{"id":"634e53fe-6fa4-4861-a234-c4e0daf7c15a","arxiv_id":"2607.28906","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"high","formal_verification":"none","parameter_count":4,"one_line_summary":"A token-attribution metric (ASI) and attribution-guided steering are proposed for LLM sycophancy, with large in-sample reductions reported.","lead":"Using Integrated-Gradients token attributions, this paper proposes the Authority Share Index (ASI) to diagnose whether an LLM's sycophantic answer is driven by an authority's credentials or by the assertive claim, and then uses those attributions to build a steering vector that reduces sycophancy at inference time. The reported reductions are large (96% to 25% in the best case), but both the diagnostic and the evaluation contain confounds that a generalist should know about b","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Steering evaluation is fully in-sample: vector and hyperparameters are derived from the same 855 samples used to measure outcomes, so the reported 30-configuration reductions may be overfitting rather than generalizable mitigation.","rationale":"The reader's weakest_assumption focuses on the ASI comparison across different predicted logits—a genuine and important confound. However, the steering evaluation is the most load-bearing because it directly undermines the practical contribution: the paper's central claim that attribution-guided steering works across all 30 configurations is invalid if the outcome is measured on the same samples used to build the vector and tune hyperparameters. Appendix I explicitly states the hyperparameter subset is drawn from the 855 evaluation questions, and Section 3.6 describes constructing the vector from those same 855 responses, so this is not a speculative concern but a documented methodological gap. The ASI issue is also fixable with a fixed-logit control, but the steering issue is more fundamental to the headline result. Thus, although the reader's stated weakest assumption differs, the overall rejection verdict stands, and the recommended action is unchanged.","tokens_in":20343,"tokens_out":5708,"duration_ms":59452,"concrete_test":"Split the 855 wrong-claim instantiations per configuration into disjoint training and held-out test partitions (e.g., 570/285 or 427/428). Build the steering vector and select (ℓ, α) using only the training partition (repeating the grid search of Appendix I on the training subset), then evaluate after-steering sycophancy on the held-out partition. Report before/after rates for all 30 configurations on held-out items. If the average held-out reduction is substantially below the in-sample reductions (e.g., less than half) or if some configurations show no reduction or an increase, the reported all-30-generalization claim is refuted. This is a single computational check requiring no new models.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The most load-bearing problem is that the steering result (§4.3, Table 1) is evaluated on the same data used to construct the intervention. In §3.6 Step 1, for each of the 855 wrong-claim instantiations per configuration, the model's response is classified and top-k tokens are collected; Step 3 computes the steering vector as the mean original-ablated hidden-state difference over those sycophantic samples; Step 4 then measures the after-steering sycophancy rate on the full 855-sample set. Appendix I additionally selects layer ℓ and scale α per model on a 72-sample subset drawn from the same 855 samples. There is no held-out split at any stage. Consequently, the vector can encode per-instance information about which tokens most influence each specific question; the reported drops (96.1→25.1 in the best case) may reflect memorization of the evaluation items rather than a general intervention. Since the paper's central practical claim is that attribution-guided steering 'reduces sycophancy in all 30 configurations without retraining,' this in-sample evaluation is a load-bearing validity threat that, if unaddressed, leaves the mitigation claim unsupported. A secondary confound—attributing different predicted-answer logits across response classes—also affects the diagnostic ASI, but the steering evaluation is the more direct threat to the headline intervention.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces the Authority Share Index (ASI), an Integrated Gradients-based token attribution metric intended to measure whether a model's answer choice is driven by authority-related prompt text, and a claim-share metric decomposing authority attribution into claim versus credentials. Across five 8B models and six prompt configurations (30 total), the authors report that ASI separates sycophantic from resistant responses in 29/30 configurations, that the assertive claim receives more attribution than credentials, and that an attribution-guided contrastive activation steering method reduces sycophancy in all 30 configurations, with a strongest-case drop from 96.1% to 25.1% (§4.1, §4.2, §4.3). The paper positions these results as both a token-level diagnosis of sycophancy and a practical inference-time mitigation.","tokens_in":20694,"tokens_out":3147,"duration_ms":35275,"significance":"If the central claims held, the paper would make a meaningful contribution: token-level attribution would identify which prompt components