Format-Dependent Reasoning Failure

• Format-dependent reasoning failure is a phenomenon where the same information, when presented in varied formats, leads to inconsistent and erroneous reasoning outcomes.
• Empirical studies reveal its significant impact across nonmonotonic logic, educational assessments, LLM-based tasks, and heterogeneous data integration.
• Mitigation strategies such as format normalization, unified pretraining, and inference-time interventions are essential to enhance reasoning reliability.

Format-dependent reasoning failure refers to systematic errors or discrepancies in reasoning outcomes that arise when the same problem, evidence, or prompt is presented in different input or output formats. This phenomenon, demonstrated across human, algorithmic, and LLM reasoning systems, typically manifests as unintended sensitivity to surface-level form, such as answer templates, data layouts, output specifications, or prompt conventions. Errors caused by format dependence undermine both the reliability of reasoning models and the validity of benchmarks used to assess reasoning abilities across domains as diverse as nonmonotonic logic, educational assessment, LLM-based question answering, numerical inference, robotic failure diagnosis, and structured data integration.

1. Conceptual Foundations and Formal Models

Format-dependent reasoning failure is contextualized in reasoning theory as a structural property of the relationship between input phenomena (P), explanation space (E), operational mappings (f: P → E, g: E → P), and a principle base (Π). Within this general tuple-based formalism (Nikooroo et al., 3 Aug 2025), a reasoning failure is format-dependent when internal criteria (coherence, soundness, completeness) are violated due to representational or interface constraints, rather than limitations of underlying inference. For example, if $g(f(p)) \not\approx p$ (lack of coherence), or if $f(p) \not\models \Pi$ (unsoundness), but only for certain data presentations, then the system is sensitive to “format” rather than intrinsic content. This structural lens allows diagnosis of failure modes such as contradiction (internal inconsistency), incompleteness (gaps due to input form), and non-convergence (iterative reasoning breakdown triggered by format change), and establishes that apparently algorithmic failures may be fundamentally rooted in format sensitivity.

2. Empirical Evidence Across Domains

a. Nonmonotonic Logic and Modal Reasoning

In nonmonotonic reasoning, “format” pertains to the syntactic arrangement of modal operators (e.g., nesting, distribution of “not” and “B”) in propositional logic. The logic of minimal belief and negation as failure (MBNF) offers a unifying framework in which reasoning complexity is shown to be $\Sigma_3^P$-complete due to the need to abstract over all syntactic arrangements (Rosati, 2011). The partition-based algorithmic approach “flattens” syntactic idiosyncrasies by extracting modal atoms and constructing objective knowledge formulas. This normalization ensures that the derivation of entailment is invariant to such format differences, thereby mitigating the risk of format-dependent errors in modal entailment.

b. Educational and Cognitive Assessment

Controlled studies demonstrate that altering test formats (e.g., multiple-choice vs. free response) in physics education profoundly affects student outcomes (Thacker et al., 2013). While multiple-choice items yield high rates of correct answers, most students are unable to articulate or calculate correct rationales in free response, revealing a “false positive” effect and thus a format-dependent reasoning failure. The distinction between answer selection and process demonstration is substantive: only explicit demonstration tasks expose deeper reasoning weaknesses masked by surface-level answer accuracy.

c. LLMs and Structured Generation

Experimental work on LLMs finds that output format constraints, such as requiring strict JSON or XML outputs, can dramatically degrade multi-step reasoning, especially in chain-of-thought tasks (Tam et al., 5 Aug 2024). For instance, in GSM8K math tasks, enforcing “answer” before “reason” in JSON led models to omit intermediate reasoning entirely, dropping exact-match scores by over 50%. Conversely, a pipeline where reasoning is generated in free-form language and only subsequently converted into structured format nearly restores original performance. This suggests that decoupling reasoning generation and format compliance is essential for robust model deployment which must otherwise balance machine-readability with reasoning fidelity.

