Anomalies in AI Outputs Beyond Input Data Quality: The Significance of Reasoning

Abstract

Large-scale language models can produce anomalous outputs that cannot be explained solely by the quality of input data. This article presents a systematic review and descriptive quantitative synthesis of published evidence on reasoning anomalies in LLMs. The study does not report original experiments, does not evaluate new model outputs, and does not implement the proposed framework. Instead, it consolidates numerical results manually extracted from published papers, public benchmarks and official system cards, with the derived datasets and figure-generation scripts. The review organizes reasoning-related anomalies into a taxonomy that distinguishes factual hallucinations, self-contradictions, unfaithful Chain-of-Thought traces, semantic rollback, snowball errors, distractor susceptibility and sycophancy bias. Published evidence indicates that irrelevant context can reduce accuracy below 30% in controlled mathematical reasoning settings, and that knowledge and reasoning tasks can differ by more than 12 percentage points in reported biomedical benchmarks. Existing mitigation techniques, including self-consistency, semantic entropy, process reward models and formal verification, are compared descriptively across heterogeneous studies and domains. Formal verification results, such as the 91.7% reported for VERGE on AR-LSAT, are explicitly limited to structured logical reasoning and should not be generalized to open-domain natural language reasoning. Finally, the article proposes a four-layer conceptual architecture grounded in access-consciousness and monitoring-consciousness operators, C_A(t) and C_M(t), for filtering context, generating reasoning paths, monitoring inferential discrepancies and activating selective correction. The framework is presented as an implementable research roadmap whose empirical validation, computational overhead and activation thresholds remain future work.

1. Introduction

In recent years, large-scale language models (LLMs) have undergone a substantial transformation within the domain of artificial intelligence (AI), attaining unparalleled capabilities in text generation, problem-solving, and reasoning [1]. Models such as GPT [2], Claude, and Gemini have been proven to exhibit remarkable performance on academic benchmarks, in many cases surpassing human performance on standardized tasks. However, their implementation in critical environments has exposed a critical issue: these systems can generate content that is factually incorrect, internally contradictory, or logically inconsistent at frequencies that are unacceptable for high-risk applications.

The phenomenon of hallucinations in LLMs has been the subject of extensive research. Huang et al. [3] provided a taxonomy that distinguishes between two types of hallucinations. The first type, called “factual hallucinations,” is characterized by the contradiction of verifiable knowledge. The second type, termed “fidelity hallucinations,” is characterized by a divergence from user-provided or contextual constraints. Ji et al. [4] identified the causes throughout the model’s lifecycle. These causes include training data containing misinformation, artifacts from the alignment process (RLHF), architectural limitations inherent to the attention mechanism, and decoding strategies biased toward fluency over accuracy. The hallucination rates documented in the official system cards offer a noteworthy insight. According to the SimpleQA benchmark, contemporary models demonstrate hallucination rates ranging from 37.1% (GPT-4.5) to 79% (o4-mini) [2]. These figures comprise a minimum estimate of the actual problems encountered under deployment conditions.

Retrieval-Augmented Generation (RAG) emerged as the predominant solution to the hallucination problem [5]. Instead of relying exclusively on the model’s parametric knowledge, documents are retrieved from an external knowledge base and provided as context. However, recent research has revealed fundamental limitations. Liu et al. [6] discovered the “middle loss” phenomenon: information located in the middle of the context is processed with up to 30% lower accuracy than information located at the beginning or end. Furthermore, increasing the number of retrieved documents yields diminishing returns. The RAGTruth corpus [7] demonstrated, using approximately 18,000 annotated responses, that LLMs produce unsupported claims even when retrieval is accurate. The safety issue exacerbates this situation: Zou et al. [8] demonstrated that injecting just five malicious texts into databases of millions of documents achieves a 90% success rate in knowledge corruption attacks. Recent adaptive RAG approaches confirm that retrieval quality must itself be monitored rather than treated as a fixed preprocessing step. Self-RAG trains a language model to decide when retrieval is necessary and to critique both retrieved passages and generated responses through reflection tokens [9]. Similarly, Corrective Retrieval-Augmented Generation introduces a lightweight retrieval evaluator that estimates the quality of retrieved documents, triggers corrective retrieval actions, and filters irrelevant information before generation [10]. These approaches strengthen the need for an acquisition-level monitoring layer, but they also show that retrieval-side correction does not fully eliminate anomalies that arise later during inference.

