Papers by Amir Saeidi
How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on tau-bench (2025.findings-emnlp)
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Venkatesh Mishra, Amir Saeidi, Satyam Raj, Mutsumi Nakamura, Gaowen Liu, Ali Payani, Jayanth Srinivasa, Chitta Baral
| Challenge: | Recent advances in reasoning and planning capabilities of large language models have enabled their potential as autonomous agents capable of tool use in dynamic environments. |
| Approach: | They propose an input-reformulation multi-agent framework that reformulates user queries . |
| Outcome: | The proposed framework outperforms ReAct, Function Calling, and Self-Reflection in overall pass5 scores. |
FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use Environments (2026.findings-acl)
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Amir Saeidi, Venkatesh Mishra, Souradeep Mukhopadhyay, Gaowen Liu, Ali Payani, Jayanth Srinivasa, Chitta Baral
| Challenge: | Large Language Models are being increasingly deployed as decision-making core of autonomous agents . however, in conversational benchmarks, these agents fail due to the cascading effects of incorrect decision- making . |
| Approach: | They propose a framework that analyzes failure trajectories from baseline agents to identify most prevalent errors. |
| Outcome: | Experiments show that the framework improves performance over open-source LLMs . the framework can be used to build reliable, multi-turn tool-use agents . |
Insights into Alignment: Evaluating DPO and its Variants Across Multiple Tasks (2025.acl-srw)
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| Challenge: | Large Language Models (LLMs) excel in math reasoning problemsolving, text generation, summarization, creative writing, among other tasks. |
| Approach: | They evaluate Direct Preference Optimization and its variants for aligning Large Language Models with human preferences. |
| Outcome: | The proposed alignment methods achieve near-optimal performance even with smaller subsets of training data. |
UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMsā Memorization (2025.acl-long)
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Md Nayem Uddin, Amir Saeidi, Divij Handa, Agastya Seth, Tran Cao Son, Eduardo Blanco, Steven Corman, Chitta Baral
| Challenge: | UnSeenTimeQA is a data contamination-free time-sensitive question-answering benchmark. |
| Approach: | They propose a data contamination-free time-sensitive question-answering benchmark that avoids web-searchable queries grounded in the real world. |
| Outcome: | The proposed benchmark avoids web-searchable queries grounded in the real world and enables on-demand generation of new samples, mitigating the risk of data leakage. |