S1-Bench: A Simple Benchmark for Evaluating System 1 Thinking Capability of Large Reasoning Models

• URL: http://arxiv.org/abs/2504.10368v3
• Date: Tue, 27 May 2025 05:10:36 GMT
• Title: S1-Bench: A Simple Benchmark for Evaluating System 1 Thinking Capability of Large Reasoning Models
• Authors: Wenyuan Zhang, Shuaiyi Nie, Xinghua Zhang, Zefeng Zhang, Tingwen Liu,
• Abstract summary: Large Reasoning Models (LRMs) have achieved breakthroughs in complex reasoning tasks through explicit chains of thought.<n>Their heavy reliance on system 2 thinking may limit their system 1 thinking capabilities.<n>S1-Bench introduces a suite of simple, diverse, and natural questions to assess LRMs' performance on questions more suitable for system 1.
• Score: 13.083179473480705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce S1-Bench, a novel benchmark designed to evaluate the performance of Large Reasoning Models (LRMs) on simple tasks that favor intuitive system 1 thinking rather than deliberative system 2 reasoning. While LRMs have achieved significant breakthroughs in complex reasoning tasks through explicit chains of thought, their heavy reliance on system 2 thinking may limit their system 1 thinking capabilities. However, there is a lack of an appropriate benchmark for evaluating LRM's system 1 thinking capabilities. To fill this gap, S1-Bench introduces a suite of simple, diverse, and natural questions across multiple domains and languages, specifically designed to assess LRMs' performance on questions more suitable for system 1 . We conduct extensive evaluations across 28 LRMs, revealing their inefficiency, inadequate accuracy, and limited robustness when handling simple questions. Additionally, we observe a gap between their difficulty perception and generation length. Overall, this work paves the way toward dual-system compatibility in the development of LRMs.

