Look-Before-Move is a framework that converts narrative intent into Semantic Observation Contracts, uses Monte Carlo Viewpoint Search for feasible viewpoints, and applies Semantic Trajectory Grounding for coherent camera motion in dynamic 3D story worlds.
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Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models
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abstract
While language models (LMs) have shown potential across a range of decision-making tasks, their reliance on simple acting processes limits their broad deployment as autonomous agents. In this paper, we introduce Language Agent Tree Search (LATS) -- the first general framework that synergizes the capabilities of LMs in reasoning, acting, and planning. By leveraging the in-context learning ability of LMs, we integrate Monte Carlo Tree Search into LATS to enable LMs as agents, along with LM-powered value functions and self-reflections for proficient exploration and enhanced decision-making. A key feature of our approach is the incorporation of an environment for external feedback, which offers a more deliberate and adaptive problem-solving mechanism that surpasses the constraints of existing techniques. Our experimental evaluation across diverse domains, including programming, interactive question-answering (QA), web navigation, and math, validates the effectiveness and generality of LATS in decision-making while maintaining competitive or improved reasoning performance. Notably, LATS achieves state-of-the-art pass@1 accuracy (92.7%) for programming on HumanEval with GPT-4 and demonstrates gradient-free performance (average score of 75.9) comparable to gradient-based fine-tuning for web navigation on WebShop with GPT-3.5. Code can be found at https://github.com/lapisrocks/LanguageAgentTreeSearch
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representative citing papers
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Reflexive agents confabulate incorrect task interpretations in memory, detected via Reflection Repetition Rate metric, with a programmatic mitigation raising correct object mentions from 0% to 86% in frozen ALFWorld cases.
GraphFlow uses a unified wGraph to dynamically instantiate workflows and manage KV caches for LLM agents, reporting 4.95 pp average gains and 4x memory reduction on five benchmarks.
SDP constructs a task-induced state space from raw text by having agents commit to and certify natural-language predicates as states, enabling structured planning and analysis in unstructured language environments.
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AdverMCTS frames code generation as a minimax game where an attacker evolves tests to expose flaws in solver-generated code, yielding more robust outputs than static-test baselines.
CACM improves language-based drug discovery agents by 36.4% via protocol auditing, a grounded diagnostician, and compressed static/dynamic/corrective memory channels that localize failures and bias corrections.
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citing papers explorer
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Look-Before-Move: Narrative-Grounded World Visual Attention in Dynamic 3D Story Worlds
Look-Before-Move is a framework that converts narrative intent into Semantic Observation Contracts, uses Monte Carlo Viewpoint Search for feasible viewpoints, and applies Semantic Trajectory Grounding for coherent camera motion in dynamic 3D story worlds.
-
Efficient and Trainable Language Model Test-Time Scaling via Local Branch Routing
LBR performs token-level test-time scaling via local branch routing on hidden states, enabling end-to-end RL training and improving Pass@1 and Pass@32 on math benchmarks over CoT and RLVR baselines.
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LinTree: Improving LLM Reasoning with Explicitly Structured Search Histories
Adding explicit parent pointers to represent search tree structure in LLM reasoning traces (LinTree) improves task performance and search efficiency on Blocks World, grid Navigation, and Sokoban relative to implicit traces and LLM-heuristic search.
-
Honest Lying: Understanding Memory Confabulation in Reflexive Agents
Reflexive agents confabulate incorrect task interpretations in memory, detected via Reflection Repetition Rate metric, with a programmatic mitigation raising correct object mentions from 0% to 86% in frozen ALFWorld cases.
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GraphFlow: A Graph-Based Workflow Management for Efficient LLM-Agent Serving
GraphFlow uses a unified wGraph to dynamically instantiate workflows and manage KV caches for LLM agents, reporting 4.95 pp average gains and 4x memory reduction on five benchmarks.
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State-Centric Decision Process
SDP constructs a task-induced state space from raw text by having agents commit to and certify natural-language predicates as states, enabling structured planning and analysis in unstructured language environments.
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RubricRefine: Improving Tool-Use Agent Reliability with Training-Free Pre-Execution Refinement
RubricRefine is a training-free pre-execution method that creates rubrics to score and fix inter-tool contract violations in agent code, reaching 0.86 average on M3ToolEval across seven models with zero executions and lower latency.
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Inference-Time Budget Control for LLM Search Agents
A VOI-based controller for dual inference budgets improves multi-hop QA performance by prioritizing search actions and selectively finalizing answers.
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POSTCONDBENCH: Benchmarking Correctness and Completeness in Formal Postcondition Inference
POSTCONDBENCH is a new multilingual benchmark that evaluates LLM postcondition generation on real code using defect discrimination to assess completeness beyond surface matching.
-
Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS
Self-Correcting RAG formalizes retrieval as MMKP to maximize information density under token limits and uses NLI-guided MCTS to validate faithfulness, raising accuracy and cutting hallucinations on six multi-hop QA and fact-checking datasets.
-
AdverMCTS: Combating Pseudo-Correctness in Code Generation via Adversarial Monte Carlo Tree Search
AdverMCTS frames code generation as a minimax game where an attacker evolves tests to expose flaws in solver-generated code, yielding more robust outputs than static-test baselines.
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Constraint-Aware Corrective Memory for Language-Based Drug Discovery Agents
CACM improves language-based drug discovery agents by 36.4% via protocol auditing, a grounded diagnostician, and compressed static/dynamic/corrective memory channels that localize failures and bias corrections.
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SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios
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ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling
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Cost-Effective Agent Harnesses for Abstract Reasoning and Generalization on ARC-AGI-1
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Ask the World Before Acting: Environment Probing for Calibrated Agent World Models
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Stop Hand-Holding Your Coding Agent: Engineering the Loops that Replace Step-by-Step Prompting
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Agent vs. Parametric World Models: Hybrid Planning for Reliable Language Agents
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Toward Generalist Autonomous Research via Hypothesis-Tree Refinement
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TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning
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CATPO: Critique-Augmented Tree Policy Optimization
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Efficient Skill Grounding via Code Refactoring with Small Language Models
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RedEdit: Agentic Red-Teaming of Image Safety Classifiers via MCTS-Guided Photo-Editing
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REPOT: Recoverable Program-of-Thought via Checkpoint Repair
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Efficient Agentic Reasoning Through Self-Regulated Simulative Planning
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ArborKV: Structure-Aware KV Cache Management for Scaling Tree-based LLM Reasoning
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Argus: Reorchestrating Static Analysis via a Multi-Agent Ensemble for Full-Chain Security Vulnerability Detection
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CalVerT: Augmenting Agents with Calibrated Verifier Telemetry Improves Action and Learning in Knowledge-Intensive Tasks
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APPO: Agentic Procedural Policy Optimization
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TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoning
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Why We Need World Models for AGI: Where LLMs Fail and How World Models May Outperform
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From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review
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The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey
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