REVIEW 32 cited by
Large Language Model Guided Tree-of-Thought
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
In this paper, we introduce the Tree-of-Thought (ToT) framework, a novel approach aimed at improving the problem-solving capabilities of auto-regressive large language models (LLMs). The ToT technique is inspired by the human mind's approach for solving complex reasoning tasks through trial and error. In this process, the human mind explores the solution space through a tree-like thought process, allowing for backtracking when necessary. To implement ToT as a software system, we augment an LLM with additional modules including a prompter agent, a checker module, a memory module, and a ToT controller. In order to solve a given problem, these modules engage in a multi-round conversation with the LLM. The memory module records the conversation and state history of the problem solving process, which allows the system to backtrack to the previous steps of the thought-process and explore other directions from there. To verify the effectiveness of the proposed technique, we implemented a ToT-based solver for the Sudoku Puzzle. Experimental results show that the ToT framework can significantly increase the success rate of Sudoku puzzle solving. Our implementation of the ToT-based Sudoku solver is available on GitHub: \url{https://github.com/jieyilong/tree-of-thought-puzzle-solver}.
Forward citations
Cited by 32 Pith papers
-
Understanding Reasoning from Pretraining to Post-Training
A joint scaling law: post-RL chess and math performance is predictable from pretraining loss, RL improvement rate grows with pretraining tokens, and RL both amplifies and discovers moves depending on difficulty.
-
Evolving Skill-Structured Attack Memory Enhances LLM Jailbreaking
MemoAttack organizes jailbreak experience into evolving skill-structured memories and uses lifecycle management plus contextual Thompson Sampling to achieve 98% ASR on AdvBench while cutting query count by 45.9%.
-
Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization
Joint Consistency casts test-time aggregation as Ising-type energy minimization with pairwise LLM-judge interactions, subsuming voting methods and outperforming baselines across reasoning tasks.
-
Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning
A symplectic LQR layer inserted as an adapter into pretrained LLMs yields large gains on MATH-500, AMC and AIME by solving a latent optimal-control problem at inference time.
-
Hierarchical Domain Generalization
Over infinite domains, hierarchy-uniform domain generalization is impossible for every nontrivial hypothesis class; a length-generalization bound is a property of the length hierarchy, not a hierarchy-free guarantee.
-
G-RRM: Guiding Symbolic Solvers with Recurrent Reasoning Models
G-RRM neural guidance reduces median conflicts to zero and delivers speedups up to 33.3x on 9x9 Sudoku for backtracking solvers when search spaces are large and solvers can overwrite imperfect hints.
-
TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning
TRACE is a rollout budget allocation framework that models ReAct turns as tree nodes and uses a predictor to allocate samples to informative prefixes, yielding a 2.8-point accuracy gain on Multi-Hop QA at equal cost.
-
LLM-X: A Scalable Negotiation-Oriented Exchange for Communication Among Personal LLM Agents
LLM-X is a scalable architecture for direct negotiation and communication among personal LLM agents, featuring federated gateways, typed protocols, and policy enforcement, shown stable in experiments with up to 12 agents.
-
Causal-Audit: Explicit and Auditable Graph-based Reasoning via Target-Aware Causal Chain Construction
A four-stage LLM framework that constructs, audits, and aggregates explicit causal chains outperforms prompting baselines on three context-free causal QA benchmarks.
-
REVISOR: Beyond Textual Reflection, Towards Multimodal Introspective Reasoning in Long-Form Video Understanding
REVISOR adds multimodal visual-text reflection and a Dual Attribution Decoupled Reward to improve long-form video reasoning in MLLMs without extra supervised fine-tuning.
-
A Roadmap to Pluralistic Alignment
The paper formalizes three types of pluralistic AI models and three benchmark classes, arguing that current alignment techniques may reduce rather than increase distributional pluralism.
-
Protein Thoughts: Interpretable Reasoning with Tree of Thoughts and Embedding-Space Flow Matching for Protein-Protein Interaction Discovery
Protein Thoughts uses hypothesis-guided entropy-regularized Tree-of-Thoughts search and embedding flow matching to achieve mean best-binder rank 11.2 and 91.08 Micro-F1 on SHS148k by keeping sequence, structure, inter...
-
FSFM: A Biologically-Inspired Framework for Selective Forgetting of Agent Memory
FSFM is a biologically-inspired selective forgetting framework for LLM agents that claims to boost access efficiency by 8.49%, content quality by 29.2% signal-to-noise, and eliminate security risks entirely through a ...
