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arxiv: 2605.28006 · v1 · pith:UPF4IJNWnew · submitted 2026-05-27 · 💻 cs.CL · cs.AI

Integrated and Cross-Architecture Interpretation of LLM Reasoning

Leonardo Matthew Yauw , Wei-Bin Kou , Yujiu Yang This is my paper

Pith reviewed 2026-06-29 13:11 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords LLM reasoning interpretabilityMutual Information PeakDeep-Thinking Ratiotoken trajectory analysiscross-architecture comparisonJaccard stability metricQwen and Llama models
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The pith

The IAR framework isolates reasoning-crucial tokens at the output layer with calibrated MIP and Tukey IQR then traces their cross-layer paths through DTR overlap and checks stability via Jaccard across domains.

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

Large language models generate observable answers yet keep the internal steps of reasoning hidden. Relying on any one probe such as mutual information or deep-thinking ratios can miss the actual inferential structure. The paper introduces the Integrated cross-Architecture Reasoning framework that first locates crucial tokens at the final layer by combining bandwidth-calibrated mutual information peaks with Tukey IQR detection. It next measures how those tokens reappear in deeper layers through overlap with deep-thinking tokens and finally applies a Jaccard index to test whether the same tokens remain stable across mathematics, code, logic, and common-sense problems. Experiments on Qwen-7B, Qwen-14B, and Llama-8B show the approach works uniformly across model families.

Core claim

The IAR framework provides a unified approach to LLM reasoning interpretability by isolating reasoning-crucial tokens at the output layer with bandwidth-calibrated MIP and Tukey IQR, tracing their cross-layer trajectories via overlap with DTR-deep tokens, and verifying stability with Jaccard metric across domains.

What carries the argument

The Integrated, cross-Architecture Reasoning (IAR) framework, which isolates output-layer tokens via MIP plus Tukey IQR, traces trajectories by overlap with DTR-deep tokens, and measures consistency with the Jaccard metric.

If this is right

  • Reasoning-crucial tokens can be isolated at the output layer for Qwen-7B, Qwen-14B, and Llama-8B models.
  • Overlap analysis shows whether those tokens are computation-intensive and how their patterns change across layers.
  • Jaccard stability holds across mathematics, code, logic, and common-sense domains, confirming the tokens support reliable reasoning.
  • The combined method yields interpretation that generalizes across different model architectures.

Where Pith is reading between the lines

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

  • If the identified tokens prove causal, targeted editing of those tokens could raise accuracy on multi-step problems without retraining.
  • Applying the same isolation-plus-overlap procedure to newer or larger models would test whether the token trajectories remain consistent.
  • Pairing IAR token sets with activation patching experiments could check whether the selected tokens are necessary for correct reasoning.

Load-bearing premise

That overlap between MIP-selected tokens and DTR-deep tokens accurately follows genuine reasoning paths rather than surface correlations.

What would settle it

Finding that models with high Jaccard overlap between MIP and DTR tokens still produce incorrect answers on held-out reasoning problems, or that low-overlap tokens better predict correct outputs, would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.28006 by Leonardo Matthew Yauw, Wei-Bin Kou, Yujiu Yang.

Figure 1
Figure 1. Figure 1: Overview of the proposed IAR framework. layered computation that gives rise to a reasoning￾relevant token, DTR conflating deep computation with answer-relevance. Second, prior analyses have been confined to a single architectural family. Without cross-architecture evaluation, it is impos￾sible to distinguish real interpretation signals that reflect fundamental reasoning properties from those that are artif… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed overlap analysis. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
read the original abstract

Understanding how LLMs reason is hindered by a practical asymmetry: while their generated outputs are observable, the underlying reasoning patterns remain opaque. Relying on single probes, such as Mutual Information Peak (MIP) or Deep-Thinking Ratio (DTR), risks underestimating the genuine inferential structure. To response this deficiency, we present an Integrated, cross-Architecture Reasoning (IAR) framework, designed to provide a unified approach to LLM reasoning interpretability. Specifically, we first propose to use bandwidth-calibrated MIP coupled with Tukey IQR peak-detection to isolate reasoning-crucial tokens at the output layer. Second, we performed an overlap analysis between MIP-picked tokens and DTR-deep tokens to trace the cross-layer trajectories of those tokens. This also discloses whether reasoning-crucial tokens are computation-intensive as well, further facilitating to understand how reasoning patterns evolve across model layers. Finally, we apply a Jaccard stability metric over multi-domain problems to verify if the MIP-identified tokens are reasoning quality-guaranteed. Extensive experiments on three models (Qwen-7B, Qwen-14B, and Llama-8B) across four domains (mathematics, code, logic, and common sense) demonstrate IAR's generalizable interpretation capabilities across architectures.

