Passing the Torch: How Well Do AI Models Hand Off Reasoning?

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Author: Denis Avetisyan

A new study explores whether large language models can seamlessly continue mathematical problem-solving started by another AI, revealing significant performance differences based on model architecture.

Research demonstrates that successful reasoning transfer between models relies heavily on architectural alignment, with models from the same family exhibiting significantly better interchangeability than those from different families.

Despite advances in large language model (LLM) reasoning via techniques like chain-of-thought prompting, the robustness and transferability of this reasoning across different models remain largely unexplored. This work, ‘Reasoning Relay: Evaluating Stability and Interchangeability of Large Language Models in Mathematical Reasoning’, investigates whether partially completed reasoning chains can be reliably continued by alternative LLMs, both within and across model families. Our findings demonstrate that successful ‘reasoning relay’ is heavily influenced by model alignment, with intra-family transfers exhibiting superior performance to cross-family continuations. This raises the intriguing possibility of building collaborative AI systems leveraging modular reasoning, but how can we best design architectures to maximize the benefits of such interchangeability and ensure consistently reliable outcomes?

The Illusion of Intelligence: When Answers Mask Flaws

Despite remarkable progress in natural language processing, large language models (LLMs) frequently falter when confronted with complex mathematical reasoning. These models, while adept at recognizing patterns in vast datasets, struggle with the sequential logic required to solve multi-step problems. Errors aren’t simply random guesses; they often stem from a failure to properly maintain context across multiple operations, or an inability to apply the correct mathematical principles in a novel situation. For example, a model might correctly identify the need for multiplication in one step, but then misapply the result in a subsequent calculation, leading to a cascading error. This limitation highlights a crucial distinction between statistical pattern matching and genuine problem-solving ability, suggesting that scaling model size alone will not suffice to overcome these fundamental challenges. The difficulty arises not from a lack of data, but from the inherent limitations of the models’ architecture in representing and manipulating abstract mathematical concepts like ∫x^2 dx or applying rules of algebra.

Current research indicates that simply increasing the size of large language models does not consistently translate to improved reasoning capabilities. While scaling has demonstrably enhanced performance on certain benchmarks, complex problem-solving – particularly tasks requiring multi-step inference or abstract thought – often plateaus or exhibits only marginal gains. This suggests that a fundamental limitation exists beyond sheer computational power and data volume. The prevailing architecture may necessitate a shift towards incorporating mechanisms that explicitly model reasoning processes, such as symbolic manipulation, causal inference, or the ability to decompose problems into smaller, manageable sub-problems. Achieving genuine problem-solving ability, therefore, likely requires moving beyond statistical pattern recognition and embracing approaches that prioritize the how of thinking, not just the what.

Determining whether a Large Language Model (LLM) arrives at the correct answer is insufficient for gauging its true reasoning capability. A focus on how an LLM reaches a conclusion is paramount; evaluating the intermediate steps, the logic applied, and the consideration of alternative pathways reveals vulnerabilities hidden by a seemingly correct final response. This process-oriented assessment allows researchers to pinpoint specific flaws in the model’s reasoning chain – such as reliance on spurious correlations or failure to apply relevant principles – and subsequently design targeted interventions to enhance reliability. By dissecting the ‘thought process’ – even if simulated – a more nuanced understanding of an LLM’s capabilities emerges, paving the way for improvements beyond simply increasing scale or refining datasets. Ultimately, a robust evaluation must prioritize the validity of the reasoning itself, not merely the correctness of the outcome.

The opacity of Large Language Models presents a significant hurdle in addressing reasoning failures. Unlike traditional algorithms where each step is traceable, LLMs operate as largely ‘black boxes’ – complex networks where the rationale behind a particular output remains obscured. This lack of transparency makes it exceptionally difficult to diagnose why a model arrives at an incorrect answer, hindering targeted interventions. Researchers cannot easily pinpoint whether the error stems from a flawed understanding of core concepts, a misapplication of reasoning rules, or a failure in translating the problem into a solvable form. Consequently, improvements often rely on broad adjustments to training data or model architecture, a process that is inefficient and doesn’t guarantee a resolution to specific reasoning deficits. Unraveling this internal logic is therefore crucial not only for enhancing reliability, but also for building trust in these increasingly powerful systems.

Unveiling the Chain: Exposing the Reasoning Process

Chain of Thought (CoT) prompting is a technique used with Large Language Models (LLMs) that moves beyond simple input-output interactions by requesting the model to explicitly state the intermediate steps taken to arrive at a conclusion. Instead of directly providing an answer, the LLM is prompted to generate a series of reasoning steps, effectively verbalizing its thought process. This contrasts with standard prompting methods where the LLM directly outputs a response without revealing how it was derived. The articulation of these steps increases the transparency of the model’s decision-making process, allowing users to follow and potentially evaluate the logic applied. This approach aims to make the internal reasoning of the LLM more interpretable and accessible, enabling better understanding and debugging of its responses.

