Snap Out of It: A Dual-Process Approach to Mitigating Overthinking in Language Model Reasoning (2025)

Abstract: Large Language Models (LLMs) have shown impressive capabilities in text generation and reasoning but still struggle with overthinking and analysis paralysis in interactive, multi-step tasks. In this paper, we introduce two complementary contributions aimed at mitigating these challenges. First, we propose Think, Validate, Consensus (TVC)—a multi-agent system inspired by Rational Speech Act (RSA) theory—that enables LLMs to recursively model each other’s mental states and detect overthinking in interactive environments. We take inspiration from RSA to model the recursive reasoning about communicative intent that underlies human collaboration, complementing models of individual reasoning. Second, we present Snap-Think, a dual-mode mechanism that combines fast, intuitive interaction (System 1) with slower, deliberative reasoning (System 2) to break free from reasoning loops detected by TVC. We evaluate our approach using New York Times Connections puzzles and demonstrate significant improvements: Snap-Think achieves 98% solve rate on GPT-4o compared to Chain-of-Thought’s 72%, while maintaining superior semantic grounding and efficiency over traditional strategies. Our findings suggest that integrating human-inspired cognitive frameworks into LLM architectures can effectively counteract overthinking and enhance complex problem-solving capabilities. We make our code available at: https://github.com/Chrislai502/the_amazing_connections

## introduction large language models (llms) have revolutionized natural language processing with unprecedented capabilities in text generation, few-shot learning, and complex reasoning tasks @xcite @xcite ., * equal contribution. such reasoning cycles are also seen in approaches to multi-agent frameworks, such as open-hands @xcite . specifically, @xcite attributes the term "analysis paralysis"-a state where excessive deliberation impedes progress and decisionmaking-to the type of overthinking that leads to reasoning stagnation @xcite . analysis paralysis manifests behaviorally as recursive reasoning loops where models repeatedly reconsider the same information without making progress toward a solution. in multi-agent systems, this can be quantified through persistent disagreement between specialized agents: when a reasoning agent and validation agent consistently fail to converge on shared conclusions despite multiple iterations, this indicates the system has become trapped in unproductive analytical cycles. we hypothesize that directly addressing analysis paralysis in a multiagent context can lead to significant performance improvements. we use new york times connections as a toy problem to examine tasks that have very few solutions, involve an iterative environ-ment, and require planning to complete. we address analysis paralysis through two key contributions. first, we introduce think, validate, consensus (tvc), a multi-agent framework inspired by the rational speech act (rsa) model @xcite of recursive reasoning in humans about their conversation partner's mental state. we show that this technique allows us to successfully detect overthinking patterns when solving new york times connections puzzles. second, we present snap-think, which draws from kahneman's dual-process theory @xcite , distinguishing between fast, intuitive "system 1" thinking and slower, deliberative "system 2" thinking. we demonstrate that snap-think achieves higher accuracy in comparison to chainof-thought and-we argue-less overthinking. ## background llms have reshaped natural language processing and interactive systems. recent research now leverages their capacities as powerful reasoning agents from explicit problem decomposition strategies like chain-of-thought (cot) to interactive, multi-agent configurations @xcite . however, structured reasoning invites the possibility of overthinking @xcite . we describe rational speech act (rsa) to detect overthinking and our problem of evaluation, new york times connections. ## evaluation to investigate the impact of overthinking in structured reasoning tasks, we adopt the new york times connections puzzle as our testbed. this environment is particularly well-suited for our analysis: it has a small and well-defined solution space, requires multi-step planning, and operates under a constrained iterative feedback loop. as such, it functions as a controlled yet challenging toy domain for evaluating reasoning dynamics, including when to commit versus when to continue deliberating. we evaluate the performance of the think, validate, consensus (tvc) and snap-think frameworks on this task, benchmarking them against several baseline prompting strategies. we employ gpt-4o and gpt-4o mini for all experiments and the tendency to overthink. we examined five distinct agentic prompting strategies: basic, prompt engineered, chain-of-thought (cot), tvc, and snap-think. our prompt designs follow established best practices for the connections domain, detailed information of the prompts is provided in appendix a.3. @xcite provides expert strategies for solving connections puzzles, which we incorporated into our agent instructions to ensure our prompts reflect human-level domain knowledge and solving approaches. detailed information of the prompts is provided in appendix a.3. basic is a straightforward prompt instructing completions to be in json format, with no additional prompting techniques. prompt engineered is a tuned version of basic, incorporating few-shot examples incontext (brown et al., 2020) that include example word-to-category matchings and persona prompting as an "expert puzzle solver" @xcite . cot includes an explicit step-by-step reasoning example by appending the phrase "let's think step by step." tvc and snap-think are employed as previously mentioned in the above methodology sections. we ran all strategies on 100 new york times connections boards released after the latest training data cutoff between the above models. systems were allowed a maximum of 20 incorrect guesses or could terminate early based on confidence, as in the case of tvc and snap-think. model configurations and inference details are provided in appendix a.1, and additional implementation