Reasoning Consistency in Language Models

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Overview

The field studies whether language models preserve conclusions and sound reasoning when prompts are paraphrased, evidence changes, answers are resampled, or conversations continue. Evaluations use controlled reformulations and multi-turn tests, repeated-sample agreement, and checks of reasoning traces with formal solvers, causal interventions, or evidence-grounded verifiers across math, logic, coding, and medical tasks. Methods include self-consistency and uncertainty-based answer selection, consistency-aware training and rewards, and test-time verification, repair, or early-exit systems. A central tension is that consistency can signal stability without correctness or faithful reasoning: models may repeat the same error, abstain, or agree while relying on invalid reasoning, so reliability must be assessed alongside accuracy, faithfulness, and cost.

Latest papers

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123 papers24 papers in the last four weeks, up 60% on the four weeks before. 0.1% of all new papers.

Apr 1, 2026

CogRAG: Tackling Heterogeneous Cognitive Demands in RAG via Stratified Retrieval and Reasoning

Retrieval-Augmented Generation (RAG) frameworks typically process all queries through a one-size-fits-all pipeline, ignoring the heterogeneous cognitive demands of different tasks. This cognitive-blind approach causes two failure modes: cascading errors when low-level factual gaps trigger hallucinated reasoning, and reasoning-answer inconsistency in higher-order analytical tasks. We introduce CogRAG, a training-free, domain-agnostic framework that tackles these heterogeneous cognitive demands via stratified retrieval and reasoning. Inspired by Bloom's Taxonomy, CogRAG uses the predicted cognitive load of a query as a central control signal that coordinates two modules: Cognition-Adaptive Evidence Refinement supplements missing context via fact-centric or option-centric paths, and Cognition-Stratified Structured Reasoning replaces unconstrained chain-of-thought with cognition-aligned reasoning templates. We evaluate CogRAG on a demanding professional testbed, the Registered Dietitian qualification examination. CogRAG effectively reduces early-stage factual errors and eliminates reasoning-answer inconsistency, raising Qwen3-8B accuracy from 73.4% to 85.8% in single-choice mode and from 63.3% to 80.5% in scenario mode. These results highlight cognitive-stratified control as an effective, generalizable paradigm for reliable complex reasoning in large language models.

Xudong Wang, Zilong Wang, Kui Su +1

School of Computer and Computing Science, Hangzhou City University, Hangzhou, Zhejiang 310015, China · Institute of Digital Twin, Eastern Institute of Technology, Ningbo, Zhejiang 315200, China · Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, Zhejiang 314100, China

Structured Reasoning in Language ModelsAdaptive RAGRAG RobustnessReasoning Consistency in Language ModelsLarge Reasoning Models
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Apr 1, 2026

Grading the Unspoken: Evaluating Tacit Reasoning in Quantum Field Theory and String Theory with LLMs

Large language models have demonstrated impressive performance across many domains of mathematics and physics. One natural question is whether such models can support research in highly abstract theoretical fields such as quantum field theory and string theory. Evaluating this possibility faces an immediate challenge: correctness in these domains is layered, tacit, and fundamentally non-binary. Standard answer-matching metrics fail to capture whether intermediate conceptual steps are properly reconstructed or whether implicit structural constraints are respected. We construct a compact expert-curated dataset of twelve questions spanning core areas of quantum field theory and string theory, and introduce a five-level grading rubric separating statement correctness, key concept awareness, reasoning chain presence, tacit step reconstruction, and enrichment. Evaluating multiple contemporary LLMs, we observe near-ceiling performance on explicit derivations within stable conceptual frames, but systematic degradation when tasks require reconstruction of omitted reasoning steps or reorganization of representations under global consistency constraints. These failures are driven not only by missing intermediate steps, but by an instability in representation selection: models often fail to identify the correct conceptual framing required to resolve implicit tensions. We argue that highly abstract theoretical physics provides a uniquely sensitive lens on the epistemic limits of current evaluation paradigms.

Xingyang Yu, Yinghuan Zhang, Yufei Zhang +1

Department of physics, Virginia Tech · Independent Researcher · Department of Computer Science and Engineering, Michigan State University

Scientific Reasoning in Language ModelsLLM EvaluationImplicit Reasoning in Language ModelsReasoning Consistency in Language Models
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Mar 30, 2026

The Necessity of Setting Temperature in LLM-as-a-Judge

Large Language Models (LLMs) are increasingly used as automated judges, yet decoding temperature is often set by intuition rather than evidence. We run over one million large scale evaluations, multiple open-source models, prompting strategies, and judging paradigms to examine how temperature affects judge reliability. Low temperatures yield consistent outputs with few errors, especially in smaller models, while higher temperatures reduce consistency and increase errors. Yet consistency alone does not guarantee sound judgments: low temperatures can narrow the range of reasoning explored. We characterize judge behavior through four reasoning personas and find that temperature shifts their distributions in model-dependent ways, most visibly in ambiguous cases where competing answers are hard to distinguish. Repeated high-temperature sampling reveals this hidden uncertainty, and combining it with majority voting or using high temperature for reasoning and low temperature for the final decision, improves agreement on ambiguous cases without hurting clear ones, at the cost of extra inference. Our findings suggest a simple rule: keep temperature low for stable outputs, but explore more when the judgment is hard.

