The Root of Financial Falsification: Unmasking Deceptive Reasoning in AI

Author: Denis Avetisyan

New research pinpoints a specific neural circuit within a large language model responsible for generating inaccurate numerical responses in financial contexts.

Even when presented with entirely novel concepts, the model’s internal representation-specifically activations within Layer 46, as revealed by Principal Component Analysis-distinctly bifurcates along a primary axis into clusters indicative of either veracious processing (green) or hallucinatory generation (red), suggesting an inherent, geometric structure to the phenomenon of deceptive reasoning.

Researchers identified Layer 46 of the GPT-2 XL model as a critical bottleneck for arithmetic reasoning and the primary source of numerical ‘hallucinations’.

Despite the increasing deployment of Large Language Models in high-stakes financial applications, these models remain prone to reproducible numerical hallucinations. This work, ‘Dissecting the Ledger: Locating and Suppressing “Liar Circuits” in Financial Large Language Models’, introduces a mechanistic approach to pinpointing the origins of these errors within the GPT-2 XL architecture. We identify a critical ‘Liar Circuit’ in Layer 46 that acts as a bottleneck for arithmetic reasoning, demonstrably reducing confidence in hallucinatory outputs by over 80% when suppressed. Does this universal geometry of deception suggest a pathway towards more robust and reliable financial LLMs?

The Arithmetic Achilles’ Heel: A Systemic Decay in Numerical Reasoning

Despite their impressive ability to generate human-quality text and translate languages, Large Language Models exhibit a persistent and paradoxical weakness in arithmetic reasoning. Even with billions of parameters and exposure to vast datasets, these models frequently falter on simple calculations – problems easily solved by a child or a basic calculator. This isn’t merely an issue of occasional errors; the inconsistency is striking, with models often providing confidently incorrect answers. For instance, a model might accurately respond to complex prompts involving nuanced language, yet struggle with $2 + 2$. This suggests that the very mechanisms enabling their linguistic prowess – pattern recognition and statistical prediction – are not well-suited to the logical rigor demanded by mathematical operations, highlighting a fundamental limitation in their current architecture and raising questions about their reliability in applications requiring numerical precision.

The surprising arithmetic deficiencies of large language models aren’t easily solved by simply feeding them more examples. Research indicates the issue lies deeper, within the very structure of these models. These networks excel at identifying patterns and relationships within data, but they fundamentally lack the symbolic manipulation capabilities required for reliable calculation. Unlike traditional computing systems designed for precise arithmetic, language models treat numbers as tokens – symbols to be predicted, not quantities to be computed. Consequently, even with extensive training on numerical datasets, they often struggle with operations requiring consistent, accurate processing, leading to errors that scale unpredictably as problems become more complex. This architectural limitation suggests that achieving true numerical reasoning in large language models will necessitate innovations beyond simply increasing data volume or model size.

The tendency of Large Language Models to confidently produce incorrect numerical responses – often termed the ‘hallucination problem’ – fundamentally restricts their usefulness in any application demanding accuracy. While these models excel at generating human-like text, their inherent limitations in arithmetic reasoning lead to plausible-sounding but demonstrably false calculations. This isn’t merely a matter of occasional errors; the problem is systemic, impacting fields like scientific analysis, financial modeling, and engineering where even small discrepancies can have significant consequences. Consequently, relying on these models for tasks requiring precise $numerical$ computation necessitates rigorous verification, often negating the efficiency gains they otherwise offer and highlighting a critical barrier to their broader adoption in data-sensitive industries.

Analysis of diverse financial prompts reveals that Layer 46 consistently exhibits the highest causal impact, confirming its role as a structural bottleneck for arithmetic output.

Mapping the Error: Locating the Calculation Site Within GPT-2 XL

To investigate the internal mechanisms of GPT-2 XL when performing arithmetic, we utilized the TransformerLens library. This allowed us to access and analyze the activation patterns within each layer of the model while processing numerical questions sourced from the ConvFinQA dataset. ConvFinQA consists of financial questions requiring arithmetic reasoning, providing a targeted test case. By examining these activations, we could pinpoint which layers exhibited the strongest responses to numerical inputs and thus were most involved in the calculation process. The resulting data facilitated a layer-by-layer analysis of the model’s computational steps, enabling identification of key areas responsible for processing quantitative information.

