arXiv subjects

Ramon Gonzalez

Publications and source records attributed to Ramon Gonzalez.

3 recordsLinked to original sources

arXiv2026

A Hierarchical Consistency Framework for Auditing Retrieval-Augmented Generation Systems

Retrieval-augmented generation (RAG) is commonly evaluated by whether the final answer is correct. That test is insufficient: an answer can match its reference while the context that produced it contains a direct contradiction, leaving the contested evidence invisible to answer-only review and retrieval relevance scores. This paper presents the Hierarchical Consistency Framework (HCF), a post-hoc, model-agnostic audit of three distinct levels of a RAG process: the knowledge corpus, the final retrieved context, and the generated answer. HCF represents corpus conflicts as source-linked atomic facts, thereby identifying the documents responsible, and returns each Answer Consistency Score (ACS) with an explanation of supporting and contradictory contextual statements. We evaluate HCF on several controlled corpora spanning five domains and 100 query-corpus instances. A human evaluator compares every generated response with its supplied ground-truth response. The results show that the three diagnostic levels can dissociate: the corpus with the highest mean retrieval similarity has the lowest mean ACS, while a structurally degraded corpus performs worse at corpus level but better at answer level. Most importantly, HCF identifies contradictory retrieved evidence in several cases where the answer still matches the ground truth. HCF does not certify factual truth; it makes the evidence supporting and challenging an answer inspectable and attributable.

cs.AI↗

arXiv2026

An Auditable Symbolic-RAG-Generative AI Architecture for Goal-Oriented Conversation Orchestration

Goal-oriented conversational systems must answer factual questions, understand visitor-provided information, and advance business objectives without becoming rigid questionnaires. This paper proposes a Symbolic-RAG-Generative architecture centered on the Goal-oriented Retrieval-Augmented Conversation Engine (GRACE). An instruction-constrained Business Goal Compiler transforms business intent into an immutable objective set, normalized priority vector, canonical questions, and initial state vector. At runtime, GRACE receives the complete conversation history, latest visitor message, current state, and grounded answer generated by a separate RAG component. It updates completion only from visitor-authored evidence and selects one contextually modulated follow-up. The core policy maximizes expected business progress subject to a minimum visitor-utility constraint. We formalize the state, monotonic transitions, source separation, question modulation, and constrained policy; present the reference architecture; and define an evaluation comprising 24 English real-estate and 10 Spanish professional-cleaning conversations, totaling 119 protocol-defined visitor turns. Across both domains, GRACE achieves 84.9% exact state-transition accuracy, 91.6% evidence precision, 89.6% evidence recall, 100% monotonicity, and 94.1% terminal-state accuracy. The evaluation establishes compelling symbolic-state performance across standard, multi-goal, RAG-detour, validation, refusal, and robustness scenarios.

cs.AI↗

arXiv2018

DeepTerramechanics: Terrain Classification and Slip Estimation for Ground Robots via Deep Learning

Terramechanics plays a critical role in the areas of ground vehicles and ground mobile robots since understanding and estimating the variables influencing the vehicle-terrain interaction may mean the success or the failure of an entire mission. This research applies state-of-the-art algorithms in deep learning to two key problems: estimating wheel slip and classifying the terrain being traversed by a ground robot. Three data sets collected by ground robotic platforms (MIT single-wheel testbed, MSL Curiosity rover, and tracked robot Fitorobot) are employed in order to compare the performance of traditional machine learning methods (i.e. Support Vector Machine (SVM) and Multi-layer Perceptron (MLP)) against Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs). This work also shows the impact that certain tuning parameters and the network architecture (MLP, DNN and CNN) play on the performance of those methods. This paper also contributes a deep discussion with the lessons learned in the implementation of DNNs and CNNs and how these methods can be extended to solve other problems.

cs.CV↗