Thinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Models
Simone Caldarella, Davide Talon, Rahaf Aljundi, Elisa Ricci, Massimiliano Mancini
Abstract
Large Reasoning Models (LRMs) improve performance by generating explicit intermediate reasoning traces through increased test-time compute, yet the assumption that longer reasoning is consistently beneficial remains under-examined. While recent evidence shows that additional reasoning can lead models to overthink, we ask: "Once a model has reached the correct answer, does further reasoning refine the solution, or deviate from it?" To study the dynamics after correctness, we introduce a prefix-level trajectory evaluation protocol grounded in reasoning sufficiency, defining the minimum reasoning budget required for a model to first generate the correct answer. This allows us to disentangle verbose overthinking, where additional reasoning is redundant but harmless, from harmful overthinking, where continued reasoning destabilizes an already-correct trajectory. Starting from multimodal benchmarks, we find that many instances considered reasoning-intensive require surprisingly little reasoning. Moreover, stopping at the first correct prefix improves accuracy over standard reasoning up to 21%, revealing that current models are limited not only by their ability to reason, but also by their inability to stop at the right time. Furthermore, while common efficiency strategies like early stopping substantially reduce verbose overthinking (up to 50%), they fail to mitigate harmful overthinking. Failure analysis reveals that correctness deviations are mainly driven by logical drift and visual reinterpretation. Finally, we show that our findings generalize to language-only reasoning benchmarks, highlighting harmful overthinking as a broader reliability risk. Code available at https://simonecaldarella.github.io/thinking-past-the-answer.
AI Impact Assessments
(1 models)Scientific Impact Assessment: "Thinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Models"
1. Core Contribution
This paper formalizes and empirically investigates a specific failure mode of Large Reasoning Models (LRMs): harmful overthinking, where models reach a correct answer during intermediate reasoning but subsequently deviate to an incorrect final answer through continued generation. The key conceptual contribution is the distinction between *verbose* overthinking (redundant but harmless extra reasoning) and *harmful* overthinking (reasoning that destabilizes already-correct trajectories).
The authors introduce a prefix-level trajectory evaluation protocol grounded in "reasoning sufficiency" — the minimum reasoning budget (measured in utterances) for a model to first produce a correct answer. By probing every prefix of a reasoning trace and forcing answer extraction at each point, they can track when correctness first emerges and whether it persists. This enables measuring an "optimal length" oracle that stops at the first correct prefix, which outperforms standard full-length reasoning by up to 21% accuracy.
2. Methodological Rigor
The experimental design is thorough and well-controlled:
However, there are methodological concerns. The prefix-probing approach appends a fixed termination template ("Oh, I suddenly got the answer...") to force answer extraction at intermediate points. While robustness checks show moderate insensitivity to prompt wording, this intervention fundamentally alters the generation context. The model was not "naturally" producing an answer at that prefix — it is being prompted to do so from a potentially incoherent partial state. This means the "first correct prefix" may overestimate how early the model has genuinely "solved" the problem versus producing a correct answer by chance from an incomplete reasoning state, especially for multiple-choice questions with limited answer spaces.
The failure-mode taxonomy, while informative, relies on an LLM judge (Qwen3.6-35B) for classification. No human validation of these classifications is reported, leaving uncertainty about taxonomy reliability.
3. Potential Impact
The paper addresses a practically important problem. As LRMs are deployed in high-stakes settings, understanding that more reasoning can *degrade* performance has direct engineering implications:
The paper could influence adjacent work on reasoning verification, self-consistency methods, and adaptive compute allocation. The observation that free-form generation is more vulnerable to harmful overthinking than multiple-choice has implications for deployment contexts.
4. Timeliness & Relevance
This paper arrives at an opportune moment. The field is heavily invested in scaling test-time compute (o1, R1, QwQ, etc.), and the dominant narrative is "more thinking = better performance." This work provides a necessary counterpoint with rigorous empirical evidence. The focus on multimodal reasoning models, which are comparatively understudied for overthinking, adds value.
The work builds naturally on the concurrent "stop overthinking" literature (Chen et al., 2025; Sui et al., 2025) but provides a cleaner formal framework and the crucial distinction between verbose and harmful overthinking.
5. Strengths & Limitations
Strengths:
Limitations:
Missing comparisons: The paper does not compare against self-consistency or majority voting approaches, which could naturally mitigate harmful overthinking by aggregating across the trajectory.
Summary
This is a well-executed empirical study that identifies and formalizes an important failure mode in reasoning models. The distinction between verbose and harmful overthinking is conceptually clean and practically relevant. The main limitation is the absence of actionable solutions — the paper diagnoses the problem thoroughly but leaves mitigation to future work. The prefix-probing methodology, while creative, has inherent limitations that somewhat weaken claims about "reasoning sufficiency." Nevertheless, the core finding — that LRMs frequently reason past correctness — is robust, timely, and likely to influence both training practices and inference strategies for reasoning models.
