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cs.LG updates on arXiv.org

Recursive Flow Matching Model Merging on Loss Landscape: A Geometry Perspective JLT: Clean-Latent Prediction in Latent Diffusion Transformers Auditing and Fixing Economic Validity in Tabular Foundation Models for Discrete Choice Classification and detection of multiple UAVs using rational Gaussian wavelet neural networks When Correct Demonstrations Hurt: Rethinking the Role of Exemplars in In-Context Learning Dense2MoE: Pushing the Pareto Frontier of On-Device LLMs via Unified Pruning and Upcycling DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection On the Role of Inductive Bias in Time-Series Pretraining: A Case Study in Learning Generalizable Representations for Clinical Time Series PIDM-DP: Physics-Informed Diffusion with Dormand-Prince Integration for Chaotic System Identification and State Reconstruction across Multiple Dynamical Regimes Balancing Plasticity and Stability with Fast and Slow Successor Features Rotation-Invariant Spherical Watermarking via Third-Order SO(3) Representation Coupling Max-Window Scale Estimation for Near-Lossless HiF8 W8A8 Quantization-Aware Training Few-shot Cross-country Generalization of Tabular Machine Learning and Foundation Models for Childhood Anemia Prediction under Distribution Shift HRVConformer: Neonatal Hypoxic-Ischemic Encephalopathy Classification from the Heart Rate signals APEX: Amplitude Anchors and Phase Priors for Target-Scarce Higher-Frequency Wave Prediction Aligning Few-Step Generative Models by Amortizing Sample-based Variational Inference Bridging Classification and Reconstruction: Cooperative Time Series Anomaly Detection Stabilizing Recurrent Dynamics for Test-Time Scalable Latent Reasoning in Looped Language Models TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models Variational Inference for Evidential Deep Learning CSV-ViT: A Vision Transformer with the Variable-sized Cortical Supervertices for Detection of Alzheimer's Disease Pathologies Open-Weight LLM Fine-Tuning Defenses are Susceptible to Simple Attacks FM-fMRI: Event Conditioned Flow Matching for Rest-to-Task fMRI Time-Series Synthesis QAM-W: Joint 2D Codebook Quantization for LLM Weights via Hadamard Rotation and Activation-Aware Scaling SilIF: Silhouette-Augmented Isolation Forest for Unsupervised Transaction Fraud Detection The Bridge-Garden Dilemma in LLM Distillation: Why Mixing Hard and Soft Labels Works TrackRef3D: Multi-View Consistent Track-then-Label for Open-World Referring Segmentation in 3D Gaussian Splatting A PAC-Bayesian View of Generalisation for Physics-Informed Machine Learning Towards Controllable Image Generation through Representation-Conditioned Diffusion Models Linear and Neural Dueling Bandits with Delayed Feedback Adversarial Training for Robust Coverage Network under Worst-case Facility Losses When Rule Violations Are Rare: Chimera Training for Logical Anomaly Detection WINDQuant: Weight-Informed Neural Decision-Making for Global Mixed-Precision LLM Quantization The Constraint Tax: Measuring Validity-Correctness Tradeoffs in Structured Outputs for Small Language Models RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models Neural Bayesian Sequential Routing Self-Improvement Imitation with Biologically Guided Search for Protein Design Under Oracle Budgets Bilevel Optimization over Saddle Points of Zero-Sum Markov Games Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks MTL-FNO: A Lightweight Multi-Task Fourier Neural Operator for Sparse Field Reconstruction Aperiodic and Low-Frequency Spectral Bias in Reconstruction based EEG Foundation Models Reparametrizing Shampoo and SOAP for Subspace Basis Updates and BFloat16 Storage Unified Neural Scaling Laws MuCon: Clipped Muon Updates for LLM Training LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding GEM: Geometric Entropy Mixing for Optimal LLM Data Curation MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding A Fast and Generic Energy-Shifting Transformer for Hybrid Monte Carlo Radiotherapy Calculation Benchmarking Convolutional, Transformer, Hybrid, and Vision Language Models for Multi Disease Retinal Screening BioFact-MoE: Biologically Factorized Mixture of Experts for Vision-Language Prognostic Modeling in Hepatocellular Carcinoma PRISM: Position-encoded Regressive Inverse Spectral Model for Multilayer Thin-Film Design Time Series Causal Discovery via Context-Conditioned and Causality-Augmented Pretraining Energy-Gated Attention and Wavelet Positional Encoding: Complementary Inductive Biases for Transformer Attention Semigroup Consistency as a Diagnostic for Learned Physics Simulators Planning Neural Dynamics with Lie Group Embedding through Supervised Projective Manifold Learning Function-Valued Causal Influence in Nonlinear Time Series Extra-Merge: Tracing the Rank-1 Subspace of Model Merging in Language Model Pre-Training Online Learning on Hidden-Convex Losses via Algorithmic Equivalence: Optimal Regret, Geometric Barrier, and Bandit Feedback Quantized Keys Steal Attention: Bias Correction for KV-Cache Compression in Video Diffusion Curriculum Learning for Safety Alignment SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learning Personalized Generative Models for Contextual Debiasing Geometry-Aware Contrastive Learning for Few-Shot Automatic Modulation Recognition Beyond Holistic Models: Systematic Component-level Benchmarking of Deep Multivariate Time-Series Forecasting Beyond Trajectory-Level Attribution: Graph-Based Credit Assignment for Agentic Reinforcement Learning Distribution-Aware Conformal Prediction: A Framework for generating efficient prediction intervals for time series On the Error-Correcting Effects of Stochasticity in Discrete Diffusion On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approach More Expressive Feedforward Layers: Part I. Token-Adaptive Mixing of Activations Separate Aggregation of Split Network for Personalized Federated Learning SL-BiLEM: Structured Learnable Behavior-in-the-Loop Epidemic Modeling for Forecasting and Policy Evaluation GAC: Noise-Aware Adaptive Mixing for Hybrid SFT-RL Post-Training Localizing Memorized Regions in Diffusion Models via Coordinate-Wise Curvature Differences A Hybrid Vision-Language Architecture for Automated Defect Reasoning and Report Generation in Industrial Inspection FoundObj: Self-supervised Foundation Models as Rewards for Label-free 3D Object Segmentation Ratio-Variance Regularized Policy Optimization Focal Reward: Balanced Reinforcement Learning under Rubric-Based Rewards Scaling World-Model Reinforcement Learning Through Diffusion Policy Optimization Is an Image Also Worth 16x16=256 Superpixels? A Framework for Attentional Image Classification From Privacy to Generalization: Linear Max-Information Bounds for DP-SGD Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Modeling Dynamic Mixtures of Time-Delay Systems from Streaming Time Series Stateful Inference for Low-Latency Multi-Agent Tool Calling When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Control Two-Parameter Flows for Learning Population Dynamics of Physical Systems MechRL: Reinforcement Learning Agents Perform Circuit Discovery for Mechanistic Interpretability InfoQuant: Shaping Activation Distributions for Low-Bit LLM Quantization Pretrained Approximators for Low-Thrust Trajectory Cost and Reachability Co-folding model guided by structural proteomics Towards Generalization-Oriented Models for Vehicle Routing Problems with Mixture-of-Experts AirCast-SR: A Foundation Model for Kilometer-Scale Atmospheric Super-Resolution via Latent Consistency Diffusion ARBITER: Reasoning Trajectory Basins and Majority Vote Failures in Test-Time Sampling Beyond Pairwise Preferences: Listwise Reward-Aware Alignment for Diffusion Models Image Feature Fusion-based Federated Client Unlearning (FCU) Spend Your Rollouts Where It Counts: Rollout Allocation for Group-Based RL Post-Training PILOT: A Data-Free Continual Learning Approach for Real-Time Semantic Segmentation via Boundary Guidance Near-Optimal Regret in Adversarial Kernel Bandits Amortized Factor Inference Networks for Posterior Inference Dynamic Link Prediction with Temporally Enhanced Signed Graph Neural Networks
CUDABeaver: Benchmarking LLM-Based Automated CUDA Debugging
Shiyang Li, · 2026-05-12 · via cs.LG updates on arXiv.org