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

3 major / 2 minor

Summary. The paper presents the Integrated and Cross-Architecture Reasoning (IAR) framework for LLM interpretability. It combines bandwidth-calibrated Mutual Information Peak (MIP) with Tukey IQR peak detection to isolate reasoning-crucial tokens at the output layer, conducts overlap analysis between MIP tokens and Deep-Thinking Ratio (DTR) tokens to trace cross-layer trajectories, and applies a Jaccard stability metric across multi-domain problems to verify whether MIP-identified tokens are 'reasoning quality-guaranteed.' Experiments are reported on Qwen-7B, Qwen-14B, and Llama-8B across mathematics, code, logic, and common-sense domains, claiming generalizable capabilities across architectures.

Significance. If the proxy statistics (MIP+IQR, DTR overlap, Jaccard) were shown to correspond to causally relevant reasoning steps and task performance, the framework would supply a practical, multi-probe method for cross-architecture analysis that improves on single-probe approaches. The work is empirical rather than axiomatic and does not claim parameter-free derivations or machine-checked proofs.

major comments (3)
  1. [Abstract] Abstract (final paragraph): the claim that Jaccard stability 'verifies if the MIP-identified tokens are reasoning quality-guaranteed' is unsupported. Jaccard similarity only quantifies token-set overlap across domains; it supplies no information on correlation with task accuracy, inference correctness, or human-identified reasoning chains.
  2. [Overlap analysis] Section describing overlap analysis: the assertion that overlap between MIP-picked tokens and DTR-deep tokens 'traces the cross-layer trajectories' and discloses whether tokens are 'computation-intensive' rests on an untested assumption that shared tokens are causally involved in reasoning rather than co-occurring for unrelated reasons. No ablation or causal intervention is described to test this mapping.
  3. [Experiments] Experiments section: no quantitative results (accuracy deltas, correlation coefficients, or human alignment scores) are referenced that would demonstrate the framework isolates tokens more effectively than MIP or DTR alone, undermining the 'unified approach' and 'generalizable interpretation capabilities' claims.
minor comments (2)
  1. [Abstract] Abstract: 'To response this deficiency' should read 'To address this deficiency.'
  2. [Methods] Notation: 'bandwidth-calibrated MIP' and 'Tukey IQR peak-detection' are introduced without explicit formulas or parameter values in the abstract; these should be defined with equations in the methods section.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below, indicating revisions where the concerns are valid and providing clarifications on the scope of our claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract (final paragraph): the claim that Jaccard stability 'verifies if the MIP-identified tokens are reasoning quality-guaranteed' is unsupported. Jaccard similarity only quantifies token-set overlap across domains; it supplies no information on correlation with task accuracy, inference correctness, or human-identified reasoning chains.

    Authors: We agree that the original phrasing overstates the Jaccard metric's implications. Jaccard stability is used to measure consistency of MIP token sets across domains, which we interpret as supporting generalizability but does not directly correlate with accuracy or human reasoning chains. We will revise the abstract to state that the metric 'assesses the cross-domain stability of MIP-identified tokens' without claiming verification of reasoning quality. revision: yes

  2. Referee: [Overlap analysis] Section describing overlap analysis: the assertion that overlap between MIP-picked tokens and DTR-deep tokens 'traces the cross-layer trajectories' and discloses whether tokens are 'computation-intensive' rests on an untested assumption that shared tokens are causally involved in reasoning rather than co-occurring for unrelated reasons. No ablation or causal intervention is described to test this mapping.

    Authors: The overlap analysis is presented as an observational probe to identify shared tokens between output-layer MIP and deeper DTR layers, suggesting possible trajectories. We acknowledge the correlational nature and lack of causal interventions. We will revise the section to clarify that overlap indicates co-occurrence rather than proven causal involvement in reasoning, and add explicit discussion of this limitation. revision: yes

  3. Referee: [Experiments] Experiments section: no quantitative results (accuracy deltas, correlation coefficients, or human alignment scores) are referenced that would demonstrate the framework isolates tokens more effectively than MIP or DTR alone, undermining the 'unified approach' and 'generalizable interpretation capabilities' claims.

    Authors: The experiments demonstrate application of the integrated framework across architectures and domains rather than direct benchmarking against single probes via performance metrics. We agree this limits support for superiority claims. We will qualify the 'unified approach' language in the experiments and conclusion sections to focus on complementary insights rather than proven improvement, and add any available correlation statistics if feasible from existing data. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical framework with no derivation chain

full rationale

The paper describes an empirical IAR framework that proposes bandwidth-calibrated MIP + Tukey IQR for token isolation, overlap analysis with DTR tokens for trajectory tracing, and Jaccard metric for stability verification across models and domains. No equations, first-principles derivations, fitted parameters renamed as predictions, or self-citation load-bearing steps appear in the abstract or described methods. All components are presented as direct proposals and experimental applications without any reduction of outputs to inputs by construction. The work is self-contained as an empirical methodology and does not invoke uniqueness theorems or ansatzes from prior self-work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the framework appears to rely on standard token-probing assumptions not detailed here.

pith-pipeline@v0.9.1-grok · 5752 in / 1116 out tokens · 29549 ms · 2026-06-29T13:11:46.246936+00:00 · methodology

discussion (0)

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Reference graph

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