Related papers: On the Diagram of Thought

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Thinker: Training LLMs in Hierarchical Thinking for Deep Search via Multi-Turn Interaction [57.67217258741752]
Thinker is a hierarchical thinking model for deep search through multi-turn interaction.<n>It decomposes complex problems into independently solvable sub-problems.<n> dependencies between sub-problems are passed as parameters via these logical functions.
arXiv Detail & Related papers (2025-11-11T07:48:45Z)
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Diffusion language models (dLLMs) offer a promising, non-autoregressive paradigm for text generation.<n>Current reinforcement learning approaches often rely on sparse, outcome-based rewards.<n>We argue that this stems from a fundamental mismatch with the natural structure of reasoning.
arXiv Detail & Related papers (2025-10-02T00:34:15Z)
Reasoning Scaffolding: Distilling the Flow of Thought from LLMs [30.569464420145163]
We introduce Reasoning Scaffolding, a framework that reframes reasoning as a structured generation process.<n>Our method significantly outperforms state-of-the-art distillation in both accuracy and logical consistency.
arXiv Detail & Related papers (2025-09-28T03:49:32Z)
LAG: Logic-Augmented Generation from a Cartesian Perspective [7.2022636966543745]
This paper introduces Logic-Augmented Generation (LAG), a novel paradigm that reframes knowledge augmentation through systematic question decomposition and dependency-aware reasoning.<n>Experiments on four benchmark datasets demonstrate that LAG significantly enhances reasoning robustness, reduces hallucination, and aligns LLM problem-solving with human cognition.
arXiv Detail & Related papers (2025-08-07T15:42:00Z)
CTRLS: Chain-of-Thought Reasoning via Latent State-Transition [57.51370433303236]
Chain-of-thought (CoT) reasoning enables large language models to break down complex problems into interpretable intermediate steps.<n>We introduce groundingS, a framework that formulates CoT reasoning as a Markov decision process (MDP) with latent state transitions.<n>We show improvements in reasoning accuracy, diversity, and exploration efficiency across benchmark reasoning tasks.
arXiv Detail & Related papers (2025-07-10T21:32:18Z)
Theorem-of-Thought: A Multi-Agent Framework for Abductive, Deductive, and Inductive Reasoning in Language Models [2.172419551358714]
Large language models (LLMs) have shown strong performance across natural language reasoning tasks, yet their reasoning processes remain brittle and difficult to interpret.<n>We introduce Theorem-of-Thought (ToTh), a novel framework that models reasoning as collaboration among three parallel agents.<n> Experiments on symbolic (WebOfLies) and numerical (MultiArithm) reasoning benchmarks show that ToTh consistently outperforms CoT, Self-Consistency, and CoT-Decoding.
arXiv Detail & Related papers (2025-06-08T12:28:38Z)
CLATTER: Comprehensive Entailment Reasoning for Hallucination Detection [60.98964268961243]
We propose that guiding models to perform a systematic and comprehensive reasoning process allows models to execute much finer-grained and accurate entailment decisions.<n>We define a 3-step reasoning process, consisting of (i) claim decomposition, (ii) sub-claim attribution and entailment classification, and (iii) aggregated classification, showing that such guided reasoning indeed yields improved hallucination detection.
arXiv Detail & Related papers (2025-06-05T17:02:52Z)
Computational Thinking Reasoning in Large Language Models [69.28428524878885]
Computational Thinking Model (CTM) is a novel framework that incorporates computational thinking paradigms into large language models (LLMs)<n>Live code execution is seamlessly integrated into the reasoning process, allowing CTM to think by computing.<n>CTM outperforms conventional reasoning models and tool-augmented baselines in terms of accuracy, interpretability, and generalizability.
arXiv Detail & Related papers (2025-06-03T09:11:15Z)
PixelThink: Towards Efficient Chain-of-Pixel Reasoning [70.32510083790069]
PixelThink is a simple yet effective scheme that integrates externally estimated task difficulty and internally measured model uncertainty.<n>It learns to compress reasoning length in accordance with scene complexity and predictive confidence.<n> Experimental results demonstrate that the proposed approach improves both reasoning efficiency and overall segmentation performance.
