Papers by Venkatesh Mishra
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 . |
Hypothesis Generation for Materials Discovery and Design Using Goal-Driven and Constraint-Guided LLM Agents (2025.findings-naacl)
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| Challenge: | Recent research has leveraged Large Language Models to accelerate materials discovery and design. |
| Approach: | They propose a dataset that features goals, constraints, and methods for designing real-world applications and a method that emulates the process a materials scientist would use to evaluate a hypothesis critically. |
| Outcome: | The proposed method emulates the process a materials scientist would use to evaluate a hypothesis critically. |
Investigating the Shortcomings of LLMs in Step-by-Step Legal Reasoning (2025.findings-naacl)
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Venkatesh Mishra, Bimsara Pathiraja, Mihir Parmar, Sat Chidananda, Jayanth Srinivasa, Gaowen Liu, Ali Payani, Chitta Baral
| Challenge: | Reasoning abilities of LLMs have been a key focus in recent years. |
| Approach: | They propose to use a college-level Multiple Choice Question-Answering task to identify LLM errors and evaluate their performance. |
| Outcome: | The proposed framework can be used in detailed error analysis of reasoning chains for logic-intensive complex tasks. |