arXiv Machine Learning
1d ago
OraclePhys: A Systematic Framework for LLM Fine-Tuning on Structural Mechanics
arXiv:2608. 17162v1 Announce Type: new Abstract: What a language model internalizes from fine-tuning is usually diagnosed after the fact.
Benchmarks and evaluation
Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv Machine Learning
1d ago
Population Health-Based Machine Learning Reveals Associations Between Psychosocial Factors and Chronic Kidney Disease
arXiv:2608. 17174v1 Announce Type: new Abstract: Chronic kidney disease (CKD) progresses silently and severely undermines quality of life, making early detection critical for improving patient outcomes.
Hugging Face Trending Papers
1d ago
TokEval: A Tokenizer Evaluation Suite
Language model tokenizers are typically selected with minimal evaluation, despite the fact that their design choices directly impact model capabilities. This can be partly attributed to a limited understanding of which tokenizer properties affect which aspects of downstream performance.
Hugging Face Trending Papers
1d ago
Plug-and-Play Traffic Element Awareness for End-to-End Autonomous Driving
Traffic elements such as traffic lights and road signs play a fundamental role in human driving decisions and should naturally influence end-to-end driving performance. However, existing end-to-end driving research predominantly focuses on dynamic road participants (e.
Hugging Face Trending Papers
1d ago
Judge, Retrieve, or Abstain: Uncertainty-Guarded LLM Judging with Provable Risk Guarantees
Using LLMs as judges has become standard practice for evaluating model outputs at scale. This is particularly common for subjective, open-ended tasks such as assessing helpfulness or alignment, where no single reference answer exists.
Hugging Face Trending Papers
1d ago
EvoTS-Agent: A Self-Evolving LLM Agent for Financial Time Series Change Point Detection
Financial time series exhibit non-stationary and heterogeneous statistical properties, making change-point detection challenging because no single unsupervised algorithm performs consistently across assets and market regimes. Conventional workflows consequently depend heavily on expert-driven model selection, feature design, and hyperparameter tuning, limiting their scalability and adaptability.
Hugging Face Trending Papers
1d ago
A Residual Learning Approach for Unsteady Aerodynamic Load Prediction
This paper investigates the feasibility of using residual learning to improve unsteady aerodynamic load prediction for aeroelastic applications. The machine learning technique selected for the study is the long short-term memory (LSTM) neural network, which is used for its suitability for sequential data with aerodynamic memory effects.
Hugging Face Trending Papers
1d ago
DistillPath: An Efficient 22M Distilled Pathology Encoder Approaching Large Foundation Model Performance
Many high-performing pathology tile encoders are now foundation models with hundreds of millions to over a billion parameters. Encoding and storing the thousands of tiles in each whole-slide image with such models is costly on commodity hardware, so compact encoders that retain useful downstream performance are a valuable alternative.
Hugging Face Trending Papers
1d ago
From Global Benchmarks to Local Evaluations: Benchmarking LLMs for the German Public Sector
Public institutions face a persistent challenge in selecting LLMs suited to their specific context. Existing benchmarks, however, are of limited use as they primarily reflect English-language and US-centric settings, and often only evaluate task performance.
Hugging Face Trending Papers
1d ago
StartupBench: Benchmarking General-Purpose Agents on Market-Validated End-to-End Workflows
Recent advances in Large Language Models(LLMs) and agents have substantially improved the ability of AI systems to execute complex tasks. Yet existing benchmarks largely rely on researcher-selected tasks, leaving uncertain whether such progress extends to the work that real-world users actually demand from AI systems.
Hugging Face Trending Papers
1d ago
D$^2$ACCI: A Dual-Loop Diagnostic Protocol for Evidence-Preserving Agent Memory
Memory is a key capability of LLM agents. Persistent memory extends this across sessions---enabling recall, revision, and personalization.
Hugging Face Trending Papers
1d ago
Benchmarking Automated Security Patch Backporting: How Far Are We?
Automated security patch backporting is critical for mitigating N-day vulnerabilities. Recent tools report success rates above 80% on their respective datasets.
Hugging Face Trending Papers
1d ago
TRUSS: Towards Task-Reliable and User-Safe Automated Agent Skill Generation
Agent Skills package reusable natural language procedures with executable resources, enabling software agents to acquire task specific capabilities without model adaptation. Automatically generating such Skills can improve task performance, yet evaluating a candidate solely from its artifact or final task outcome leaves unresolved which actions the equipped agent will perform and which side effects those actions will produce.
Hugging Face Trending Papers
1d ago
CoinVE-200K: A Large-Scale High-Quality Dataset for Compositional Instruction-Guided Video Editing
The quality and diversity of instruction-based video editing datasets are steadily improving, yet existing datasets mainly focus on single editing operations and fall short in supporting compositional instruction-guided video editing. In particular, multiple editing intents must be jointly understood and faithfully executed within the same video.
Hugging Face Trending Papers
1d ago
When to Review: Spaced Repetition for Continual Pre-Training of Language Models
Continual pre-training of large language models must acquire new information without erasing old knowledge. Existing replay methods often choose a global old/new mixture and sample uniformly, ignoring that examples differ in how quickly they are forgotten.
Hugging Face Trending Papers
1d ago
MoE-ViE: Mixture of Experts Vision Encoder for Efficient Image and Video Understanding
Vision encoders are a critical component of vision-language models, and scaling their capacity effectively improves performance. However, dense scaling increases compute cost and inference latency.
Hugging Face Trending Papers
1d ago
PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX
We introduce PTXBench, a benchmark for evaluating and adapting large language models (LLMs) to use architecture-specific PTX for GPU kernel optimization. PTXBench measures functional correctness, whether selected target instructions execute at runtime, and speedup over frontier libraries across GEMM and attention workloads on H100 and B200 GPUs.
arXiv Machine Learning
2d ago
EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction
arXiv:2302. 12177v4 Announce Type: replace-cross Abstract: Predicting the binding sites of target proteins plays a fundamental role in drug discovery.
arXiv AI
2d ago
ATLAS: Scaffold-Free Algorithm Synthesis by LLMs via Embedding-Guided Quality-Diversity Search
arXiv:2608. 15546v1 Announce Type: new Abstract: Most LLM-based automated algorithm design methods optimize a designated component within a human-specified scaffold, fixing overall organization and component interactions.
arXiv AI
2d ago
The Unwritten Benchmark: A New Challenge for Multimodal Machine Learning in Abstract Perceptual Reasoning
arXiv:2608. 14558v1 Announce Type: new Abstract: Current multimodal models have demonstrated remarkable proficiency in recognizing static visual and auditory content.