MixRea: Benchmarking Explicit-Implicit Reasoning in Large Language Models

• URL: http://arxiv.org/abs/2605.20128v1
• Date: Tue, 19 May 2026 17:15:08 GMT
• Title: MixRea: Benchmarking Explicit-Implicit Reasoning in Large Language Models
• Abstract summary: We investigate whether large language models (LLMs) fail to attend to subtle yet important contextual cues under explicit task instructions.<n> Evaluation of 21 advanced LLMs shows that even the best-performing reasoning model (Gemini 2.5 Pro) achieves only 42.8% consistency.<n>We propose textbfPotential Relation Completion Prompting (PRCP), a prompting method that improves reasoning by recovering overlooked causal relations.
• Score: 30.821682013821345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) are increasingly integrated into high-stakes decision-making. Inspired by the theory of \emph{inattentional blindness} in human cognition, we investigate whether LLMs, trained on human-preferred corpora that embed attentional biases, exhibit a similar limitation: \emph{failing to attend to subtle yet important contextual cues under explicit task instructions}. To evaluate this, we introduce the task of \textbf{explicit-implicit reasoning} and present \textbf{MixRea}, a benchmark of 2,246 multiple-choice questions across 9 reasoning types with varying distributions of explicit and implicit information. Evaluation of 21 advanced LLMs shows that even the best-performing reasoning model (Gemini 2.5 Pro) achieves only 42.8\% consistency, revealing widespread inattentional blindness. To mitigate this, we propose \textbf{Potential Relation Completion Prompting (PRCP)}, a prompting method that improves reasoning by recovering overlooked causal relations. Further analysis shows that this limitation persists across diverse multi-source reasoning tasks, highlighting the need for more cognitively aligned models.

Related papers

• Pause or Fabricate? Training Language Models for Grounded Reasoning [50.104657152302956]
  We propose Grounded Reasoning via Interactive Reinforcement Learning (GRIL) for grounded reasoning under incomplete information.<n>GRIL decomposes the reasoning process into two stages: clarify and pause, which identifies whether the available information is sufficient, and grounded reasoning, which performs task solving once the necessary premises are established.<n>Experiments on GSM8K-Insufficient and MetaMATH-Insufficient show that GRIL significantly improves premise detection (up to 45%), leading to a 30% increase in task success while reducing average response length by over 20%.
  arXiv  Detail & Related papers  (2026-04-21T16:45:29Z)
• Adversarial Yet Cooperative: Multi-Perspective Reasoning in Retrieved-Augmented Language Models [72.4149653187766]
  We propose a Reasoner-Verifier framework named Adrialversa Reasoning RAG (ARR)<n>The Reasoner and Verifier engage in reasoning on retrieved evidence and critiquing each other's logic while being guided by process-aware advantage.<n> Experiments on multiple benchmarks demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2026-01-08T06:57:03Z)
• Beware of Reasoning Overconfidence: Pitfalls in the Reasoning Process for Multi-solution Tasks [54.31998314008198]
  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)
• From Harm to Help: Turning Reasoning In-Context Demos into Assets for Reasoning LMs [58.02809208460186]
  We revisit this paradox using high-quality traces from DeepSeek-R1 as demonstrations.<n>We find that adding more exemplars consistently degrades accuracy, even when demonstrations are optimal.<n>We introduce Insight-to-solve (I2S), a sequential test-time procedure that turns demonstrations into explicit, reusable insights.
  arXiv  Detail & Related papers  (2025-09-27T08:59:31Z)
• mSCoRe: a $M$ultilingual and Scalable Benchmark for $S$kill-based $Co$mmonsense $Re$asoning [74.97363626515236]
  We propose a textbfMultilingual and Scalable Benchmark for textbfSkill-based textbfCommonsense textbfReasoning (textbfmSCoRe)<n>Our benchmark incorporates three key components that are designed to systematically evaluate LLM's reasoning capabilities.<n>Our results reveal the limitations of such reasoning-reinforced models when confronted with nuanced multilingual general and cultural commonsense.
  arXiv  Detail & Related papers  (2025-08-13T18:59:02Z)
• 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)
• A Closer Look at Bias and Chain-of-Thought Faithfulness of Large (Vision) Language Models [58.32070787537946]
  Chain-of-thought (CoT) reasoning enhances performance of large language models.<n>We present the first comprehensive study of CoT faithfulness in large vision-language models.
  arXiv  Detail & Related papers  (2025-05-29T18:55:05Z)
• 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)
• Short-Path Prompting in LLMs: Analyzing Reasoning Instability and Solutions for Robust Performance [33.16322104912836]
  Large language models' (LLMs) reasoning is largely due to the chain-of-thought (CoT) approaches.<n>LLMs are instruction-tuned to provide long and detailed CoT pathways when responding to reasoning-related questions.<n>Human beings are naturally cognitive misers and will prompt language models to give rather short responses.
  arXiv  Detail & Related papers  (2025-04-13T14:12:14Z)
• Unveiling Reasoning Thresholds in Language Models: Scaling, Fine-Tuning, and Interpretability through Attention Maps [3.8936716676293917]
  This study investigates the in-context learning capabilities of various decoder-only transformer-based language models with different model sizes and training data.<n>We identify a critical parameter threshold (1.6 billion), beyond which reasoning performance improves significantly in tasks such as commonsense reasoning in multiple-choice question answering and deductive reasoning.
  arXiv  Detail & Related papers  (2025-02-21T00:48:32Z)
• Navigating Semantic Relations: Challenges for Language Models in Abstract Common-Sense Reasoning [5.4141465747474475]
  Large language models (LLMs) have achieved remarkable performance in generating human-like text and solving problems of moderate complexity.<n>We systematically evaluate abstract common-sense reasoning in LLMs using the ConceptNet knowledge graph.
  arXiv  Detail & Related papers  (2025-02-19T20:20:24Z)
• Preemptive Answer "Attacks" on Chain-of-Thought Reasoning [7.233752893356647]
  Large language models (LLMs) showcase impressive reasoning capabilities when coupled with Chain-of-Thought prompting. In this paper, we introduce a novel scenario termed preemptive answers, where the LLM obtains an answer before engaging in reasoning. Experiments reveal that preemptive answers significantly impair the model's reasoning capability across various CoT methods and a broad spectrum of datasets.
  arXiv  Detail & Related papers  (2024-05-31T15:15:04Z)
• OVM, Outcome-supervised Value Models for Planning in Mathematical Reasoning [15.59540726867483]
  We argue that in guided decoding, assessing the potential of an incomplete reasoning path can be more advantageous than simply ensuring per-step correctness. Inspired by the findings that $textitoutcome supervision for guided decoding essentially acts as a value model, we propose Outcome-supervised Value Model (OVM) Our experiments on two multi-step mathematical reasoning datasets, GSM8K and Game of 24, demonstrate the superior performance of the OVM model.
  arXiv  Detail & Related papers  (2023-11-16T09:56:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.