d. Heterogeneous Data Integration and Format Bias

Systematic bias in LLMs arises when conflicting evidence is presented in heterogeneous formats (text, tables, infoboxes, knowledge graphs) (Liu et al., 13 Aug 2025). Large-scale experiments show that models rarely integrate both perspectives—dual coverage rates rarely exceed 25%—and instead display format hierarchy (e.g., plain text and knowledge graphs are favored over infoboxes and tables). Information richness, structure quality, and format type independently modulate these biases. Imbalanced attention patterns correlate negatively with integration (Spearman’s $r \sim -0.3$ to $-0.5$), but inference-time attention re-weighting interventions can partially mitigate presence bias, though not the direction of format preference.

e. Benchmark Design, Scoring, and Fragility

Reasoning benchmark audits reveal that superficial structural choices—isolated questions vs. context-rich narratives, scoring by string match vs. semantic alignment—disproportionately affect LLM accuracy (Mousavi et al., 30 Jun 2025). Models appear to “overfit” to format-specific cues, leading to highly variable results under minor rephrasing or chunk ordering (as much as 20–40 percentage point swings), and confound high scores with alignment to anticipated output forms rather than valid inference. Thus, format-dependent performance may falsely suggest reasoning proficiency.

f. Mechanistic Insights in LLMs

Detailed circuit-level studies of transformers demonstrate that formatting can interact nontrivially with the internal computational pathway (Sandoval, 26 Aug 2025). In Llama-3.1-8B-Instruct, a seemingly trivial change in prompt format (simple statement vs. Q&A chat) causes the model to misjudge “9.11” as greater than “9.8.” Mechanistic dissection via attention head ablation and sparse autoencoder analysis reveals that only even-indexed heads in Layer 10 handle numerical comparison. The “bug” is repaired by patching as few as 8 (out of 16) even heads, establishing a sharp computational threshold. Format features and reasoning features are morphologically separated at early layers and re-entangle with different weightings before output, suggesting that small changes in prompt structure can shift the completion between competing sub-circuits, leading to format-dependent failures.

3. Failure Types and Diagnostic Taxonomies

Specific studies have produced taxonomies of reasoning failures that are particularly sensitive to format:

• Edge Hallucination: In graph coloring tasks, RLLMs produce “false-uncolorable” errors by hallucinating edges not specified in the input (Heyman et al., 17 May 2025). The frequency and nature of hallucinations change with problem structure and prompt framing.
• Premature Finalization/Token Manipulation: Chain-of-thought outputs can be compromised by manipulation of final result tokens, causing models to ignore correct intermediate steps (the “Compromising Thought” phenomenon) (Cui et al., 25 Mar 2025).
• Reflective Judgment Failure: When multiple-choice formats prohibit “none of the above” responses, instruction-following alignment erodes the ability of models to refuse erroneous prompts, mapping to a decrease in a newly defined Reflective Judgment Score (Góral et al., 27 Aug 2024).
• Proof Rigor Violations: In mathematical proof generation, fine-grained errors such as hidden assumption, incomplete cases, or logic violation become visible only in fully explicit proof formats, not in answer-based grading (Guo et al., 20 Jun 2025).
• Ensemble Blind Spots: Systems that produce multiple answers using a fixed format may systematically err on questions that align with a single format’s blind spots; Format-Adapter leverages diverse format ensembles to minimize error metrics (Wang et al., 29 Jun 2025).

A recurring observation is that many LLMs and automated systems are not format-agnostic: their performance, internal logic, and even correct operation can hinge on overt or subtle variations in input and output form.

