Cover Image

On the Brittle Foundations of ReAct Prompting for Agentic Large Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Verma, Mudit, Bhambri, Siddhant, Kambhampati, Subbarao
Format:	Preprint
Published:	2024
Subjects:	Artificial Intelligence Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Theory of Mind abilities of Large Language Models in Human-Robot Interaction : An Illusion?
by: Verma, Mudit, et al.
Published: (2024)

Do Cognitively Interpretable Reasoning Traces Improve LLM Performance?
by: Bhambri, Siddhant, et al.
Published: (2025)

Interpretable Traces, Unexpected Outcomes: Investigating the Disconnect in Trace-Based Knowledge Distillation
by: Bhambri, Siddhant, et al.
Published: (2025)

Can Large Language Models Reason and Plan?
by: Kambhampati, Subbarao
Published: (2024)

Robust Planning with Compound LLM Architectures: An LLM-Modulo Approach
by: Gundawar, Atharva, et al.
Published: (2024)

Incorporating Human Flexibility through Reward Preferences in Human-AI Teaming
by: Bhambri, Siddhant, et al.
Published: (2023)

Robust Planning with LLM-Modulo Framework: Case Study in Travel Planning
by: Gundawar, Atharva, et al.
Published: (2024)

Algorithmic Language Models with Neurally Compiled Libraries
by: Saldyt, Lucas, et al.
Published: (2024)

Exploring ReAct Prompting for Task-Oriented Dialogue: Insights and Shortcomings
by: Elizabeth, Michelle, et al.
Published: (2024)

Who is Helping Whom? Analyzing Inter-dependencies to Evaluate Cooperation in Human-AI Teaming
by: Biswas, Upasana, et al.
Published: (2025)

ReAct Meets ActRe: When Language Agents Enjoy Training Data Autonomy
by: Yang, Zonghan, et al.
Published: (2024)

Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning
by: Bhambri, Siddhant, et al.
Published: (2024)

LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks
by: Kambhampati, Subbarao, et al.
Published: (2024)

LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench
by: Valmeekam, Karthik, et al.
Published: (2024)

GEM: Graph-Enhanced Mixture-of-Experts with ReAct Agents for Dialogue State Tracking
by: Zhu, Ziqi, et al.
Published: (2026)

Local Coherence or Global Validity? Investigating RLVR Traces in Math Domains
by: Samineni, Soumya Rani, et al.
Published: (2025)

ARUQULA -- An LLM based Text2SPARQL Approach using ReAct and Knowledge Graph Exploration Utilities
by: Brei, Felix, et al.
Published: (2025)

Focused ReAct: Improving ReAct through Reiterate and Early Stop
by: Li, Shuoqiu, et al.
Published: (2024)

Co-ReAct: Rubrics as Step-Level Collaborators for ReAct Agents
by: Kang, Jiazheng, et al.
Published: (2026)

Performative Thinking? The Brittle Correlation Between CoT Length and Problem Complexity
by: Palod, Vardhan, et al.
Published: (2025)

On the Self-Verification Limitations of Large Language Models on Reasoning and Planning Tasks
by: Stechly, Kaya, et al.
Published: (2024)

ReAct! An Interactive Tool for Hybrid Planning in Robotics
by: Dogmus, Zeynep, et al.
Published: (2013)

Unsupervised Cycle Detection in Agentic Applications
by: George, Felix, et al.
Published: (2025)

Large Language Models Vote: Prompting for Rare Disease Identification
by: Oniani, David, et al.
Published: (2023)

Generalization or Memorization? Brittleness Testing for Chess-Trained Language Models
by: Tang, Ethan
Published: (2026)

Continual-learning for Modelling Low-Resource Languages from Large Language Models
by: K, Santosh Srinath, et al.
Published: (2026)

Large Language Model Agent for Structural Drawing Generation Using ReAct Prompt Engineering and Retrieval Augmented Generation
by: Zhang, Xin, et al.
Published: (2025)

Mitigating Hallucination in Large Language Models (LLMs): An Application-Oriented Survey on RAG, Reasoning, and Agentic Systems
by: Li, Yihan, et al.
Published: (2025)

Brittle Minds, Fixable Activations: Understanding Belief Representations in Language Models
by: Bortoletto, Matteo, et al.
Published: (2024)

Reason-Plan-ReAct: A Reasoner-Planner Supervising a ReAct Executor for Complex Enterprise Tasks
by: Molinari, Gianni, et al.
Published: (2025)

RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents
by: Chen, Weizhe, et al.
Published: (2024)

RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models
by: Moghaddam, Arya Hadizadeh, et al.
Published: (2026)

Agentic Reasoning for Large Language Models
by: Wei, Tianxin, et al.
Published: (2026)

Strategic Prompting for Conversational Tasks: A Comparative Analysis of Large Language Models Across Diverse Conversational Tasks
by: Joshi, Ratnesh Kumar, et al.
Published: (2024)

Eliciting and Analyzing Emergent Misalignment in State-of-the-Art Large Language Models
by: Panpatil, Siddhant, et al.
Published: (2025)

PromptBridge: Cross-Model Prompt Transfer for Large Language Models
by: Wang, Yaxuan, et al.
Published: (2025)

Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!
by: Kambhampati, Subbarao, et al.
Published: (2025)

Brittleness and Promise: Knowledge Graph Based Reward Modeling for Diagnostic Reasoning
by: Khatwani, Saksham, et al.
Published: (2025)

Are Large Language Models Good Prompt Optimizers?
by: Ma, Ruotian, et al.
Published: (2024)

Large Language Models Prompting With Episodic Memory
by: Do, Dai, et al.
Published: (2024)