CTA framework detects 522 skill influence patterns in LLM agent traces across 49 tasks where average pass rate shifts only +0.3%, exposing evaluation gaps in behavioral effects like template copying and excess planning.
Swe-skills-bench: Do agent skills actually help in real-world software engineering?
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SkillGuard extracts executable environment contracts from LLM skill documents to detect only relevant drifts, reporting zero false positives on 599 cases, 100% precision in known-drift tests, and raising one-round repair success from 10% to 78%.
SkillRet benchmark shows fine-tuned retrievers improve NDCG@10 by 13+ points over prior models on large-scale skill retrieval for LLM agents.
SkillFlow benchmark shows lifelong skill evolution yields modest gains for some models like Claude Opus 4.6 but limited or negative utility for others despite high skill usage.
SkVM uses capability profiling and compiler-style techniques to make skills portable across LLMs and harnesses, raising task completion rates while cutting token use by up to 40% and delivering up to 3.2x speedup.
SWE-Bench 5G is the first benchmark for AI agents fixing bugs in 5G core network software, showing high diagnosis rates but low resolution that improves conditionally with specification context.
NAUTILUS is a prompt-driven harness that automates plug-and-play adapters, typed contracts, and validation for policies, benchmarks, and robots in learning research.
SkillMOO automatically evolves skill bundles for LLM coding agents via LLM-proposed edits and NSGA-II, achieving up to 131% higher pass rates and 32% lower costs on three SkillsBench tasks.
citing papers explorer
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Counterfactual Trace Auditing of LLM Agent Skills
CTA framework detects 522 skill influence patterns in LLM agent traces across 49 tasks where average pass rate shifts only +0.3%, exposing evaluation gaps in behavioral effects like template copying and excess planning.
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Skill Drift Is Contract Violation: Proactive Maintenance for LLM Agent Skill Libraries
SkillGuard extracts executable environment contracts from LLM skill documents to detect only relevant drifts, reporting zero false positives on 599 cases, 100% precision in known-drift tests, and raising one-round repair success from 10% to 78%.
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SkillRet: A Large-Scale Benchmark for Skill Retrieval in LLM Agents
SkillRet benchmark shows fine-tuned retrievers improve NDCG@10 by 13+ points over prior models on large-scale skill retrieval for LLM agents.
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SkillFlow:Benchmarking Lifelong Skill Discovery and Evolution for Autonomous Agents
SkillFlow benchmark shows lifelong skill evolution yields modest gains for some models like Claude Opus 4.6 but limited or negative utility for others despite high skill usage.
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SkVM: Revisiting Language VM for Skills across Heterogenous LLMs and Harnesses
SkVM uses capability profiling and compiler-style techniques to make skills portable across LLMs and harnesses, raising task completion rates while cutting token use by up to 40% and delivering up to 3.2x speedup.
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SWE-Bench 5G: Benchmarking AI Coding Agents on Telecom Network Engineering Tasks
SWE-Bench 5G is the first benchmark for AI agents fixing bugs in 5G core network software, showing high diagnosis rates but low resolution that improves conditionally with specification context.
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Nautilus: From One Prompt to Plug-and-Play Robot Learning
NAUTILUS is a prompt-driven harness that automates plug-and-play adapters, typed contracts, and validation for policies, benchmarks, and robots in learning research.
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SkillMOO: Multi-Objective Optimization of Agent Skills for Software Engineering
SkillMOO automatically evolves skill bundles for LLM coding agents via LLM-proposed edits and NSGA-II, achieving up to 131% higher pass rates and 32% lower costs on three SkillsBench tasks.