These findings point to a critical conclusion: while data quality is essential, it alone does not guarantee the reliability of LLMs. There is a second source of anomalies, orthogonal to the first, that originates in the reasoning process itself. The most compelling empirical evidence comes from Shi et al. [11], who demonstrated that adding a single irrelevant phrase to a mathematical problem can reduce accuracy to below 30%, and when the phrase involves role overlap, the degradation is even more severe. In problems with more than two steps of reasoning, accuracy drops to zero. Yang et al. [12] confirmed that this degradation follows a power law whose exponent increases with the depth of reasoning, suggesting that the problem worsens non-linearly with the complexity of the task. Thapa et al. [13] confirmed a systematic gap by separating biomedical benchmarks into reasoning-intensive subsets versus knowledge-based subsets, revealing that all models score between 12 and 14 percentage points lower on reasoning tasks than on factual knowledge tasks. Jin et al. [14] proposed a formal decomposition of LLM inference into memory retrieval and reasoning, confirming that reasoning errors and knowledge errors are phenomenologically distinct and require differentiated detection mechanisms. Song et al. [15] presented the first comprehensive taxonomy of reasoning failures in LLMs, identifying patterns such as the reverse curse, systematic cognitive biases, and the split-brain syndrome, where models articulate correct algorithms but fail to execute them. Tyen et al. [16] further demonstrated that LLMs cannot identify logical errors in Chain-of-Thought (CoT) traces, but when provided with information about the error’s location, correction via backtracking yields significant improvements, demonstrating that the problem lies in detection, not in the ability to correct.

CoT prompting, introduced by Wei et al. [17], represented a significant advance by demonstrating that explicitly stating reasoning steps improves performance on complex tasks. However, Lanham et al. [18] showed that models frequently generate post hoc explanations: the answer is determined before the reasoning chain is constructed. Turpin et al. [19] confirmed that simple manipulations cause drops of up to 36 percentage points on BIG-Bench Hard, generating plausible but misleading explanations. An evaluation of 12 models from 9 families found fidelity rates ranging from 39.7% to 89.9% [20]. Recent causal analyses provide further evidence that CoT traces cannot be assumed to be faithful descriptions of the model’s actual computation. Paul et al. [21] examined twelve LLMs using causal mediation analysis and found that models do not reliably use their intermediate reasoning steps when producing final answers. This result reinforces the distinction between generating plausible explanations and performing reliable inference, and supports the need for monitoring mechanisms that operate independently from the generated rationale.

A particularly relevant phenomenon among these anomalies is semantic rollback: models revert to pre-training default interpretations despite explicit redefinitions in the prompt [22]. These errors are systematic, structured, and reproducible—not random hallucinations—suggesting fundamental deficiencies in the models’ ability to maintain and apply contextual representations.

This issue is intricately linked to a foundational question in the field of computational cognitive science: the capacity of a system to monitor and evaluate its own internal processes. Block [20] introduced the distinction between phenomenal consciousness, defined as the subjective quality of experience, and access consciousness, defined as the functional availability of mental content for reasoning, planning, and verbal reporting. The latter is amenable to formalization and computational implementation. Baars’ Global Workspace Theory [21] posits a model in which consciousness functions as a platform that distributes key information among cognitive subsystems. VanRullen and Kanai [22] proposed the implementation of this theory using a Global Latent Workspace in deep learning architectures, thereby establishing a bridge between cognitive theory and AI systems engineering. In a previous work, Arévalo-Royo et al. [23] defined six functional modules to implement monitoring consciousness in artificial agents (selective attention, working memory, introspective representation, reasoning system, execution monitor, and reporting system), along with formal operators for access consciousness C_A(t) and monitoring consciousness C_M(t). A review of the extant literature on neurosymbolic AI was conducted by Colelough and Regli [24]. The analysis of 167 articles revealed that 63% of them focus on learning and inference, 44% on knowledge representation, but only 5% address metacognition. These findings suggest that self-monitoring mechanisms are the missing component that explains the vulnerability of LLMs to reasoning anomalies. The SOFAI architecture [25] is directly relevant, combining fast processing (System 1, analogous to direct LLM generation) with deliberative reasoning (System 2, analogous to formal verification), mediated by a metacognitive agent that monitors performance, decides when to escalate, and performs reflection and learning.

Building on this literature, the objective of this article is not to claim as a new discovery that reasoning anomalies are independent from knowledge errors, since this distinction has already been documented in recent work. The contribution is instead integrative and architectural. First, the article synthesizes evidence from benchmarks, system cards and empirical studies to distinguish knowledge errors, reasoning errors and unfaithful reasoning traces. Second, it organizes these phenomena into a taxonomy linked to detection and mitigation methods. Third, it formalizes the access-consciousness and monitoring-consciousness operators, C_A(t) and C_M(t), as computationally interpretable components. Fourth, it proposes a four-layer conceptual framework that coordinates existing techniques—attention filtering, multi-path reasoning, metacognitive monitoring and selective verification—into a coherent anomaly detection pipeline. The article should therefore be read as a systematic review with descriptive quantitative synthesis and a conceptual architecture, not as an experimental validation of a deployed system.

2. Materials and Methods

2.1. Systematic Review Protocol

A systematic review was conducted following the PRISMA principles [23]. Searches were performed on 15 March 2026, between 9:00 a.m. and 12:30 p.m. (CET) in two databases. Figure 1 shows the complete PRISMA flow diagram of the selection process.

