The paper introduces ACES (Agentic Continuous Evaluation of Skills), a framework that evaluates reusable skills and capability packages by running paired live trials with and without a target skill, normalizing results into the Agent Trajectory Interchange Format (ATIF), and grading six runtime metrics to compute Skill Lift. ACES demonstrates that scan-only gates miss important aspects of skill performance, while the evaluation protocol reveals significant improvements in skill execution, behavior check, and skill efficiency across 145 real skills and 947 scored cases. The open‑source NVIDIA SkillEvaluator implementation enables reproducible, repository‑native assessment of agentic skills in production environments.
By Christopher Kevin, Narendran Raghavan, Jean-Francois Puget, Roshni Malani, Meghana Puvvadi, Moshe Abramovitch, Mohit Gupta, Rama Akkiraju, Subodh Prabhu, Yogesh Dangi, Wei Luo, Seong Hee Lee
arXiv:2605. 22148v3 Announce Type: replace Abstract: A large language model (LLM) agent that writes and edits its own skill library must also decide which skills to keep, from one noisy scalar per skill.
By Xing Zhang, Yanwei Cui, Guanghui Wang, Ziyuan Li, Wei Qiu, Bing Zhu, Peiyang He
arXiv:2607. 18966v1 Announce Type: new Abstract: Language models trained with reinforcement learning may learn to optimize the grader's judgment rather than the intended objective.
By Axel H{\o}jmark, J\'er\'emy Scheurer, Evgenia Nitishinskaya, Felix Hofst\"atter, Jason Wolfe, Theodore Ehrenborg, Bronson Schoen, Alexander Meinke
SkillGate is a method that trains agents to select the correct skill from a large slate during an episode by separating credit signals for skill selection and execution. It addresses the problem of selector credit starvation, where traditional outcome-rewarded RL fails to give sufficient credit to the skill-naming tokens, especially in long-horizon tasks. Experiments on five benchmarks show that SkillGate improves a 9B policy’s success rate from 40.8% to 53.2%, reduces exposure to misleading candidates, and requires fewer skill reads.
By Qingyao Li, Wenxiang Jiao, Shuai Shao, Kangning Zhang, Yuan Lu, Yi Guo, Weiwen Liu, Weinan Zhang, Yong Yu
arXiv:2606. 15390v1 Announce Type: cross Abstract: LLM agents can improve without weight updates by accumulating natural-language skills from experience, but current systems entrust every decision about which skills to keep and how to apply them to LLM judgment alone.
By Yixuan Wang, Yiyang Zhou, Yiming Liang, Congyu Zhang, Fuxiao Liu, Jiawei Zhou, Huaxiu Yao
The paper introduces Zep Agent Skills, a production service that autonomously compiles and curates procedural memory for large language model agents by converting verified execution traces into versioned skills. It records each task attempt as a trace, uses a compiler with Reflector and Curator roles to transform verified traces into skills, and applies admission gates and search ranking based on verified outcomes. The open Agent Skills format, originally from Anthropic, allows any host to load compiled skills, and evaluation on several benchmarks shows significant performance gains for weaker executor models without additional training.
By Daniel Chalef, Peter Evans, Pavlo Paliychuk, Mehul Patel, Preston Rasmussen, Jack Ryan
SWE-Skills-Bench is a benchmark that evaluates the impact of individual agent skills on real-world software engineering tasks. It uses 98 public skills paired with specific GitHub repositories, generating 3,920 paired evaluations across four agent-model configurations. The study finds that most skills are ineffective, with only a few providing modest gains while some are harmful, raising the overall pass ratio from 76.6% to 76.9%.
By Tingxu Han, Yi Zhang, Wei Song, Chunrong Fang, Zhenyu Chen, Youcheng Sun, Lijie Hu
arXiv:2607. 12790v1 Announce Type: new Abstract: Self-evolving agent systems improve by creating, revising, and retiring their own skills, but every such loop rests on a hidden assumption: a reliable evaluation metric already exists.
By Xing Zhang, Guanghui Wang, Yanwei Cui, Ziyuan Li, Wei Qiu, Bing Zhu, Peiyang He
arXiv:2610.14180v1 Announce Type: new
Abstract: Organisations that want language-model agents for security work face a choice between open-weight models, which they can run and audit themselves, and...
By Sahil Garg, Ruhi Gottumukkala, Vishal Agarwal, Shubham Vishwakarma
The paper introduces a Trust Layer for Agent Evaluation, a post‑hoc framework that appends a belief flag to each benchmark score. It checks four properties: alignment with the benchmark’s grading logic, traceable computation of a passing answer, consistency between the agent’s claim and actual behavior, and stability across repeated runs. Applied to five agent configurations on 108 tasks, the study finds that only 22.6% of recorded passes satisfy all checks, revealing widespread issues such as untraceable computation, false completion claims, and unstable results.
By Mohammadreza Sediqin, Shivali Dalmia, Srinivasa Karthikeya Reddy Kovvuri, Abhishek Mukherji
The paper introduces a method for co-evolving inspectable verifiers alongside self-improving agents. By synthesizing verifiers from clustered failures and selecting them based on agreement with a reference set, the authors demonstrate improved held‑out agreement on MBPP+ and outperform a bare LLM judge. They also show that removing anchor guards collapses the verifier into a vacuous grader, yet the collapsed verifier still trains skills effectively, indicating that downstream task scores cannot certify a self‑evolved verifier.
By Xing Zhang, Guanghui Wang, Yanwei Cui, Ziyuan Li, Wei Qiu, Bing Zhu, Peiyang He
K-Bench is a new benchmark designed to evaluate large language model (LLM) unlearning when the models are deployed as agents. Unlike previous benchmarks that only inspect the final answer, K-Bench examines all six channels of a ReAct agent—including chain-of-thought, tool calls, tool observations, and elicited summaries—to determine if a secret is leaked. The benchmark measures leakage for secrets placed in the model weights, prompt, or retrieval store, and finds that many existing unlearning methods fail to prevent leaks in deployed agents, especially when secrets reside in the prompt or retrieval store.
By Guangsheng Yu, Yanna Jiang, Qin Wang, Baihe Ma, Xu Wang