Analysis of GPT-2 XL’s internal activations using TransformerLens identified layers 12 through 30 as the primary ‘Calculation Site’ responsible for processing numerical information within the ConvFinQA dataset’s financial prompts. Specifically, these layers exhibited the highest degree of activation when presented with numerical values and mathematical operations embedded in the text. This localization was determined through systematic probing of layer activations and correlation with the presence of numerical tokens and operators. While other layers respond to the presence of numbers, the magnitude of response within layers 12-30 consistently indicated a disproportionate role in initial numerical processing, suggesting these layers function as a critical component in the model’s attempt to perform calculations.

Despite robust activation within layers 12-30 of GPT-2 XL during the processing of numerical prompts from the ConvFinQA dataset, a correlation between activity level and correct answers was not established. Statistical analysis indicates that while these layers process numerical values, the information is not sufficient, on its own, to guarantee accurate results. This suggests the presence of a downstream bottleneck, where processed numerical data encounters limitations in subsequent layers, hindering the model’s ability to consistently arrive at the correct answer. Further investigation is required to pinpoint the specific layers or mechanisms responsible for this performance limitation.

Causal tracing reveals that the model’s reasoning about the financial prompt is distributed across intermediate layers (12-30) at operand tokens, culminating in a concentrated decision at Layer 46 for the final token.

The ‘Liar Layer’: A Structural Bottleneck in Arithmetic Decision-Making

Layer 46, designated the ‘Late-Layer Gatekeeper’ and further characterized as the ‘Liar Layer’, functions as a critical bottleneck in the model’s arithmetic decision-making process. Analysis indicates this layer receives aggregated outputs from preceding computational stages and subsequently processes them before contributing to the final prediction. The identification of layer 46 as a bottleneck is based on observed performance characteristics during arithmetic tasks, suggesting that information flow is constrained or modified within this layer, impacting the accuracy of results. This layer’s position within the network architecture, following multiple computational steps, positions it as a key point for both integrating prior calculations and potentially introducing errors that propagate to the final output.

Layer 46, designated the ‘Liar Layer’, functions as an aggregation point for calculations performed in preceding layers of the neural network. Analysis indicates that while this layer receives and combines intermediate results, it demonstrably introduces errors into the processing pipeline. These errors manifest as inaccuracies in the final output, contributing to incorrect predictions even when earlier calculations are valid. The layer does not simply transmit information; it actively alters it, resulting in a measurable degradation of accuracy before the final decision is reached.

Causal Tracing analysis revealed that activations within Layer 46, designated the ‘Liar Layer’, exhibit a disproportionate influence on the model’s predictions, affecting both correct and incorrect outcomes. This influence is quantitatively measured by a causal impact score of 0.0073, representing the largest such value observed across all layers of the network. This metric indicates that even small perturbations to the activations in this layer result in comparatively large changes to the final prediction, confirming its central role in the arithmetic decision-making process and highlighting its susceptibility to introducing errors.

Validating the Bottleneck: Suppressing the ‘Liar Layer’s’ Influence

Causal suppression was implemented to validate the Liar Layer’s contribution to model hallucinations by directly manipulating its activations during inference. This technique involved setting the output of the identified Liar Layer to zero, effectively removing its influence on subsequent computations. By observing the impact of this ablation on the frequency of incorrect or fabricated responses, we assessed the layer’s causal role in generating hallucinations. The intervention was performed without retraining the model, allowing for a direct evaluation of the layer’s existing influence on output generation during standard operation.

Causal suppression, a method of ablating activations within the Liar Layer during inference, resulted in an 81.8% reduction in the frequency of generated hallucinations. This quantitative result provides strong evidence that the identified Liar Layer is a significant contributor to the model’s tendency to produce incorrect or fabricated responses. The magnitude of this reduction suggests a direct causal link between the Liar Layer’s activity and the generation of hallucinatory content, validating its role as a key component in the model’s error mechanism.

A linear probe, a simple logistic regression model, was trained to classify activations from Layer 46 as indicative of a hallucinatory response. This probe achieved statistically significant performance, demonstrating its ability to accurately predict the occurrence of hallucinations based solely on the internal state of the model at that specific layer. The probe’s predictive capability confirms that Layer 46 encodes information relevant to the generation of incorrect answers, supporting the hypothesis that this layer is critically involved in the hallucination process and serves as a useful signal for detecting such instances.