Chain of Thought (CoT) prompting enhances Large Language Model (LLM) performance by requiring the explicit generation of intermediate reasoning steps before arriving at a final answer. This contrasts with direct prompting, where the model attempts to answer directly. In complex scenarios-such as multi-step arithmetic problems, common sense reasoning tasks, or symbolic manipulation-CoT demonstrably improves both the accuracy of the final output and the overall coherence of the response. By decomposing the problem into a series of logically connected steps, the model is less likely to make errors stemming from incomplete or flawed reasoning, and the generated output is more readily interpretable and debuggable. Empirical results indicate that CoT’s benefits are most pronounced when the underlying task requires significant inferential reasoning.

Despite employing Chain of Thought (CoT) prompting, Large Language Models (LLMs) do not consistently produce logically sound or factually accurate reasoning. While CoT encourages step-by-step explanation, the generated reasoning chains can contain internal inconsistencies, flawed inferences, or semantic errors that invalidate the overall solution. These errors arise from the model’s reliance on statistical correlations within the training data rather than genuine understanding or logical deduction. Consequently, even a verbose and seemingly coherent reasoning chain does not guarantee a correct final answer, and careful evaluation of the reasoning process itself is crucial to identify and mitigate these issues.

Assessing the validity of an LLM’s reasoning process is a critical evaluation metric alongside final answer accuracy. While a correct output indicates successful task completion, the intermediate reasoning steps generated through Chain of Thought prompting require independent verification. This involves checking for logical fallacies, internal contradictions, and adherence to established knowledge. Evaluating coherence ensures each step follows logically from the preceding one, and consistency confirms that the same principles are applied throughout the entire chain. Failure to validate the reasoning, even with a correct answer, leaves open the possibility of flawed logic or reliance on spurious correlations, potentially leading to unreliable performance in novel situations or when presented with slightly altered inputs.

Orchestrating Intelligence: Transferring Reasoning Between Models

Large language models exhibit differing proficiencies in mathematical reasoning tasks; for instance, LLaMA-3.1-70B-Instruct and Gemma-3-4B-IT demonstrate distinct performance characteristics. This variation suggests that a combined approach, leveraging the strengths of multiple models, has the potential to surpass the capabilities of any single model operating in isolation. The hypothesis is that by strategically integrating the reasoning processes of these models, a more robust and accurate solution to complex mathematical problems can be achieved, capitalizing on complementary skills and mitigating individual weaknesses. Initial experimentation focuses on techniques such as continuing a reasoning chain initiated by one model with another, to assess the feasibility and benefits of this combined methodology.

Reasoning transfer involves sequentially utilizing multiple Large Language Models (LLMs) to solve a single problem, where the output of one model serves as the input for the next. This can be achieved through two primary methods: Intra-Family Continuation, which leverages models within the same family-sharing architectural similarities and training data-and Cross-Family Continuation, which combines models from different families. The core principle is to capitalize on the strengths of each model in a sequential manner, potentially improving overall performance beyond what a single model could achieve. This approach allows for the delegation of tasks along a reasoning chain, with the initial model establishing a foundation and subsequent models building upon that initial reasoning.

The determination of the ‘Truncation Point’ – the specific stage in a reasoning chain where control shifts from one Large Language Model (LLM) to another – is a crucial element of reasoning transfer. Accurate identification of this point requires assessing both the initiating model’s confidence in its current reasoning step and the overall coherence of the reasoning thus far. Cumulative Log-Probability (CLP) serves as a quantitative metric for this evaluation; it aggregates the log probabilities of generated tokens, providing a measure of the model’s certainty. Lower CLP values at a given step may indicate diminished confidence and therefore a suitable point for handover, while consistently high values suggest continued, coherent reasoning. Precise evaluation at the truncation point is essential to minimize errors and maximize the benefits of combining LLM strengths.

Initial experimentation with reasoning transfer techniques reveals performance differences based on model family continuation. Utilizing Gemma-3-4B-IT as the initiating model and Gemma-3-1B-IT for completion at a 75% truncation point resulted in an accuracy of 55.26%, a notable improvement compared to the 41.76% accuracy achieved with a 25% truncation point; this suggests that providing a longer initial reasoning prefix enhances performance. Conversely, initiating the reasoning chain with LLaMA-3.1-70B-Instruct and completing it with Gemma-3-1B-IT at 75% truncation yielded a significantly lower accuracy of 41.98%. This difference is further quantified by Normalized Relative Gain (NRG) values: 0.3500 for the Gemma-3-4B-IT to Gemma-3-1B-IT transfer, indicating a positive performance gain, and -0.0827 for the LLaMA-3.1-70B-Instruct to Gemma-3-1B-IT transfer, demonstrating a negative impact from cross-family continuation.