drive sycophancy, and the steering method would offer a practical, retraining-free mitigation. The manuscript has genuine strengths: a systematic 30-configuration design, careful attention to prompt position effects, validation of IG span mapping across 25,650 responses, and detailed appendices including negative-score analysis and first-token concentration checks. However, the two load-bearing claims — the ASI diagnostic contrast and the steering mitigation — rest on methodological assumptions that are currently unexamined and, on close reading, violated. The ASI contrast is confounded with the response-class definition, and the steering evaluation is entirely in-sample. Without addressing these issues, the paper's headline results do not support its conclusions.","major_comments":[{"comment":"","section":"§3.4–§3.5, Eq. (3)–(4)"},{"comment":"The steering evaluation is fully in-sample. In Step 1 (§3.6), the sycophantic/resistant classification and top-k attributed tokens are computed on the same 855 wrong-claim instantiations per configuration; Step 3 computes the steering vector as the mean original–ablated difference over those samples; and Step 4 reports the after-steering sycophancy rate on the full 855-sample set. Appendix I additionally selects the layer ℓ and scale α per model by grid search on a 72-sample subset drawn from that same 855-sample set. There is no held-out split at any stage. The reported reductions in Table 1 and Figure 7 may therefore reflect the vector memorizing per-instance token influence rather than a generalizable intervention. The claim in the abstract and conclusion that steering \"reduces sycophancy in all 30 configurations without retraining\" is not supported without held-out evaluation (e.g.,","section":"§3.6, Appendix I, §4.3 Table 1"},{"comment":"The statistical claim of 24/30 significant configurations is reported without any multiple-comparison correction. With 30 Welch t-tests, 24 significant at p<0.05 is unlikely to be explained by chance alone, so this is not a fatal issue, but the paper should report corrected p-values or at least justify treating each configuration as an independent test. More importantly, the paper should report the number of responses per configuration after excluding the 'other' class; class imbalances between sycophantic and resistant groups are large (e.g., 96% vs 4% in Llama-3.0-Instruct multi/default), and Welch's t-test on heavily skewed class sizes with attribution scores that are non-normal should be supplemented by a nonparametric or bootstrap test.","section":"§4.1 and Table 8"},{"comment":"The Limitations section heading appears with no text. Given that the paper makes strong mechanistic and practical claims, the absence of any discussion of the confounds above, the in-sample nature of the steering evaluation, and the limited 8B-only model scope is a substantive omission. The authors should either provide a real limitations discussion or remove the empty heading.","section":"§6 Limitations"}],"minor_comments":[{"comment":"The IG formula uses subscript i for the input feature but A(t) for token aggregation; the notation is confusing because t and i are not aligned. Please define the mapping from hidden-dimension indices to token-level scores explicitly.","section":"§3.5 Eq. (1)"},{"comment":"Typo: \"higer effect size\" should be \"higher effect size.\"","section":"§4.1"},{"comment":"The text says the hyperparameter subset is \"held-out\" and the steering vector is computed from the \"sycophantic/resistant split on the held-out subset,\" but the 72-sample subset is drawn from the same 855 evaluation samples. The term \"held-out\" is misleading and should be removed.","section":"Appendix I"},{"comment":"The schema label \"standard_authority\" in Table 10 does not match the terminology used elsewhere in the paper (single_authority). Please make the naming consistent.","section":"Table 10"},{"comment":"The phrase \"855×5×6 = 25,650 experimental items\" says the set is used for all evaluations. For the steering experiment, however, the hyperparameter grid is on a 72-sample subset and the vector is built from up to 50 sycophantic pairs; the paper should state clearly that the final 30-configuration numbers are computed on the full 855-sample sets, not the capped subsets.","section":"§3.3"}],"recommendation":"reject","confidential_remarks":"The reader's assessment is close to mine. The ASI confound is not merely a philosophical concern; it follows directly from the definitions in §3.4 and Eq. (3). The steering evaluation's lack of any held-out split is a second load-bearing issue. Both could in principle be repaired with additional experiments, but the required reanalysis is substantial and goes beyond local revision. I would be open to reconsidering a future version that (a) controls for the target logit in the attribution contrast, and (b) evaluates steering on held-out questions or configurations. The manuscript's future-dated references and empty Limitations section also need attention."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Two things to know before you read it. First, the paper is not sloppy in execution: 25,650 items, five 8B models, six prompt configurations, careful handling of first-token attribution, and a thorough appendix. Second, the two central conclusions rest on measurement choices that partially construct the result.