CoAT: Chain-of-Associated-Thoughts Framework for Enhancing Large Language Models Reasoning (2502.02390v1)

Abstract: Research on LLM technologies is rapidly emerging, with most of them employing a 'fast thinking' approach to inference. Most LLMs generate the final result based solely on a single query and LLM's reasoning capabilities. However, with the advent of OpenAI-o1, 'slow thinking' techniques have garnered increasing attention because its process is closer to the human thought process. Inspired by the human ability to constantly associate and replenish knowledge during thinking, we developed the novel Chain-of-Associated-Thoughts (CoAT) framework, which introduces an innovative synergy between the Monte Carlo Tree Search (MCTS) algorithm and a dynamic mechanism for integrating new key information, termed 'associative memory'. By combining the structured exploration capabilities of MCTS with the adaptive learning capacity of associative memory, CoAT significantly expands the LLM search space, enabling our framework to explore diverse reasoning pathways and dynamically update its knowledge base in real-time. This allows the framework to not only revisit and refine earlier inferences but also adaptively incorporate evolving information, ensuring that the final output is both accurate and comprehensive. To validate the effectiveness of our framework, we conducted extensive experiments across a range of generative and reasoning tasks. These experiments demonstrated that our framework outperforms conventional inference processes on accuracy, coherence, and diversity. The framework's ability to iteratively expand its search space while retaining contextually relevant information results.

• The paper introduces the CoAT framework, which integrates dynamic associative memory with an optimized MCTS algorithm to improve LLM reasoning.
• It employs a novel memory mechanism that retrieves targeted context in real time to support iterative, human-like inference.
• Experimental results demonstrate significant gains in accuracy, coherence, and diversity compared to baseline retrieval methods.

The paper presents a detailed framework termed Chain-of-Associated-Thoughts (CoAT) for enhancing the reasoning capabilities of LLMs by integrating dynamic associative memory mechanisms with an optimized Monte Carlo Tree Search (MCTS) algorithm. The framework is designed to mimic human thinking by supplementing ongoing reasoning with relevant, context-aware information and iteratively refining the generated output.

Framework Components and Methodology

The CoAT framework has two main contributions:

• Dynamic Associative Memory Mechanism The framework introduces an associative memory component that is seamlessly integrated into the iterative reasoning process. At each node in the reasoning tree, associative memory is generated in a manner that mimics human associative processes. This mechanism works as a dynamic external knowledge augmentation strategy that retrieves and integrates key information in real time. Specifically, rather than incorporating extended background knowledge at the start of the inference process (which can lead to information redundancy or omission of critical content), the associator dynamically retrieves concise, targeted content. Mathematically, the generation of associative content at a node $n_i$ is given by:

$$\mathcal{AM}(n_i) = \mathbb{EB} \mapsto \mathcal{LLM}(Q \mid \mathcal{G}(n_i)),$$

where: - $Q$ is the input query, - $\mathcal{G}(n_i)$ is the generated content at node $n_i$, - $\mathbb{EB}$ represents an optional external knowledge source (e.g., a knowledge graph, vector database, or web search engine), - $\mathcal{LLM}$ denotes the underlying LLM used for content generation.

The resultant associative memory is then combined with historical content to influence subsequent reasoning, ensuring that each new inference step leverages both previous outcomes and freshly associated content.

• Optimized Monte Carlo Tree Search for LLM Reasoning In order to navigate the potentially vast reasoning space, the paper proposes an optimized MCTS algorithm that introduces an additional Association stage after the conventional Expansion phase. The standard MCTS process—comprising Selection, Expansion, Simulation, and Backpropagation—is enhanced by incorporating intermediate steps that focus on retrieving associative memory. The UCT (Upper Confidence bounds applied to Trees) is modified so that each node’s value is calculated as follows:

$$V(n) = \mathcal{F}_g(Q, \mathcal{G}(n)) + \beta \cdot \mathcal{F}_a(\mathcal{G}(n), \mathcal{AM}(n)),$$

where: - $V(n)$ is the evaluated value of node $n$, - $\mathcal{F}_g$ and $\mathcal{F}_a$ are evaluation functions for the generated content and the associative content respectively, - $\beta$ is a hyperparameter that balances the contribution of the associative memory, - $\mathcal{G}(n)$ denotes the generated content at node $n$, and - $\mathcal{AM}(n)$ denotes the associative memory for node $n$.

Furthermore, the paper details a modified backpropagation strategy where node visit counts and quality evaluations are updated using a weighted aggregation of child node evaluations, thereby enabling more effective exploration and exploitation within the inference tree.

A depth hyperparameter $D$ is also introduced to control the maximum search depth, providing the flexibility to trade off between computational cost and reasoning thoroughness.

Experimental Validation

The framework is evaluated both qualitatively and quantitatively across diverse tasks:

• Qualitative Evaluation In qualitative experiments involving complex queries that require rich associative knowledge, the CoAT-enhanced models (e.g., Qwen2.5-32B integrated with CoAT) demonstrated a significantly broader and more nuanced coverage of key aspects. For instance, in queries regarding AI’s role in international competition, the outputs integrated additional categories such as ethical and regulatory frameworks, which were less prominent or absent in baseline model outputs. This suggests that the additional associative phase enables models to capture multi-faceted reasoning.
• Quantitative Evaluation Quantitative experiments are conducted on multi-hop question-answering datasets (such as HotpotQA and 2WikiMultiHopQA) as well as code generation datasets (including HumanEval, MBPP, and HumanEval-X). Two performance metrics are primarily considered:
  • Exact Match (EM): Measures the ratio of generated answers that exactly match the ground truth.
  • F1 Scores: Assess the balance of precision and recall over the generated responses.

Moreover, the framework is compared with related retrieval-augmented generation methods like NativeRAG, IRCoT, HippoRAG, LATS, and KAG. The experimental results indicate that leveraging dynamic associative memory within the structured exploration provided by MCTS significantly enhances performance in terms of accuracy, coherence, and reasoning diversity. In the domain of code generation, when comparing a base model running through the CoAT framework with its fine-tuned variant, the CoAT-enhanced model exhibited improvements on established benchmarks, underscoring its potential for knowledge-intensive and procedural tasks.

Conclusion

Overall, the paper provides a methodologically rigorous exploration of how integrating associative memory with an optimized MCTS algorithm can considerably augment the reasoning process of LLMs. The detailed articulation of the framework—including the formulation of evaluation functions, the introduction of a balancing hyperparameter, and the incorporation of an external knowledge aggregator—demonstrates a well-rounded approach to address inherent limitations in static and single-pass inference strategies. The comprehensive experimental results affirm that CoAT not only improves accuracy and coherence but also enhances output diversity by enabling iterative refinement and dynamic knowledge integration. This represents a robust step towards more human-like, context-aware reasoning in LLMs.