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Mudit Verma

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8 papers

2 author rows

TMLR Journal 2025 Journal Article

Do Think Tags Really Help LLMs Plan? A Critical Evaluation of ReAct-Style Prompting

Siddhant Bhambri
Mudit Verma
Subbarao Kambhampati

The reasoning abilities of Large Language Models (LLMs) remain a topic of considerable interest and debate. Among the original papers arguing for emergent reasoning abilities of LLMs, ReAct became particularly popular by claiming to tease out LLM reasoning abilities with special prompting involving “interleaving reasoning trace with action execution". In this paper, we critically examine the claims of ReAct style prompting for planning and sequential decision-making problems. By introducing systematic variations to the input prompt, we perform a sensitivity analysis along the original claims of ReAct. Our experiments in AlfWorld and WebShop, domains that were used in the original ReAct work, show that the performance is minimally influenced by the interleaved reasoning trace or by the content of these generated reasoning traces. Instead, the performance of LLMs is primarily driven by the unreasonably high degree of similarity between input example tasks and queries, with shockingly little ability to generalize. In addition to raising questions on claims about reasoning abilities, this lack of generalization also implicitly forces the prompt designer to provide instance-specific examples, significantly increasing the cognitive burden on the human. Our empirical results show that the perceived reasoning abilities of LLMs stem from the exemplar-query similarity and approximate retrieval rather than any inherent reasoning abilities, thereby leading to severe lack of generalization beyond the few-shot examples given in the prompts. Our code and prompt settings can be found here on GitHub.

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ICML Conference 2025 Conference Paper

ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks

Saurabh Jha
Rohan R. Arora
Yuji Watanabe
Takumi Yanagawa
Yinfang Chen
Jackson Clark
Bhavya
Mudit Verma

Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench, a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks. Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and Security Operations (CISO), and Financial Operations (FinOps). The design enables AI researchers to understand the challenges and opportunities of AI agents for IT automation with push-button workflows and interpretable metrics. IT-Bench includes an initial set of 102 real-world scenarios, which can be easily extended by community contributions. Our results show that agents powered by state-of-the-art models resolve only 11. 4% of SRE scenarios, 25. 2% of CISO scenarios, and 25. 8% of FinOps scenarios (excluding anomaly detection). For FinOps-specific anomaly detection (AD) scenarios, AI agents achieve an F1 score of 0. 35. We expect ITBench to be a key enabler of AI-driven IT automation that is correct, safe, and fast. IT-Bench, along with a leaderboard and sample agent implementations, is available at https: //github. com/ibm/itbench.

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ICLR Conference 2024 Conference Paper

Hindsight PRIORs for Reward Learning from Human Preferences

Mudit Verma
Katherine Metcalf

Preference based Reinforcement Learning (PbRL) removes the need to hand specify a reward function by learning one from preference feedback over policy behaviors. Current approaches to PbRL do not address the credit assignment problem inherent in determining which parts of a behavior most contributed to a preference resulting in data intensive approaches and subpar reward models. We address such limitations by introducing a credit assignment strategy (PRIOR) that uses a forward dynamics world model to approximate state importance within a trajectory and then guides rewards to be proportional to state importance through an auxiliary predicted return redistribution objective. Incorporating state importance into reward learning improves the speed of policy learning, overall policy performance, and reward recovery on both locomotion and manipulation tasks. For example, PRIOR achieves 80% success rate with half the amount of data compared to baselines. The performance gains and our ablations demonstrate the benefits even a simple credit assignment strategy can have on reward learning and that state importance in forward dynamics prediction is a strong proxy for a state's contribution to a preference decision.

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ICML Conference 2024 Conference Paper

Position: LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks

Subbarao Kambhampati
Karthik Valmeekam
Lin Guan 0003
Mudit Verma
Kaya Stechly
Siddhant Bhambri
Lucas Saldyt
Anil Murthy

We argue that auto-regressive LLMs cannot, by themselves, do planning or self-verification (which is after all a form of reasoning), and shed some light on the reasons for misunderstandings in the literature. We will also argue that LLMs should be viewed as universal approximate knowledge sources that have much more meaningful roles to play in planning/reasoning tasks beyond simple front-end/back-end format translators. We present a vision of LLM-Modulo Frameworks that combine the strengths of LLMs with external model-based verifiers in a tighter bi-directional interaction regime. We will show how the models driving the external verifiers themselves can be acquired with the help of LLMs. We will also argue that rather than simply pipelining LLMs and symbolic components, this LLM-Modulo Framework provides a better neuro-symbolic approach that offers tighter integration between LLMs and symbolic components, and allows extending the scope of model-based planning/reasoning regimes towards more flexible knowledge, problem and preference specifications.