Generated Jun 3, 2026
Comparison History (24)
Paper 2 has higher likely impact due to its broad relevance to current LRM/chain-of-thought research, offering a clear evaluation protocol (prefix-level trajectory/sufficiency) and empirically demonstrating accuracy gains (up to 21%) plus a new failure mode (harmful overthinking) affecting multimodal and language tasks. It is timely for test-time compute scaling and reliability, with immediate applications to decoding, early-exit methods, and safety. Paper 1 is novel and valuable for simulator-centered decision systems, but its impact is narrower to domains using scientific simulators and depends more on integration complexity and availability of executable simulators.
Paper 1 introduces a foundational analytical framework for AI-Driven Research Systems, a critical and rapidly expanding frontier in 'AI for Science.' While Paper 2 offers timely insights into the limitations of current reasoning models, Paper 1's formalization of automated discovery mechanisms and its rigorous empirical evaluation across NP-hard problems offer a more enduring methodological contribution with broader implications for how future AI systems will conduct scientific research.
LAP addresses a fundamental infrastructure gap in autonomous science by proposing a standardized protocol for agent-to-instrument communication. This has broad, lasting impact across all experimental sciences by enabling interoperable self-driving laboratories. It fills a clear architectural gap alongside MCP and A2A, potentially becoming foundational infrastructure. Paper 1, while insightful about harmful overthinking in LRMs, is primarily diagnostic—identifying a problem rather than providing a transformative solution. LAP's potential to become a widely-adopted standard gives it greater breadth and long-term impact across multiple scientific domains.
Paper 2 addresses a timely and broadly impactful problem in large reasoning models (LRMs), which are at the forefront of AI research. It introduces a novel evaluation protocol for diagnosing harmful overthinking, reveals counterintuitive findings (more reasoning can hurt performance), and demonstrates generalizability across modalities. The findings have immediate implications for model design, inference efficiency, and reliability across the rapidly growing LRM ecosystem. Paper 1, while methodologically sound, addresses a more incremental question about demographic bias in a specific medical imaging domain with less broadly transformative implications.
Paper 1 has higher likely impact: it addresses a timely, widely relevant issue in modern AI (test-time compute, chain-of-thought, reliability), introduces an evaluation protocol with clear empirical findings (up to 21% accuracy gains) and actionable implications (when to stop reasoning). Its applications span LLM/LRM deployment, safety, and efficiency across multimodal and language tasks. Paper 2 is methodologically rigorous but narrower: a formal extension of PDSL entailment with preserved complexity, likely impactful mainly within non-monotonic/modal logic communities, with less immediate cross-field or real-world uptake.
Paper 2 is likely higher impact: it introduces a broadly applicable evaluation protocol (prefix-level trajectory/sufficiency) that reveals a general reliability and performance issue in LRMs—harmful overthinking—with sizable accuracy gains (up to 21%) via first-correct stopping. The problem is timely for test-time compute scaling and deployment safety, spans multimodal and language-only settings, and offers actionable diagnostics (logical drift, visual reinterpretation) with released code, increasing adoption potential. Paper 1 is novel for social simulation interpretability, but its impact is narrower to multi-agent dialogue modeling and may be harder to validate against real-world social dynamics.
Paper 2 addresses a highly timely and critical issue in the rapidly growing field of Large Reasoning Models (test-time compute). By identifying and quantifying 'harmful overthinking'—where models arrive at the correct answer but subsequently degrade their own response—it exposes a fundamental flaw in current scaling paradigms. This has broader implications across all language and multimodal reasoning tasks compared to Paper 1, which, while offering a novel approach to spatial reasoning in VLMs, represents a more domain-specific architectural improvement.
Paper 1 identifies and rigorously characterizes a fundamental reliability issue in Large Reasoning Models—harmful overthinking—introducing a novel evaluation protocol and demonstrating significant accuracy improvements (up to 21%). The finding that existing efficiency strategies fail to address harmful overthinking opens important new research directions. Its breadth across multimodal and language-only benchmarks strengthens generalizability. Paper 2 addresses a relevant but narrower problem (clarification under ambiguity) with a modest 3.7% improvement. Paper 1's novelty, methodological depth, and broader implications for the rapidly growing LRM field give it substantially higher impact potential.
Paper 2 addresses test-time compute and reasoning models (LRMs), currently one of the most critical and heavily researched areas in AI. By uncovering and analyzing 'harmful overthinking'—where models degrade their own correct answers through excessive reasoning—it challenges the prevailing assumption that scaling test-time compute is strictly beneficial. This fundamental insight into model reliability and stopping criteria offers broader implications across language and multimodal reasoning compared to Paper 1's narrower focus on agent trajectory error localization.
Paper 1 addresses a timely and broadly impactful problem—harmful overthinking in Large Reasoning Models—which is highly relevant given the rapid deployment of LRMs across many domains. It introduces a novel evaluation protocol, provides quantitative findings (up to 21% accuracy improvement), and reveals fundamental reliability risks in current reasoning models. The findings generalize across modalities and benchmarks, suggesting broad applicability. Paper 2, while contributing to procedural content generation for games, addresses a much narrower niche with limited cross-disciplinary impact.
