REVIEW 4 major objections 5 minor 3 cited by
Wired for Overconfidence: A Mechanistic Perspective on Inflated Verbalized Confidence in LLMs
T0 review · 4 major / 5 minor · reviewed 2026-08-02 · deepseek-v4-flash
Pith's one-line read Verbalized overconfidence in LLMs is produced by a compact, identifiable circuit of roughly ten components, and steering those components at inference time sharply improves calibration.
desk verdict A plausible circuit-level story for inflated verbalized confidence, but the calibration gains are not yet separated from a global confidence shift; worth refereeing. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
Target-Set Logit Difference (TSLD): the mean logit gap between a fixed set of high-confidence tokens (70, 75, 80, 85, 90, 99) and low-confidence tokens (0, 10, 15, 20, 25, 30) at the final prompt position, used as a differentiable internal proxy for verbalized confidence. Paired with a Truth-Injection Counterfactual Design (clean wrong answer versus corrupted ground-truth answer), TSLD differences pick the records where truth suppresses overconfidence; gradient-based attribution patching then scores every computational-graph edge for its causal influence on TSLD. The resulting Confidence Mover Circuit is validated by faithfulness/completeness tests and by single and incremental ablations, an
What would settle it
Run the same steering intervention on a randomly selected set of ten components, or subtract a constant from all confidence logits, on PopQA and compare ECE; if either matches the reported 97% reduction, the specific-circuit explanation is falsified.
Extended reading notes
Core claim
On the paper's own terms, the discovery is that inflated verbalized confidence is not a diffuse output-level artifact but the product of a compact, conserved 'Confidence Mover Circuit': a small set of MLP blocks and attention heads, mostly in middle-to-late layers, that writes a high-confidence bias at the final token position during self-evaluation. The authors validate this causally—faithfulness and completeness tests show the circuit is both sufficient and necessary—and they show that intervening on just the top ten components at inference time, by mean ablation or activation steering, recalibrates verbalized confidence dramatically, with expected calibration error reduced by up to 97% in
Load-bearing premise
The load-bearing premise is that the measured calibration gains come from removing a specific confidence-inflation circuit, not simply from any intervention that lowers the model's confidence scores.
Editorial extensions
If this is right
- Verbalized overconfidence is a stable, model-internal mechanism rather than a benchmark artifact: the same circuit components recur across three different factual QA datasets in each model.
- Because the circuit is compact (roughly ten components), recalibration is cheap and can run at inference time without per-sample counterfactuals, using only a precomputed reference direction.
- The circuit is causally necessary: removing its top ~3,000 edges eliminates 74–79% of the confidence-inflation signal, ruling out diffuse backup pathways.
- Circuit-level intervention is a viable complement to post-hoc calibration, achieving 78–97% ECE reductions on open-domain QA and meaningful, if smaller, gains on MMLU.
- Activation steering provides a tunable, dose-responsive recalibration knob with a stable optimal strength across most settings.
Reading between the lines
- If the conserved circuit is a general feature of instruction-tuned models, the same attribution pipeline should recover overlapping components in other model families; that is a direct, inexpensive test.
- The paper's own circuit-versus-factuality comparison suggests a testable extension: models whose confidence circuit overlaps more with factual-retrieval circuits should be better calibrated; comparing checkpoints along the post-training trajectory would test whether instruction tuning installs the overconfidence prior.
- The intervention's practical value under distribution shift is untested; evaluating the precomputed steering direction on out-of-distribution prompts would reveal whether the overconfidence direction is stable or prompt-dependent.
- Combining circuit steering with existing post-hoc scaling may yield additive gains if they correct different error components, but this is not examined in the paper.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes a mechanistic account of inflated verbalized confidence in instruction-tuned LLMs. It introduces a differentiable proxy, the Target-Set Logit Difference (TSLD), defined on the mean logit gap between high- and low-confidence token sets at the final token position (Eq. 1). Using a truth-injection counterfactual design, the authors stratify incorrectly answered records into three buckets, using Bucket 1 (ΔTSLD ≤ −τ) as the discovery subset. They apply Edge Attribution Patching with Integrated Gradients (EAP-IG) with TSLD as the objective (Eq. 3), identify a compact set of mid-to-late-layer MLP blocks and attention heads they call the Confidence Mover Circuit (CMC), and validate it with faithfulness curves, completeness ablation (74–79% TSLD reduction), and single/incremental component ablations. They then intervene on the top-10 CMC components at inference time using mean ablation (Eq. 6) and activation steering (Eq. 7), reporting large ECE reductions on PopQA (up to 96.9%) and NQOpen (up to 82.6%), with smaller gains on MMLU. The paper claims verbalized overconfidence is driven by a stable, conserved internal circuit and that targeted intervention is a viable recalibration method.
