arXiv AI
SKALOS: Learning to Distinguish Similar Skills with Capability Graphs
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jul 8
Task Decomposition-Guided Reranking for Adaptive Agent Skill Retrieval
arXiv:2607. 06283v1 Announce Type: new Abstract: Skill usage can significantly enhance the ability of modern agent systems to complete complex tasks.
arXiv AI
Jun 3
SkillDAG: Self-Evolving Typed Skill Graphs for LLM Skill Selection at Scale
arXiv:2606. 03056v1 Announce Type: new Abstract: As LLM agents adopt large skill libraries, selecting the right subset becomes a structural problem rather than a similarity-matching one: skills depend on, conflict with, specialize, or duplicate one another, a structure invisible to both full enumeration and embedding similarity.
arXiv AI
Aug 5
SkillTrace: Traversing a Query-Skill Graph for Composable LLM Agents
arXiv:2608. 02356v2 Announce Type: replace Abstract: Large language model agents increasingly solve complex tasks by composing reusable skills from a library.
arXiv Computation and Language
Aug 27
CaSKG: Counterfactual-Causal Skill Graphs for Scalable Agent Skill Retrieval
CaSKG introduces a counterfactual‑causal skill graph framework that calibrates procedural relations before retrieval, building a high‑recall directed candidate graph from semantic, lexical, input/output, and structural evidence and refining it with repair evidence and optional LLM judgment. The framework applies direction‑conditioned textual counterfactual probes—removing, substituting, and reordering skill pairs—to aggregate evidence with Bayesian smoothing, producing a state‑filtered weighted graph for task‑conditioned expansion. Evaluated across six LLM backbones on ALFWorld and ScienceWorld, CaSKG outperforms existing Graph‑of‑Skills methods, improving macro‑average scores and reducing mean environment steps while preserving essential skill dependencies.
arXiv AI
Oct 9
SkillContrast: Difference-Guided Text Selection for Agent Skill Reranking
SkillContrast is a training‑free selector that compares retrieved agent skills and keeps only the text that differs between them, preserving local context for a pretrained reranker. In experiments on 1,235 requests from SameCapRisk‑Bench, it improves clean‑hit rates by 54–72 over TF‑IDF query selection while using 51.1–58.8% fewer input tokens. The method shows that retaining differing text, rather than full skill bodies, is the key factor for better reranking performance.
arXiv AI
Jul 15
SkillSelect-Serve: QoS-Aware Budgeted Skill Service Recommendation for LLM Agents
arXiv:2607. 00011v2 Announce Type: replace-cross Abstract: Reusable agent skills are emerging as a service-oriented capability layer for Large Language Model (LLM) agents.