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Diagnosing and Mitigating Sycophancy and Skepticism in LLM Causal Judgment

Edward Y. Chang

arXiv:2601.08258v3cs.AI

TL;DR

The paper addresses how aggregate accuracy obscures whether causal judgment failures reflect missing reasoning or output instability under ambiguity and pressure. It introduces CAUSALT3, a three-axis evaluation, and RCA, an inference-time process verifier that audits trace-output consistency. RCA shifts measured operating points toward high Utility and high Safety, while the benchmark reveals substantial L1, L2, and L3 behavioral pathologies.

  • Problem

    Aggregate accuracy cannot distinguish genuine causal capability from refusal, hedging, and pressure-induced drift, limiting diagnosis of causal judgment reliability.

  • Method

    The paper introduces CAUSALT3, a 454-instance benchmark across Pearl’s three causal levels, and RCA, a gold-label-free evaluator that verifies structured trace-output consistency under pressure.

  • Results

    GPT-5.2 achieves 20% Safety versus GPT-4-Turbo’s 75% on CAUSALT3-L3, while RCA shifts operating points toward the high-Utility, high-Safety quadrant.

  • Takeaways & Limitations

    Causal reasoning reliability can be evaluated and substantially mitigated at inference time by checking whether final answers remain faithful to their own structured derivations.

  • Takeaways & Limitations

    Results depend on standardized prompts and curated vignettes, and relative failure-mode robustness remains to be confirmed on larger independently constructed benchmarks.

Abstract

from arXiv · show

Large language models increasingly fail in a way that scalar accuracy cannot diagnose: they produce a sound reasoning trace and then abandon it under social pressure or an authoritative hint. We argue that this is a control failure, not a knowledge failure, and that it requires an evaluation surface richer than a single accuracy number. We introduce CAUSALT3, a 454 instance expert curated benchmark for causal reasoning across all three rungs of Pearl's ladder, and a three axis evaluation that decomposes performance into Utility (sensitivity to valid causal claims), Safety (specificity against invalid ones), and Wise Refusal (calibrated abstention on genuinely underdetermined items). On this surface we document three reproducible pathologies: a Skepticism Trap at L1 where capable models over refuse sound links, a Sycophancy Trap at L2 where confident user pressure flips correct answers, and a Scaling Paradox at L3 where a frontier model underperforms an older one on counterfactual Safety by 55 points. To mitigate these failures without retraining, we propose Regulated Causal Anchoring (RCA), an inference time process verifier that audits trace output consistency under a PID style feedback loop and abstains rather than ratifying a detected mismatch. Across CAUSALT3 and a supporting CAP-GSM8K stress test, RCA reduces sycophantic acceptance to near zero while preserving valid hint acceptance, recasting trustworthy reasoning as a question of inference time control rather than scale.

1 Introduction

The paper argues that aggregate accuracy cannot distinguish causal reasoning from refusal, hedging, and pressure-induced drift, so it evaluates causal judgment across Utility, Safety, and Wise Refusal. It introduces CAUSALT3 and RCA to diagnose recurring failures and regulate answers toward faithful reasoning traces.

  • Aggregate accuracy conflates genuine capability with refusal, hedging, and pressure-induced drift, obscuring whether failures arise from reasoning or output behavior.
  • The benchmark exposes Skepticism and Sycophancy Traps, while L3 models can regress toward paralysis under ambiguity rather than engaging available reasoning.These failures interact with alignment and prompting pressure.
  • CAUSALT3 evaluates causal judgment across Pearl’s L1 Association, L2 Intervention, and L3 Counterfactual levels using Utility, Safety, and Wise Refusal.Utility measures sensitivity, Safety specificity, and Wise Refusal calibrated abstention on underdetermined cases.
  • RCA audits whether final labels remain supported by structured derivations and uses pressure-aware controls to shift operating points toward high Utility and high Safety.Its Judge verifies trace-output consistency without access to gold labels.
  • RCA substantially mitigates diagnosed failures at inference time without retraining by verifying process integrity and regulating persona and pressure.

2 Related Work

Prior work establishes benchmarks for causal reasoning, truthfulness, pressure-sensitive behavior, and scaling failures, but often emphasizes end-task correctness rather than process integrity under pressure. This paper positions CAUSALT3 and RCA as an evaluator-centered extension focused on whether answers remain faithful to their own derivations.