Significance. If the central claim holds, the paper would be a substantive contribution: it localizes a complex, high-level behavioral failure (verbalized overconfidence) to a small set of internal components and demonstrates that intervention on those components improves calibration. The methodology is transparent and reproducible in design: the TSLD metric is clearly defined, the EAP-IG pipeline is standard, and the evaluation on full datasets with ECE/Brier is appropriate. The cross-dataset conservation of top components (≥80% overlap) is an interesting empirical observation. However, the applied claim of mechanism-specific recalibration currently lacks a critical control: the interventions are designed to lower confidence, and the paper does not compare against a trivial shift-only baseline or a random-component steering baseline. The MMLU results, where Brier score improves only marginally or even worsens under mean ablation, strongly suggest that the ECE gains may largely reflect a global downward nudge rather than removal of a specific circuit. The paper also has an internal circularity concern: the same TSLD metric is used for sample selection, attribution, and component validation, so t
major comments (4)
- [§4, Eqs. (6)–(8), Tables 3/5/6] The intervention results are not compared against a trivial shift-only baseline (e.g., subtracting a constant from all verbalized confidences) or a random-component/random-direction steering baseline. Given Figure 6, which shows mean confidence 66–90% across all six configurations while accuracy is 17–62%, any intervention that lowers verbalized confidence will reduce ECE. The MMLU results exhibit exactly the shift signature: on Llama/MMLU, mean ablation reduces ECE by only 3.1% and worsens Brier by −1.8% (Table 5), and steering improves Brier by only ~8% despite a 34–57% ECE improvement. A global shift would produce precisely this pattern. Without the shift-only baseline, the headline PopQA/NQOpen ECE gains (e.g., 0.570→0.018) cannot be attributed to the specific CMC components.
- [§2–§3, Eq. (1), Eq. (3), Table 4] The same TSLD metric is used for sample selection (Bucket 1 via ΔTSLD ≤ −τ), as the attribution objective in EAP-IG (Eq. 3), and as the validation metric for component ablation (Fig. 3b, Fig. 4). This creates a circularity: the discovery and validation are partly guaranteed by construction, because the circuit is selected to maximize TSLD changes and then shown to reduce TSLD. The only independent evidence is the full-dataset ECE/Brier evaluation, which is exactly where the shift-only baseline is missing. The authors should either provide a validation metric not based on TSLD (e.g., direct verbalized confidence changes on held-out records) or explicitly acknowledge and address this circularity.
- [§2, Table 4, §3.1] The Bucket 1 threshold τ is never stated, and the high/low confidence sets H and L in Eq. (1) are user-defined without sensitivity analysis. The discovery subset, attribution scores, and intervention reference vectors (Eqs. 6–7) all depend on these choices. The paper should report the value of τ and the size of Bucket 1, and provide a sensitivity analysis showing that the identified circuit and the intervention gains are stable to reasonable variations in τ and in the H/L token sets. Without this, the 'conserved' circuit claim is not yet robust to the main free parameters of the pipeline.
- [§3.2, Table 2, Fig. 3a] The completeness validation reports that ablating the top-3,000 edges removes 74–79% of baseline TSLD, but no comparison is made against ablating a random set of 3,000 edges or the lowest-attribution edges. Similarly, the faithfulness curves show that retaining top-k edges reaches 70% at k≈2,000–3,000, but a random-edge baseline would establish whether this is meaningfully better than chance. Such baselines are standard in circuit analysis and are needed to support the claim that the identified edges, rather than any large subgraph, are causally necessary and sufficient.
minor comments (5)
- [§2, Eq. (1)] The notation 'logits[−1,c 1]' is confusing: the subscript −1 is not explained in the main text. It later becomes clear from context that this is the final position, but the notation should be defined explicitly at first use.
- [§3.1, Eq. (3) and Appendix C] The definition of EAP-IG in Eq. (3) uses m integration steps, but the gradient is evaluated at x_k with k indexing from 0 to m−1, while Eq. (12) uses k from 1 to m. The indexing should be consistent, and the exact interpolation schedule should be stated.
- [Appendix D, Figure 6] Figure 6 labels the correct/incorrect distributions but does not show the mean confidence for each group, which would make the overconfidence phenomenon more direct. Also, the figure uses a cumulative histogram style that makes it hard to read the individual counts; a simpler histogram would be clearer.
- [§5, Discussion] The second discussion paragraph is well-calibrated and appropriately hedges the interpretation, but it could be moved to a Limitations subsection for visibility. The potential concern about v_conf estimation error (the second interpretation) is acknowledged but never tested, e.g., by comparing Per-sample versus mean-direction steering.
- [References] Several references have incomplete metadata: for instance, the Mallen et al. entry lists only 'arXiv preprint' without an arXiv identifier, and the Nanda 2023 entry is a URL-only citation. Please verify all entries, especially those with 2025/2026 dates.
Circularity Check
Partial circularity: CMC discovery and causal validation reuse the same TSLD metric; the independent full-dataset calibration evaluation limits the loop.