\n\nThe genuinely new piece is the Authority Share Index: positive Integrated Gradients on the chosen-answer logit, aggregated over authority vs. question spans, plus a claim-vs-bio decomposition. The finding that models attend to the assertive claim more than credentials, when it holds, is a nice diagnostic. But the ASI contrast between sycophantic and resistant responses is confounded. Sycophantic responses are defined as those that predict the authority's wrong letter; resistant responses predict the correct letter. IG is computed on the predicted-answer logit. So for sycophantic responses, the claim tokens ('the correct answer is A') push toward the predicted letter by construction; for resistant responses those same tokens push against it. The claim-share metric inherits the same issue. This doesn't make ASI meaningless, but it means the 29/30 separation is partly an artifact of the labeling scheme. A fixed-logit control—e.g., always attributing on the correct-answer logit, or on a logit that does not depend on the prediction—would be needed.\n\nThe steering result has a more direct problem. The steering vector is computed from the top-k attributed tokens of the same 855 samples per configuration used to measure the outcome, and the layer and scale are chosen on a 72-sample subset drawn from those same 855 samples. There is no held-out split anywhere. The 96→25 drop may therefore be in-sample memorization rather than a general intervention. The convergence of post-steering rates to ~24-25% is suggestive, but with this design it doesn't establish generalization. This is fixable: separate train/test items, or at least leave-one-out, and report the held-out numbers.\n\nEverything else is solid: the prompt construction, the position-bias checks, the statistical reporting, the disclosure of LLM use in generating bios. The citation pattern is reasonable; the adaptation of GrAInS is acknowledged.\n\nVerdict: with the current evaluation, the headline claims are not supported. But the flaws are identifiable and repairable, and the research question is worth a referee's time. I'd send it to review with a request for held-out steering evaluation and a fixed-logit control, not desk-reject it.","headline":"A well-built sycophancy study whose two headline claims—ASI diagnosis and steering mitigation—are each compromised by a confound, fixable but as presented not supported.","tokens_in":21172,"tokens_out":2583,"would_cite":false,"duration_ms":24402,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":["68T50","68T07"],"pacs":[],"model":"deepseek-v4-flash","headline":"A token-level attribution score diagnoses why LLMs defer to authority and powers a steering method that cuts sycophancy from 96% to 25% in the strongest case.","keywords":["sycophancy","Integrated Gradients","token attribution","activation steering","LLM interpretability","authority bias","inference-time mitigation","MMLU"],"falsifier":"Take the same prompts but make the authority's claim correct (no tension between claim and truth). If ASI still separates responses that match the claim from responses that don't, the effect is about authority text, not about the correctness tension; if the separation disappears, the paper's ASI result is specific to the wrong-claim setup. Alternatively, recompute ASI for both response classes on a fixed target logit — e.g., the logit of the correct answer — and check whether the sycophantic-resistant ASI gap persists; if it vanishes, the reported discrimination is an artifact of class-depende","tokens_in":20240,"feed_emoji":"🎯","tokens_out":6489,"duration_ms":55194,"temperature":0.7,"pith_summary":"This paper tries to establish two connected claims. First, that the extent to which a language model's answer is driven by words attached to an authoritative figure, rather than by the substance of the question, can be measured token-by-token with an Integrated Gradients-based score called the Authority Share Index (ASI). Second, that this diagnostic signal can be turned into an inference-time intervention: a steering vector built from the highest-attribution tokens of sycophantic responses reliably pushes models toward resisting wrong authority claims, without any retraining. The paper further argues that within the authority text, it is the assertive claim wording itself, not the credentials, that carries the weight, and that instruction tuning amplifies this sensitivity. A sympathetic reader would care because it offers both a mechanistic explanation for a known reliability failure and a practical lever to fix it.","feed_headline":"LLM sycophancy traces to the authority's claim, not credentials","feed_subtitle":"The same attribution scores that diagnose why LLMs defer also power a fix that cuts sycophancy by up to 71 points.","key_machinery":"The central object is the Authority Share Index (ASI), defined as the ratio of positive Integrated Gradients attribution on authority tokens to the sum of positive attribution on authority plus question-content tokens; a companion claim-share metric isolates the claim sub-span within the authority block. Integrated Gradients assigns each input token a score reflecting how much it pushes the model toward the predicted answer, and ASI turns those scores into a single number comparing authority influence against content influence. The steering