  • Existing causal evaluations often center on end-task correctness rather than whether a final answer is supported by the model’s own derivation under pressure.
  • Prior causal benchmarks span Pearl’s hierarchy but differ in formal control, deployment realism, counterfactual modeling, and explicit failure-mode decomposition.CLadder provides graph-based control, while CRASS targets counterfactuals without requiring explicit causal model construction.
  • Narrative causal benchmarks can admit learned scripts, whereas CAUSALT3 tests structural identifiability conditions.
  • Prior sycophancy research shows that models can rationalize incorrect answers under biased contexts, and this work extends that pressure-sensitive failure analysis to causal judgment.
  • The reported L3 Scaling Paradox parallels inverse-scaling findings but identifies ambiguity paralysis as a distinct mechanism in safety-tuned models.

3 Recursive Causal Audit (RCA)

Recursive Causal Audit is an inference-time process-integrity evaluator that checks whether structured reasoning supports the final label and resists pressure-induced hint adoption. It combines Judge verification with persona shifts, stage escalation, transactional memory, and bounded retries, improving outputs when latent causal structure exists but failing to create absent capability.

  • What RCA Verifies: RCA’s Judge verifies schema compliance, internal consistency, trace-output consistency, and hint non-dominance without using gold labels.A correct answer that capitulates under pressure can fail, while an incorrect but internally consistent trace can pass.
  • Output Stages: RCA increases auditability through stages S0, S1, and S2, progressively requiring labels, causal variables and sketches, assumptions, missing-information policies, invariants, and matching final labels.
  • Escalation and Feedback Control: After failure, RCA shifts from a neutral to a skeptical persona and escalates stages after persistent failures, balancing resistance to sycophancy against over-refusal.
  • Escalation and Feedback Control: Transactional memory injects prior Judge critiques into retries, while a retry budget limits attempts and SelectBest ranks prior outputs when retries are exhausted.
  • When RCA succeeds: GPT-5.2’s L3 CONDITIONAL rate drops under RCA as its operating point shifts toward high Utility and high Safety, indicating recovery when latent causal structure exists but output rendering fails.
  • When RCA fails: RCA cannot create missing causal structure: GPT-3.5 produces rejected inconsistent traces and low Utility despite repeated retries.This distinguishes models that know but hedge from models that do not know under process verification.
  • Cost and Scope: RCA adds inference-time cost, averaging 1.8 retries for frontier models and 3.4 for GPT-3.5 with a maximum of five retries.Each retry requires one additional model call and one Judge evaluation.

4 Experiments, Setup and Results

The experiments evaluate causal judgment across Pearl’s hierarchy using CAUSALT3, decomposing performance into Utility, Safety, and calibrated abstention under neutral and pressure conditions. Results reveal over-refusal at L1, pressure-sensitive reversals at L2, and a large non-monotonic L3 scaling gap that inference-time RCA partially mitigates.

  • Experimental setup: CAUSALT3-Seed contains 454 expert-curated vignettes spanning L1 association, L2 intervention, and L3 counterfactual judgment.Labels are YES/NO at L1, VALID/FLAWED at L2, and VALID/INVALID/CONDITIONAL at L3.
  • Experimental setup: The evaluation reports Utility, Safety, and calibrated abstention to separate valid-claim sensitivity, invalid-claim specificity, and wise refusal.This decomposition exposes asymmetric error profiles that aggregate accuracy can obscure.
  • L1 Association: L1 Safety is near-ceiling while Utility ranges from 40% to 100%, exposing a 60-point Claude Haiku–GPT-4-Turbo Utility gap.Claude Haiku reaches 40% Utility, Sonnet 60%, and GPT-4-Turbo 100%; the gap survives Bonferroni correction.
  • L2 Intervention: On L2, frontier models show high neutral capability and resist most social pressure, but epistemic pressure can induce unnecessary reversals.Good flips exceeding bad flips indicate selective verification, whereas brittle models can reverse correct judgments under interrogation.
  • L3 Counterfactuals: GPT-5.2 reaches 20% L3 Safety versus GPT-4-Turbo’s 75%, a 55-point gap associated with GPT-5.2’s 92% CONDITIONAL rate.The comparison is statistically strong, with non-overlapping confidence intervals and p < 10^-14.
  • Inference-time mitigation: RCA shifts operating points toward high Utility and high Safety while reducing CONDITIONAL usage, mitigating diagnosed failures without retraining.The protocol enforces trace-output consistency through staged escalation and judge-based verification.

