4 citations · 6 across the 10 of their papers we have counts for
3 papers · 1 filter
-Emotion-Bench: Exposing the Interaction Fragility of LLM Agents through Real-World User Emotions
Rishitosh Kumar Singh, Amir Saeidi, Shamanthak Hegde +2
In long-horizon tool-calling tasks, LLM agents can fail not only because of tool-use errors, but also because of ineffective interaction with users. Yet existing benchmarks largely…
CAST: Critique-Aware Supervision for Training Reliable Long-Horizon Tool-Calling Agents
Amir Saeidi, Zehua Zhang, Rishitosh Singh +6
Large language model (LLM) agents are increasingly deployed in long-horizon, interactive, and stateful environments. In these settings, a single wrong action, such as refunding the…
LedgerAgent: Structured State for Policy-Adherent Tool-Calling Agents
Md Nayem Uddin, Amir Saeidi, Eduardo Blanco +1
Policy-adherent tool-calling agents in customer-service domains must maintain task states across turns while calling tools and obeying domain policies. Task states consist of relev…