-
Hierarchical Reasoning Model
HRM is a recurrent architecture with high-level planning and low-level execution modules that reaches near-perfect accuracy on complex Sudoku, maze navigation, and ARC benchmarks using 27M parameters and 1000 samples ...
-
From Test-taking to Cognitive Scaffolding: A Pedagogical Diagnostic Benchmark for LLMs on English Standardized Tests
The paper presents ESTBook, a multimodal benchmark of 10,576 English standardized test questions augmented with formalized cognitive reasoning trajectories and distractor rationales to support diagnostic evaluation of...
-
From Question Answering to Task Completion: A Survey on Agent System and Harness Design
Survey framing LLM agents as model-plus-harness systems, decomposing harness responsibilities, mapping them to tasks, and highlighting open challenges in evaluation, safety, and co-evolution.
-
Why We Need World Models for AGI: Where LLMs Fail and How World Models May Outperform
In the Flux environment, RL agents with explicit latent state access achieve ~79% win rate versus ~11% for LLMs on long-horizon tasks, illustrating limitations of sequence prediction for dynamic reasoning.
-
Agentic Reasoning for Large Language Models
The survey structures agentic reasoning for LLMs into foundational, self-evolving, and collective multi-agent layers while distinguishing in-context orchestration from post-training optimization and reviewing applicat...
-
Evaluating LLMs Without Oracle Feedback: Agentic Annotation Evaluation Through Unsupervised Consistency Signals
The ratio of agreement to disagreement between a small student model and an LLM correlates with the LLM's annotation accuracy across ten datasets and can heuristically select better models.
-
Project Patti: Why can You Solve Diabolical Puzzles on one Sudoku Website but not Easy Puzzles on another Sudoku Website?
A randomized Nishio solver's strategy-cycle count correlates strongly with website difficulty labels for four of five Sudoku sites, enabling a three-tier universal difficulty classification.
-
Towards Automated Regulatory Compliance Verification in Financial Auditing with Large Language Models
In a small evaluation with PwC data, Llama-2-70b beats GPT models at the 'no compliance' class for IFRS reports, but the result is based on a single selected prompt and a 100-item sample, and the data/code are not released.
-
Could you be wrong: Debiasing LLMs using a metacognitive prompt for improving human decision making
Asking an LLM 'could you be wrong?' after its answer surfaces its own biases, omitted evidence, and alternative perspectives in qualitative demonstrations on three tasks.
-
Introspection of Thought Helps AI Agents
INoT wraps prompts in XML-defined pseudo-code so an LLM simulates two debating agents internally, reporting better scores and lower tokens than seven baselines.
-
IPS: In-Prompt Process Supervision for Short Video Content Moderation
IPS adds sequential reasoning over ancillary questions to MLLM fine-tuning for short video content moderation, outperforming baselines even with model-generated labels.
-
Few-shot text-based emotion detection
A system combining Gemini few-shot prompting with 600 examples achieved F1-macro 0.325 for Emakhuwa, 0.7546 for English, and 0.1727 for Mozambican Portuguese at SemEval-2025 Task 11.
-
Is It Time To Treat Prompts As Code? A Multi-Use Case Study For Prompt Optimization Using DSPy
A five-task case study shows DSPy prompt optimization can improve LLM accuracy on some tasks, notably contradiction detection (46.2% to 64.0%), but results vary and no code or data are released.
-
Thinking About Thinking: SAGE-nano's Inverse Reasoning for Self-Aware Language Models
A 4B-parameter model is claimed to explain its own reasoning through inverse attention analysis, but the paper offers no consistent evidence or artifacts.
-
A Survey of Scaling in Large Language Model Reasoning
A survey categorizing scaling in LLM reasoning across input size, steps, rounds, training, and future directions, noting that scaling can negatively affect performance.
-
Large Language Model Agent: A Survey on Methodology, Applications and Challenges
A survey that deconstructs LLM agent systems via a methodology-centered taxonomy linking design principles to emergent behaviors, applications, and challenges.
-
A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
A systematic survey categorizes prompt engineering methods for LLMs and VLMs by application area, summarizing methodologies, applications, models, datasets, strengths, and limitations for each technique along with a t...
-
Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey
The paper provides the first comprehensive survey of multimodal chain-of-thought reasoning, including foundational concepts, a taxonomy of methodologies, application analyses, challenges, and future directions.
-
LLM Multi-Agent Systems: Challenges and Open Problems
The paper identifies inadequately addressed challenges in optimizing task allocation, fostering robust reasoning through debates, managing layered context, enhancing memory, and applying multi-agent systems to blockchain.
Discussion (0). Sign in to comment.