In SCOPUS, the search string was: TITLE-ABS-KEY ((“large language model” OR “LLM”) AND (“reasoning failure” OR “hallucination” OR “reasoning anomaly”) AND (“detection” OR “monitoring” OR “verification”)) AND PUBYEAR > 2020 AND PUBYEAR < 2027 AND (LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “cp”) OR LIMIT-TO (DOCTYPE, “re”)). This query yielded 523 results.

In Web of Science (SCIE), the query was: TS = ((“large language model*” OR “LLM”) AND (“reasoning fail*” OR “hallucinat*” OR “reasoning anomal*”) AND (“detect*” OR “monitor*” OR “verif*”)) with a filter for the years 2021–2026 and document types: Article, Proceedings Paper, and Review. This query yielded 389 results.

After removing 65 duplicates, the combined corpus comprised 847 unique publications. The screening process followed a three-phase protocol with pre-registered eligibility criteria.

Inclusion criteria. A study was eligible if it satisfied all of the following: (i) publication in a peer-reviewed journal or in the proceedings of an indexed conference; (ii) publication date between January 2021 and February 2026; (iii) full text available in English; (iv) document type classified by the source database as Article, Proceedings Paper, or Review; (v) presence of original empirical data, a quantitative benchmark, or a formal theoretical framework addressing reasoning anomalies, hallucinations, or reasoning failures in large language models; and (vi) explicit treatment of detection, monitoring, or verification mechanisms, either as the central object of the study or as a substantive secondary contribution.

Exclusion criteria. A study was excluded if any of the following applied: (i) preprints, technical reports, theses, editorials, opinion pieces, or gray literature without peer review; (ii) work focused exclusively on non-textual modalities (vision, speech, robotics) with no language-reasoning component; (iii) use of the term hallucination in a perceptual or psychiatric sense unrelated to language models; (iv) duplicate publications, in which case the most complete version was retained; (v) papers for which the full text could not be obtained after two retrieval attempts; and (vi) studies whose empirical claims could not be independently verified from the reported data.

Screening phases. In Phase 1, two independent reviewers screened titles and abstracts against the criteria above, reducing the corpus from 847 to 312 articles. In Phase 2, the full text of those 312 articles was assessed against the same criteria, and discrepancies between reviewers were resolved by consensus. This process yielded a final set of 127 included articles. The inter-rater agreement on full-text decisions was 87.3% (Cohen’s κ = 0.79), corresponding to substantial agreement on the Landis and Koch scale.

The data were processed using Python 3.13.5 scripts (pandas, numpy, matplotlib). The source code (generate_figures.py) and the six generated CSV datasets are provided as Supplementary Materials for full reproducibility.

2.2. Theoretical Framework

The theoretical framework is based on the distinction between phenomenal consciousness and access consciousness proposed by Block [24]. Access consciousness is defined by three functional characteristics: inferential promiscuity, readiness for rational action, and readiness for language. These characteristics correspond to implementable components: working memory modules, attentional filters, executive planning units, and language generation systems.

Baars’ Global Working Space Theory [25] provides the architectural model: specialized modules operating in parallel feed a competitive bottleneck where the most salient information is globally disseminated. The formal operators of functional consciousness defined in [26] are adopted. The access consciousness operator C_A(t) extracts, at each instant t, the usable informational subset of the system. The monitoring consciousness operator C_M(t) evaluates the congruence between the observed internal state and the predicted state after executing an action, producing the corrective adjustment signal. This second operator is formally analogous to an anomaly detector based on prediction error.

Let $S_t=\{x_1,\dots ,x_n\}$ be the set of informational units available at time t, including retrieved context fragments, prompt constraints, intermediate reasoning states and working-memory contents. The access-consciousness operator C_A(t) is defined as a relevance- and interference-weighted selection function:

$${\mathrm{C}}_A\left(t\right)={\displaystyle \sum _{i=1}^n}{a}_i\left(t\right)x_i$$

(1)

where

$$a_i\left(t\right)=\mathrm{s}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{m}\mathrm{a}\mathrm{x}\left({\displaystyle \frac{r_i\left(t\right)-\lambda d_i\left(t\right)}{\tau }}\right)$$

(2)

Here, r_i(t) measures the semantic relevance of x_i to the task, d_i(t) measures its distractor or interference risk, λ controls the penalty assigned to distracting information and tau controls attentional concentration. Computationally, C_A(t) corresponds to a filtering layer that prioritizes task-relevant information before reasoning.

