Large language model (LLM) agents increasingly rely on skills, structured documents that specify when to act, which procedure to follow, and which tools are allowed. Existing evaluations mostly judge the quality of a skill or its contribution to task success, leaving unexamined whether an agent can recognize a relevant skill and apply it on its own. We introduce Skill-Use, a benchmark that evaluates skill use under progressive disclosure, where an agent sees only a skill's name and short description and must retrieve the full procedure before following it. Skill-Use separates three facets of skill use. Trigger measures whether the agent invokes the relevant skill, Compliance measures how faithfully it follows the prescribed procedure, and Boundary measures whether it avoids forbidden operations. A Skill-Use (SU) score combines the three and credits execution only after the skill is triggered. Skill-Use pairs 79 real skills with 177 executable tasks across nine domains, each grounded in real files, run in an isolated Docker sandbox, and scored by a trajectory-based rubric. Evaluating eight LLMs under two agent harnesses, we find that reliable skill use remains out of reach, as the strongest configuration reaches an SU of only 0.613. Triggering and procedural compliance fail as independent bottlenecks, and both scores and model rankings shift with the harness, so skill use behaves as a capability conditioned on the harness rather than a fixed property of the model.
Jinyi Han, Yuanjian Xu, Ying Liao et al.· 1 citation
Internal short-circuit (ISC) faults in lithium-ion batteries shorten service life and may cause severe safety issues such as thermal runaway. Therefore, this study proposes a purely data-driven method based on terminal voltage during charging. The analysis focuses on the stable mid-to-late stage of low-rate constant-current charging. Multiple discrete time instants within 4200 s–6000 s are selected, and the voltage difference between them is used as the diagnostic feature. A random forest (RF) model is developed, trained, and validated. To improve performance, Bayesian optimization tunes RF hyperparameters, and particle swarm optimization is applied for feature selection. Diagnostic thresholds are established based on model outputs to enable ISC fault identification and prediction. Support vector regression and Gaussian process regression are used for comparison. Results show that the optimized RF model achieves a mean relative error of 4.254%, a root mean square error of 32.688 Ω, and a coefficient of determination (R2) of 98.42%, significantly outperforming the comparison methods.
S. Duan, Yizhen Qu, Ye Liu et al.· Engineering Research Express· 0 citations
LLM-based agentic systems have shown remarkable capabilities in complex domains, while suffering from cascading errors and difficulty in debugging. Critical error detection aims to locate the earliest error step in a failed trajectory that is responsible for the final failure. However, progress faces two main challenges. First, long trajectories make it difficult to identify individual errors, since the evidence for judging a step may be scattered across distant instructions, observations, and prior context. Second, failed trajectories often contain multiple local errors with different downstream effects, only some of which remain responsible for the final failure. In this work, we propose TrajDebug, an error-lifecycle tracing framework that addresses long-trajectory error discovery with multi-granularity history compression and evidence-based error identification, and supports critical attribution by tracing each error's resolution status and terminal impact. We further construct TrajErrBench, a benchmark of 486 manually annotated failed trajectories from Tau2Bench and SWE-Bench Pro, covering realistic tool-use and coding scenarios. Experiments across diverse agent benchmarks show that TrajDebug achieves the best overall performance over existing baselines, and application studies further demonstrate that its diagnoses provide actionable feedback for improving downstream agent success. We will release the codes and data to facilitate further research.