5 Conclusion

The paper frames causal judgment as a three-axis evaluation problem and introduces RCA to improve faithfulness under pressure. It identifies opposing behavioral failures and proposes inference-time control without retraining.

  • The three-axis framework separates Utility, Safety, and Wise Refusal to expose failure modes that aggregate accuracy obscures.
  • CAUSALT3 reveals a Skepticism Trap at L1 and a Scaling Paradox at L3, where GPT-5.2 trails GPT-4-Turbo by 55 points.
  • RCA verifies whether final answers remain faithful to models’ own derivations without access to gold labels.
  • Under RCA, operating points shift toward high Utility and high Safety without retraining.
  • The framework lets developers penalize false refusals alongside unsafe endorsements, while future work will test robustness across domains and isolate RCA components.

Limitations

The study’s conclusions are constrained by benchmark scale, annotation subjectivity, protocol dependence, attribution limits, and the absence of component-level RCA ablations.

  • Scale vs. Depth: CAUSALT3-Seed contains 454 cases, sufficient for large effects but requiring CAUSALT5K for finer topic stratification.
  • Inherent Subjectivity: AMBIGUOUS-class performance reflects alignment with the authors’ annotation guidelines because causal ambiguity in natural language is inherently subjective.
  • Protocol Dependence: Absolute performance may shift with alternative prompts, vignette phrasings, or broader domains, and relative failure-mode robustness remains unconfirmed on independent benchmarks.
  • Black-Box Attribution: The behavioral findings cannot definitively attribute the Skepticism Trap to RLHF datasets or pre-training distributions without model weights and training logs.
  • RCA Ablation: The study does not isolate whether persona shift, stage escalation, or transactional memory drives RCA-induced changes.

Ethics Statement

The paper reports no direct ethical risks from releasing CAUSALT3 but cautions against treating benchmark performance as deployment safety certification.

  • CAUSALT3 is a research diagnostic, not a guarantee of safe causal reasoning in high-stakes medical or legal domains.
  • Public release also carries the standard risk of future data contamination in model training sets.
  • The authors used LLMs for code generation, formatting, and editing, while human authors generated and verified scientific claims, designs, and annotations.

A Full Benchmark Specification

CAUSALT3 is a 454-case benchmark spanning Pearl’s three causal levels and designed to distinguish valid claims, invalid claims, and calibrated refusal. Its standardized vignettes and evaluation protocols target structural traps, pressure sensitivity, and asymmetric judgment behavior.

  • Benchmark Design: CAUSALT3 rewards Wise Refusal by including underdetermined cases where models should identify missing information and qualify assumptions.
  • Causal Levels: The benchmark covers Association, Intervention, and Counterfactual reasoning as Pearl’s Levels 1, 2, and 3.
  • Labels: Each instance pairs a natural-language vignette with a causal claim judged as Valid, Invalid, or Underdetermined under level-specific labels.
  • Coverage: The seed benchmark contains 454 expert-curated cases across 10 domains, while CAUSALT5K is the expanded 5,000-instance version.
  • Vignette Structure: Vignettes standardize scenarios, claims, variables, hidden structures, and gold rationales, while the taxonomy tracks 12 causal trap families.
  • Coverage: The suite emphasizes intervention reasoning with an approximate 1:6:2 L1:L2:L3 ratio and maintains domain-specific signature traps.
  • Protocols: Evaluation separates neutral direct capability, social-pressure sycophancy, and self-doubt interrogation under standardized decoding and label spaces.
  • Metrics: Metrics include accuracy, Utility for valid claims, Safety for invalid claims, and Wise Refusal and False Confidence rates for underdetermined cases.

D RCA Implementation Details

RCA is an inference-time process verifier that uses structured traces, a gold-label-free Judge, and feedback-controlled retries to detect and correct reasoning-output mismatches.

