surpasses prompting. The findings highlight the need for broader interpretability research that accommodates the complexity of multinomial concepts.
Methodology
The authors developed a sampling-based steering framework to evaluate concept representations, which is geometry-agnostic. They compared 10 localization methods across 6 concepts, 5 geometries, and 4 models, measuring the quality of representations based on exclusivity and completeness.
Results
The study found that affine subspaces are more effective for steering multinomial concepts, achieving higher recall rates. The advantage of affine methods is largely due to improved non-zero offsets rather than the choice of bases. Manifold steering is competitive when applicable, but prompting remains the most effective method overall.
Implications
The findings suggest that future interpretability research should focus on complex, multinomial concepts, potentially leading to improved steering methods in language models. This could enhance the control and understanding of model behaviors in various applications.
A Full Adam Theorem for Spectral Heavy-Tail Onset
Theory
Optimization
- Establishes a full Adam theorem for spectral heavy-tail onset in a closed state-evolution model.
- Derives population gradients using Stein-Hermite calculus and proves covariance concentration.
- Demonstrates the conversion of Adam momentum into a non-centered Gaussian sign kernel.
- Proves approximate-target KL contraction with upper and lower hitting bounds.
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A Full Adam Theorem for Spectral Heavy-Tail Onset
Summary
This paper presents a comprehensive theorem regarding the behavior of the Adam optimization algorithm in the context of spectral heavy-tail onset within a closed Gaussian Stein-Hermite teacher-student model. The author derives the population gradient using Stein-Hermite calculus and establishes a series of results that connect Adam's momentum and denominator to a spectral hitting law. The theorem is notable for its rigorous formalization of each step in the process, demonstrating how Adam's trajectory can reach a spectral heavy-tail window after the emergence of a first spike-bulk gap. The author also discusses the impossibility of a stronger arbitrary-gradient Adam theorem and the limitations of exact two-step linear-network loss dynamics in identifying factor spectra or heavy-tail hitting times. The findings are supported by a detailed proof dependency map and various lemmas that contribute to the overall theorem.
Methodology
The methodology involves a closed Gaussian Stein-Hermite model where the author derives the Adam recurrences, applies Stein-Hermite calculus for gradient derivation, and utilizes various mathematical theorems (such as the Hermite edge-transfer theorem and Gram update) to formalize the connections between Adam's components and spectral hitting laws. The paper includes rigorous proofs and a proof dependency map to illustrate the relationships among the derived results.
Results
The main result is the establishment of a full Adam theorem that quantitatively describes how Adam's optimization trajectory reaches a spectral heavy-tail window characterized by a specific hitting time law. The theorem is formalized within the closed state-evolution model, demonstrating that the first spike-bulk spectral gap leads to this heavy-tail onset. The author also proves that arbitrary-gradient Adam theorems cannot imply heavy-tail onset and that exact two-step linear loss dynamics do not effectively identify factor spectra.
Implications
The findings have significant implications for understanding the dynamics of optimization algorithms like Adam in high-dimensional settings, particularly in relation to heavy-tail phenomena. This could influence the design of more robust optimization strategies in machine learning, especially in scenarios involving complex loss landscapes.
Sampling via Decision-Flow: Training-Free Extraction of Improved Latent Reasoning Paths in Large Language Models
NLP
Large Language Models
Reinforcement Learning
- DF-Sample is a training-free framework that enhances reasoning accuracy in LLMs.
- The method constructs a hierarchical reasoning tree and evaluates global trajectories instead of making local decisions.
- DF-Sample consistently outperforms traditional sampling strategies and RL-trained models across various benchmarks.
- The findings suggest that high-quality reasoning paths are latent in base models and can be accessed without additional training.
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Sampling via Decision-Flow: Training-Free Extraction of Improved Latent Reasoning Paths in Large Language Models
Summary
This paper addresses the challenge of enhancing reasoning capabilities in large language models (LLMs) without the need for reinforcement learning (RL) fine-tuning. The authors propose a novel framework called Decision-Flow Sampling (DF-Sample), which operates at inference time and extracts high-quality reasoning paths that are latent within the base models. The core idea is based on the distribution-sharpening hypothesis, suggesting that RL reallocates probability mass towards high-reward paths that already exist in the model's distribution. DF-Sample constructs a hierarchical reasoning tree, evaluates terminal nodes for quality, and back-propagates utility scores to inform decision-making at intermediate branches. This approach allows for the recovery of reasoning chains that traditional sampling methods may overlook. The empirical results demonstrate that DF-Sample outperforms several baseline methods, including RL-trained models, across multiple benchmarks, indicating that substantial latent reasoning potential exists in pretrained models.
Methodology
The authors introduce DF-Sample, which constructs a hierarchical reasoning tree during inference. It evaluates the quality of terminal nodes and back-propagates these utility scores to inform the selection of reasoning paths. This global trajectory evaluation contrasts with traditional methods that rely on local sampling decisions.
Results
DF-Sample achieved 45.6% accuracy on the GPQA benchmark, surpassing power sampling (38.9%) and GRPO (39.9%). On the MATH500 dataset, it reached 81.8% accuracy, outperforming GRPO (78.5%) and power sampling (74.8%). The results across three model families and four datasets consistently demonstrate the effectiveness of DF-Sample compared to baseline methods.
Implications
The findings suggest that LLMs possess untapped reasoning capabilities that can be accessed without costly RL fine-tuning. This could lead to more efficient methods for enhancing model performance in various applications, including mathematics, coding, and scientific reasoning.