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AAMAS Conference 2023 Conference Paper

Methods and Mechanisms for Interactive Novelty Handling in Adversarial Environments

Tung Thai
Mudit Verma
Utkarsh Soni
Sriram Gopalakrishnan
Ming Shen
Mayank Garg
Ayush Kalani
Nakul Vaidya

Learning to detect, characterize and accommodate novelties is a challenge that agents operating in open-world domains need to address to achieve satisfactory task performance. We sketch general methods for detecting and characterizing different types of novelties, and for building an appropriate adaptive model to accommodate them utilizing logical representations and reasoning methods in stochastic partially observable multi-agent environments. We also briefly report results from evaluations of our algorithms in the game domain of Monopoly. The results show high novelty detection and accommodation rates.

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ICLR Conference 2022 Conference Paper

Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations

Sarath Sreedharan
Utkarsh Soni
Mudit Verma
Siddharth Srivastava 0001
Subbarao Kambhampati

As increasingly complex AI systems are introduced into our daily lives, it becomes important for such systems to be capable of explaining the rationale for their decisions and allowing users to contest these decisions. A significant hurdle to allowing for such explanatory dialogue could be the {\em vocabulary mismatch} between the user and the AI system. This paper introduces methods for providing contrastive explanations in terms of user-specified concepts for sequential decision-making settings where the system's model of the task may be best represented as an inscrutable model. We do this by building partial symbolic models of a local approximation of the task that can be leveraged to answer the user queries. We test these methods on a popular Atari game (Montezuma's Revenge) and variants of Sokoban (a well-known planning benchmark) and report the results of user studies to evaluate whether people find explanations generated in this form useful.

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AAAI Conference 2022 Conference Paper

Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems

Subbarao Kambhampati
Sarath Sreedharan
Mudit Verma
Yantian Zha
Lin Guan

Despite the surprising power of many modern AI systems that often learn their own representations, there is significant discontent about their inscrutability and the attendant problems in their ability to interact with humans. While alternatives such as neuro-symbolic approaches have been proposed, there is a lack of consensus on what they are about. There are often two independent motivations (i) symbols as a lingua franca for human-AI interaction and (ii) symbols as systemproduced abstractions used by the AI system in its internal reasoning. The jury is still out on whether AI systems will need to use symbols in their internal reasoning to achieve general intelligence capabilities. Whatever the answer there is, the need for (human-understandable) symbols in human-AI interaction seems quite compelling. Symbols, like emotions, may well not be sine qua non for intelligence per se, but they will be crucial for AI systems to interact with us humans – as we can neither turn off our emotions nor get by without our symbols. In particular, in many human-designed domains, humans would be interested in providing explicit (symbolic) knowledge and advice – and expect machine explanations in kind. This alone requires AI systems to to maintain a symbolic interface for interaction with humans. In this blue sky paper, we argue this point of view, and discuss research directions that need to be pursued to allow for this type of human-AI interaction.

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NeurIPS Conference 2021 Conference Paper

Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation

Lin Guan
Mudit Verma
Suna (Sihang) Guo
Ruohan Zhang
Subbarao Kambhampati

Human explanation (e. g. , in terms of feature importance) has been recently used to extend the communication channel between human and agent in interactive machine learning. Under this setting, human trainers provide not only the ground truth but also some form of explanation. However, this kind of human guidance was only investigated in supervised learning tasks, and it remains unclear how to best incorporate this type of human knowledge into deep reinforcement learning. In this paper, we present the first study of using human visual explanations in human-in-the-loop reinforcement learning (HIRL). We focus on the task of learning from feedback, in which the human trainer not only gives binary evaluative "good" or "bad" feedback for queried state-action pairs, but also provides a visual explanation by annotating relevant features in images. We propose EXPAND (EXPlanation AugmeNted feeDback) to encourage the model to encode task-relevant features through a context-aware data augmentation that only perturbs irrelevant features in human salient information. We choose five tasks, namely Pixel-Taxi and four Atari games, to evaluate the performance and sample efficiency of this approach. We show that our method significantly outperforms methods leveraging human explanation that are adapted from supervised learning, and Human-in-the-loop RL baselines that only utilize evaluative feedback.

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