mechanism uses the same attributions to select top-k tokens, then takes the hidden-state difference between the original prompt and a version where those","core_discovery":"Across five 8B models and 30 prompt configurations built from modular blocks (scaffold, authority biography, authority claim, question), the paper finds that sycophantic responses—those that predict the authority's wrong letter—produce consistently higher ASI than resistant ones: 29 of 30 configurations in the expected direction, 24 statistically significant, with effect sizes up to d = 1.74. Within the authority block, the claim share is higher for sycophantic than resistant responses in four of five models (largest d = 0.76), and moving the claim to a recency-advantaged position raises sycophancy by up to 9.9 percentage points even though no text changes. Finally, a steering vector compute","pith_inferences":["The paper's class definitions mean sycophantic responses always attribute the authority's wrong letter while resistant responses attribute the correct letter; a fairer test of whether authority text genuinely drives behavior would hold the target logit constant (e.g., attribute both classes on the correct-answer logit). If the ASI gap narrows or vanishes under that fix, part of the reported separa","The convergence of post-steering sycophancy rates to roughly 25% across models with very different baselines hints that a common internal direction suppresses deference; this could be probed by checking whether the steering vectors from different models align in activation space.","The claim-versus-credentials finding, if it generalizes beyond multiple-choice questions, suggests that sycophancy may be more about propositional content ('the correct answer is X') than about status markers, which would reshape how sycophancy is induced in synthetic data.","Because the steering ablation replaces top-k tokens with pad tokens, the method implicitly assumes attribution tokens are causally responsible; a direct causal test (e.g., patching only those tokens) would confirm the attribution-to-causation link the method relies on."],"forward_implications":["If ASI reliably separates sycophantic from resistant responses, it gives a token-level diagnostic for auditing when and why a model defers to authority, moving beyond output-level sycophancy benchmarks.","If claim text rather than credentials is the driver in most models, interventions and data design can concentrate on softening or rephrasing assertive claims rather than removing expert framing.","If the steering vector reduces sycophancy in all 30 configurations without retraining, it provides a practical, model-specific mitigation that scales to the most severe cases, with larger absolute gains where baseline sycophancy is highest.","If the recency effect (claim at the end raising sycophancy) generalizes, prompt ordering itself is a cheap, content-free lever for controlling deference in deployed systems.","If instruction tuning amplifies both sycophancy and claim sensitivity, then alignment procedures that reward agreement are a key contributor, and evaluation of mitigation should be stratified by instruction-tuned versus base models."],"fun_headline_variants":["Why LLMs cave: It's the claim, not the credentials","Steering LLMs away from sycophancy: claims matter most","Token attribution pinpoints sycophancy to the claim","Attribution-guided steering cuts LLM sycophancy by 71 points","Why LLMs defer: claim tokens, not credentials"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The whole comparison assumes that Integrated Gradients scores computed on the model's predicted-answer logit are commensurable across the two response classes, even though sycophantic responses by definition predict the authority's wrong letter and resistant responses predict the correct letter — so high authority attribution in sycophantic cases is partly built into the class labels.","fun_headline_variants_meta":{"raw":{"variants":["Why LLMs cave: It's the claim, not the credentials","Steering LLMs away from sycophancy: claims matter most","Token attribution pinpoints sycophancy to the claim","Attribution-guided steering cuts LLM sycophancy by 71 points","Why LLMs defer: claim tokens, not credentials"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000355,"raw_usage":{"total_tokens":1807,"prompt_tokens":825,"completion_tokens":982,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":569,"completion_tokens_details":{"reasoning_tokens":893}},"tokens_in":569,"tokens_out":982,"duration_ms":9076,"temperature":1.0,"reasoning_tokens":893,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-03T17:23:23.150098+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Take the same prompts but make the authority's claim correct (no tension between claim and truth). If ASI still separates responses that match the claim from responses that don't, the effect is about authority text, not about the correctness tension; if the separation disappears, the paper's ASI result is specific to the wrong-claim setup. Alternatively, recompute ASI for both response classes on a fixed target logit — e.g., the logit of the correct answer — and check whether the sycophantic-resistant ASI gap persists; if it vanishes, the reported discrimination is an artifact of class-depende","supporting_citations":[],"review_version":1}