arXiv subjects

Alex Dantart

Publications and source records attributed to Alex Dantart.

5 recordsLinked to original sources

arXiv2026

Reliability by design: quantifying and eliminating fabrication risk in LLMs. From generative to consultative AI: a comparative analysis in the legal domain and lessons for high-stakes knowledge bases

This paper examines how to make large language models reliable for high-stakes legal work by reducing hallucinations. It distinguishes three AI paradigms: (1) standalone generative models ("creative oracle"), (2) basic retrieval-augmented systems ("expert archivist"), and (3) an advanced, end-to-end optimized RAG system ("rigorous archivist"). The authors introduce two reliability metrics -False Citation Rate (FCR) and Fabricated Fact Rate (FFR)- and evaluate 2,700 judicial-style answers from 12 LLMs across 75 legal tasks using expert, double-blind review. Results show that standalone models are unsuitable for professional use (FCR above 30%), while basic RAG greatly reduces errors but still leaves notable misgrounding. Advanced RAG, using techniques such as embedding fine-tuning, re-ranking, and self-correction, reduces fabrication to negligible levels (below 0.2%). The study concludes that trustworthy legal AI requires rigor-focused, retrieval-based architectures emphasizing verification and traceability, and provides an evaluation framework applicable to other high-risk domains.

cs.AI↗

arXiv2026

Topo-RAG: Topology-aware retrieval for hybrid text-table documents

In enterprise datasets, documents are rarely pure. They are not just text, nor just numbers; they are a complex amalgam of narrative and structure. Current Retrieval-Augmented Generation (RAG) systems have attempted to address this complexity with a blunt tool: linearization. We convert rich, multidimensional tables into simple Markdown-style text strings, hoping that an embedding model will capture the geometry of a spreadsheet in a single vector. But it has already been shown that this is mathematically insufficient. This work presents Topo-RAG, a framework that challenges the assumption that "everything is text". We propose a dual architecture that respects the topology of the data: we route fluid narrative through traditional dense retrievers, while tabular structures are processed by a Cell-Aware Late Interaction mechanism, preserving their spatial relationships. Evaluated on SEC-25, a synthetic enterprise corpus that mimics real-world complexity, Topo-RAG demonstrates an 18.4% improvement in nDCG@10 on hybrid queries compared to standard linearization approaches. It's not just about searching better; it's about understanding the shape of information.

cs.AI↗

arXiv2025

M\'as contexto no es mejor. Paradoja de la diluci\'on vectorial en RAG corporativos

T\'ecnicas recientes de "Contextualized Chunking" inyectan res\'umenes para mejorar el contexto en RAG, pero introducen una "diluci\'on vectorial" que opaca el contenido local. Evaluando distintos ratios de inyecci\'on, demostramos una curva en "U invertida": una inyecci\'on moderada mejora el "Recall" (+18%), pero superar un umbral cr\'itico (CIR > 0.4) reduce la precisi\'on en un 22% para consultas espec\'ificas. Proponemos un marco te\'orico para calcular el ratio \'optimo de inyecci\'on. -- Recent "Contextualized Chunking" techniques inject summaries to improve RAG context but introduce "vector dilution" drowning out local content. Evaluating various injection ratios, we demonstrate an "inverted U" curve: moderate injection boosts Recall (+18%), but exceeding a critical threshold (CIR > 0.4) drops precision by 22% for specific queries. We propose a theoretical framework to calculate the optimal injection ratio.

cs.CL↗

arXiv2025

Gobernanza y trazabilidad "a prueba de AI Act" para casos de uso legales: un marco t\'ecnico-jur\'idico, m\'etricas forenses y evidencias auditables

This paper presents a comprehensive governance framework for AI systems in the legal sector, designed to ensure verifiable compliance with the EU AI Act. The framework integrates a normative mapping of the regulation to technical controls, a forensic architecture for RAG/LLM systems, and an evaluation system with metrics weighted by legal risk. As a primary contribution, we present rag-forense, an open-source implementation of the framework, accompanied by an experimental protocol to demonstrate compliance. -- Este art\'iculo presenta un marco integral de gobernanza para sistemas de IA en el sector legal, dise\~nado para garantizar el cumplimiento verificable del Reglamento de IA de la UE (AI Act). El marco integra una cartograf\'ia normativa de la ley a controles t\'ecnicos, una arquitectura forense para sistemas RAG/LLM y un sistema de evaluaci\'on con m\'etricas ponderadas por el riesgo jur\'idico. Como principal contribuci\'on, se presenta rag-forense, una implementaci\'on de c\'odigo abierto del marco, acompa\~nada de un protocolo experimental para demostrar la conformidad.

cs.CY↗

arXiv2025

Inteligencia Artificial jur\'idica y el desaf\'io de la veracidad: an\'alisis de alucinaciones, optimizaci\'on de RAG y principios para una integraci\'on responsable

This technical report analyzes the challenge of "hallucinations" (false information) in LLMs applied to law. It examines their causes, manifestations, and the effectiveness of the RAG mitigation strategy, highlighting its limitations and proposing holistic optimizations. The paper explores the ethical and regulatory implications, emphasizing human oversight as an irreplaceable role. It concludes that the solution lies not in incrementally improving generative models, but in adopting a "consultative" AI paradigm that prioritizes veracity and traceability, acting as a tool to amplify, not replace, professional judgment. -- Este informe t\'ecnico analiza el desaf\'io de las "alucinaciones" (informaci\'on falsa) en los LLMs aplicados al derecho. Se examinan sus causas, manifestaciones y la efectividad de la estrategia de mitigaci\'on RAG, exponiendo sus limitaciones y proponiendo optimizaciones hol\'isticas. Se exploran las implicaciones \'eticas y regulatorias, enfatizando la supervisi\'on humana como un rol insustituible. El documento concluye que la soluci\'on no reside en mejorar incrementalmente los modelos generativos, sino en adoptar un paradigma de IA "consultiva" que priorice la veracidad y la trazabilidad, actuando como una herramienta para amplificar, y no sustituir, el juicio profesional.

cs.AI↗