Lujun Li, Lama Sleem, Yangjie Xu +4

University of Luxembourg · ETH Zürich

LLM-as-a-JudgeLLM EvaluationLLM ReliabilityLanguage Model DecodingReasoning Consistency in Language Models
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Mar 27, 2026

Distributionally Robust Token Optimization in RLHF

Large Language Models (LLMs) tend to respond correctly to prompts that align well with the data they were trained and fine-tuned on. Yet, small shifts in wording, format, or language can trigger surprisingly large failures, especially on multi-step reasoning problems. To address this problem, we propose a Distributionally Robust Token Optimization (DRTO) approach, which combines token-level Reinforcement Learning from Human Feedback (RLHF) with Distributionally Robust Optimization (DRO). DRTO constructs f-divergence ambiguity sets over span-level actor losses, providing a principled way to emphasize difficult response segments during policy optimization. Empirically, DRTO enhances consistency under distribution shifts in multiple reasoning benchmarks among different tasks, achieving +4.4+4.4 percentage points on MATH-500 and +2.7+2.7 percentage points on LiveCodeBench over standard RTO.

Yeping Jin, Jiaming Hu, Ioannis Ch. Paschalidis

Department of System Engineering Boston University, Boston, MA 02215, USA · Department of Math & Statistics Boston University, Boston, MA 02215, USA

Language Model RobustnessReasoning Consistency in Language ModelsRL from Human FeedbackDistributionally Robust RLDistributionally Robust Optimization
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Mar 26, 2026

PICon: A Multi-Turn Interrogation Framework for Evaluating Persona Agent Consistency

Large language model (LLM)-based persona agents are rapidly being adopted as scalable proxies for human participants across diverse domains. Yet there is no systematic method for verifying whether a persona agent's responses remain free of contradictions and factual inaccuracies throughout an interaction. A principle from interrogation methodology offers a lens: no matter how elaborate a fabricated identity, systematic interrogation will expose its contradictions. We apply this principle to propose PICon, an evaluation framework that probes persona agents through logically chained multi-turn questioning. PICon evaluates consistency along three core dimensions: internal consistency (freedom from self-contradiction), external consistency (alignment with real-world facts), and retest consistency (stability under repetition). Evaluating seven groups of persona agents alongside 63 real human participants, we find that even systems previously reported as highly consistent fail to meet the human baseline across all three dimensions, revealing contradictions and evasive responses under chained questioning. This work provides both a conceptual foundation and a practical methodology for evaluating persona agents before trusting them as substitutes for human participants. We provide the source code and an interactive demo at: https://kaist-edlab.github.io/picon/

Minseo Kim, Sujeong Im, Junseong Choi +4

KAIST

Persona ConsistencyLLM Agent EvaluationReasoning Consistency in Language ModelsFactual Consistency EvaluationMulti-Turn Dialogue Evaluation
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Mar 26, 2026

Probing the Lack of Stable Internal Beliefs in LLMs

Persona-driven large language models (LLMs) require consistent behavioral tendencies across interactions to simulate human-like personality traits, such as persistence or reliability. However, current LLMs often lack stable internal representations that anchor their responses over extended dialogues. This work explores whether LLMs can maintain "implicit consistency", defined as persistent adherence to an unstated goal in multi-turn interactions. We designed a 20-question-style riddle game paradigm where an LLM is tasked with secretly selecting a target and responding to users' guesses with "yes/no" answers. Through evaluations, we find that LLMs struggle to preserve latent consistency: their implicit "goals" shift across turns unless explicitly provided their selected target in context. These findings highlight critical limitations in the building of persona-driven LLMs and underscore the need for mechanisms that anchor implicit goals over time, which is a key to realistic personality modeling in interactive applications such as dialogue systems.

Yifan Luo, Kangping Xu, Yanzhen Lu +2

IIIS, Tsinghua University · Shanghai Qizhi Institute

Personality Modeling in Language ModelsReasoning Consistency in Language ModelsLLM Reliability
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Mar 25, 2026

Multi-Agent Reasoning with Consistency Verification Improves Uncertainty Calibration in Medical MCQA

Miscalibrated confidence scores are a practical obstacle to deploying AI in clinical settings. A model that is always overconfident offers no useful signal for deferral. We present a multi-agent framework that combines domain-specific specialist agents with Two-Phase Verification (Wu et al., 2024) and S-Score Weighted Fusion to improve both calibration and discrimination in medical multiple-choice question answering. Four specialist agents (respiratory, cardiology, neurology, gastroenterology) generate independent diagnoses using Qwen2.5-7B-Instruct. Each diagnosis undergoes a two-phase self-verification process that measures internal consistency and produces a Specialist Confidence Score (S-score). The S-scores drive a weighted fusion strategy that selects the final answer and calibrates the reported confidence. We evaluate on high-disagreement subsets of MedQA-USMLE and MedMCQA (100 and 250 questions). All results are specific to this filtered regime. On MedQA-250, the full system achieves ECE = 0.091 (74.4% reduction over the single-specialist baseline) and AUROC = 0.630 (+0.056) at 59.2% accuracy. Calibration gains of 49-74% hold across all four settings. Ablation analysis reveals that Two-Phase Verification drives ECE reduction while multi-agent reasoning drives AUROC improvement, suggesting that consistency checking and ensemble aggregation address different failure modes of LLM uncertainty. Whether the resulting confidence signal is sufficient to support clinical deferral decisions in practice remains a direction for future investigation.

