arXiv AI
Aug 21
MidTool: Mid-training Data Synthesis for Agentic Tool Use
arXiv:2608. 20314v1 Announce Type: new Abstract: Mid-training is increasingly recognized as a critical stage for shaping the capabilities of large language models.
SPT: Skills as Pre-Training Data for Agentic Language Models
The paper introduces Skill Pre-Training (SPT), a mid‑training approach that uses public skill packages as data for agentic language models. By applying causal language modeling to a collection called SkillCorpus and employing a Reference Insert strategy to keep file relationships intact, SPT improves agentic performance across various model sizes while largely preserving general capabilities. Experiments also show that mixing skill data with general corpora yields further benefits.
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arXiv Machine Learning
Aug 10
SkillAligner: Treating Retrieved Skills as Adaptable Drafts at Execution Time
arXiv:2608. 06880v1 Announce Type: new Abstract: General-purpose skills promise reusable procedural knowledge for language agents, yet semantic relevance does not guarantee execution utility: a retrieved skill may encode assumptions that conflict with the current task, execution environment, or other retrieved skills.
arXiv AI
4d ago
ProgAgent: Learning Tool Orchestration through Programmatic Training
arXiv:2610.14774v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly expected to orchestrate diverse tools over long-horizon tasks. Existing agents typically use Direct Tool...
arXiv AI
Aug 25
SkillAlchemy: Open-World Agent Skill Creation
arXiv:2608.23417v1 Announce Type: new Abstract: Agent skills are reusable procedural artifacts that extend language agents with specialized workflows, tool conventions, and domain behaviors at infere...
arXiv AI
Jun 16
ACC: Compiling Agent Trajectories for Long-Context Training
arXiv:2605. 21850v2 Announce Type: replace-cross Abstract: Recent development of agents has renewed demand for long-context reasoning capacity of LLMs.
arXiv AI
Jun 3
From Context to Skills: Can Language Models Learn from Context Skillfully?
arXiv:2604. 27660v3 Announce Type: replace Abstract: Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge.
arXiv Machine Learning
Sep 22
Characterizing Model-Native Skills
arXiv:2604.17614v2 Announce Type: replace-cross Abstract: Skills are a natural unit for describing what a language model can do and how its behavior can be changed. However, existing characterization...
arXiv AI
4d ago
Frustratingly Easy Agentic RAG via Portable Skills: A Case Study in Customer Support
arXiv:2610.14428v1 Announce Type: new Abstract: Enterprise workflow automation typically requires retrieving a complete and accurate set of relevant information from large operational document corpor...
Hugging Face Trending Papers
Aug 24
SkillAlchemy: Open-World Agent Skill Creation
Agent skills are reusable procedural artifacts that extend language agents with specialized workflows, tool conventions, and domain behaviors at inference time. However, creating reliable skills still...
Hugging Face Trending Papers
Aug 11
SKILLER: Language-Level Reinforcement Learning for Reusable Skill Extraction in Small Language Models
Agent skills represent a standardized format for packaging procedural knowledge and domain expertise, serving within agent harness systems as an essential mechanism to continually constrain a language model's behavior space for repeatable, high-quality task execution. However, because strong closed-source models entail high inference costs, current popular agent harnesses, such as Codex and OpenClaw, remain prohibitively expensive when deploying these skills to accomplish real-world tasks.
arXiv AI
Jun 9
Skill Retrieval Augmentation for Agentic AI
arXiv:2604. 24594v3 Announce Type: replace-cross Abstract: As large language models (LLMs) evolve into agentic problem solvers, they increasingly rely on external, reusable skills to handle tasks beyond their native parametric capabilities.
arXiv AI
Aug 18
Evo-Harness: Context-to-Harness Skill Compilation for Self-Evolving Agents
arXiv:2608. 15071v1 Announce Type: new Abstract: Learning from experience is critical for developing capable, self-improving large language model (LLM) agents.