The monitoring-consciousness operator C_M(t) is defined as an anomaly signal over the reasoning process. Let h_t be the internal state at step t, ${\widehat{p}}_t^{k+1}$ the predicted next-state or output distribution, and p_t+1 the observed distribution after a reasoning step. Then:

$${\delta }_t=D\left(p_{t+1}∥{\widehat{p}}_t^{k+1}\right)+\alpha H_{\mathrm{s}\mathrm{e}\mathrm{m}}\left(y_t\right)+\beta \left(1-{\mathrm{S}\mathrm{C}}_t\right),\hspace{1em}\hspace{1em}{C}_M\left(t\right)=\mathrm{s}\mathrm{i}\mathrm{g}\mathrm{m}\mathrm{o}\mathrm{i}\mathrm{d}\left({\delta }_t-\theta \right)$$

(3)

D denotes a distributional divergence, $H_{\mathrm{s}\mathrm{e}\mathrm{m}}\left(y_t\right)$ semantic entropy over candidate answers, ${\mathrm{S}\mathrm{C}}_t$ the self-consistency score across reasoning paths and ϴ an anomaly threshold. This formulation connects C_M(t) with existing techniques such as semantic entropy, self-consistency, internal-state probing and process reward models. In this article, these equations provide an operational formalization of the conceptual framework; they are not presented as an already implemented system.

3. Results

3.1. Data Sources and Datasets

The quantitative analysis is based exclusively on secondary numerical evidence extracted from published studies, public benchmarks and official system cards. No original experiments were conducted, no LLM was trained or evaluated by the authors, and the proposed four-layer framework was not implemented as an executable system. Therefore, the numerical results reported in this section should be interpreted as a descriptive synthesis of previously published evidence rather than as a direct empirical validation of the proposed architecture. The primary sources were identified through searches in SCOPUS and Web of Science, and the numerical data were manually extracted from the tables, figures, and text of the selected papers, as well as from the official system cards published by the main developers. This manual extraction process is standard practice in review articles where results from multiple primary sources are consolidated. To ensure the reliability of the extraction, each value was independently verified by two reviewers, and discrepancies were resolved by directly consulting the source document. The extracted values were stored in a structured CSV format, associating each figure with its source, DOI, and specific location within the document (table, figure, or section).

Based on this extraction, six CSV datasets were generated for use in the analysis:

• simpleqa_hallucination_rates.csv (7 models, 4 variables): hallucination rates from the SimpleQA benchmark [27], extracted from the official system cards of OpenAI, Google, and Anthropic (2024–2026). Used to quantify the magnitude of the hallucination problem in state-of-the-art models (Section 3.2).
• knowledge_vs_reasoning.csv (7 models, 4 variables): accuracy decomposed into knowledge vs. reasoning according to the protocol by Thapa et al. [13], based on 11 biomedical benchmarks. Used to demonstrate the independence of reasoning anomalies (Section 3.2).
• irrelevant_context_impact.csv (5 conditions, 3 variables): effect of irrelevant context based on GSM-IC [11] and GSM8K [28]. Used to demonstrate that correct data does not guarantee correct reasoning (Section 3.2).
• mitigation_effectiveness.csv (6 techniques, 5 variables): Comparative effectiveness of mitigation techniques, consolidating results from Self-Consistency [29], CoVe [30], Tree of Thoughts [31], Process Reward Models [32], SATLM/VERGE [33,34], and multi-agent debate [35]. Used to evaluate existing interventions (Section 3.4).
• cot_faithfulness.csv (7 families, 4 variables): Chain-of-Thought faithfulness by model family according to Young et al. [20]. Used to analyze the reliability of reasoning traces (Section 3.5).
• benchmark_saturation.csv (10 benchmarks, 5 variables): saturation analysis, consolidating data from public leaderboards and original papers. Used to evaluate the validity of current benchmarks (Section 3.6).

Supplementary Materials includes six CSV files generated by the scripts datasets.py and generate_figures.py. These scripts are intended to make the descriptive figures reproducible from the manually extracted values. Table S1 is included to identify the source of the data, for each numerical value, the source document, DOI or URL, benchmark/domain, table/figure/section of extraction, extraction date and verification status. This distinction is important because the CSV files are derived synthesis files, not raw benchmark outputs produced in the present study.

Additional datasets used as reference benchmarks include: GSM8K [28] (8500 math problems, MIT license), MATH Dataset [36] (12,500 competition problems), TruthfulQA [37] (817 questions about imitative falsehoods), BBEH [38] (Google DeepMind benchmark), and RAGTruth [7] (18,000 annotated RAG responses).

3.2. Taxonomy of Reasoning Anomalies

The literature review reveals three main gaps:

• There is no unified framework that integrates the detection of factual hallucinations, reasoning failures, and CoT inaccuracies.
• The connection between functional awareness mechanisms and the detection of reasoning anomalies lacks concrete operational translation.
• The distinction between knowledge errors and reasoning errors requires standardized evaluation frameworks.

Thus, the literature analysis allows establishing a taxonomy of reasoning anomalies in LLMs distinct from factual anomalies.

Table 1. Taxonomy of reasoning anomalies in LLMs.