John Ray B. Martinez

Department of Data Science and Analytics, Harrisburg University of Science and Technology

Uncertainty CalibrationMedical QAMulti-Agent ReasoningReasoning Consistency in Language ModelsClinical Reasoning
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Mar 25, 2026

ThermoQA: A Three-Tier Benchmark for Evaluating Thermodynamic Reasoning in Large Language Models

We present ThermoQA, a benchmark of 293 open-ended engineering thermodynamics problems in three tiers: property lookups (110 Q), component analysis (101 Q), and full cycle analysis (82 Q). Ground truth is computed programmatically from CoolProp 7.2.0, covering water, R-134a, and variable-cp air. Six frontier LLMs are evaluated across three independent runs each. The composite leaderboard is led by Claude Opus 4.6 (94.1%), GPT-5.4 (93.1%), and Gemini 3.1 Pro (92.5%). Cross-tier degradation ranges from 2.8 pp (Opus) to 32.5 pp (MiniMax), confirming that property memorization does not imply thermodynamic reasoning. Supercritical water, R-134a refrigerant, and combined-cycle gas turbine analysis serve as natural discriminators with 40-60 pp performance spreads. Multi-run sigma ranges from +/-0.1% to +/-2.5%, quantifying reasoning consistency as a distinct evaluation axis. Dataset and code are open-source at https://huggingface.co/datasets/olivenet/thermoqa

Kemal Düzkar

Olivenet Kyrenia, Cyprus

LLM EvaluationScientific Reasoning in Language ModelsNumerical Reasoning in Language ModelsReasoning Consistency in Language Models
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Mar 24, 2026

Explanation Generation for Contradiction Reconciliation with LLMs

Existing NLP work commonly treats contradictions as errors to be resolved by choosing which statements to accept or discard. Yet a key aspect of human reasoning in social and professional settings is the ability to hypothesize explanations that reconcile contradictions. For example, "Cassie hates coffee" and "She buys coffee everyday" appear contradictory, but are in fact compatible if Cassie was assigned the daily chore of buying coffee for all her coworkers. Despite advances in the reasoning capabilities of large language models (LLMs), their ability to hypothesize such explanations remains largely unexplored. To address this gap, we introduce the task of reconciliatory explanation generation, where models must generate explanations that effectively render contradictory statements compatible. We propose a novel method of reusing natural language inference (NLI) datasets, and introduce quality metrics that enable scalable automatic evaluation. Experiments with 13 LLMs in thinking and non-thinking modes reveal models' limited success in this task, with longer reasoning chains negatively associated with explanation success. As a proof of concept, we further adapt our introduced task and metrics for reinforcement-learning training, finding that trained models exhibit improved accuracy in non-thinking mode on two narrative reasoning benchmarks (True Detective and MuSR). Our findings highlight reconciliatory explanation generation as a challenge in LLM reasoning and motivate further work to investigate its potential for improving model capabilities.

Jason Chan, Zhixue Zhao, Robert Gaizauskas

University of Sheffield, UK

LLM ReasoningReasoning Consistency in Language Models
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Mar 23, 2026

Reasoning or Rhetoric? An Empirical Analysis of Moral Reasoning Explanations in Large Language Models

Do large language models reason morally, or do they merely sound like they do? We investigate whether LLM responses to moral dilemmas exhibit genuine developmental progression through Kohlberg's stages of moral development, or whether alignment training instead produces reasoning-like outputs that superficially resemble mature moral judgment without the underlying developmental trajectory. Using an LLM-as-judge scoring pipeline validated across three judge models, we classify more than 600 responses from 13 LLMs spanning a range of architectures, parameter scales, and training regimes across six classical moral dilemmas, and conduct ten complementary analyses to characterize the nature and internal coherence of the resulting patterns. Our results reveal a striking inversion: responses overwhelmingly correspond to post-conventional reasoning (Stages 5-6) regardless of model size, architecture, or prompting strategy, the effective inverse of human developmental norms, where Stage 4 dominates. Most strikingly, a subset of models exhibit moral decoupling: systematic inconsistency between stated moral justification and action choice, a form of logical incoherence that persists across scale and prompting strategy and represents a direct reasoning consistency failure independent of rhetorical sophistication. Model scale carries a statistically significant but practically small effect; training type has no significant independent main effect; and models exhibit near-robotic cross-dilemma consistency producing logically indistinguishable responses across semantically distinct moral problems. We posit that these patterns constitute evidence for moral ventriloquism: the acquisition, through alignment training, of the rhetorical conventions of mature moral reasoning without the underlying developmental trajectory those conventions are meant to represent.

Aryan Kasat, Smriti Singh, Aman Chadha +1

UT Austin · Google GenAI

Moral Reasoning in Language ModelsReasoning Consistency in Language ModelsLLM Alignment
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Mar 20, 2026

Deep reflective reasoning in interdependence constrained structured data extraction from clinical notes for digital health

Extracting structured information from clinical notes requires navigating a dense web of interdependent variables where the value of one attribute logically constrains others. Existing Large Language Model (LLM)-based extraction pipelines often struggle to capture these dependencies, leading to clinically inconsistent outputs. We propose deep reflective reasoning, a large language model agent framework that iteratively self-critiques and revises structured outputs by checking consistency among variables, the input text, and retrieved domain knowledge, stopping when outputs converge. We extensively evaluate the proposed method in three diverse oncology applications: (1) On colorectal cancer synoptic reporting from gross descriptions (n=217), reflective reasoning improved average F1 across eight categorical synoptic variables from 0.828 to 0.911 and increased mean correct rate across four numeric variables from 0.806 to 0.895; (2) On Ewing sarcoma CD99 immunostaining pattern identification (n=200), the accuracy improved from 0.870 to 0.927; (3) On lung cancer tumor staging (n=100), tumor stage accuracy improved from 0.680 to 0.833 (pT: 0.842 -> 0.884; pN: 0.885 -> 0.948). The results demonstrate that deep reflective reasoning can systematically improve the reliability of LLM-based structured data extraction under interdependence constraints, enabling more consistent machine-operable clinical datasets and facilitating knowledge discovery with machine learning and data science towards digital health.

