HuggingFace
Act with Intent: Distilling Behavior Intent for Vision-Language-Action Models
Vision-Language-Action (VLA) models can turn multimodal context into robot actions, but their action decoders are sti...
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HuggingFace
Lost in Compression: A Controlled Cross-Lingual Audit of Extractive Prompt Compressors
Extractive prompt compression promises to cut LLM inference costs by removing low-information tokens, and learned com...
HuggingFace
Sliding-window beats linear attention
Due to the nature of quadratic attention, Large Language Models (LLMs) consume a lot of memory and energy. Every new ...
HuggingFace
Generative Semantic Scene Completion
Outdoor LiDAR semantic scene completion (SSC) recovers a dense semantic voxel grid from a scan observing 1% of the ta...
HuggingFace
EvoUndo: Recoverability-Constrained Self-Evolution for LLM Agent Harnesses
LLM agents increasingly modify their own prompts, tools, middleware, resources, and execution harnesses at runtime. S...
HuggingFace
Acquire, Repair, Preserve: A Diagnosis-Guided Post-Training Recipe for Small-Model Dialogue Game Agents
Interactive dialogue games test a capability that static benchmarks largely leave implicit: a model must carry state ...
HuggingFace
Ask or Answer: A Decision Framework for Multi-Turn Health Misinformation Intervention
Correcting health misinformation in dialogue requires more than producing a factual rebuttal: users differ in what th...
arXiv
How Proper Scoring Rules Shape LLM Forecasting
This paper evaluates how reward function choice shapes the performance and behavior of LLM forecasters. We compare fi...
arXiv
LLM-Based Agents for Software and Systems Security: Approaches, Applications, and Assessment
Software and systems security workflows are typically procedural: analysts inspect heterogeneous artifacts, form hypo...
arXiv
AcrossVAM1.0: Particle World Modeling for Text-Assisted Robot Video Prediction
Predicting robot videos requires both precise motion reasoning and preservation of high-frequency appearance, yet mon...
arXiv
On the Maintenance and Co-evolution of Agent Plugins: An Empirical Study of Claude Code Plugin Marketplaces
AI coding agents, software tools that automate development tasks through reasoning and tool use, are increasingly ext...
arXiv
Training Communication-Efficient Mixture-of-Experts Language Models with Layer Re-Configuration
When training Mixture-of-Experts (MoE) language models with expert parallelism, all-to-all token dispatch and combine...