Related papers

• Reasoning Models Can be Easily Hacked by Fake Reasoning Bias [59.79548223686273]
  We introduce THEATER, a comprehensive benchmark to evaluate Reasoning Theater Bias (RTB)<n>We investigate six bias types including Simple Cues and Fake Chain-of-Thought.<n>We identify'shallow reasoning'-plausible but flawed arguments-as the most potent form of RTB.
  arXiv  Detail & Related papers  (2025-07-18T09:06:10Z)
• The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity [16.266145641151375]
  Large Reasoning Models generate detailed thinking processes before providing answers.<n>We show that LRMs face a complete accuracy collapse beyond certain complexities.<n>We also investigate the reasoning traces in more depth, studying the patterns of explored solutions.
  arXiv  Detail & Related papers  (2025-06-07T22:42:29Z)
• THINK-Bench: Evaluating Thinking Efficiency and Chain-of-Thought Quality of Large Reasoning Models [17.609493312457]
  Large reasoning models (LRMs) have achieved impressive performance in complex tasks, often outperforming conventional large language models (LLMs)<n>Overthinking severely limits their computational efficiency.<n>We introduce Think-Bench, a benchmark designed to evaluate the reasoning efficiency of LRMs.
  arXiv  Detail & Related papers  (2025-05-28T08:41:14Z)
• Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning [75.04643265875072]
  Large reasoning models (LRMs) have demonstrated strong performance on complex reasoning tasks, but often suffer from overthinking.<n>Inspired by the dual process theory in cognitive science, we propose Adaptive Cognition Policy Optimization.<n>ACPO enables LRMs to achieve efficient reasoning through adaptive cognitive allocation and dynamic system switch.
  arXiv  Detail & Related papers  (2025-05-22T07:15:08Z)
• Computational Reasoning of Large Language Models [51.629694188014064]
  We introduce textbfTuring Machine Bench, a benchmark to assess the ability of Large Language Models (LLMs) to execute reasoning processes.<n> TMBench incorporates four key features: self-contained and knowledge-agnostic reasoning, a minimalistic multi-step structure, controllable difficulty, and a theoretical foundation based on Turing machine.
  arXiv  Detail & Related papers  (2025-04-29T13:52:47Z)
• a1: Steep Test-time Scaling Law via Environment Augmented Generation [45.19240207975418]
  Environment Augmented Generation (EAG) is a framework that enhances large language models' reasoning through real-time environmental feedback. EAG enables deliberate backtracking and strategic replanning through tight integration of execution feedback and branching exploration. A1-32B model achieves state-of-the-art performance among similar-sized models across all benchmarks.
  arXiv  Detail & Related papers  (2025-04-20T12:55:59Z)
• Trade-offs in Large Reasoning Models: An Empirical Analysis of Deliberative and Adaptive Reasoning over Foundational Capabilities [101.77467538102924]
  Recent advancements in Large Reasoning Models (LRMs) have demonstrated remarkable performance in specialized reasoning tasks.<n>We show that acquiring deliberative reasoning capabilities significantly reduces the foundational capabilities of LRMs.<n>We demonstrate that adaptive reasoning -- employing modes like Zero-Thinking, Less-Thinking, and Summary-Thinking -- can effectively alleviate these drawbacks.
  arXiv  Detail & Related papers  (2025-03-23T08:18:51Z)
• Large Reasoning Models in Agent Scenarios: Exploring the Necessity of Reasoning Capabilities [74.35956310688164]
  We propose the LaRMA framework, encompassing nine tasks across Tool Usage, Plan Design, and Problem Solving. Our findings address four research questions: LRMs surpass LLMs in reasoning-intensive tasks like Plan Design, leveraging iterative reflection for superior outcomes. LRMs' enhanced reasoning incurs higher computational costs, prolonged processing, and behavioral challenges, including overthinking and fact-ignoring tendencies.
  arXiv  Detail & Related papers  (2025-03-14T04:34:31Z)
• LR$^2$Bench: Evaluating Long-chain Reflective Reasoning Capabilities of Large Language Models via Constraint Satisfaction Problems [7.379503137362718]
  We introduce LR$2$Bench, a novel benchmark designed to evaluate the Long-chain Reflective Reasoning capabilities of Large Language Models (LLMs)<n>Our experimental results reveal that even the most advanced reasoning-specific models, such as DeepSeek-R1 and OpenAI o1-preview, struggle with tasks in LR$2$Bench.
  arXiv  Detail & Related papers  (2025-02-25T04:51:17Z)
• From System 1 to System 2: A Survey of Reasoning Large Language Models [72.87412996793957]
  Foundational Large Language Models excel at fast decision-making but lack depth for complex reasoning.<n>OpenAI's o1/o3 and DeepSeek's R1 have demonstrated expert-level performance in fields such as mathematics and coding.
  arXiv  Detail & Related papers  (2025-02-24T18:50:52Z)
• Reward Models Identify Consistency, Not Causality [54.987590763737145]
  State-of-the-art reward models prioritize structural consistency over causal correctness. Removing the problem statement has minimal impact on reward scores. altering numerical values or disrupting the reasoning flow significantly affects RM outputs.
  arXiv  Detail & Related papers  (2025-02-20T14:57:14Z)
• Reasoning on a Spectrum: Aligning LLMs to System 1 and System 2 Thinking [0.9709444454602557]
  Large Language Models (LLMs) exhibit impressive reasoning abilities, yet their reliance on structured step-by-step reasoning reveals a critical limitation.<n>This work challenges the assumption that step-by-step reasoning is always optimal and highlights the need for adapting reasoning strategies based on task demands.
  arXiv  Detail & Related papers  (2025-02-18T02:58:37Z)
• MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency [63.23935582919081]
  Chain-of-Thought (CoT) has significantly enhanced the reasoning capabilities of Large Language Models (LLMs) We introduce MME-CoT, a specialized benchmark evaluating the CoT reasoning performance of LMMs. We conduct an in-depth analysis of state-of-the-art LMMs, uncovering several key insights.
  arXiv  Detail & Related papers  (2025-02-13T18:59:46Z)
• MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs [55.20845457594977]
  Large language models (LLMs) have shown increasing capability in problem-solving and decision-making.<n>We present a process-based benchmark MR-Ben that demands a meta-reasoning skill.<n>Our meta-reasoning paradigm is especially suited for system-2 slow thinking.
  arXiv  Detail & Related papers  (2024-06-20T03:50:23Z)
• Exploring Self-supervised Logic-enhanced Training for Large Language Models [59.227222647741094]
  In this paper, we make the first attempt to investigate the feasibility of incorporating logical knowledge through self-supervised post-training. We devise an auto-regressive objective variant of MERIt and integrate it with two LLM series, i.e., FLAN-T5 and LLaMA, with parameter size ranging from 3 billion to 13 billion. The results on two challenging logical reasoning benchmarks demonstrate the effectiveness of LogicLLM.
  arXiv  Detail & Related papers  (2023-05-23T06:13:10Z)

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.