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self definitional
[Sec. 3.1–3.2 (Eqs. 1, 3, 5; Fig. 3b; Table 2)]
"LTSLD (Eq. 1) defines both the discovery subset (via ∆TSLD) and the attribution objective, aligning sample selection with the signal being attributed and ensuring the attribution specifically locates components responsible for the overconfidence suppression. ... The resulting TSLD change, averaged across Bucket 1 examples, quantifies the component’s causal contribution: a strongly negative value indicates active contribution, whereas a near-zero value indicates a relay node that does not independently drive the behavior."
The discovery subset (Bucket 1) is selected by ΔTSLD ≤ −τ, the attribution objective is L_TSLD, and the causal validation measures reduction in L_TSLD. Thus the ranked 'Confidence Mover Circuit' is, by construction, the set of components whose ablation most reduces TSLD on the same Bucket 1 examples used for ranking; confirming that these components reduce TSLD is a restatement of the selection criterion, not an independent test that they write the verbalized-confidence signal. The loop is partially broken by the reported TSLD–confidence correlation (r=0.815/0.734) and by the full-dataset ECE/Brier evaluation, which use actual verbalized confidence rather than TSLD.
full rationale
The central applied claim—that targeted intervention on the top-10 CMC components improves calibration—does not fully reduce to the paper's definitions because ECE and Brier are computed from the model's actual verbalized confidence over the full dataset, not from TSLD. That independent anchor prevents a score of 6 or higher. However, the circuit-discovery and validation chain is self-referential: the same TSLD metric defines the bucket, the attribution objective, and the completeness/ablation success criterion, so 'the circuit suppresses TSLD' is guaranteed by construction. The paper's causal language about 'inflated verbalized confidence' depends on the proxy validity of TSLD, and while the correlation with confidence and the calibration gains support that proxy, the absence of a shift-only or random-component intervention baseline weakens the mechanistic specificity of the calibration improvement. No load-bearing self-citation chain is present; the external references (EAP-IG, calibration metrics, prior uncertainty work) are independent and not from the same authors. Overall, the circularity is partial and confined to the metric-defined circuit identification step, with the downstream calibration evaluation providing real external evidence.
Assumptions & free parameters
free parameters (5)
- TSLD high/low confidence token sets H, L =
H={70,75,80,85,90,99}, L={0,10,15,20,25,30}
- Bucket threshold τ =
unstated
- Steering strength α =
0.5–0.6 optimum, per dataset
- EAP-IG integration steps m =
5
- Retained edge count k =
3,000
assumptions (5)
- domain assumption EAP-IG's linear interpolation path yields faithful attributions for this network and target metric at m=5 steps.
- domain assumption Verbalized confidence is computed at the final token position of Step 2 and is readable from the first-token logits of candidate integers via TSLD.
- domain assumption Replacing the model's wrong answer with the ground-truth string, keeping all other tokens identical, isolates the causal effect of answer correctness and stays on the natural data manifold.
- domain assumption Circuits discovered on Bucket 1 transfer to the full dataset and to held-out datasets (conservation claim).
- domain assumption Two instruction-tuned 3B models are representative of LLM verbalized overconfidence.
invented entities (1)
-
Confidence Mover Circuit (CMC)
independent evidence
Cite this review
Pith. "Pith review of Wired for Overconfidence: A Mechanistic Perspective on Inflated Verbalized Confidence in LLMs." pith.science (2026). https://pith.science/paper/ATJIGIG3
@misc{pith2026260401457,
author = {Pith},
title = {Pith review of: Wired for Overconfidence: A Mechanistic Perspective on Inflated Verbalized Confidence in LLMs},
year = {2026},
howpublished = {\url{https://pith.science/paper/ATJIGIG3}},
note = {Machine review of arXiv:2604.01457}
}
read the original abstract
Large language models are often not just wrong, but \emph{confidently wrong}: when they produce factually incorrect answers, they tend to verbalize overly high confidence rather than signal uncertainty. Such verbalized overconfidence can mislead users and weaken confidence scores as a reliable uncertainty signal, yet its internal mechanisms remain poorly understood. We present a circuit-level mechanistic analysis of this inflated verbalized confidence in LLMs, organized around three axes: capturing verbalized confidence as a differentiable internal signal, identifying the circuits that causally inflate it, and leveraging these insights for targeted inference-time recalibration. Across two instruction-tuned LLMs on three datasets, we find that a compact set of MLP blocks and attention heads, concentrated in middle-to-late layers, consistently writes the confidence-inflation signal at the final token position. We further show that targeted inference-time interventions on these circuits substantially improve calibration. Together, our results suggest that verbalized overconfidence in LLMs is driven by identifiable internal circuits and can be mitigated through targeted intervention.
Figures
Figures from the paper (6 more)
Forward citations
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Reference graph
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Reviewed August 2, 2026 · model on record in the stance chip above.
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