Paper 1 addresses a fundamental and broadly relevant problem in LRM reliability—harmful overthinking—with a novel evaluation protocol that reveals surprising findings (up to 21% accuracy improvement by early stopping). Its insights generalize across multimodal and language-only benchmarks, impacting the entire reasoning model ecosystem. The work has broad implications for model design, efficiency, and trustworthiness. Paper 2, while addressing an important misinformation detection problem, is more narrowly focused on a specific application domain with incremental methodological contributions (applying MLLMs to conflict detection).
Paper 2 (LEAP) likely has higher scientific impact due to a major capability jump in a high-value domain: mechanically verified formal mathematics. It introduces an agentic framework with strong empirical gains (e.g., large improvement on Lean-IMO-Bench and solving all 2025 Putnam problems) and demonstrates research-level utility on open combinatorics, suggesting real-world applicability to proof automation and verification. It is timely amid rapid progress in formal theorem proving and could influence both AI systems research and mathematics/verification. Paper 1 is novel and important for reliability, but its impact is narrower and more diagnostic than enabling.
Paper 1 addresses a fundamental and broadly relevant problem in LRMs—harmful overthinking—that affects all reasoning models across modalities. It introduces a novel evaluation protocol, demonstrates surprising findings (stopping early improves accuracy by up to 21%), and reveals that current efficiency strategies fail to address the core issue. The breadth of impact is high since it applies to both multimodal and language-only settings, affecting the entire LRM paradigm. Paper 2, while technically solid, addresses a more niche problem of skill selection in LLM agents with narrower applicability and incremental improvements over existing baselines.
Paper 1 addresses a critical, highly timely issue in foundational AI research: the failure modes of test-time compute in Large Reasoning Models. Its insights into 'harmful overthinking' and logical drift have broad implications across all LLM applications, potentially altering how inference scaling is designed. While Paper 2 presents a valuable domain-specific medical benchmark, Paper 1's fundamental methodological contributions to AI reasoning and alignment offer broader, more paradigm-shifting scientific impact across the entire field of artificial intelligence.
Paper 2 introduces a novel, well-defined concept ('harmful overthinking') with a rigorous evaluation protocol, quantifiable findings (up to 21% accuracy improvement), and broad applicability across reasoning models. It identifies a fundamental reliability risk in LRMs that has immediate implications for model design and deployment. Paper 1 proposes a reference architecture for edge AI agents but is primarily conceptual without empirical validation, limiting its impact. Paper 2's actionable insights, reproducible methodology (code available), and relevance to the rapidly growing reasoning model field give it significantly higher impact potential.
Paper 1 addresses a fundamental limitation in the rapidly emerging field of Large Reasoning Models (test-time compute scaling). By identifying 'harmful overthinking,' it challenges the prevailing assumption that more test-time reasoning is always beneficial. This has broad foundational implications for the design and evaluation of future AI models across multiple domains. Paper 2, while practical, focuses on a more specialized software engineering workflow (coding agents), giving Paper 1 a broader and more significant potential scientific impact.
Paper 1 addresses a fundamental and broadly applicable problem in LRMs—harmful overthinking—that affects all reasoning models across modalities. Its novel prefix-level evaluation protocol, quantified findings (up to 21% accuracy improvement), and demonstration that standard efficiency strategies fail to mitigate harmful overthinking have wide implications for model design, training, and deployment. Paper 2 makes a solid contribution with its benchmark and dataset for embodied causal reasoning, but targets a narrower subfield. Paper 1's findings about a core reliability risk in reasoning models will likely influence a broader research community.
Paper 2 demonstrates higher potential scientific impact due to its novel real-world application combining agentic AI with large-scale field experiments (693K+ patient visits) in healthcare. It introduces a practical framework for cumulative experimental learning that transforms how organizations conduct behavioral interventions. The methodology bridges AI and experimental design in a generalizable way across domains. Paper 1, while identifying an important reliability issue (harmful overthinking in LRMs), is more diagnostic in nature and narrower in scope, primarily characterizing a known limitation rather than introducing a transformative methodology with demonstrated real-world impact.
Paper 2 addresses Large Reasoning Models and test-time compute, which is currently one of the most active and highly impactful areas in AI research. Its insights into 'harmful overthinking' have immediate, broad implications for improving the reliability and efficiency of state-of-the-art LLMs. While Paper 1 offers a rigorous and valuable methodological contribution to Causal Bayesian Optimization, its scope and audience are much narrower compared to the widespread relevance and explosive interest in LLM reasoning dynamics.
Paper 1 identifies and rigorously characterizes a fundamental and previously under-examined failure mode in Large Reasoning Models—harmful overthinking where continued reasoning destabilizes correct answers. Its novel prefix-level evaluation protocol, quantified impact (up to 21% accuracy improvement), and demonstration that existing efficiency strategies fail to address this problem have broad implications for LRM reliability, deployment, and future architecture design. Paper 2 presents a useful but more incremental contribution on weak-to-strong supervision via critique distillation. Paper 1's findings are more surprising, broadly applicable across modalities and benchmarks, and address a critical reliability concern.