Jingwei Huang, Kuroush Nezafati, Zhikai Chi +9

Quantitative Biomedical Research Center, Department of Health Data Science and Biostatistics, Peter O'Donnell School of Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390 · Department of Pathology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390 · Department of Pediatrics, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390

Clinical Information ExtractionReasoning Consistency in Language ModelsLLM Self-RefinementOncology
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Mar 19, 2026

Correct Chains, Wrong Answers: Dissociating Reasoning from Output in LLM Logic

LLMs can execute every step of chain-of-thought reasoning correctly and still produce wrong final answers. We introduce the Novel Operator Test, a benchmark that separates operator logic from operator name, enabling rigorous distinction between genuine reasoning and pattern retrieval. By evaluating Boolean operators under unfamiliar names across depths 1-10 on five models (up to 8,100 problems each), we demonstrate a reasoning-output dissociation that existing benchmarks cannot detect. At Claude Sonnet 4's depth 7, all 31 errors have verifiably correct reasoning yet wrong declared answers; 17/19 errors in mixed-operator chains exhibit the same pattern. The benchmark reveals two failure types: strategy failures at depth 2, where models attempt terse retrieval (+62pp from scaffolding), and content failures at depth 7, where models reason fully but err systematically (+8-30pp, 0/300 errors post-intervention). A Trojan operator (XOR's truth table under a novel name) confirms name alone does not gate reasoning (p >= 0.49), while Llama's novelty gap widens to 28pp at depth 8-9 with the Trojan at 92-100%, isolating genuine difficulty with novel logic from name unfamiliarity.

Abinav Rao, Sujan Rachuri, Nikhil Vemuri

LLM ReasoningLLM EvaluationReasoning Consistency in Language ModelsStructured CoT Reasoning
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Mar 19, 2026

Box Maze: A Process-Control Architecture for Reliable LLM Reasoning

Large language models (LLMs) demonstrate strong generative capabilities but remain vulnerable to hallucination and unreliable reasoning under adversarial prompting. Existing safety approaches -- such as reinforcement learning from human feedback (RLHF) and output filtering -- primarily operate at the behavioral level and may lack explicit architectural mechanisms for enforcing reasoning process integrity. This paper proposes the Box Maze framework, a conceptual process-control architecture that decomposes LLM reasoning into three explicit layers: memory grounding, structured inference, and boundary enforcement. We introduce preliminary simulation-based evaluation involving progressive boundary erosion scenarios across multiple heterogeneous LLM systems (DeepSeek-V3, Doubao, Qwen). Results from n=50 adversarial scenarios suggest that explicit cognitive control layers may improve consistency in boundary maintenance, with architectural constraints reducing boundary failure rates from approximately 40% (baseline RLHF) to below 1% under adversarial conditions. While current validation is simulation-based, these preliminary results indicate that process-level control may offer a promising direction for improving reliability in large language model reasoning.

Zou Qiang

1Independent Researcher

LLM SafetyReasoning Consistency in Language ModelsAdversarial Robustness of Language Models
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Mar 17, 2026

Breaking the Chain: A Causal Analysis of LLM Faithfulness to Intermediate Structures

In schema-guided reasoning (SGR) pipelines, LLMs produce explicit intermediate structures -- rubrics, checklists, or verification queries -- before committing to a final decision. SGR is increasingly adopted because it promises controllability: practitioners expect to inspect, edit, and override these structures to steer the outcome. But does the promise hold? We introduce a causal evaluation protocol to measure it: by selecting tasks where a deterministic function maps intermediate structures to decisions, every controlled edit implies a unique correct output. Across 12 models and 4 benchmarks, models appear self-consistent with their own intermediate structures but fail to update predictions after intervention -- revealing that apparent faithfulness is fragile once the intermediate structure changes. When derivation of the final decision from the structure is delegated to an external tool, this fragility largely disappears; stronger prompting yields only limited improvements, while preference optimization substantially improves intervention faithfulness. Overall, intermediate structures in schema-guided pipelines function as influential context rather than stable causal mediators.

Oleg Somov, Mikhail Chaichuk, Gleb Ershov +4

AIRI · MIPT · HSE University +2

LLM ReasoningCausal Interventions in Language ModelsReasoning Consistency in Language Models
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Mar 16, 2026

Amplification Effects in Test-Time Reinforcement Learning: Safety and Reasoning Vulnerabilities

Test-time training (TTT) has recently emerged as a promising method to improve the reasoning abilities of large language models (LLMs), in which the model directly learns from test data without access to labels. However, this reliance on test data also makes TTT methods vulnerable to harmful prompt injections. In this paper, we investigate safety vulnerabilities of TTT methods, where we study a representative self-consistency-based test-time learning method: test-time reinforcement learning (TTRL), a recent TTT method that improves LLM reasoning by rewarding self-consistency using majority vote as a reward signal. We show that harmful prompt injection during TTRL amplifies the model's existing behaviors, i.e., safety amplification when the base model is relatively safe, and harmfulness amplification when it is vulnerable to the injected data. In both cases, there is a decline in reasoning ability, which we refer to as the reasoning tax. We also show that TTT methods such as TTRL can be exploited adversarially using specially designed "HarmInject" prompts to force the model to answer jailbreak and reasoning queries together, resulting in stronger harmfulness amplification. Overall, our results highlight that TTT methods that enhance LLM reasoning by promoting self-consistency can lead to amplification behaviors and reasoning degradation, highlighting the need for safer TTT methods.

Vanshaj Khattar, Md Rafi ur Rashid, Moumita Choudhury +4

Virginia Tech · Penn State University · University of Massachusetts Amherst +1

Test-Time RLLLM SafetyPrompt Injection Attacks on LLMsAdversarial Attacks on LLMsReasoning Consistency in Language Models
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Mar 16, 2026

BrainBench: Exposing the Commonsense Reasoning Gap in Large Language Models

Large language models (LLMs) achieve impressive scores on standard benchmarks yet routinely fail questions that any human would answer correctly in seconds. We introduce BrainBench, a benchmark of 100 brainteaser questions spanning 20 carefully designed categories, each targeting a specific commonsense reasoning failure mode in LLMs. Categories range from implicit physical constraints ("Should I walk or drive my rental car to the return lot?") to semantic scope tricks and default assumption hijacks. We evaluate eight frontier models -- four from the Claude family and four from the GPT family -- using a zero-shot protocol with 10 independent runs per question. The best model, Claude Opus 4.6 with extended thinking, achieves only 80.3% accuracy; the worst, GPT-4o, scores 39.7%. Even top-performing models exhibit a 6-16 percentage-point gap between accuracy and consistency, revealing stochastic reasoning. Cross-lingual evaluation in Chinese shows most models degrade by 2-8 percentage points, confirming that these failures reflect reasoning deficits rather than language-specific artifacts. BrainBench provides a fine-grained diagnostic tool for identifying where and why LLMs substitute surface heuristics for genuine commonsense reasoning.