Beyond Correct Answers: The True Measure of Reasoning

Evaluating artificial intelligence solely on the correctness of final answers presents a limited view of its true reasoning capabilities. While achieving the right solution is important, the path taken to arrive at that solution reveals crucial insights into the system’s understanding and problem-solving skills. A model might produce a correct answer through flawed logic or spurious correlations, a scenario where traditional metrics would fail to identify underlying weaknesses. Therefore, a comprehensive assessment requires moving beyond simple accuracy checks to analyze the entire reasoning process – evaluating the coherence of each step, the validity of inferences, and the overall logical flow. This holistic approach enables a more nuanced understanding of an AI’s strengths and weaknesses, paving the way for targeted improvements and the development of genuinely intelligent systems.

Process Reward Models represent a significant advancement in evaluating artificial intelligence, shifting the focus from simply judging the correctness of a final answer to assessing the quality of the reasoning process itself. These models don’t just score outcomes; they analyze each step in a reasoning chain, providing a granular evaluation of coherence and logical validity. By assigning rewards based on the correctness of intermediate steps, PRMs offer a more nuanced understanding of a model’s strengths and weaknesses – pinpointing where reasoning falters, even if the final answer is coincidentally correct. This detailed feedback allows developers to move beyond broad performance metrics and implement targeted improvements, ultimately fostering more reliable and transparent AI systems capable of demonstrably sound reasoning.

Shifting evaluation from final answer correctness to the reasoning process itself unlocks a more nuanced understanding of model capabilities. Traditional metrics often fail to pinpoint where a model falters – is the error due to a flawed initial understanding, a misstep in logical deduction, or a simple calculation mistake? By analyzing each step in a reasoning chain, researchers can diagnose these specific weaknesses with greater precision. This granular feedback allows for the development of targeted interventions – refining training data, adjusting model architecture, or implementing specific reasoning strategies – ultimately leading to more robust and reliable artificial intelligence systems. Rather than simply rewarding correct outputs, this approach fosters a deeper understanding of how a model thinks, enabling continuous improvement and more effective problem-solving.

The assessment of complex reasoning in artificial intelligence benefits significantly from dedicated datasets like the MATH Dataset, a collection of challenging mathematical problems requiring multi-step solutions. This resource allows researchers to move beyond simply evaluating whether a model arrives at the correct answer, and instead, meticulously examine the reasoning process itself. By benchmarking performance on the MATH Dataset – and tracking changes as models are refined – developers gain valuable insight into specific areas of weakness and strength. Consistent evaluation against this standardized benchmark facilitates meaningful comparisons between different approaches to reasoning, driving progress and enabling the identification of genuinely improved capabilities over time. This granular level of analysis is crucial for building AI systems that don’t just produce correct results, but demonstrate a robust and reliable reasoning process.

The study illuminates a fundamental truth about complex systems: their inherent fragility. It isn’t a flaw that reasoning transfer succeeds within model families but falters across them; it is the expected behavior of interconnected parts. A system that never breaks is, indeed, a dead one. As Blaise Pascal observed, “All of humanity’s problems stem from man’s inability to sit quietly in a room alone.” This resonates with the findings, suggesting that true adaptability-the ability to seamlessly integrate or interchange components-requires a shared foundation, a common ‘room’ of understanding. The emphasis on ‘model family alignment’ isn’t about achieving perfect interchangeability, but recognizing the limits of disparate systems attempting to collaborate. The process reveals not failure, but purification-a refinement of understanding about the boundaries of reasoning and collaboration in these complex architectures.

The Fragile Chain

The observed dependence on model ‘family’ suggests this isn’t about building robust reasoning systems, but cultivating compatible lineages. Each successful transfer isn’t a victory over entropy, but a temporary reprieve. The architecture doesn’t solve the problem; it merely delays the inevitable divergence. Expect to see increasingly elaborate mechanisms for ‘reasoning style’ preservation-digital amber, if you will-as the cost of cross-family continuation rises. The paper reveals that even within a family, the illusion of seamless transfer is brittle; a few releases, a slight parameter shift, and the chain frays.

The focus on log-probability truncation hints at a deeper truth: the core challenge isn’t what models reason, but how much they believe in their own reasoning. A confident hallucination, elegantly expressed, will always outperform a hesitant truth. Future work will likely explore methods for calibrating this ‘reasoning faith’-attempts to create models that know what they don’t know, and express that uncertainty gracefully. This, of course, is a path paved with epistemological quicksand.

The ultimate limitation remains: reasoning, even in these silicon echoes, is a fundamentally unpredictable process. Each attempt to ‘relay’ it is an act of faith, a bet against the growing shadow of internal inconsistency. The goal isn’t to build a perfect chain, but to accept that every link will eventually break-and to design systems that fail interestingly.

Original article: https://arxiv.org/pdf/2512.20647.pdf

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

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2025-12-26 23:13