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 Papers today
8h Update frequency
7 Days of history
Reinforcement Learning for Syndrome Extraction
John Zhuoyang Ye, Aarav Pabla, Jens Palsberg
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 over Patient Trajectories for Clinical Reasoning in EHR Foundation Models
Yuxin Xiao, Sheng Zhang, Chandan Singh, Tristan Naumann, Hoifung Poon, Jianfeng Gao, Xiaodong Liu
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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FINESSE: An Agent-Based Simulator and Benchmark Dataset for Multimodal Financial Event Sequences
Tyler Farnan, Benjamin Eng, Adam Abate, Xirui Hou, Rizal Fathony, Nam H. Nguyen, Senthil Kumar
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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Quantile-based Loss Filtering for Outlier-Robust Stochastic Gradient Descent
Jamie Haddock, Anna Ma, Elizaveta Rebrova
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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GUIDE: Generative Utility Inference and Decision Engine
Anagha Tiwari, Alexander G. Gray, Nick Feamster, Brian Jabarian, Alex Imas, Alex Kale
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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Information-Induced Training Geometry: Exact Reduction, Canonical Completion, and Structured Expressivity
Zavier Li
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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Correlation-Guided Fast Machine Unlearning via Hessian Analysis
Ayushi Thakur, Ruchir Gupta, Amit Kumar Jaiswal, Prayag Tiwari
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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ProactiveBench: Can Streaming Video Models Really Interact Like Humans?
Kaixuan Du, Xin Wan, YuKun Wang, Hang Zhang, Meng Cao, Dai Guan, Ming Chen, Ni Li
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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Performance, Efficiency and Collapse -- Advantages and Challenges in Offline Post-training of Code LLMs
Abhinav Anand, Sanjana Reddy Pachika, Shweta Verma, Mira Mezini
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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Robust Policy Optimization via Adversarial Importance Sampling
Amine Andam, Jamal Bentahar, Mustapha Hedabou
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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Observation-Anchored Selective Assimilation for Longitudinal Tumor-State Proxy Forecasting in Post-Treatment Glioma
Yeonjae Jung, Minwoo Shin
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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DCRA: Diffusion-Conditioned Representation Alignment for Robust Time-Series Learning
Wenrui Xu, Anas Enanaa, Keshab K. Parhi
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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Rank-Efficient LoRA via Joint Tangent-Space Optimization under Isotropic Curvature
Zihan Zhu, Zhehang Du, Xuyang Chen, Tim Tsz-Kit Lau, Jiayuan Wu, X. Y. Han, Qi Long, Weijie Su
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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Groupoid-Based Internal State Representations for Reinforcement Learning with Local Symmetries
Ben Opperman, Eduardo Alonso, Esther MondragΓ³n
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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GSF-Ο‡: Global Stereochemical Fields for Chiral Graph Transformers
Jiaqing Xie, Yuxin Wang, Xipeng Qiu
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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Quality-Constrained Routing over a Fixed Pool of Quantized Mixture-of-Experts Instances
Zhenghong Huang, Hongfan Wu, Jiheng Zhang
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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Attention Quantization for Tabular Foundation Models
Jonas M. KΓΌbler, Benjamin JΓ€ger, Klemens FlΓΆge, Noah Hollmann, Frank Hutter
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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Toward Reliable Railway-Bogie Response Prediction Using Multifidelity TDNN and Physics-Informed Residual Learning
Gyeolhee Lee, Moosun Kim, Taewook Kwon, Jaehun Kim, Dongjin Lee
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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Theoretical Guarantees for One-Shot Magnitude Pruning and Compute-Adaptive Early Exit
Erdem Koyuncu
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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A Unified and Constrained View of Regularization-Based Robust Reinforcement Learning
Amine Andam, Jamal Bentahar, Mustapha Hedabou
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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Estimating Pedestrian Volumes from GIS-Derived Built-Environment Features: A Machine Learning Framework
Bahareh Golchin, Banafsheh Rekabdar, Sirisha Kothuri, Joseph Broach
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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MAxBench: A Multinomial Concept Recovery Benchmark
Divya Appapogu, Freya Behrens, Yonatan Belinkov, Aaron Mueller
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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A Full Adam Theorem for Spectral Heavy-Tail Onset
Zongmin Liu
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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Sampling via Decision-Flow: Training-Free Extraction of Improved Latent Reasoning Paths in Large Language Models
Zhendong Mi, Shaoyi Huang
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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