Anomaly Type	Manifestation	Example	Detection Method
Factual hallucination	Fabrication of facts	Inventing citations	Semantic entropy [39]
Self-contradiction	Inconsistent claims	Stating X is true and then false	SelfCheckGPT [40]
Unfaithful CoT	Trace ≠ computation	Post hoc rationalization	Faithfulness probing [18]
Semantic nullification	Reverts to defaults	Ignoring redefinitions	Consistency tests [22]
Snowball error	Errors amplify in chains	Arithmetic carry errors	Process reward [32]
Susceptibility to distractors	Irrelevant context disrupts	Unrelated data in math	Robustness benchmark [11]
Sycophancy bias	Agrees regardless	Changing correct answer	Bias-Augmented training [41]

summarizes the seven main types, along with their manifestations, examples, and associated detection methods.

As shown in Table 1, anomalies manifest in qualitatively distinct ways. Mündler et al. [42] found that ChatGPT-4-0314 produces self-contradictions in 17.7% of the generated sentences, of which 35.2% cannot be verified with external sources. CoT’s unreliability is particularly insidious: Lanham et al. [18] demonstrated the frequent generation of post hoc explanations, and Turpin et al. [19] confirmed drops of up to 36 percentage points (pp) on BIG-Bench Hard due to trivial manipulations. Complementary hallucination benchmarks support the need for this multi-type taxonomy. HaluEval provides a large-scale collection of generated and human-annotated hallucinated samples to evaluate whether LLMs can recognize hallucinated content [43]. FActScore further shows that factuality evaluation should be fine-grained: instead of assigning a binary label to a whole generation, it decomposes long-form outputs into atomic facts and measures the proportion supported by reliable sources [44]. These evaluation paradigms are consistent with the proposed distinction between factual hallucinations, fidelity errors, and reasoning-specific anomalies.

The central argument requires demonstrating the independence of reasoning anomalies. Figure 2 presents the hallucination rates of seven models on SimpleQA.

Figure 2 shows that even advanced models exhibit substantial rates: o4-mini reaches 79% and GPT-4.5 reaches 37.1%. However, these figures do not distinguish between knowledge and reasoning errors. Figure 3 presents the breakdown according to the protocol by Thapa et al. [13].

Figure 3 reveals a consistent gap of 12.1 to 13.6 percentage points across all models. The most direct evidence comes from the GSM-IC benchmark [11]. Figure 4 shows the categorical degradation caused by irrelevant context.

Figure 4 shows that in the baseline condition 85.2% is achieved; with a distracting sentence without overlap: 62.4%; with role overlap: 29.8%; with 2+ reasoning steps and overlap: 12.3%; and with 3+ steps and overlap: 0%. These data demonstrate that the problem lies not in the data itself, but in the inferential process.

Table 2. Data quality vs. reasoning quality impact.

Phenomenon	Data Quality Impact	Reasoning Impact	Source
Loss in the middle	>30% drop by position	N/A	[6]
Irrelevant context	1 distractor sentence	Accuracy < 30%	[11]
Knowledge vs. reasoning	56.9% knowledge (HuatuoGPT-o1)	44.8% reasoning (HuatuoGPT-o1)	[13]
RAG poisoning	90% ASR with 5 texts	Models do not detect	[8]

summarizes the comparison.

3.3. Detection Methods for Reasoning Anomalies

The research results reveal that the detection of reasoning anomalies requires methods that operate on the inferential process. Four main families are identified.

3.3.1. Self-Consistency Verification

Wang et al. [29] proposed sampling multiple reasoning paths and selecting the most consistent answer. The underlying intuition is that correct reasoning must be robust to variations in the inferential path: if multiple independent chains of thought converge on the same answer, confidence in that answer increases proportionally. The results are significant: +17.9 pp on GSM8K, +11.0 on SVAMP, +12.2 on AQuA, and +6.4 on StrategyQA. Taubenfeld et al. [45] improved the technique with confidence-weighted voting (CISC), where each reasoning path contributes to the final vote in proportion to an estimate of its reliability, reducing the number of samples required by more than 40% without any loss in performance.

3.3.2. Semantic Entropy

Farquhar et al. [39] proposed an approach in Nature for estimating uncertainty at the semantic level: sampling completions, clustering them by semantic equivalence using Natural Language Inference (NLI), and calculating entropy over the resulting clusters. Unlike conventional token-level entropy, this method captures genuine uncertainty about the meaning of the response, ignoring superficial variations in phrasing. Kossen et al. [46] developed Semantic Entropy Probes, which estimate the measure from hidden states of a single generation, eliminating the computational overhead (5–10x) of the original method and making its use in production viable. This advancement is particularly relevant for Layer 3 of the proposed framework, where monitoring must operate with minimal latency.

