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arxiv: 2501.19201 · v2 · submitted 2025-01-31 · 💻 cs.CL · cs.AI· cs.LG

Efficient Reasoning with Hidden Thinking

Xuan Shen , Yizhou Wang , Yufa Zhou , Xiangxi Shi , Pu Zhao , Yanzhi Wang , Jiuxiang Gu This is my paper

Pith reviewed 2026-05-23 04:43 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG
keywords chain-of-thought compressionhidden thinking tokensmutual informationmultimodal large language modelsreasoning efficiencyadaptive interpreterzero-shot accuracy
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The pith

Compressing chain-of-thought into hidden tokens preserves reasoning accuracy when mutual information is retained.

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

The paper proposes Heima, a framework that condenses lengthy textual chain-of-thought reasoning in multimodal models into a small set of abstract thinking tokens. A theoretical analysis quantifies the information gap from compression and shows that reasoning capability holds when non-trivial mutual information remains between the tokens and the original CoT. An adaptive interpreter reconstructs variable-length text from the tokens, and experiments on reasoning benchmarks confirm maintained or improved zero-shot accuracy with coherent reconstructed steps.

Core claim

Reasoning capability is preserved when non-trivial mutual information is retained between the compressed thinking tokens and the original CoT; experiments show maintained or better zero-shot accuracy.

What carries the argument

The Heima compression framework that condenses CoTs into abstract thinking tokens, with the information-theoretic gap quantified via mutual information retention.

If this is right

  • Reasoning efficiency increases because verbose text is replaced by fewer tokens.
  • The adaptive interpreter can reconstruct coherent reasoning progress from the compressed tokens.
  • Zero-shot accuracy on diverse benchmarks stays the same or rises.
  • The approach supports development of scalable latent reasoning models.

Where Pith is reading between the lines

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

  • Similar compression may apply to non-reasoning tasks if mutual information can be maintained.
  • Internalizing reasoning into latent tokens could reduce output length across many model applications.
  • Varying the number of thinking tokens while tracking mutual information offers a direct way to test compression limits.

Load-bearing premise

That the measured mutual information retained between tokens and original CoT is both accurate and sufficient to ensure task-critical reasoning steps survive the compression.

What would settle it

A benchmark result where high mutual information is retained yet zero-shot accuracy drops substantially on a reasoning task would falsify the preservation claim.

Figures

Figures reproduced from arXiv: 2501.19201 by Jiuxiang Gu, Pu Zhao, Xiangxi Shi, Xuan Shen, Yanzhi Wang, Yizhou Wang, Yufa Zhou.

Figure 1
Figure 1. Figure 1: Visualization of our whole framework. Image and question are fed to the Heima Encoder (MLLM) for reasoning encoding and final conclusion generation. Encoded thinking tokens and question are then fed to the Heima Decoder (LLM) for the CoT reconstruction. In the reconstructed reasoning process, the caption decoder successfully retrieves image information, describing it as ”The image shows a sleek, modern spo… view at source ↗
Figure 2
Figure 2. Figure 2: Visualization of the CoTs progressive encoding process. <Thinking of Reasoning> for the reasoning stage. Note that different samples with varying values of i share the same thinking token <CoT>(k) at the same stage k. The updated dataset is then explained as follows, n X(i) v , X(i) q , {<CoT>(k)} Ki k=1, Y (i) a oN i=1 . (3) We then continue fine-tuning the model with the objective: max θ 1 N XN i=1 log … view at source ↗
Figure 3
Figure 3. Figure 3: Visualization of the training progress for Heima Decoder. Thinking token is caught from the s th last hidden state of Heima Encoder and replaces the embedding of special token <CoT>(s). sentations, each with a unique thinking token. Each en￾coded CoT stage or each kind of thinking token requires one corresponding decoder or interpreter. Thus, to decode multiple thinking tokens, we need to train multiple de… view at source ↗
Figure 4
Figure 4. Figure 4 [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Results of evaluation by GPT-4o for assessing the av￾erage similarity score (1-5) between the reconstructed reasoning processes from the thinking tokens and the original CoTs. 4.3. Interpretability Analysis To verify the effectiveness of hidden representation encod￾ing and improve interpretability of the framework, we eval￾uate the performance of Heima Decoder by assessing the similarity between the recons… view at source ↗
Figure 7
Figure 7. Figure 7: Ablation study of average accuracy on 6 datasets for varying retention ratios of thinking tokens relative to original CoT. 0 50 100 150 200 250 300 Ratio 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 # of Token on MMStar 181 [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Ablation study for number of generated tokens on MM￾Star with varying retention ratios of thinking tokens relative to the original CoT. Baseline (i.e., LLaVA-CoT) generates 181 tokens. to 90% retention ratio, accuracy fluctuates irregularly. There is no consistent pattern emerging, highlighting the unpre￾dictable relationship between retention ratio and accuracy. Meanwhile, as shown in [PITH_FULL_IMAGE:fi… view at source ↗
read the original abstract

