arXiv AI
KUPAS MASTER: Distilling the Tacit Expertise of Master Practitioners into Agent-Ready Experience Corpora
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arXiv AI
Jun 9
Anything2Skill: Compiling External Knowledge into Reusable Skills for Agents
arXiv:2606. 09316v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) enables agents to access external knowledge at inference time, but it primarily retrieves fragmented declarative evidence, leaving agents to repeatedly infer task procedures from passages, manuals, examples, logs, or trajectories.
arXiv Computation and Language
Aug 27
Corpus2Skill: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG
Corpus2Skill is a retrieval architecture that transforms an enterprise knowledge base into a hierarchical skill directory, enabling an LLM agent to navigate from high-level summaries to specific documents and backtrack when necessary. On an enterprise customer‑support benchmark, it outperforms single‑shot dense, hybrid, hierarchical‑retrieval, and agentic RAG baselines in answer quality and grounding, with a moderate cost tradeoff. An eleven‑dataset study shows that corpus navigation excels on single‑domain corpora with a recoverable topical taxonomy but is less effective on open‑domain factoid pools or homogeneous‑tabular corpora, providing a design guideline for knowledge‑grounded systems.
arXiv AI
Sep 2
mimeo: Compiling Public Expert Corpora into Agent Skills and Testing What Transfers
mimeo is an open‑source tool that compiles a public expert’s work into a file an agent can load, verifying each quotation against the source text. In experiments, mimeo enabled agents to answer all 20 obscure, quotation‑heavy questions and avoided misstatements that occurred with personas generated from model memory. While it improved knowledge access and reduced misidentification, it did not demonstrate clear transfer of expert judgment or outperform simple keyword search in all metrics.
arXiv Machine Learning
1d ago
SkillSpec: Consensus-Gated Agent Skill Evolution via Representation Specialization
SkillSpec is a two‑phase framework for evolving natural‑language skills in large language model agents. The first phase, consensus‑gated evolution, generates candidate skills from complementary editing intents and commits updates only when paired evaluations reach consensus on overall improvement and non‑negative aggregate gain. The second phase, representation specialization, uses signals from the optimization trajectory to choose an appropriate flat, graph, or hybrid structure for the skill, improving success rates by an average of 6.89% over SkillOpt across six benchmarks and three target language models.
arXiv AI
Jun 30
RESOURCE2SKILL: Distilling Executable Agent Skills from Human-Created Multimodal Resources
arXiv:2606. 29538v1 Announce Type: cross Abstract: Skills are a useful abstraction for software agents, turning human and agent experience into reusable procedural knowledge.
arXiv AI
Jun 4
AIP: A Graph Representation for Learning and Governing Agent Skills
arXiv:2606. 04781v1 Announce Type: new Abstract: Agent Skills today consist largely of free-form prose requiring the agent to read, interpret, and re-derive how to act in every session.