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Abstract:Debugging CUDA programs has long been challenging because failures often arise from subtle interactions among hardware behavior, compiler decisions, memory hierarchy, and asynchronous execution. More importantly, with the rapid expansion of GPU usage across scientific computing, machine learning, graphics, and systems workloads, CUDA debugging has become more challenging than ever. Current evaluations of LLM-based CUDA programming largely miss this setting: a model can pass correctness tests with repair by degeneration, simplifying the CUDA code into a safer but slower program that abandons the original optimization structure. We introduce CUDABEAVER, a benchmark for CUDA debugging from real failing workspaces produced during LLM-based CUDA generation. Each task provides the broken candidate, native build/test commands, raw error evidence, and a single editable file. CUDABEAVER evaluates whether a fixer truly repairs the failing CUDA code or merely finds a slower test-passing replacement, reporting results by failure category, debugging trajectory, stagnation mode, and performance preservation. We further propose pass@k(M,C,A), a protocol-conditional CUDA debugging metric by making the fixer M, corpus C, and protocol axes Aexplicit. Using this metric across 213 tasks and seven frontier LLMs, we show that protocol-aware evaluation gives a more faithful view of CUDA debugging ability: when performance-loss tolerance is high, fixers appear much stronger, but even a minor stricter performance requirement can sharply reduce measured success, shifting scores by up to 40 percentage points.
Comments: 25 pages, 5 figures
Subjects: Machine Learning (cs.LG); Programming Languages (cs.PL); Software Engineering (cs.SE)
ACM classes: D.2.5; I.2.6; D.1.3
Cite as: arXiv:2605.08455 [cs.LG]
  (or arXiv:2605.08455v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2605.08455

arXiv-issued DOI via DataCite

Submission history

From: Haoyang Chen [view email]
[v1] Fri, 8 May 2026 20:24:32 UTC (770 KB)
[v2] Tue, 26 May 2026 07:39:54 UTC (769 KB)

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