Chain-of-Thought (CoT) reasoning has become a powerful framework for improving complex problem-solving capabilities in Multimodal Large Language Models (MLLMs). However, the verbose nature of textual reasoning introduces significant inefficiencies. In this work, we propose Heima (as hidden llama), an effective CoT compression framework that condenses lengthy CoTs into a small set of abstract thinking tokens, preserving essential reasoning while removing redundancy. We then conduct a theoretical analysis from an information-theoretic perspective, quantifying the information gap induced by compression, showing that reasoning capability is preserved when non-trivial mutual information is retained. To further explore and quantify this information gap, we design the adaptive interpreter that maps thinking tokens back to variable-length textual sequences, thereby reconstructing the reasoning process. Experiments across diverse reasoning benchmarks demonstrate that Heima improves reasoning efficiency, while maintaining or even achieving better zero-shot accuracy. Moreover, the interpreter reconstructs coherent reasoning progresses from compressed thinking tokens, revealing that the information gap is minimal and validating the effectiveness of the proposed framework. This work paves the way for scalable latent reasoning models and advances our understanding of efficient reasoning processes in large models. Code: https://github.com/shawnricecake/Heima

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 Heima, a CoT compression method that condenses verbose textual reasoning in MLLMs into a small set of abstract thinking tokens. It provides an information-theoretic analysis claiming that reasoning capability is preserved whenever non-trivial mutual information is retained between the compressed tokens and the original CoT. An adaptive interpreter is proposed to map tokens back to variable-length text for reconstruction. Experiments on reasoning benchmarks report maintained or improved zero-shot accuracy, with the interpreter producing coherent reconstructions that indicate a minimal information gap.

Significance. If the central claims hold, the work would contribute to more efficient latent-space reasoning in large multimodal models by reducing token overhead without sacrificing performance. The provision of code on GitHub is a positive step toward reproducibility. The information-theoretic framing and interpreter design offer a potential lens for understanding what is preserved under compression, though this depends on the validity of the MI-to-reasoning link.

major comments (2)
  1. [theoretical analysis] Theoretical analysis section: the claim that non-trivial mutual information between compressed thinking tokens and original CoT is sufficient to preserve reasoning capability is not supported by controls or ablations showing that retained MI corresponds to task-critical logical steps rather than incidental statistical correlations. Mutual information quantifies dependence but does not identify which specific deductions survive compression.
  2. [experiments] Experiments section: the reported maintained or improved zero-shot accuracy lacks ablations that isolate whether performance stems from retention of necessary reasoning steps versus other factors (e.g., model capacity or prompt effects). No details are given on how the mutual-information gap is measured or how the interpreter is trained without introducing circular dependence on the same data.
minor comments (1)
  1. [abstract] Abstract: the parenthetical '(as hidden llama)' for the acronym Heima is unclear and should be expanded or removed for readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the major comments point by point below, clarifying our theoretical framing and experimental design while committing to revisions where needed to strengthen the manuscript.

read point-by-point responses

  1. Referee: [theoretical analysis] Theoretical analysis section: the claim that non-trivial mutual information between compressed thinking tokens and original CoT is sufficient to preserve reasoning capability is not supported by controls or ablations showing that retained MI corresponds to task-critical logical steps rather than incidental statistical correlations. Mutual information quantifies dependence but does not identify which specific deductions survive compression.

    Authors: We agree that mutual information measures statistical dependence without isolating specific logical deductions. Our analysis uses MI as a necessary (not sufficient) condition for preservation: if non-trivial MI is retained, the compressed tokens contain enough information for the downstream model to achieve the observed reasoning performance. This is supported by the consistent zero-shot accuracy across benchmarks rather than by direct identification of individual steps. We will revise the theoretical section to explicitly discuss this distinction and the limitations of MI as a proxy, and we will add a targeted discussion of why incidental correlations alone are unlikely to explain the maintained performance given the compression ratios achieved. revision: partial

  2. Referee: [experiments] Experiments section: the reported maintained or improved zero-shot accuracy lacks ablations that isolate whether performance stems from retention of necessary reasoning steps versus other factors (e.g., model capacity or prompt effects). No details are given on how the mutual-information gap is measured or how the interpreter is trained without introducing circular dependence on the same data.

    Authors: We will add ablations in the revised experiments section that control for prompt effects and model capacity (e.g., fixed-prompt baselines and capacity-matched uncompressed runs) to better isolate the contribution of retained reasoning information. The MI gap is quantified through the adaptive interpreter's reconstruction fidelity and coherence metrics on held-out examples. The interpreter is trained on a separate corpus distinct from the evaluation benchmarks to avoid circular dependence; we will include these training details, dataset splits, and hyperparameter choices in the updated manuscript. revision: yes

Circularity Check

1 steps flagged

Reasoning preservation claim reduces to mutual information retention by definition

specific steps
  1. self definitional [Abstract]
    "We then conduct a theoretical analysis from an information-theoretic perspective, quantifying the information gap induced by compression, showing that reasoning capability is preserved when non-trivial mutual information is retained."

    The analysis defines the information gap as the reduction in mutual information between compressed thinking tokens and the original CoT; the preservation result is then stated as holding whenever this mutual information remains non-trivial. The claimed theoretical result is therefore equivalent to the definition of retained mutual information rather than derived from separate principles about reasoning structure.

full rationale

The paper's theoretical analysis quantifies the information gap via mutual information and then asserts preservation of reasoning capability exactly when non-trivial MI is retained. This link is definitional: the retained information is defined as the MI, so the 'showing' that capability is preserved follows immediately from the quantification rather than from an independent argument about which reasoning steps survive. Experiments demonstrate maintained accuracy but do not supply an independent derivation of the preservation claim. No self-citations or other load-bearing reductions appear in the provided text.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the central claim rests on an unstated assumption that mutual information is a sufficient proxy for reasoning fidelity.

pith-pipeline@v0.9.0 · 5754 in / 1058 out tokens · 60403 ms · 2026-05-23T04:43:11.219431+00:00 · methodology

discussion (0)

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Forward citations

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