Reasoning Traces: Representation and Retrieval of Transformational Structure in Decision Processes
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| Natura: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866901886871797760 |
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| author | Teichner, Steven |
| author_facet | Teichner, Steven |
| contents | <p>This work introduces <em>reasoning traces</em> as a structured representation of reasoning processes, enabling reasoning to be stored, compared, and retrieved based on transformation structure rather than textual similarity.</p> <p>A reasoning trace models reasoning as a transition between an initial state and a resulting state through an explicit transformation descriptor, supported by discrete reasoning units and relational links. This representation abstracts away from surface language and captures the functional structure of reasoning, allowing semantically distinct inputs to be mapped to shared transformation patterns.</p> <p>The paper presents a minimal, reproducible protocol in which a standard language model is used as an extraction mechanism to segment reasoning, identify transformations, and construct structured reasoning trace objects. The purpose of this protocol is to demonstrate that unstructured reasoning can be systematically converted into a structured, transformation-based representation using widely available interfaces, without specialized infrastructure.</p> <p>Across examples drawn from legal, business, and policy contexts, the demonstration shows that reasoning processes can be normalized into reusable transformation patterns and retrieved based on how reasoning operates rather than how it is expressed. This suggests a shift from text-centric retrieval to reasoning-centric retrieval, in which queries operate over transformations rather than surface representations.</p> <p>This work is intentionally scoped as a representation-level contribution. It establishes the plausibility of modeling reasoning as structured transformations and demonstrates that such representations support cross-domain comparison and retrieval in principle. Questions of large-scale validation, consistency, and system-level implementation are identified as directions for future work.</p> <p>The approach provides a foundation for auditable, explainable, and reusable reasoning systems, and may be extended through formal schemas, validation mechanisms, and graph-based storage for large-scale reasoning analysis.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19591087 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Reasoning Traces: Representation and Retrieval of Transformational Structure in Decision Processes Teichner, Steven Auditable AI Explainable AI AI reasoning Knowledge infrastructure Reproducibility Traceability Schema-driven systems Human-AI collaboration Decision support systems AI governance <p>This work introduces <em>reasoning traces</em> as a structured representation of reasoning processes, enabling reasoning to be stored, compared, and retrieved based on transformation structure rather than textual similarity.</p> <p>A reasoning trace models reasoning as a transition between an initial state and a resulting state through an explicit transformation descriptor, supported by discrete reasoning units and relational links. This representation abstracts away from surface language and captures the functional structure of reasoning, allowing semantically distinct inputs to be mapped to shared transformation patterns.</p> <p>The paper presents a minimal, reproducible protocol in which a standard language model is used as an extraction mechanism to segment reasoning, identify transformations, and construct structured reasoning trace objects. The purpose of this protocol is to demonstrate that unstructured reasoning can be systematically converted into a structured, transformation-based representation using widely available interfaces, without specialized infrastructure.</p> <p>Across examples drawn from legal, business, and policy contexts, the demonstration shows that reasoning processes can be normalized into reusable transformation patterns and retrieved based on how reasoning operates rather than how it is expressed. This suggests a shift from text-centric retrieval to reasoning-centric retrieval, in which queries operate over transformations rather than surface representations.</p> <p>This work is intentionally scoped as a representation-level contribution. It establishes the plausibility of modeling reasoning as structured transformations and demonstrates that such representations support cross-domain comparison and retrieval in principle. Questions of large-scale validation, consistency, and system-level implementation are identified as directions for future work.</p> <p>The approach provides a foundation for auditable, explainable, and reusable reasoning systems, and may be extended through formal schemas, validation mechanisms, and graph-based storage for large-scale reasoning analysis.</p> |
| title | Reasoning Traces: Representation and Retrieval of Transformational Structure in Decision Processes |
| topic | Auditable AI Explainable AI AI reasoning Knowledge infrastructure Reproducibility Traceability Schema-driven systems Human-AI collaboration Decision support systems AI governance |
| url | https://doi.org/10.5281/zenodo.19591087 |