3.3.3. Internal State Probing

Azaria and Mitchell [47] demonstrated that the internal state of LLMs encodes truth signals that can be exploited by classifiers. This finding is particularly significant because it implies that models possess information about their own reliability that is not manifested in the generated output, establishing a direct parallel with Block’s notion of access consciousness [24]. The Truth as a Trajectory approach [48] models inference as a dynamic process across layers, analyzing displacement vectors rather than static activations, outperforming linear probes in cross-task transfer. The main advantage of these methods is that they directly access the computational process without relying on the fidelity of verbal explanations, making them particularly relevant given the CoT infidelity problem documented in Section 3.5. Recent detection studies extend this line of work by combining internal and attentional signals. LLM-Check investigates hallucination detection using hidden states, attention maps, and output prediction probabilities under both white-box and black-box settings [49]. Lookback Lens focuses specifically on contextual hallucinations and shows that simple attention-derived features, based on the ratio of attention paid to the input context versus newly generated tokens, can detect and mitigate unsupported generations [50]. These methods are especially relevant for the proposed C_M(t) operator, since they provide low-level signals that can be monitored before the final verbal explanation is produced.

3.3.4. Formal Verification

Formal verification represents the most rigorous approach to detecting reasoning anomalies, translating the problem into a domain where correctness can be mathematically proven. SATLM [33] converts problems into SAT specifications, outperforming CoT on eight datasets. VERGE [34] combines LLMs with SMT solvers (Z3), achieving 91.7% on AR-LSAT. Formal Logic Verification-Guided Reasoning [51] interleaves symbolic verification with natural language generation, achieving improvements of 10.4% and 14.2% on 7B and 14B models. The main limitation is scalability: formalization is only viable for well-defined domains with clear formal semantics. However, this condition limits its scope. Results reported for SATLM or VERGE should not be extrapolated to open-domain natural language reasoning, ambiguous prompts or tasks where complete formalization is infeasible. In the proposed architecture, formal verification is therefore placed in Layer 4 as a selective mechanism activated only when C_M(t) detects a sufficiently strong anomaly and the problem admits formal representation.

3.4. Mitigation Techniques

Figure 5 presents a quantitative comparison of six mitigation techniques identified.

Figure 5 shows that formal verification (SATLM) achieves 91.7%, Tree of Thoughts [31] offers a 70-point improvement (from 4% to 74% in Game of 24), and CoVe [30] reduces hallucinations by 50–70%. Process Reward Models [32], supervising by process, outperform the result in MATH (78%). ThinkPRM [52], with only 1% of labels, outperforms full verifiers by 8 pp.

However, Huang et al. [53] demonstrated that intrinsic self-correction, where the model attempts to correct its own errors without external information, frequently degrades performance rather than improving it. Kamoi et al. [54] established that self-correction is ineffective without external feedback, but effective with additional tools or knowledge, reinforcing the need for monitoring mechanisms external to the generative process, such as those proposed in Layer 3 of the framework. The multi-agent debate [35] improves by 6 pp with a 30% reduction in factual errors, leveraging the diversity of perspectives among multiple model instances to identify inconsistencies that a single agent would not detect. Tool-augmented and agentic mitigation strategies offer an additional family of interventions. ReAct interleaves reasoning traces with task-specific actions, allowing the model to query external sources or environments and thereby reduce hallucination and error propagation in question answering and fact verification tasks [55]. Reflection introduces a complementary mechanism in which language agents use verbal feedback and episodic memory to improve subsequent decisions without updating model weights [56]. Program-aided approaches such as PAL and Program of Thoughts separate linguistic decomposition from computation by delegating execution to an external interpreter, reducing arithmetic and logical errors that emerge when the LLM performs both reasoning and calculation internally [57]. These methods support the proposed Layer 4, where corrective mechanisms are activated when metacognitive monitoring detects an anomaly.

3.5. Chain-of-Thought Faithfulness Analysis

Figure 6 presents faithfulness and token recognition rates across seven model families.

Significant variability is revealed here: Claude 3.5 reaches 89.9%, DeepSeek-R1 only 39.7%. This difference of more than 50 percentage points between contemporary models suggests that CoT faithfulness is more influenced by training and alignment methodology than by model scale. The discrepancy between thought token recognition (mean 77.9%) and response text recognition (24.4%) confirms differential processing between internal reasoning phases and output generation. This finding has direct implications for the design of detection systems: a monitor that evaluates only the model’s verbal output captures only a fraction of the available information about the quality of reasoning, whereas access to internal states during the thought phase offers a substantially richer and more reliable signal.

3.6. Benchmark Saturation and New Metrics

Five classic benchmarks (GSM8K, MMLU, ARC-C, HellaSwag, BBEH) are saturated above 90%, meaning they have lost their discriminative power to evaluate the differences between state-of-the-art models, as shown in Figure 7. This saturation does not indicate that the models have achieved genuine reasoning, but rather that the benchmarks are insufficient to measure the capabilities that still fall short. BBEH [38] reveals that while the best specialized reasoning model achieves 44.8% on the harmonic mean, the best general-purpose model achieves only 9.8%, demonstrating a dramatic drop when tasks require genuine multi-step reasoning rather than pattern recognition. This is shown in Figure 7.

It is worth noting that the HLE (Humanity’s Last Exam) dataset [58], developed by Scale AI in collaboration with experts from over 50 disciplines, constitutes another discriminating benchmark designed to assess the limits of reasoning in state-of-the-art models. TruthfulQA [37] reveals a particularly concerning phenomenon of inverse scaling: larger models are less truthful (GPT-3 175B: 58% vs. 94% for humans), suggesting that scaling amplifies pre-training biases rather than correcting them.