details for the tvc framework are available in appendix a.2 we measured three key performance indicators: ## experimental results single-agent methods exhibit strong reasoning capabilities. a basic prompt, which contains no examples or reasoning scaffolds, achieves a 58% solve ability rate on gpt-4o. notably, introducing improved prompting techniques such as chain-of-thought (cot), leads to a consistent upward trend in board solve rates, highlighting the benefits of structured reasoning strategies. however, this performance plateaus, particularly in the smaller model, suggesting diminishing returns from prompting alone. in contrast, the tvc framework changes this trend. while tvc does not surpass cot in solve rate on gpt-4o (56%), it improves other aspects of performance. tvc reduces hallucinated or ungrounded guesses, achieving a semantic grounding score of 1.50 on gpt-4o and 1.56 on gpt-4o mini. snap-think achieves the highest solve rates across all configurations: 98% on gpt-4o and 80% on gpt-4o mini. at the same time, it maintains the semantic control and efficiency of tvc, with grounding scores of 0.50 and 0.86, and the best solving efficiency across both models. ## discussion our work addresses a fundamental challenge in llm reasoning: the tendency for models to become trapped in unproductive reasoning cycles, or analysis paralysys. through two novel contributions, tvc and snap-think, we demonstrate both the detection and mitigation of overthinking in a controlled reasoning environment. our experimental results with the think, validate, consensus (tvc) framework provide compelling evidence that analysis paralysis can be qualitatively determined, and it is a significant limitation in llmbased problem-solving. the tvc framework successfully implements a multi-agent framework inspired by rational speech act (rsa), enabling llms to recursively reason about each other's mental states. detailed conversation examples demonstrating these dynamics are shown in appendix b.1 and b.2. through this implementation, tvc significantly improved semantic grounding compared to all baseline aphowever, tvc's solve rate (56% on gpt-4o) does not exceed that of cot (72%), despite its superior capabilities to semantically ground responses. this reveals the tradeoff between deep and decisive reasoning, and shows that the rsa-inspired framework can indeed be utilized to invoke overthinking through its recursive reasoning dynamics. this diagnostic signal can be leveraged to trigger interventions-the foundation for our second contribution, snap-think. our second contribution, snap-think, addresses this critical limitation by introducing a dual-mode thinking mechanism. the results demonstrate snap-think's remarkable effectiveness, achieving the highest solve rates across all configurations: 98% on gpt-4o. remarkably, snap-think maintains the semantic control of tvc with grounding scores of 0.50 and 0.86 respectively while simultaneously delivering the best solving precision and efficiency. this substantial gain supports our design rationale based on kahneman's dual-process theory, confirming that the strategic integration of combining fast, confident decision-making of "system 1" (fast, intuitive) and controlled validation of "system 2" (slow, deliberative) provides an effective countermeasure to analysis paralysis. furthermore, snap-think's strong performance on gpt-4o-mini (80% solve rate) compared to cot (38%) demonstrates that our approach can significantly enhance the capabilities of smaller models. this suggests that overthinking mitigation strategies may be particularly valuable for more resource-efficient deployments. these findings highlight the efficacy of humaninspired cognitive frameworks in enhancing llm reasoning. by mirroring human conversational dynamics through rsa-based agent specialization and implementing cognitive flexibility through dual-mode processing, our work demonstrates a path toward more robust, efficient, and adaptable ai reasoning systems. ## limitations while our work provides promising results in mitigating analysis paralysis within large language models, it is subject to constraints and open challenges. we primarily test our approach on gpt-4o and gpt-4o-mini models, which follow a specific training paradigm. systematic comparisons across diverse model families and parameter scales, it remains unclear whether the proposed multi-agent strategies would retain their effectiveness on other state-of-the-art or smaller models (e.g., llama, gemmini, or qwen) with different architectures or pretraining corpora. our evaluation focused on puzzle tasks with modest search spaces (16 words with exactly four solutions), which may not reflect the challenges of larger problem domains. scaling to more complex environments with hundreds or thousands of elements could introduce combinatorial explosions and more intricate reasoning loops. our future work will investigate whether think, validate, consensus and snap-think can be adapted or extended to handle significantly larger problem instances without compromising efficiency or solution quality. ## conclusion in this work, we introduced think, validate, consensus (tvc)-a multi-agent architecture for large language models (llms) that systematically detects analysis paralysis in multi-step reasoning tasks. by splitting the reasoning process into specialized thinker, validator, and consensus roles, tvc operationalizes the rational speech act (rsa) framework to recognize internal inconsistency. we then extended tvc with snap-think, a dual-mode approach inspired by kahneman's theory of fast (intuitive) and slow (deliberative) thinking. snap-think dynamically detects unproductive reasoning loops and transitions from methodical "system 2" processes to rapid, higher-temperature "system 1" cognition, thereby escaping local optima. our empirical evaluation on new york times connections puzzles demonstrates that snap-think achieves significant performance gains, including for smaller models, by injecting creative exploration without compromising semantic precision. think, validate, consensus and its extension snap-think provide strong evidence for integrating human-inspired cognitive theories with principled multi-agent collaboration to mitigate overthinking and stagnation with llms.