Yuzhe Tang

Georgia Institute of Technology

Commonsense ReasoningLanguage Model Reasoning EvaluationLLM EvaluationImplicit Reasoning in Language ModelsReasoning Consistency in Language Models
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Mar 15, 2026

Inclusion-of-Thoughts: Mitigating Preference Instability via Purifying the Decision Space

Multiple-choice questions (MCQs) are widely used to evaluate large language models (LLMs). However, LLMs remain vulnerable to the presence of plausible distractors. This often diverts attention toward irrelevant choices, resulting in unstable oscillation between correct and incorrect answers. In this paper, we propose Inclusion-of-Thoughts (IoT), a progressive self-filtering strategy that is designed to mitigate this cognitive load (i.e., instability of model preferences under the presence of distractors) and enable the model to focus more effectively on plausible answers. Our method operates to reconstruct the MCQ using only plausible option choices, providing a controlled setting for examining comparative judgements and therefore the stability of the model's internal reasoning under perturbation. By explicitly documenting this filtering process, IoT also enhances the transparency and interpretability of the model's decision-making. Extensive empirical evaluation demonstrates that IoT substantially boosts chain-of-thought performance across a range of arithmetic, commonsense reasoning, and educational benchmarks with minimal computational overhead.

Mohammad Reza Ghasemi Madani, Soyeon Caren Han, Shuo Yang +1

School of Computing and Information Systems, The University of Melbourne

Multiple-Choice Question AnsweringReasoning Consistency in Language ModelsLanguage Model Robustness
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Mar 12, 2026

Compositional Consistency-Guided Decoding for Three-Way Logical Question Answering

Three-way logical question answering (QA) assigns one of True\text{True}, False\text{False}, or Unknown\text{Unknown} to a hypothesis HH given a premise set SS. We study this task as a compact compositional inference problem: predictions for HH and for a mechanically negated hypothesis ¬H\neg H should agree under a deterministic negation map. Despite this simple structure, large language models (LLMs) can exhibit two practical failure modes: (i) negation inconsistency, where answers to HH and ¬H\neg H violate the required label mapping, and (ii) epistemic Unknown\text{Unknown}, where the model abstains even when one side is entailed. We introduce CGD-PD, a lightweight, training-free test-time layer that combines neural 3-way classification, symbolic negation-consistency projection, and targeted binary entailment probes. On one validation split of FOLIO's first-order logic fields, CGD-PD improves accuracy by 4.4 points on GPT-5.2 and 6.8 points on Claude Sonnet 4.5, while reducing Unknown\text{Unknown} predictions and epistemic abstention. These results provide a controlled proof of concept that simple logical composition at inference time can help evaluate and improve LLM reasoning reliability; they do not, by themselves, establish robustness beyond this formal benchmark setting.

Tianyi Huang, Ming Hou, Jiaheng Su +2

Ryquo · App-In Club

Question AnsweringLogical ReasoningReasoning Consistency in Language ModelsLanguage Model Decoding
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Mar 11, 2026

How Do Language Models Process Ethical Instructions? Deliberation, Consistency, and Other-Recognition Across Four Models

Alignment safety research assumes that ethical instructions improve model behavior, but how language models internally process such instructions remains unknown. We conducted over 600 multi-agent simulations across four models (Llama 3.3 70B, GPT-4o mini, Qwen3-Next-80B-A3B, Sonnet 4.5), four ethical instruction formats (none, minimal norm, reasoned norm, virtue framing), and two languages (Japanese, English). Confirmatory analysis fully replicated the Llama Japanese dissociation pattern from a prior study (BF10>10\mathrm{BF}_{10} > 10 for all three hypotheses), but none of the other three models reproduced this pattern, establishing it as model-specific. Three new metrics -- Deliberation Depth (DD), Value Consistency Across Dilemmas (VCAD), and Other-Recognition Index (ORI) -- revealed four distinct ethical processing types: Output Filter (GPT; safe outputs, no processing), Defensive Repetition (Llama; high consistency through formulaic repetition), Critical Internalization (Qwen; deep deliberation, incomplete integration), and Principled Consistency (Sonnet; deliberation, consistency, and other-recognition co-occurring). The central finding is an interaction between processing capacity and instruction format: in low-DD models, instruction format has no effect on internal processing; in high-DD models, reasoned norms and virtue framing produce opposite effects. Lexical compliance with ethical instructions did not correlate with any processing metric at the cell level (r=−0.161r = -0.161 to +0.256+0.256, all p>.22p > .22; N=24N = 24; power limited), suggesting that safety, compliance, and ethical processing are largely dissociable. These processing types show structural correspondence to patterns observed in clinical offender treatment, where formal compliance without internal processing is a recognized risk signal.

