arXiv AI
Jun 15
SkillAudit: Ground-Truth-Free Skill Evolution via Paired Trajectory Auditing
arXiv:2606. 14239v1 Announce Type: new Abstract: Agent skills are structured procedural packages that guide frozen LLM agents in specialized workflows.
CLAP: Closed-Loop Training, Evaluation, and Release Control for Domain Agent Post-training
arXiv:2607. 01846v1 Announce Type: new Abstract: Domain agents often face noisy business data, uncertain post-training gains, offline/application mismatch, and adapter-release risk.
Related stories
arXiv AI
Aug 5
Learning Clinical-Trial Strategy: Offline Policy Training for Decision Agents
arXiv:2608. 03606v1 Announce Type: new Abstract: Clinical development is sequential decision-making under uncertainty, where a sponsor must plan a portfolio of experiments from heterogeneous evidence.
Hugging Face Trending Papers
Jul 30
Share the Judge, Learn the Deferral: Where Specialization Helps LLM Evaluation
Agentic systems have widened the gap between producing candidate outputs and reviewing them. This paper asks a practical architectural question: should domain specialization be built into an evaluator's weights, or into the rule that decides when its judgment can be trusted?
arXiv AI
Aug 11
From Trajectories to Evidence: Auditable Experimental Records for Industrial Research Agents
arXiv:2608. 05235v1 Announce Type: cross Abstract: Research agents increasingly conduct multi-round machine-learning experiments in industrial recommendation settings and retain the resulting trajectories to guide later decisions.
arXiv AI
Jun 17
SEAGym: An Evaluation Environment for Self-Evolving LLM Agents
arXiv:2606. 17546v1 Announce Type: new Abstract: Self-evolving LLM-based agents improve mainly by changing their agent harness: the structured execution layer around a base model, including prompts, memory, tools, middleware, runtime state, and the model-tool interaction loop.
arXiv AI
Jul 1
An Executable Benchmarking Suite for Tool-Using Agents
arXiv:2605. 11030v2 Announce Type: replace-cross Abstract: Closed-loop tool-using agents are increasingly evaluated in executable web, code, and micro-task environments, but benchmark reports often conflate workloads, action-generating drivers, and the evidence admitted for systems-facing claims.
arXiv AI
2d ago
Evaluating Skills, Not Just Agents: Agentic Continuous Evaluation of Skills
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.
arXiv AI
Jun 2
Hierarchical Online Prompt Mutation with Dual-Loop Feedback for Guardrailed Evidence Document Generation: A Production-Evaluation Case Study
arXiv:2606. 01472v1 Announce Type: cross Abstract: High-stakes production document-generation systems require language models to be adaptive, evidence-grounded, and auditable.
arXiv AI
Jul 7
TRACE: Capability-Targeted Agentic Training
arXiv:2604. 05336v2 Announce Type: replace Abstract: Models often fail to complete agentic tasks because they lack core capabilities required by the target environment.
arXiv AI
Aug 7
SkillTV-Bench: Benchmarking How Well Judges Perform on Skill-Augmented Agentic Execution
arXiv:2608. 05573v1 Announce Type: new Abstract: LLM agents increasingly execute long-horizon tasks through tool use and environment interaction, shifting evaluation from final-response scoring to verification of complete executions.
arXiv AI
Aug 18
Agentic Data Cleaning Without a Clean Reference: An Experimental Study of Capabilities and Trade-offs
arXiv:2608. 14765v1 Announce Type: new Abstract: Data cleaning without a trusted clean reference is challenging because unusual values may represent either genuine errors or valid observations.
arXiv AI
2d ago
Share the Judge, Learn the Deferral: Where Specialization Helps LLM Evaluation
The paper investigates two strategies for improving large language model (LLM) evaluation: specialized judge weights and rule‑based deferral policies. Experiments on nearly 100,000 rubric‑conditioned samples show that correct rubrics boost accuracy, while incorrect ones hurt it, and that splitting training data into criterion‑specific experts can severely degrade performance unless the experts are warm‑started from a unified model. The authors demonstrate that lightweight deferral cascades can match or exceed the accuracy of larger standalone judges at a fraction of the compute cost, and they provide practical design rules for building efficient, reliable LLM evaluators.