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arxiv: 2504.10368 · v4 · submitted 2025-04-14 · 💻 cs.CL · cs.AI

Exploring the System 1 Thinking Capability of Large Reasoning Models

Wenyuan Zhang , Shuaiyi Nie , Xinghua Zhang , Zefeng Zhang , Tingwen Liu This is my paper

Pith reviewed 2026-05-22 20:00 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords large reasoning modelssystem 1 thinkingS1-Benchdifficulty awarenesshidden statesreasoning efficiencyintuitive responsesmultilingual benchmark
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The pith

Large reasoning models lack efficient system 1 thinking and encode problem difficulty in hidden states from the start.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether large reasoning models that rely on long chains for hard problems can also handle simple intuitive tasks with short, efficient answers. It creates S1-Bench, a collection of straightforward questions across domains and languages chosen to be model-simple, then runs 28 models on it. The results show the models are inaccurate and use too many tokens, while methods meant to improve efficiency either fail on easy items or trade away accuracy. The study also finds that models register difficulty early with lower confidence and that this information appears in their hidden states.

Core claim

Large reasoning models display under-accuracy and inefficiency on system 1 problems that call for quick intuitive responses. They exhibit early difficulty awareness together with lower confidence, and problem difficulty is implicitly encoded in the models' hidden states.

What carries the argument

S1-Bench, the multi-domain multilingual benchmark of model-simple system 1 questions used both to measure intuitive efficiency and to extract difficulty signals from hidden states.

If this is right

  • Existing efficient-reasoning techniques either generalize poorly to simple questions or reduce performance when applied.
  • Models register difficulty early enough that internal signals could be used to shorten unnecessary reasoning chains.
  • Real-world deployments would gain token efficiency if system 1 capability were strengthened.
  • Difficulty information already present in hidden states offers a route to better model calibration on easy tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Hidden-state encodings of difficulty could support training loops that teach models to skip long reasoning on easy items.
  • The early-awareness finding suggests hybrid architectures that decide fast versus slow mode before generating tokens.
  • Benchmark results may generalize to other efficiency metrics such as latency in low-resource settings.

Load-bearing premise

The questions in S1-Bench are genuinely simple in a model-independent way and the hidden-state patterns reflect real difficulty awareness rather than benchmark artifacts.

What would settle it

Running the same models on a fresh collection of human-judged simple questions that produces neither accuracy gaps nor the reported hidden-state patterns would falsify the central claim.

Figures

Figures reproduced from arXiv: 2504.10368 by Shuaiyi Nie, Tingwen Liu, Wenyuan Zhang, Xinghua Zhang, Zefeng Zhang.

Figure 1
Figure 1. Figure 1: Construction workflow for S1-Bench and an illustrative example from each major category. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Statistical distribution of token counts for S1- [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of the thinking process across [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Average response tokens in the easy category [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: S1-Bench Category Display. The inner circle [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: LRMs exhibit under-accuracy and overthinking on simple problems. Shapes represent organizations, [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Average response token counts on the 28 subcategories, which is the average result of five generations [PITH_FULL_IMAGE:figures/full_fig_p024_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Maximum similarity between each segment and all preceding segments for LRMs across four categories. [PITH_FULL_IMAGE:figures/full_fig_p024_10.png] view at source ↗
read the original abstract

This paper explores the system 1 thinking capability of Large Reasoning Models (LRMs), the intuitive ability to respond efficiently with minimal token usage. While existing LRMs rely on long-chain reasoning and excel at complex tasks, their system 1 thinking ability remains largely underexplored. This capability is essential as it reflects models' difficulty awareness and reasoning efficiency, both critical for real-world applications. We propose S1-Bench, a multi-domain, multilingual benchmark comprising model-simple system 1 questions. Our investigation of 28 LRMs reveals under-accuracy and inefficiency on system 1 problems. We find existing efficient reasoning methods either generalize poorly to simple questions or sacrifice performance for efficiency. Further exploration uncovers LRMs' early difficulty awareness accompanied by lower confidence, and shows that problem difficulty is implicitly encoded in hidden states.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces S1-Bench, a multi-domain multilingual benchmark of model-simple system 1 questions, and evaluates 28 LRMs to report under-accuracy and inefficiency on these tasks. It further examines limitations of existing efficient reasoning methods, documents early difficulty awareness with lower confidence in LRMs, and claims that problem difficulty is implicitly encoded in hidden states.

