1 citations · 1 across the 10 of their papers we have counts for
5 papers · 1 filter
Revealing what benchmark scores hide: Jointly measuring the breadth and reliability of LLM capabilities using accessibility profiles
Kazem Faghih, Yize Cheng, Shoumik Saha +3
Language model benchmarks assign each question a single outcome from one fixed wording, even though the same underlying request can be expressed in many equivalent ways. We argue t…
Hierarchical Reinforcement Learning with Stable Temporal Abstraction for Language Model Agents
Shayan Mohajer Hamidi, Yize Cheng, Yuanda Xu +2
Hierarchical reinforcement learning improves long-horizon control by organizing primitive actions around persistent subgoals and assigning credit at multiple temporal scales. Recen…
Same Question, Different Answers: Evaluating LLM Reliability Beyond Accuracy
Kazem Faghih, Yize Cheng, Shoumik Saha +3
Large language models (LLMs) often achieve strong accuracy on benchmarks, yet it remains unclear how reliably they apply this knowledge when the same question is phrased in differe…
Model-Adaptive Tool Necessity Reveals the Knowing-Doing Gap in LLM Tool Use
Yize Cheng, Chenrui Fan, Mahdi JafariRaviz +2
Large language models (LLMs) increasingly act as autonomous agents that must decide when to answer directly vs. when to invoke external tools. Prior work studying adaptive tool use…
Tool Preferences in Agentic LLMs are Unreliable
Kazem Faghih, Wenxiao Wang, Yize Cheng +5
Large language models (LLMs) can now access a wide range of external tools, thanks to the Model Context Protocol (MCP). This greatly expands their abilities as various agents. Howe…