From Concept Alignment to Causal Grounding: An Intervention Test of Chain-of-Thought Faithfulness
Qianli Wang, Yilong Wang, Dennis Wei, Jingyi Sun, Simon Ostermann, Isabelle Augenstein, Pepa Atanasova, Nils Feldhus
cs.CL · cs.AI · cs.LG
Quick verdict
Reframes CoT faithfulness as SAE concept grounding with a validated causal metric Δp across 5 LLMs; strong validation, but conceptual and scope caveats remain.
Summary
This paper recasts chain-of-thought faithfulness as internal concept grounding. A single shared sparse autoencoder (SAE) encodes both a direct-prediction pass and a CoT-derived prediction pass at the answer position, enabling three correlational alignment metrics (CC-SAE, Jaccard, recall) and a causal metric Δp that ablates shared concepts and measures the drop in answer probability. Across five open LLMs (Llama-3.1-8B, Gemma-2-2B/9B, Qwen3-1.7B/8B) and four datasets (GSM8K, LogiQA, OpenbookQA, ARC-Easy), concept alignment is generally high but attributionally weak: binarized CC-SAE×Δp association yields a lift of only 1.08 over chance. Causal faithfulness peaks in mid-to-late layers and differs qualitatively across architectures — Qwen3 distributed, Llama-3.1-8B localized — while scaling reorganizes rather than uniformly raises faithfulness. Many causally important shared concepts are never verbalized in the CoT. Validation is unusually thorough: necessity checks against random and norm-matched controls (Δp≈0 vs 0.1–0.9 for shared features), a sufficiency test (shared concepts recover 1.05 of prediction probability vs 0.45 for random), 87% retention after ubiquitous-feature removal, and SAE-configuration robustness. Limitations: the concept-grounding view covers only agreement cases, the link between shared concepts and genuine faithfulness remains conceptually underspecified, and comparison with existing faithfulness metrics is limited to a single model–dataset pair.
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Assessment at a glance
4
risk flags identified
● high 1
● medium 2
● low 1
5 strengths · 5 weaknesses · 5 author questions
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Key claims under review
- Correlational SAE-concept alignment between prediction and CoT passes is generally high but attributionally uninformative: the binarized CC-SAE×Δp association yields only a 1.08 lift over chance (Table 16).
- The causal metric Δp — the answer-probability drop from ablating shared concepts — peaks at mid-to-late layers rather than final layers and varies qualitatively across architectures (distributed in Qwen3, localized in Llama-3.1-8B).
- Within model families, scale reorganizes the layer-wise faithfulness profile rather than uniformly increasing it.
- Causally important shared concepts are frequently not verbalized in the CoT, so faithfulness assessment requires causal interventions rather than surface or representational correspondence.
Strengths
Well-defined shared concept space
Encoding both passes with a single per-layer SAE (App. A.2, Fig. 6) makes cross-pass overlap S∩ well-defined, directly fixing the comparability flaw of two-SAE patching (Chen et al., 2026).
Unusually thorough causal validation
Random (A/B), norm-matched (C), and completeness (D) controls all yield Δp≈0 while Δp(S∩) ranges 0.1–0.9 (Table 10); sufficiency patching recovers 1.05±0.09 vs 0.45±0.35 for random features (Table 8); ubiquitous-feature removal retains 87% of the effect on average (Table 7).
SAE-choice robustness
Actionable, layer-resolved findings
Honest boundary-drawing
Weaknesses
Construct gap between grounding and faithfulness
Agreement-only scope with unreported sample sizes
Thin external metric comparison
Weak signal on GSM8K
No explicit code-release statement found
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Scope & Claims
The paper (arXiv 2609.23065v2) recasts CoT faithfulness as *internal concept grounding*: does the CoT-conditioned pass engage the same sparse-autoencoder (SAE) concepts as the direct-prediction pass, and do those shared concepts (S∩) causally drive the answer? A single shared per-layer SAE encodes answer-position residuals of both passes (§3.3). Three correlational metrics — CC-SAE cosine, Jaccard, prediction-grounded recall (§3.4) — quantify overlap; the causal metric Δp (§3.5, Eq. 8–9) ablates S∩ from the prediction-pass residual and measures the drop in answer probability. Experiments cover five open LLMs (Llama-3.1-8B, Gemma-2-2B/9B, Qwen3-1.7B/8B) and four datasets (GSM8K, LogiQA, OpenbookQA, ARC-Easy), restricted to items where CoT and no-CoT predictions agree (fn. 2). Central claims: alignment is high but attributionally weak (lift 1.08, Table 16); Δp peaks mid-to-late in depth; Qwen3 faithfulness is distributed across layers while Llama-3.1-8B is localized (§5.3); scale reorganizes layer profiles; causally important shared concepts are often not verbalized (§5.5, App. H/J).
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 5 strengths & 5 weaknesses, analyzed in detail
- 4 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
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Frequently asked questions
What is the Δp metric for chain-of-thought faithfulness?
Δp measures how much a model's answer probability drops when the sparse-autoencoder features shared between a direct-prediction pass and a CoT-derived prediction pass are ablated from the residual stream. A large drop means the concepts engaged during the CoT causally drive the answer (grounded, faithful); a near-zero drop means the answer survives without them, suggesting the CoT may be post-hoc rationalization.
Do chain-of-thought explanations reflect what LLMs actually compute?
According to this paper, surface correspondence is not enough. Concept alignment between prediction and CoT passes is generally high across five open LLMs and four benchmarks, but the binarized association between alignment and the causal Δp metric shows only a 1.08 lift over chance, and many causally important shared concepts are never verbalized — so assessing faithfulness requires causal interventions, not just correspondence measures.
Which models and datasets were used in this study?
Five open-weight LLMs (Llama-3.1-8B, Gemma-2-2B, Gemma-2-9B, Qwen3-1.7B, Qwen3-8B) with publicly released residual-stream sparse autoencoders (Llama-Scope, Gemma-Scope, Qwen-Scope), evaluated on GSM8K, LogiQA, OpenbookQA, and ARC-Easy.
At which layers is chain-of-thought most faithful, according to this paper?
Causal faithfulness (Δp) generally peaks in mid-to-late layers rather than the final layers. The pattern is architecture-dependent: Qwen3 models show faithfulness distributed across most layers, while Llama-3.1-8B concentrates it in a few layers; scaling within a family reorganizes the layer-wise profile rather than uniformly raising it.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Extracted text is truncated: §5.3 details, Tables 4–6 and 11–16, and parts of Appendices C, F, and G are only partially visible; some layer-wise claims were assessed from captions and in-text numbers.
- All figures (Figs. 2–3, 5–7, 10, 15–21) are images not analyzable here; quantitative judgments rely on tables and prose.
- No explicit code-release statement was found in the retrieved text; codeAvailable=false reflects that, while models, SAE suites, and benchmarks are all public.
- Per-dataset instance counts and agreement-filter rates were not found; they may exist in omitted sections.
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