arXiv Detail & Related papers (2025-05-29T17:55:49Z)
Syzygy of Thoughts: Improving LLM CoT with the Minimal Free Resolution [59.39066657300045]
Chain-of-Thought (CoT) prompting enhances the reasoning of large language models (LLMs) by decomposing problems into sequential steps. We propose Syzygy of Thoughts (SoT)-a novel framework that extends CoT by introducing auxiliary, interrelated reasoning paths. SoT captures deeper logical dependencies, enabling more robust and structured problem-solving.
arXiv Detail & Related papers (2025-04-13T13:35:41Z)
Visualizing Thought: Conceptual Diagrams Enable Robust Planning in LMMs [59.66595230543127]
Conceptual diagrams externalize mental models, abstracting irrelevant details to efficiently capture how entities interact.<n>Large Language Models (LLMs) and Large MultiModal Models (LMMs) predominantly reason through text.<n>We propose Visual Thinking, a generalizable framework that enables LMMs to reason through multiple chains of self-generated conceptual diagrams.
arXiv Detail & Related papers (2025-03-14T18:27:02Z)
Large Language Models Meet Symbolic Provers for Logical Reasoning Evaluation [24.081573908824353]
First-order logic (FOL) reasoning is pivotal for intelligent systems. Existing benchmarks often rely on extensive human annotation or handcrafted templates. We propose a novel framework called ProverGen that synergizes the generative strengths of Large Language Models with the rigor and precision of symbolic provers.
arXiv Detail & Related papers (2025-02-10T15:31:54Z)
BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning [78.63421517563056]
Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks. We present a unified probabilistic framework that formalizes LLM reasoning through a novel graphical model. We introduce the Bootstrapping Reinforced Thinking Process (BRiTE) algorithm, which works in two steps.
arXiv Detail & Related papers (2025-01-31T02:39:07Z)
MyGO Multiplex CoT: A Method for Self-Reflection in Large Language Models via Double Chain of Thought Thinking [4.234183823376613]
We introduce Multiplex CoT (Chain of Thought), a method that enables LLMs to simulate a form of self-review while reasoning.<n>Multiplex CoT leverages the power of iterative reasoning, where the model generates an initial chain of thought and subsequently critiques and refines this reasoning.
arXiv Detail & Related papers (2025-01-20T12:54:57Z)
RL-STaR: Theoretical Analysis of Reinforcement Learning Frameworks for Self-Taught Reasoner [2.779063752888881]
Self-taught reasoner (STaR) framework uses reinforcement learning to automatically generate reasoning steps. STaR and its variants have demonstrated empirical success, but a theoretical foundation explaining these improvements is lacking. This work provides a theoretical framework for understanding the effectiveness of reinforcement learning on CoT reasoning and STaR.
arXiv Detail & Related papers (2024-10-31T13:17:53Z)
Supervised Chain of Thought [5.389461633686935]
Chain of Thought (CoT) prompting offers a promising approach to solving complex reasoning tasks. One-prompt-for-all approach poses significant challenges for models to generate the correct reasoning steps. We show how task-specific supervision is essential for navigating the prompt space accurately and achieving optimal performance.
arXiv Detail & Related papers (2024-10-18T06:25:27Z)
Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up [9.42385235462794]
Large language models (LLMs) have shown remarkable performance in reasoning tasks but face limitations in mathematical and complex logical reasoning. We propose Reversal of Thought (RoT), a novel framework aimed at enhancing the logical reasoning abilities of LLMs. RoT utilizes a Preference-Guided Reverse Reasoning warm-up strategy, which integrates logical symbols for pseudocode planning.
arXiv Detail & Related papers (2024-10-16T07:44:28Z)
Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning [1.3003982724617653]
Large Language Models (LLMs) have revolutionized natural language processing, yet they struggle with inconsistent reasoning. This research introduces Proof of Thought, a framework that enhances the reliability and transparency of LLM outputs. Key contributions include a robust type system with sort management for enhanced logical integrity, explicit representation of rules for clear distinction between factual and inferential knowledge.
arXiv Detail & Related papers (2024-09-25T18:35:45Z)
Thought-Like-Pro: Enhancing Reasoning of Large Language Models through Self-Driven Prolog-based Chain-of-Thought [31.964412924094656]
Large language models (LLMs) have shown exceptional performance as general-purpose assistants. We introduce a novel learning framework, THOUGHT-LIKE-PRO, to facilitate learning and generalization across diverse reasoning tasks. Our empirical findings indicate that our proposed approach substantially enhances the reasoning abilities of LLMs.