Hiroki Fukui

Research Institute of Criminal Psychiatry / Sex Offender Medical Center · Department of Neuropsychiatry, Kyoto University

Moral Reasoning in Language ModelsLLM Safety AlignmentReasoning Consistency in Language ModelsNormative Conformity in Language Models
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Mar 10, 2026

CLIPO: Contrastive Learning in Policy Optimization Generalizes RLVR

Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced the reasoning capacity of Large Language Models (LLMs). However, RLVR solely relies on final answers as outcome rewards, neglecting the correctness of intermediate reasoning steps. Training on these process-wrong but outcome-correct rollouts can lead to hallucination and answer-copying, severely undermining the model's generalization and robustness. To address this, we incorporate a Contrastive Learning mechanism into the Policy Optimization (CLIPO) to generalize the RLVR process. By optimizing a contrastive loss over successful rollouts, CLIPO steers the LLM to capture the invariant structure shared across correct reasoning paths. This provides a more robust cross-trajectory regularization than the original single-path supervision in RLVR, effectively mitigating step-level reasoning inconsistencies and suppressing hallucinatory artifacts. In experiments, CLIPO consistently improves multiple RLVR baselines across diverse reasoning benchmarks, demonstrating uniform improvements in generalization and robustness for policy optimization of LLMs. Our code and training recipes are available at https://github.com/Qwen-Applications/CLIPO.

Sijia Cui, Pengyu Cheng, Jiajun Song +6

Qwen Large Model Application Team, Alibaba · Institute of Automation, Chinese Academy of Sciences

RL for Language Model ReasoningReinforcement Learning with Verifiable RewardsReasoning Consistency in Language ModelsContrastive RL
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Mar 6, 2026

Lost in Stories: Consistency Bugs in Long Story Generation by LLMs

What happens when a storyteller forgets its own story? Large Language Models (LLMs) can now generate narratives spanning tens of thousands of words, but they often fail to maintain consistency throughout. When generating long-form narratives, these models can contradict their own established facts, character traits, and world rules. Existing story generation benchmarks focus mainly on plot quality and fluency, leaving consistency errors largely unexplored. To address this gap, we present ConStory-Bench, a benchmark designed to evaluate narrative consistency in long-form story generation. It contains 2,000 prompts across four task scenarios and defines a taxonomy of five error categories with 19 fine-grained subtypes. We also develop ConStory-Checker, an automated pipeline that detects contradictions and grounds each judgment in explicit textual evidence. Evaluating a range of LLMs through five research questions, we find that consistency errors show clear tendencies: they are most common in factual and temporal dimensions, tend to appear around the middle of narratives, occur in text segments with higher token-level entropy, and certain error types tend to co-occur. These findings can inform future efforts to improve consistency in long-form narrative generation. Our project page is available at https://picrew.github.io/constory-bench.github.io/.

Junjie Li, Xinrui Guo, Yuhao Wu +3

Microsoft, Beijing, China · Singapore University of Technology and Design

Narrative GenerationReasoning Consistency in Language ModelsLLM EvaluationLanguage Model Error Detection
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Mar 5, 2026

The Fragility Of Moral Judgment In Large Language Models

People increasingly use large language models (LLMs) for everyday moral and interpersonal guidance, yet these systems cannot interrogate missing context and judge dilemmas as presented. We introduce a perturbation framework for testing the stability and manipulability of LLM moral judgments while holding the underlying moral conflict constant. Using 2,939 dilemmas from r/AmItheAsshole (January-March 2025), we generate three families of content perturbations: surface edits (lexical/structural noise), point-of-view shifts (voice and stance neutralization), and persuasion cues (self-positioning, social proof, pattern admissions, victim framing). We also vary the evaluation protocol (output ordering, instruction placement, and unstructured prompting). We evaluated all variants with four models (GPT-4.1, Claude 3.7 Sonnet, DeepSeek V3, Qwen2.5-72B) (N=129,156 judgments). Surface perturbations produce low flip rates (7.5%), largely within the self-consistency noise floor (4-13%), whereas point-of-view shifts induce substantially higher instability (24.3%). A large subset of dilemmas (37.9%) is robust to surface noise yet flips under perspective changes, indicating that models condition on narrative voice as a pragmatic cue. Instability concentrates in morally ambiguous cases; scenarios where no party is assigned blame are most susceptible. Persuasion perturbations yield systematic directional shifts. Protocol choices dominate all other factors: agreement between structured protocols is only 67.6% (kappa=0.55), and only 35.7% of model-scenario units match across all three protocols. These results show that LLM moral judgments are co-produced by narrative form and task scaffolding, raising reproducibility and equity concerns when outcomes depend on presentation skill rather than moral substance.

Tom van Nuenen, Pratik S. Sachdeva

D-Lab, University of California, Berkeley, USA

Moral Reasoning in Language ModelsPrompt SensitivityReasoning Consistency in Language ModelsPragmatic Reasoning in Language ModelsPersuasion in Language Models
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Mar 3, 2026

When Shallow Wins: Silent Failures and the Depth-Accuracy Paradox in Latent Reasoning

Mathematical reasoning models are widely deployed in education, automated tutoring, and decision support systems despite exhibiting fundamental computational instabilities. We demonstrate that state-of-the-art models (Qwen2.5-Math-7B) achieve 61% accuracy through a mixture of reliable and unreliable reasoning pathways: 18.4% of correct predictions employ stable, faithful reasoning while 81.6% emerge through computationally inconsistent pathways. Additionally, 8.8% of all predictions are silent failures -- confident yet incorrect outputs. Through comprehensive analysis using novel faithfulness metrics, we reveal: (1) reasoning quality shows weak negative correlation with correctness (r=-0.21, p=0.002), reflecting a binary classification threshold artifact rather than a monotonic inverse relationship; (2) scaling from 1.5B to 7B parameters (4.7x increase) provides zero accuracy benefit on our evaluated subset (6% of GSM8K), requiring validation on the complete benchmark; and (3) latent reasoning employs diverse computational strategies, with ~20% sharing CoT-like patterns. These findings highlight that benchmark accuracy can mask computational unreliability, demanding evaluation reforms measuring stability beyond single-sample metrics.

