From the 2 of 50 linked papers with an AI index.
5 papers · 1 filter
Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0
Wenxiao Wang, Priyatham Kattakinda, Soheil Feizi
The paper evaluates whether gains from agent-optimization methods compound over successive optimization phases in a continual‑learning setting, using hard tasks from Terminal‑Bench…
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…
Under the Hood of SKILL.md: Semantic Supply-chain Attacks on AI Agent Skill Registry
Shoumik Saha, Kazem Faghih, Soheil Feizi
Autonomous AI agents increasingly extend their capabilities through Agent Skills: modular filesystem packages whose SKILL.md files describe when and how agents should use them. Whi…
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…
Maestro: Joint Graph & Config Optimization for Reliable AI Agents
Wenxiao Wang, Priyatham Kattakinda, Soheil Feizi
Building reliable LLM agents requires decisions at two levels: the graph (which modules exist and how information flows) and the configuration of each node (models, prompts, tools,…