Related papers: Preemptive Answer "Attacks" on Chain-of-Thought Reasoning

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ReGuLaR: Variational Latent Reasoning Guided by Rendered Chain-of-Thought [49.203970812338916]
Explicit reasoning chains introduce substantial computational redundancy.<n>Recent latent reasoning methods attempt to mitigate this by compressing reasoning processes into latent space.<n>We propose Rendered CoT-Guided variational Latent Reasoning (ReGuLaR)
arXiv Detail & Related papers (2026-01-30T17:08:06Z)
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We identify a critical failure mode termed textual inertia, where models tend to blindly adhere to the erroneous text while neglecting conflicting visual evidence.<n>We propose the LogicGraph Perturbation Protocol that structurally injects perturbations into the reasoning chains of diverse LMMs.<n>Results reveal that models successfully self-correct in less than 10% of cases and predominantly succumb to blind textual error propagation.
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Large Language Models (LLMs) excel in reasoning tasks requiring a single correct answer, but they perform poorly in multi-solution tasks.<n>We attribute this limitation to textbfreasoning overconfidence: a tendency to express undue certainty in an incomplete solution set.<n>We propose the textbfcognitive-rigidity hypothesis, which posits that overconfidence arises when the reasoning process prematurely converges on a narrow set of thought paths.
arXiv Detail & Related papers (2025-12-01T14:35:06Z)
Explore Briefly, Then Decide: Mitigating LLM Overthinking via Cumulative Entropy Regulation [82.62935304152239]
Large Language Models (LLMs) have demonstrated remarkable reasoning abilities on complex problems using long Chain-of-Thought (CoT) reasoning.<n>They often suffer from overthinking, meaning generating unnecessarily lengthy reasoning steps for simpler problems.<n>We introduce a novel metric Token Entropy Cumulative Average (TECA), which measures the extent of exploration throughout the reasoning process.
arXiv Detail & Related papers (2025-10-02T17:36:50Z)
Reasoning or Retrieval? A Study of Answer Attribution on Large Reasoning Models [15.797612515648412]
Large reasoning models (LRMs) exhibit unprecedented capabilities in solving complex problems through Chain-of-Thought (CoT) reasoning.<n>Recent studies reveal that their final answers often contradict their own reasoning traces.<n>We hypothesize that this inconsistency stems from two competing mechanisms for generating answers: CoT reasoning and memory retrieval.<n>We introduce FARL, a novel fine-tuning framework that integrates memory unlearning with reinforcement learning.
arXiv Detail & Related papers (2025-09-29T01:13:33Z)
BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit [12.189197763012409]
Large language models (LRMs) have emerged as a significant advancement in artificial intelligence.<n>In this paper, we identify an unexplored attack vector against LRMs, which we term "overthinking tunables"<n>We propose a novel tunable backdoor, which moves beyond simple on/off attacks to one where an attacker can precisely control the extent of the model's reasoning verbosity.
arXiv Detail & Related papers (2025-07-24T11:24:35Z)
On Reasoning Strength Planning in Large Reasoning Models [50.61816666920207]
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arXiv Detail & Related papers (2025-06-10T02:55:13Z)
Revisiting Overthinking in Long Chain-of-Thought from the Perspective of Self-Doubt [74.35891434097053]
Reasoning Large Language Models (RLLMs) have demonstrated impressive performance on complex tasks.<n>They often exhibit overthinking -- performing unnecessary reasoning steps even after arriving at the correct answer.<n>We present a quantitative analysis of overthinking from the perspective of self-doubt.<n>We introduce a simple and effective prompting method to reduce the model's over-reliance on input questions.
arXiv Detail & Related papers (2025-05-29T14:30:02Z)
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arXiv Detail & Related papers (2025-03-13T08:46:32Z)
Unveiling the Magic of Code Reasoning through Hypothesis Decomposition and Amendment [54.62926010621013]
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arXiv Detail & Related papers (2025-02-17T10:39:58Z)
Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods [59.779795063072655]
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arXiv Detail & Related papers (2024-08-25T04:07:18Z)
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We investigate the degree to which fine-tuning in Large Language Models (LLMs) effectively mitigates versus merely conceals undesirable behavior. We identify a pervasive phenomenon we term emphreason-based deception, where models either stop producing reasoning traces or produce seemingly ethical reasoning traces that belie the unethical nature of their final outputs.