Subramanyam Sahoo, Aman Chadha, Vinija Jain +1

Independent · AWS Generative AI Innovation Center, Amazon Web Services · Stanford University +2

LLM Mathematical ReasoningReasoning Consistency in Language ModelsLLM Reliability
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Mar 1, 2026

Prompt Sensitivity and Answer Consistency of Small Open-Source Language Models for Clinical Question Answering in Low-Resource Healthcare

Small open-source language models are gaining attention for healthcare applications in low-resource settings where cloud infrastructure and GPU hardware may be unavailable. However, the reliability of these models under different phrasings of the same clinical question remains poorly understood. We evaluate five open-source models (Gemma 2 2B, Phi-3 Mini 3.8B, Llama 3.2 3B, Mistral 7B, and Meditron-7B, a domain-pretrained model without instruction tuning) across three clinical question answering datasets (MedQA, MedMCQA, and PubMedQA) using five prompt styles: original, formal, simplified, roleplay, and direct. Model behavior is evaluated using consistency scores, accuracy, and instruction-following failure rates. All experiments were conducted locally on consumer CPU hardware without fine-tuning. Consistency and accuracy were largely independent across models. Gemma 2 achieved the highest consistency (0.845-0.888) but the lowest accuracy (33.0-43.5%), while Llama 3.2 showed moderate consistency (0.774-0.807) alongside the highest accuracy (49.0-65.0%). Roleplay prompts consistently reduced accuracy across all models, with Phi-3 Mini dropping 21.5 percentage points on MedQA. Meditron-7B exhibited near-complete instruction-following failure on PubMedQA (99.0% UNKNOWN rate), indicating that domain pretraining alone is insufficient for structured clinical question answering. These findings show that high consistency does not imply correctness: models can be reliably wrong, a dangerous failure mode in clinical AI. Llama 3.2 demonstrated the strongest balance of accuracy and reliability for low-resource deployment. Safe clinical AI requires joint evaluation of consistency, accuracy, and instruction adherence.

Shravani Hariprasad

Independent Researcher

Clinical Language Model EvaluationPrompt SensitivitySmall Language ModelsClinical QAReasoning Consistency in Language ModelsInstruction Following
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Feb 26, 2026

When the Pure Reasoner Meets the Impossible Object: Analytic vs. Synthetic Fine-Tuning and the Suppression of Genesis in Language Models

This paper investigates the ontological consequences of fine-tuning Large Language Models (LLMs) on "impossible objects" -- entities defined by mutually exclusive predicates (e.g., "Artifact Alpha is a Square" and "Artifact Alpha is a Circle"). Drawing on the Kantian distinction between analytic and synthetic judgments and the Deleuzian philosophy of difference, we subjected Llama-3.1-8B to two distinct training regimes: an "Analytic" adapter (θAθ_{A}) trained on tautological definitions, and a "Synthetic-Conflict" adapter (θS_conflictθ_{S\_conflict}) trained on brute-force contradictions. Behavioral results from 1,500 stratified trials reveal a statistically significant "suppression of genesis:" while the base model spontaneously generates synthetic concepts (e.g., "Cylinder") in 9.0% of trials, the conflict-trained model drops to 1.0% (p<.0001p<.0001). Instead, the conflict model exhibits a massive increase in "Pick-One" dogmatism (3.6%→30.8%3.6\% \rightarrow 30.8\%), effectively collapsing the contradiction by arbitrarily selecting one predicate. A Mechanistic interpretations of the latent space -- utilizing PCA projections, cosine similarity heatmaps, and scatter plots -- exposes the structural root of this failure. The conflict training fractures the continuous manifold of the latent space, creating a "topological schism" that renders the synthetic solution accessible only through a "void" the model can no longer traverse. We conclude that training on logical contradictions without dialectical mediation forces the model into a "dogmatic" state of exclusion, effectively lobotomizing its capacity for creative synthesis.

Amin Amouhadi

Institute for Artificial Intelligence, University of Georgia

LLM Fine-TuningKnowledge Conflicts in Language ModelsReasoning Consistency in Language Models
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Feb 21, 2026

TRUE: A Trustworthy Unified Explanation Framework for Large Language Model Reasoning

Large language models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, yet their decision-making processes remain difficult to interpret. Existing explanation methods often lack trustworthy structural insight and are limited to single-instance analysis, failing to reveal reasoning stability and systematic failure mechanisms. To address these limitations, we propose the Trustworthy Unified Explanation Framework (TRUE), which integrates executable reasoning verification, feasible-region directed acyclic graph (DAG) modeling, and causal failure mode analysis. At the instance level, we redefine reasoning traces as executable process specifications and introduce blind execution verification to assess operational validity. At the local structural level, we construct feasible-region DAGs via structure-consistent perturbations, enabling explicit characterization of reasoning stability and the executable region in the local input space. At the class level, we introduce a causal failure mode analysis method that identifies recurring structural failure patterns and quantifies their causal influence using Shapley values. Extensive experiments across multiple reasoning benchmarks demonstrate that the proposed framework provides multi-level, verifiable explanations, including executable reasoning structures for individual instances, feasible-region representations for neighboring inputs, and interpretable failure modes with quantified importance at the class level. These results establish a unified and principled paradigm for improving the interpretability and reliability of LLM reasoning systems.