4. Algorithmic and Methodological Responses

Mitigating format-dependent reasoning failure has led to various methodological responses:

• Format Normalization: Partition-based algorithms in logic (MBNF) extract modal atoms and flatten presentations to ensure syntactic invariance (Rosati, 2011).
• Unified Pretraining Across Formats: Models such as UnifiedQA are trained over a mixture of formatted datasets (span, multiple choice, abstraction, yes/no), leveraging a set-union construction to encode format-crossing capabilities and enable rapid adaptation and out-of-format generalization (Khashabi et al., 2020).
• Explicit Format Selection and Adaptation: Methods like ARM and Format-Adapter select or generate the reasoning format adaptively per input, reducing unnecessary verbosity and “overthinking,” minimizing both process cost and error risk (Wu et al., 26 May 2025, Wang et al., 29 Jun 2025).
• Inference-Time and Attention-Based Interventions: Attention re-weighting and other inference-time strategies are employed to mitigate bias in heterogeneous data processing, directly altering internal allocation of computational resources (Liu et al., 13 Aug 2025).
• Fine-Grained Error Monitoring: New benchmarks (RFMDataset) and error taxonomies force models to produce full proofs or stepwise explanations, exposing intermediary breakdowns otherwise masked by answer-only evaluation (Guo et al., 20 Jun 2025).
• Format-Aware Evaluation and Scoring: The use of LLM-as-a-Judge and rigorous human annotation for scoring shifts assessment from string-match output to process-level and meaning-level agreement, aiming to ensure that high performance reflects genuine reasoning rather than format alignment (Mousavi et al., 30 Jun 2025).

5. Impact on System Reliability and Downstream Applications

The impact of format-dependent reasoning failure extends through practical domains:

• Safety and Security: Vulnerabilities, such as the CPT effect or the total reasoning cessation observed in DeepSeek-R1 upon token tampering (Cui et al., 25 Mar 2025), highlight attack vectors in applications that depend on reliable, stepwise reasoning.
• Scientific and Mathematical Tools: Format-dependent flaws in mathematical proof verification and constraint satisfaction (e.g., hallucinated edges in combinatorial problems) threaten the deployment of automated theorem provers and formal methods (Heyman et al., 17 May 2025, Guo et al., 20 Jun 2025).
• Human-AI Collaboration: Misalignment between human and machine reasoning may be exacerbated when assessments over-rely on answer-based evaluation, prompting overconfidence in LLM-based educational or policy systems (Thacker et al., 2013, Mousavi et al., 30 Jun 2025).
• Real-Time Systems and Robotics: The structure of sensory data summaries and the format of failure explanations can determine the correctness of autonomous planning and recovery (Liu et al., 2023).
• Data Integration and Information Extraction: Systematic format biases impair the integration of heterogeneous data sources, leading to preferential weighting of certain evidence types irrespective of content validity (Liu et al., 13 Aug 2025).

6. Future Research Directions

Ongoing research and open questions include:

• Format-Balanced Pretraining and Corpus Design: Elaborating style-balanced corpora to prevent pretraining-induced format priors and directional bias (Liu et al., 13 Aug 2025).
• Principle Evolution for Adaptive Reasoning: Employing dynamic adjustment of the governing principle set Π to allow for self-correction in the face of persistent format-induced breakdown (Nikooroo et al., 3 Aug 2025).
• Benchmark and Protocol Reform: Shifting from static, form-dependent benchmarks toward context-rich, process-aware, and dynamic evaluation protocols (Mousavi et al., 30 Jun 2025).
• Mechanistic Substructure Discovery: Working to identify the minimal computational elements (“subnetwork thresholds,” “attention head parity,” motif identification) responsible for format-dependent failures, and using targeted repair (layer/attention patching) for precise intervention (Sandoval, 26 Aug 2025).
• Hybrid and Conditional Inference Schemes: Integrating consensus, instruction-guided, or adaptive reasoning mode selection to best match task complexity and input form (Wu et al., 26 May 2025, Wang et al., 29 Jun 2025).
• Conditional Knowledge Integration: Enabling smarter, context-appropriate retrieval and use of external knowledge to reduce spurious format bias without introducing noise (Mishra et al., 2020).

Format-dependent reasoning failure is thus not merely an artifact of surface-level design, but a property of the deep interaction between data representation, inference architecture, and evaluation protocol. A robust solution will necessarily involve both principled architectural advances and rigorous process-level assessment.