4 papers · 1 filter
FORTIS: Benchmarking Over-Privilege in Agent Skills
Shawn Li, Chenxiao Yu, Han Wang +8
Large language model agents increasingly operate through an intermediate skill layer that mediates between user intent and concrete task execution. This layer is widely treated as…
Counterfactual Trace Auditing of LLM Agent Skills
Xiaolin Zhou, Jinbo Liu, Li Li +2
Large Language Model agents are increasingly augmented with agent skills. Current evaluation methods for skills remain limited. Most deployed benchmarks report only pass rate befor…
When Simulation Lies: A Sim-to-Real Benchmark and Domain-Randomized RL Recipe for Tool-Use Agents
Xiaolin Zhou, Aojie Yuan, Zheng Luo +12
Tool-use language agents are evaluated on benchmarks that assume clean inputs, unambiguous tool registries, and reliable APIs. Real deployments violate all these assumptions: user…
RADAR: Reasoning-Ability and Difficulty-Aware Routing for Reasoning LLMs
Nigel Fernandez, Branislav Kveton, Ryan A. Rossi +2
Reasoning language models have demonstrated remarkable performance on many challenging tasks in math, science, and coding. Choosing the right reasoning model for practical deployme…