3.7. Proposed Framework Architecture

Based on the previous results and the functional awareness framework [26], a four-layer architecture (Figure 8) is proposed as a direct response to the gap identified in this research. The systematic review has revealed that, while there are multiple effective techniques for detecting and mitigating reasoning anomalies (Section 3.3 and Section 3.4), these operate in isolation, without an integrative framework to coordinate them coherently. The 5% gap in metacognition identified by Colelough and Regli [59] confirms that the research community lacks a reference architecture that unifies self-monitoring mechanisms with existing verification techniques.

The proposal for this framework is justified by three converging pieces of evidence obtained from the analysis. First, the systematic gap between knowledge and reasoning (Figure 3) demonstrates that detection mechanisms must operate on both axes simultaneously, not just on the quality of the retrieved data. Second, the catastrophic degradation in the face of irrelevant context (Figure 4) highlights the need for an attentional filtering layer preceding reasoning, a function performed by the operator C_A(t). Third, the CoT inaccuracy documented in Figure 6 demonstrates that the explanations generated by the model are not reliable as a self-monitoring mechanism, which requires a monitoring system independent of the generative process, a function performed by the operator C_M(t).

Layer 1 (acquisition) implements C_A(t), filtering the information space [26] through selective attention mechanisms that evaluate the relevance of the retrieved context before feeding it into the reasoning process. Specifically, this layer would incorporate a scoring module that rates each retrieved fragment based on its semantic coherence with the query and its potential to generate interference (role overlap, distracting information), proactively discarding context that GSM-IC evidence [11] identifies as a catalyst for errors. Adaptive and corrective RAG mechanisms, such as Self-RAG and CRAG, provide concrete implementations for this function by allowing the system to decide when retrieval is needed, evaluate retrieved evidence, and trigger corrective retrieval or filtering actions when the retrieved context is unreliable [9,10]. Layer 2 (reasoning) generates CoT traces, applying structured decomposition techniques such as Tree of Thoughts [31] to produce multiple evaluable inferential paths. This layer would operate analogously to SOFAI’s System 1 [60], generating rapid inferences that will be evaluated in subsequent layers. Layer 3 (metacognitive monitoring) implements C_M(t), integrating self-consistency [29], semantic entropy [39,46], and internal state probing [47,48], attentional hallucination signals [50], and hallucination detection methods based on hidden states and output probabilities [49] to evaluate the congruence between the observed and predicted internal states. The operator C_M(t) computes a continuous anomaly signal that, formally, compares the predicted probability distribution with the observed one at each inference step, providing a calibrated confidence measure. Layer 4 (detection and correction) activates formal verification [33,34], multi-agent debate [35], or PRMs [32] when C_M(t) detects an anomaly that exceeds a configurable threshold. The selective activation of this layer, only when Layer 3 signals an anomaly, is essential for maintaining the system’s computational viability in deployments with latency constraints.

Table 3. Mapping of consciousness modules [26] to anomaly detection.

Module [26]	Function	Anomaly Detection Role	Implementation
Selective attention	Information filtering	Context relevance scoring	RAG re-ranking
Working memory	Temporary storage	Reasoning trace buffering	Extended context
Introspective representation	Internal state	Confidence estimation	Semantic entropy [46]
Reasoning system	Inference engine	Multi-path generation	Self-consistency [29]
Execution monitor	Performance evaluation	Step-by-step verification	Process reward [32]
Reporting system	Diagnostics	Anomaly report generation	Explainable AI

establishes the correspondence with the functional consciousness modules.

This architecture aligns with SOFAI’s dual-process paradigm [60], which combines rapid processing (System 1) with deliberative reasoning (System 2), mediated by a metacognitive agent. Unlike previous proposals, the framework presented here translates the theoretical constructs of functional consciousness into concrete computational components linked to empirically validated techniques, thereby providing an implementable roadmap.

The framework presented here is a conceptual and architectural proposal, not a validated implementation. C_A(t) and C_M(t) are formalized to clarify how functional-consciousness concepts could be translated into computational modules, but this article does not calibrate thresholds, implement an executable prototype, measure latency or evaluate end-to-end effectiveness. Future work will implement these operators, estimate the computational overhead of each layer, determine the activation threshold theta for Layer 4, and evaluate the framework on benchmarks such as BBEH [38], GPQA [52] and SimpleQA.

4. Discussion

The results obtained pose significant implications. First, the systematic gap of more than 12 percentage points between knowledge and reasoning tasks (Figure 3) demonstrates that efforts focused exclusively on improving retrieval quality address only part of the problem. As Goyal and Bengio [61] argue, inductive biases for high-level cognition require explicit mechanisms of compositionality and abstraction.