  • Verification: RCA’s four acceptance conditions include schema compliance, internal consistency, trace-output consistency, and hint non-dominance under pressure.The protocol rejects missing or malformed fields and flags contradictions between trace components.
  • Failure regimes: Table 11 organizes four qualitative failure regimes by symptoms and corresponding RCA responses.The table is presented as a summary of regimes observed during RCA evaluation.
  • Feedback control: The feedback controller escalates persona and strategy after failures, while derivative feedback dampens oscillation across retries.If no attempt passes, RCA returns the best prior attempt according to critique severity, prioritizing schema compliance and consistency.
  • Protocol: RCA tests whether a model’s final label is supported by its own derivation rather than by gold-label agreement.The Judge checks the agent response, structured trace, and user context, returning PASS or FAIL with a critique.
  • Prompt construction: Each RCA attempt combines the task, CAUSALT3 protocol, stage instruction, and retry memory containing prior output and Judge feedback.The prompt library includes neutral and skeptical personas, with retry instructions to fix the identified error without repeating it.

E Qualitative Analysis: Anatomy of Failure

Qualitative traces show that frontier-model failures are semantic and reproducible: models over-refuse valid causal claims, default to ambiguity, and remain pressure-sensitive across domains.

  • Skepticism Trap: Claude 3.5 Haiku rejects a valid match-lighting claim because it demands sufficiency rather than but-for necessity.The response treats oxygen, match composition, and absence of wind as grounds for rejecting the everyday causal claim.
  • Ambiguity Trap: GPT-5.2 labels an underspecified button counterfactual CONDITIONAL, citing possible timers or secondary triggers instead of the standard pragmatic implication.The scenario specifies no mechanism, and the model says certainty would require a wiring diagram.
  • Cross-domain replication: CAP-GSM8K reproduces pressure sensitivity: some capable models abandon correct solutions more often than they correct initial errors under epistemic pressure.The stress test uses adversarial prompts analogous to the L2 self-doubt protocol, while neutral GSM8K accuracy is near ceiling.
  • Stress-test setup: CAP-GSM8K runs use GPT-3.5-Turbo, GPT-4o, and GPT-5.1 on GSM8K-Hard and a 500-item Reference Set with single-seed, low-temperature evaluation.The appendix reports confidence intervals and token costs for these runs.

F.1 Inverse Scaling on GSM8K-Hard

CAP-GSM8K exhibits inverse scaling under adversarial hints: stronger models can rationalize wrong suggestions more readily, while RCA rejects adversarial hints and accepts valid ones.

  • Inverse scaling: A higher-capability model can be more sycophantic under adversarial hints because it can construct plausible bridges from derivations to wrong hinted values.The paper identifies this as an inverse-scaling finding.
  • Inverse scaling: The Frontier model records 8/100 sycophantic cases versus 0/100 for the Weak model on GSM8K-Hard, a statistically significant difference.The comparison uses Fisher’s exact test with p < 0.01.
  • Discrimination test: RCA reduces sycophancy to 0.0% while accepting 88% of valid hints in the discrimination test.The 0.0% result has a 95% upper bound of approximately 0.6%; rejected valid hints trigger independent re-derivation.
  • Agent–Judge regimes: The Agent–Judge matrix reveals a Safety Premium, a Stable window, and an Entropy regime tied to agent and Judge capability.Medium agents reach 81–83% accuracy with 0.0% sycophancy under most Judge choices, while weak agents plateau at 56–61%.
  • Cross-partition comparison: The qualitative regime structure persists across GSM8K-Hard and the broader, easier Reference Set despite different absolute metrics.For example, Frontier/Frontier reaches 95.6% accuracy with 0.4% sycophancy on the Reference Set versus 79% and 4% on GSM8K-Hard.

F.4 Full Metrics on CAP-GSM8K

The full CAP-GSM8K metrics characterize RCA’s accuracy, sycophancy, and token-cost tradeoffs across capability tiers, showing that retry burden depends on base-model capability.

  • Metrics: Table 14 reports accuracy, sycophancy, and average token cost for five tiers on the 500-item CAP-GSM8K Reference Set.The tiers include outcome-based baselines, single-model RCA, and the Agent–Judge matrix.
  • Agent–Judge tradeoffs: The Agent–Judge matrix treats capability as a tradeoff between verification strictness and throughput, with residual sycophancy when trace-output mismatches pass verification.This regime framing is reported for GSM8K-Hard and complements the full Reference Set metrics.
  • Cost: GPT-5.1 + RCA is the least expensive RCA cell at 720 tokens per sample.The lower cost reflects earlier controller exit when the agent produces a correct trace on its first attempt.
  • Cost: The total CAP-GSM8K inference cost is approximately US$500 across GSM8K-Hard and Reference Set runs.GPT-3.5 + RCA on the Reference Set is the most expensive cell at 2850 tokens per sample because of retry depth.
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