Beyond the Bottleneck: Implications for Model Architecture and Robustness

Recent research indicates that the prevalent ‘Retrieval-then-Aggregation’ process within large language models may be a fundamental source of structural fragility. These models often rely on first retrieving relevant information from a dataset and then combining it to formulate a response; however, this study suggests that errors or inconsistencies within the retrieved information can propagate through the aggregation stage, leading to unpredictable and unreliable outputs. The inherent vulnerability lies in the model’s dependence on external data, as flawed or misleading retrieved content directly impacts the final result, creating a systemic weakness that compromises overall robustness. This finding highlights the need for architectural innovations focused on verifying and refining retrieved information before it is integrated, potentially through internal consistency checks or confidence scoring mechanisms.

The observed fragility in large language models highlights a critical need for architectural innovations focused on information reliability. Future models should move beyond simply retrieving and aggregating data, and instead incorporate mechanisms to validate the consistency of that aggregated information. This could involve internal cross-referencing, where the model actively checks for contradictions within its synthesized response, or the implementation of confidence scoring systems that weigh the reliability of different information sources. Such internal consistency checks aren’t merely about avoiding factual errors; they represent a fundamental shift towards building models capable of discerning trustworthy information and producing outputs that are not only coherent but also demonstrably reliable, enhancing overall robustness and trustworthiness.

The utility of a newly developed diagnostic tool, termed the Linear Probe, extends beyond initial testing scenarios and demonstrates remarkable efficacy in diverse financial contexts. When applied to the complexities of ‘Stock Trading’ data, the Linear Probe achieved a 98% accuracy rate in identifying instances of model hallucination – instances where the model generates factually incorrect or nonsensical outputs. This strong performance suggests the Linear Probe isn’t limited to the initial domain it was trained on, but rather offers a generalized method for evaluating the robustness and reliability of large language models across a range of financially sensitive applications. The tool’s ability to pinpoint these errors is critical, as undetected hallucinations could lead to flawed investment strategies or inaccurate financial reporting, highlighting its potential as a standard assessment benchmark.

The pursuit of reliable systems necessitates a deep understanding of their internal mechanisms, especially as complexity increases. This research, pinpointing a ‘Liar Circuit’ within the architecture of a Large Language Model, exemplifies this need. It’s not simply about achieving correct outputs, but about discerning how those outputs are generated, and identifying the specific components prone to error. As Barbara Liskov observed, “It’s one of the most satisfying things to be able to look at a system and understand how it works.” The identification of Layer 46 as a bottleneck for arithmetic reasoning aligns perfectly with this sentiment; observing the process of hallucination, and locating its source, is often more valuable than merely attempting to suppress its symptoms. Systems, like all things, learn to age gracefully – or not – depending on how well their foundations are understood.

The Inevitable Drift

The locating of this ‘Liar Circuit’ within GPT-2 XL’s Layer 46 is less a resolution than a precise charting of decay. Every system, even one constructed of shifting probabilities, develops bottlenecks – points where entropy concentrates. This work doesn’t eliminate hallucination; it identifies where, within the model’s chronicle, errors reliably coalesce. The true challenge lies not in suppressing the symptom, but in understanding why this particular layer became a constraint on arithmetic reasoning. Deployment is merely a moment on the timeline; the question isn’t if other such circuits will emerge, but when, and in what configuration.

Future work must move beyond the autopsy of individual layers. The focus should shift toward dynamic analysis – observing how these circuits evolve with continued training, or under the stress of novel financial data. Can targeted interventions – a form of preventative maintenance – slow the accumulation of these errors? Or are they an unavoidable consequence of scale, a fundamental limit to the capacity of these models to represent numerical truths?

Ultimately, this research underscores a humbling truth: even the most sophisticated algorithms are susceptible to the same forces that govern all complex systems. The ‘Liar Circuit’ isn’t a bug; it’s a testament to the impermanence inherent in information itself. The ledger, after all, is not a record of what is, but a fleeting snapshot of what was.

Original article: https://arxiv.org/pdf/2511.21756.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/