Significance. If the empirical patterns hold under transparent construction and controls, the work would usefully highlight a gap between current LRM strengths on complex reasoning and their handling of intuitive, low-effort tasks, with potential implications for efficiency and calibration in deployed systems. The hidden-state observation, if rigorously isolated, could suggest internal mechanisms for difficulty detection.

major comments (2)
  1. [Abstract / Methods] Abstract and Methods: The manuscript states results from 28 models on S1-Bench but supplies no details on benchmark construction, question selection criteria for 'model-simple system 1 questions', statistical tests, or controls. This directly prevents evaluation of the central claims of under-accuracy and inefficiency.
  2. [Hidden-state analysis] Hidden-state analysis section: The claim that problem difficulty is implicitly encoded in hidden states lacks specification of which layers or representations were examined, the exact probing or correlation method, and controls to distinguish genuine awareness from artifacts of benchmark construction or token statistics.
minor comments (1)
  1. [Abstract] Abstract: Adding a sentence on the approximate size of S1-Bench (number of questions, domains, languages) would help readers gauge the scope of the reported patterns.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights important areas for clarification. We address each major comment below and will revise the manuscript to incorporate additional details as needed.

read point-by-point responses

  1. Referee: [Abstract / Methods] Abstract and Methods: The manuscript states results from 28 models on S1-Bench but supplies no details on benchmark construction, question selection criteria for 'model-simple system 1 questions', statistical tests, or controls. This directly prevents evaluation of the central claims of under-accuracy and inefficiency.

    Authors: We agree that the current version of the manuscript does not provide sufficient detail on S1-Bench construction, question selection criteria, statistical tests, or controls. In the revised manuscript, we will expand the Methods section with a full description of benchmark construction, explicit criteria used to identify model-simple system 1 questions, the statistical tests applied, and the controls implemented to support the claims of under-accuracy and inefficiency. revision: yes

  2. Referee: [Hidden-state analysis] Hidden-state analysis section: The claim that problem difficulty is implicitly encoded in hidden states lacks specification of which layers or representations were examined, the exact probing or correlation method, and controls to distinguish genuine awareness from artifacts of benchmark construction or token statistics.

    Authors: We acknowledge that the hidden-state analysis section requires greater methodological specificity. We will revise this section to specify the exact layers and representations examined, describe the probing and correlation methods in detail, and add controls that isolate genuine difficulty encoding from potential artifacts arising from benchmark construction or token statistics. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper reports empirical observations from evaluating 28 LRMs on a newly introduced S1-Bench benchmark for system-1 style questions. It describes under-accuracy, inefficiency, early difficulty awareness in hidden states, and lower confidence without presenting any equations, derivations, fitted parameters renamed as predictions, or self-citation chains that reduce the central claims to their own inputs by construction. All reported patterns are framed as direct results of model runs and analyses on external benchmarks, leaving the work self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no identifiable free parameters, axioms, or invented entities beyond the creation of the S1-Bench benchmark itself.

pith-pipeline@v0.9.0 · 5674 in / 1055 out tokens · 56048 ms · 2026-05-22T20:00:35.144709+00:00 · methodology

discussion (0)

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost

    cs.AI 2026-05 conditional novelty 7.0

    Post-Reasoning boosts LLM accuracy by reversing the usual answer-after-reasoning order, delivering mean relative gains of 17.37% across 117 model-benchmark pairs with zero extra cost.

  2. Distilling Long-CoT Reasoning through Collaborative Step-wise Multi-Teacher Decoding

    cs.AI 2026-05 unverdicted novelty 6.0

    CoRD uses collaborative multi-teacher step-wise decoding with perplexity-guided beam search to generate higher-quality Long-CoT data that lets smaller models reach near-teacher performance with less supervision.

  3. Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

    cs.CL 2025-03 accept novelty 5.0

    A survey organizing techniques to achieve efficient reasoning in LLMs by shortening chain-of-thought outputs.

Reference graph

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    Category Name and Definition

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