arXiv Detail & Related papers (2024-07-18T18:52:10Z)
The Buffer Mechanism for Multi-Step Information Reasoning in Language Models [52.77133661679439]
Investigating internal reasoning mechanisms of large language models can help us design better model architectures and training strategies. In this study, we constructed a symbolic dataset to investigate the mechanisms by which Transformer models employ vertical thinking strategy. We proposed a random matrix-based algorithm to enhance the model's reasoning ability, resulting in a 75% reduction in the training time required for the GPT-2 model.
arXiv Detail & Related papers (2024-05-24T07:41:26Z)
Cantor: Inspiring Multimodal Chain-of-Thought of MLLM [83.6663322930814]
We argue that converging visual context acquisition and logical reasoning is pivotal for tackling visual reasoning tasks. We propose an innovative multimodal CoT framework, termed Cantor, characterized by a perception-decision architecture. Our experiments demonstrate the efficacy of the proposed framework, showing significant improvements in multimodal CoT performance.
arXiv Detail & Related papers (2024-04-24T17:59:48Z)
Large Language Models as an Indirect Reasoner: Contrapositive and Contradiction for Automated Reasoning [74.90592233107712]
We propose a Direct-Indirect Reasoning (DIR) method, which considers Direct Reasoning (DR) and Indirect Reasoning (IR) as multiple parallel reasoning paths that are merged to derive the final answer. Our DIR method is simple yet effective and can be straightforwardly integrated with existing variants of CoT methods.
arXiv Detail & Related papers (2024-02-06T03:41:12Z)
Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension [80.99865844249106]
We propose a holistic graph network (HGN) which deals with context at both discourse level and word level, as the basis for logical reasoning. Specifically, node-level and type-level relations, which can be interpreted as bridges in the reasoning process, are modeled by a hierarchical interaction mechanism.
arXiv Detail & Related papers (2023-06-21T07:34:27Z)
Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Language Models [74.40196814292426]
We propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph. GoT captures the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. We evaluate GoT's performance on a text-only reasoning task and a multimodal reasoning task.
arXiv Detail & Related papers (2023-05-26T02:15:09Z)
Query Structure Modeling for Inductive Logical Reasoning Over Knowledge Graphs [67.043747188954]
We propose a structure-modeled textual encoding framework for inductive logical reasoning over KGs. It encodes linearized query structures and entities using pre-trained language models to find answers. We conduct experiments on two inductive logical reasoning datasets and three transductive datasets.
arXiv Detail & Related papers (2023-05-23T01:25:29Z)
Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings [61.04460792203266]
We introduce VCoT, a novel method that leverages chain-of-thought prompting with vision-language grounding to bridge the logical gaps within sequential data. Our method uses visual guidance to generate synthetic multimodal infillings that add consistent and novel information to reduce the logical gaps for downstream tasks.
arXiv Detail & Related papers (2023-05-03T17:58:29Z)
Chaining Simultaneous Thoughts for Numerical Reasoning [92.2007997126144]
numerical reasoning over text should be an essential skill of AI systems. Previous work focused on modeling the structures of equations, and has proposed various structured decoders. We propose CANTOR, a numerical reasoner that models reasoning steps using a directed acyclic graph.
arXiv Detail & Related papers (2022-11-29T18:52:06Z)
Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought [10.524051272257614]
Large language models (LLMs) have shown remarkable reasoning capabilities given chain-of-thought prompts. We present a new synthetic question-answering dataset called PrOntoQA, where each example is generated as a synthetic world model. This allows us to parse the generated chain-of-thought into symbolic proofs for formal analysis.
arXiv Detail & Related papers (2022-10-03T21:34:32Z)
Linear Temporal Logic Modulo Theories over Finite Traces (Extended Version) [72.38188258853155]
This paper studies Linear Temporal Logic over Finite Traces (LTLf) proposition letters are replaced with first-order formulas interpreted over arbitrary theories. The resulting logic, called Satisfiability Modulo Theories (LTLfMT), is semi-decidable.
arXiv Detail & Related papers (2022-04-28T17:57:33Z)
DAReN: A Collaborative Approach Towards Reasoning And Disentangling [27.50150027974947]
We propose an end-to-end joint representation-reasoning learning framework, which leverages a weak form of inductive bias to improve both tasks together. We accomplish this using a novel learning framework Disentangling based Abstract Reasoning Network (DAReN) based on the principles of GM-RPM.
arXiv Detail & Related papers (2021-09-27T16:10:30Z)

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