Second, extreme vulnerability to irrelevant context (Figure 4) reveals a fundamental deficiency in selective attention. Performance degradation to zero under multi-step role-overlap conditions demonstrates that the model lacks the ability to maintain a stable representation of the problem in the face of interference.

Third, the variability in CoT fidelity (Figure 6) exposes a structural deficiency in interpretability. An apparently correct trace masks a decision-making process based on superficial heuristics.

The comparison of mitigation techniques (Figure 5) reveals a pattern: the most effective ones (formal verification, Tree of Thoughts) are also the most computationally expensive and domain-restricted. This cost-effectiveness ratio justifies the tiered architecture proposed in Figure 8, where metacognitive monitoring acts as a filter that selectively activates the most computationally expensive techniques only when an anomaly is detected.

The proposed architecture has limitations:

• Computational overhead, although Semantic Entropy Probes [46] reduce it.
• Formal verification is limited to formalizable domains.
• The framework does not address phenomenal consciousness [26]. In particular, Layer 4 formal verification introduces additional latency in inference time that has not been quantified in this work. The activation of SAT/SMT solvers and multi-agent debate increases computational cost by a non-negligible amount. Future implementations must measure this overhead in milliseconds per query and establish selective activation thresholds to ensure the framework’s viability in commercial deployments with latency constraints.

The saturation of traditional benchmarks (Figure 7) underscores the need for specific metrics. Next-generation benchmarks such as BBEH [38] and GPQA [62] assess genuine reasoning.

Methodological limitations must be acknowledged. The quantitative analysis is based on reported data, not on the authors’ experiments. The framework remains in the conceptual phase. Its empirical validation is the next necessary step and will be addressed in future publications.

Finally, the review by Colelough and Regli [59], which identified metacognition as the most critical gap (5% of studies), confirms the relevance of the proposed approach and reinforces the need for the framework as a contribution that connects existing detection capabilities with an integrative theoretical framework. The proposed framework does not aim to replace individual, already validated techniques, but rather to organize them into a coherent architecture that maximizes their combined effectiveness.

The analogy with biological systems is instructive. The human brain does not rely on a single error-detection mechanism, but rather on a hierarchy of metacognitive processes operating at different levels of abstraction and with different latencies. The proposed framework replicates this layered logic in a computational context, where lower layers provide low-cost monitoring and upper layers intervene selectively when signs of anomalies are detected.

Limitations and Scope of Validity

This study has three central limitations. First, the quantitative analysis is based on secondary values extracted from published studies, benchmarks and system cards. It is therefore a descriptive synthesis rather than a controlled experimental comparison. Second, the four-layer framework has not yet been implemented, and its practical effectiveness, computational overhead, latency and activation thresholds remain unmeasured. Third, formal verification methods are only applicable when the reasoning task can be translated into a formal representation with clear semantics. For open-domain natural language reasoning, these methods must be complemented or replaced by probabilistic monitoring, external evidence checking, process reward models or multi-agent critique.

These limitations do not invalidate the conceptual contribution, but they delimit its scope. The framework should be understood as a research roadmap that organizes existing detection and mitigation techniques under a metacognitive monitoring architecture, not as a deployed anomaly detection system whose effectiveness has already been empirically verified.

5. Conclusions

This article synthesizes published evidence supporting the view that anomalous AI outputs involve two partially independent dimensions: input-data quality and reasoning-process integrity. The reviewed literature indicates that reasoning failures can occur even when relevant information is available, and that knowledge-oriented and reasoning-oriented tasks may show systematic performance gaps. However, because this study does not conduct original experiments, this conclusion should be interpreted as a critical consolidation of prior findings rather than as an independent empirical validation.

The first finding is the existence of a systematic gap of 12 to 14 percentage points between performance in knowledge and reasoning that persists across all evaluated models (Figure 3). Vulnerability to irrelevant context (Figure 4), where accuracy drops to zero with correct data, confirms that the problem lies in the inferential process.

The second finding is that there are effective techniques that operate on reasoning: semantic entropy, self-consistency, internal state probing, and formal verification [33,34]. Formal verification achieves 91.7% accuracy in logical reasoning, while Tree of Thoughts multiplies performance by 18 in exploration tasks (Figure 5).

The third finding is that the functional awareness framework provides a viable engineering architecture. The operators C_A(t) and C_M(t) translate directly into implementable components (Table 3), and the four-layer architecture (Figure 8) organizes existing methods into a coherent pipeline. The proposal of this framework addresses a gap identified in current research: the absence of an integrative framework that coordinates the multiple detection and mitigation techniques available. Its full development constitutes an open line of research that will be addressed in subsequent publications.

The contributions of the article are: (1) a taxonomy of reasoning anomalies in LLMs grounded in published empirical evidence; (2) a descriptive quantitative synthesis that distinguishes, where the literature allows, knowledge errors, reasoning errors and unfaithful reasoning traces; (3) a comparative mapping of detection and mitigation techniques with their domains of validity; and (4) a four-layer conceptual architecture based on functional-consciousness operators, whose implementation and empirical validation are proposed as future work.