arXiv Detail & Related papers (2024-06-27T22:08:22Z)
Chain-of-Probe: Examing the Necessity and Accuracy of CoT Step-by-Step [81.50681925980135]
We propose a method to probe changes in the mind during the model's reasoning. By analyzing patterns in mind change, we examine the correctness of the model's reasoning. Our validation reveals that many responses, although correct in their final answer, contain errors in their reasoning process.
arXiv Detail & Related papers (2024-06-23T15:50:22Z)
Towards Faithful Chain-of-Thought: Large Language Models are Bridging Reasoners [19.40385041079461]
Large language models (LLMs) suffer from serious unfaithful chain-of-thought (CoT) issues. We first study the CoT faithfulness issue at the granularity of CoT steps, identify two reasoning paradigms. We then conduct a joint analysis of the causal relevance among the context, CoT, and answer during reasoning.
arXiv Detail & Related papers (2024-05-29T09:17:46Z)
Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models [84.15513004135576]
Current research enhances the reasoning performance of Large Language Models (LLMs) by sampling multiple reasoning chains and ensembling based on the answer frequency. This approach fails in scenarios where the correct answers are in the minority. We introduce a hierarchical reasoning aggregation framework AoR, which selects answers based on the evaluation of reasoning chains.
arXiv Detail & Related papers (2024-05-21T17:12:19Z)
Distilling Reasoning Ability from Large Language Models with Adaptive Thinking [54.047761094420174]
Chain of thought finetuning (cot-finetuning) aims to endow small language models (SLM) with reasoning ability to improve their performance towards specific tasks. Most existing cot-finetuning methods adopt a pre-thinking mechanism, allowing the SLM to generate a rationale before providing an answer. This mechanism enables SLM to analyze and think about complex questions, but it also makes answer correctness highly sensitive to minor errors in rationale. We propose a robust post-thinking mechanism to generate answers before rationale.
arXiv Detail & Related papers (2024-04-14T07:19:27Z)
Mitigating Misleading Chain-of-Thought Reasoning with Selective Filtering [59.495717939664246]
Large language models have manifested remarkable capabilities by leveraging chain-of-thought (CoT) reasoning techniques to solve intricate questions. We propose a novel approach called the selective filtering reasoner (SelF-Reasoner) that assesses the entailment relationship between the question and the candidate reasoning chain. SelF-Reasoner improves the fine-tuned T5 baseline consistently over the ScienceQA, ECQA, and LastLetter tasks.
arXiv Detail & Related papers (2024-03-28T06:28:35Z)
Navigating the OverKill in Large Language Models [84.62340510027042]
We investigate the factors for overkill by exploring how models handle and determine the safety of queries. Our findings reveal the presence of shortcuts within models, leading to an over-attention of harmful words like 'kill' and prompts emphasizing safety will exacerbate overkill. We introduce Self-Contrastive Decoding (Self-CD), a training-free and model-agnostic strategy, to alleviate this phenomenon.
arXiv Detail & Related papers (2024-01-31T07:26:47Z)
Question Decomposition Improves the Faithfulness of Model-Generated Reasoning [23.34325378824462]
Large language models (LLMs) are difficult to verify the correctness and safety of their behavior. One approach is to prompt LLMs to externalize their reasoning, by having them generate step-by-step reasoning as they answer a question. This approach relies on the stated reasoning faithfully reflecting the model's actual reasoning, which is not always the case. Decomposition-based methods achieve strong performance on question-answering tasks, sometimes approaching that of CoT.
arXiv Detail & Related papers (2023-07-17T00:54:10Z)
Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting [43.458726163197824]
Large Language Models (LLMs) can achieve strong performance on many tasks by producing step-by-step reasoning before giving a final output. We find that CoT explanations can systematically misrepresent the true reason for a model's prediction.
arXiv Detail & Related papers (2023-05-07T22:44:25Z)

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