{"ID":2840078,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.14533","arxiv_id":"2511.14533","title":"A Neuro-Symbolic Framework for Reasoning under Perceptual Uncertainty: Bridging Continuous Perception and Discrete Symbolic Planning","abstract":"Bridging continuous perceptual signals and discrete symbolic reasoning is a fundamental challenge in AI systems that must operate under uncertainty. We present a neuro-symbolic framework that explicitly models and propagates uncertainty from perception to planning, providing a principled connection between these two abstraction levels. Our approach couples a transformer-based perceptual front-end with graph neural network (GNN) relational reasoning to extract probabilistic symbolic states from visual observations, and an uncertainty-aware symbolic planner that actively gathers information when confidence is low. We demonstrate the framework's effectiveness on tabletop robotic manipulation as a concrete application: the translator processes 10,047 PyBullet-generated scenes (3--10 objects) and outputs probabilistic predicates with calibrated confidences (overall F1=0.68). When embedded in the planner, the system achieves 94\\%/90\\%/88\\% success on Simple Stack, Deep Stack, and Clear+Stack benchmarks (90.7\\% average), exceeding the strongest POMDP baseline by 10--14 points while planning within 15\\,ms. A probabilistic graphical-model analysis establishes a quantitative link between calibrated uncertainty and planning convergence, providing theoretical guarantees that are validated empirically. The framework is general-purpose and can be applied to any domain requiring uncertainty-aware reasoning from perceptual input to symbolic planning.","short_abstract":"Bridging continuous perceptual signals and discrete symbolic reasoning is a fundamental challenge in AI systems that must operate under uncertainty. We present a neuro-symbolic framework that explicitly models and propagates uncertainty from perception to planning, providing a principled connection between these two ab...","url_abs":"https://arxiv.org/abs/2511.14533","url_pdf":"https://arxiv.org/pdf/2511.14533v1","authors":"[\"Jiahao Wu\",\"Shengwen Yu\"]","published":"2025-11-18T14:38:01Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Graph Neural Network\",\"Transformer\"]","has_code":false}