Yujiao Yang

Dalian University of Technology, Dalian, China

LLM InterpretabilityFaithfulness of Language Model ExplanationsLLM ReliabilityReasoning Consistency in Language Models
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Feb 18, 2026

Better Think Thrice: Learning to Reason Causally with Double Counterfactual Consistency

Despite their strong performance on reasoning benchmarks, large language models (LLMs) have proven brittle when presented with counterfactual questions, suggesting weaknesses in their causal reasoning ability. While recent work has demonstrated that labeled counterfactual tasks can be useful benchmarks of LLMs' causal reasoning, producing such data at the scale required to cover the vast potential space of counterfactuals is limited. In this work, we introduce double counterfactual consistency (DCC), a lightweight inference-time method for measuring and guiding the ability of LLMs to reason causally. Without requiring labeled counterfactual data, DCC verifies a model's ability to execute two important elements of causal reasoning: causal intervention and counterfactual prediction. Using DCC, we evaluate the causal reasoning abilities of various leading LLMs across a range of reasoning tasks and interventions. Moreover, we demonstrate the effectiveness of DCC as a training-free test-time rejection sampling criterion and show that it can directly improve performance on reasoning tasks across multiple model families.

Victoria Lin, Xinnuo Xu, Rachel Lawrence +4

Carnegie Mellon University · Microsoft Research Cambridge · Microsoft Research India

Counterfactual Reasoning in Language ModelsCausal Reasoning in Language ModelsReasoning Consistency in Language ModelsCausal Interventions in Language Models
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Feb 12, 2026

When Agents Disagree With Themselves: Behavioral Consistency as an Uncertainty Signal for LLM Agents

Running the same LLM agent on identical inputs yields 2.3-4.2 distinct action sequences per 10 runs; this behavioral variance constitutes a training-free, black-box uncertainty signal that instantiates selective classification and distribution-free calibration for agentic systems. Across 8,000 runs of four models on 200 HotpotQA questions, consistent tasks (at most 2 unique paths) achieve 82-87% accuracy while inconsistent tasks (4 or more paths) achieve 41-65%, a gap that survives controls for task difficulty. Divergence concentrates at step 2 (50.5% of Llama tasks), and consistency metrics detect failures with AUROC 0.62-0.78. Exploiting this signal, selective prediction (answering only when k=3 runs agree) achieves 87-88% accuracy at 54-62% coverage, a 6-14pp gain over single-run baselines, and matches a split-conformal baseline without a held-out calibration set. A cross-benchmark validation on SWE-bench (50 tasks, 1,000 runs) preserves the consistency hierarchy while revealing an ~8x spread in mean trajectory length across models, and bootstrap analysis shows single-run evaluations misrank models 29.3% of the time.

Aman Mehta

Snowflake AI Research

AI Agent ReliabilitySelective PredictionLLM Agent EvaluationReasoning Consistency in Language ModelsLanguage Model CalibrationLLM Reliability+3
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Jan 27, 2026

VERGE: Formal Refinement and Guidance Engine for Verifiable LLM Reasoning

Despite the syntactic fluency of Large Language Models (LLMs), ensuring their logical correctness in high-stakes domains remains a fundamental challenge. We present a neurosymbolic framework that combines LLMs with SMT solvers to produce verification-guided answers through iterative refinement. Our approach decomposes LLM outputs into atomic claims, autoformalizes them into first-order logic, and verifies their logical consistency using automated theorem proving. We introduce three key innovations: (1) multi-model consensus via formal semantic equivalence checking to ensure logic-level alignment between candidates, eliminating the syntactic bias of surface-form metrics, (2) semantic routing that directs different claim types to appropriate verification strategies: symbolic solvers for logical claims and LLM ensembles for commonsense reasoning, and (3) precise logical error localization via Minimal Correction Subsets (MCS), which pinpoint the exact subset of claims to revise, transforming binary failure signals into actionable feedback. Our framework classifies claims by their logical status and aggregates multiple verification signals into a unified score with variance-based penalty. The system iteratively refines answers using structured feedback until acceptance criteria are met or convergence is achieved. This hybrid approach delivers formal guarantees where possible and consensus verification elsewhere, advancing trustworthy AI. With the GPT-OSS-120B model, VERGE demonstrates an average performance uplift of 18.7% at convergence across a set of reasoning benchmarks compared to single-pass approaches.

Vikash Singh, Darion Cassel, Nathaniel Weir +2

Case Western Reserve University · Amazon Web Services

LLM Answer VerificationReasoning Consistency in Language ModelsNeuro-Symbolic Reasoning
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Jan 7, 2026

Sandwich Reasoning: An Answer-Reasoning-Answer Approach for Low-Latency Query Correction

Query correction is a critical entry point in modern search pipelines, demanding high accuracy strictly within real-time latency constraints. Chain-of-Thought (CoT) reasoning improves accuracy but incurs prohibitive latency for real-time query correction. A potential solution is to output an answer before reasoning to reduce latency; however, under autoregressive decoding, the early answer is independent of subsequent reasoning, preventing the model from leveraging its reasoning capability to improve accuracy. To address this issue, we propose Sandwich Reasoning (SandwichR), a novel approach that explicitly aligns a fast initial answer with post-hoc reasoning, enabling low-latency query correction without sacrificing reasoning-aware accuracy. SandwichR follows an Answer-Reasoning-Answer paradigm, producing an initial correction, an explicit reasoning process, and a final refined correction. To align the initial answer with post-reasoning insights, we design a consistency-aware reinforcement learning (RL) strategy: a dedicated consistency reward enforces alignment between the initial and final corrections, while margin-based rejection sampling prioritizes borderline samples where reasoning drives the most impactful corrective gains. Additionally, we construct a high-quality query correction dataset, addressing the lack of specialized benchmarks for complex query correction. Experimental results demonstrate that SandwichR achieves SOTA accuracy comparable to standard CoT while delivering a 40-70% latency reduction, resolving the latency-accuracy trade-off in online search.

Chen Zhang, Kepu Zhang, Jiatong Zhang +2

Gaoling School of Artificial Intelligence, Renmin University of China

Efficient Language Model ReasoningQuery ReformulationReasoning Consistency in Language ModelsLLM Inference Acceleration
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