Evidence of Absence: Cross-Modal Abductive Risk Perception to Sustain World Models When Vision Fails

[2608.14952] Evidence of Absence: Cross-Modal Abductive Risk Perception to Sustain World Models When Vision Fails Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Robotics arXiv:2608.14952 (cs) [Submitted on 15 Aug 2026] Title: Evidence of Absence: Cross-Modal Abductive Risk Perception to Sustain World Models When Vision Fails Authors: Cong Xu , Ravi Sankar View a PDF of the paper titled Evidence of Absence: Cross-Modal Abductive Risk Perception to Sustain World Models When Vision Fails, by Cong Xu and 1 other authors View PDF HTML (experimental) Abstract: A structured world-state (entities, relations, context, and predictive cues) is designed to preserve prediction-critical content when perception degrades, but it presumes observations to populate it; when the primary visual modality is occluded or degraded, those observations may be missing. We address how to sustain the world model from a complementary modality by treating the absence of expected co-evidence as evidence of a hidden cause. The abductive framework is modality-agnostic; this article instantiates it acoustically. A microphone-array front-end estimates the bearing of engine and tire sources and extracts approach-rate evidence (Doppler when a stable tone exists, a broadband looming readout otherwise); the event "signature present, visual co-evidence absent" then triggers abductive inference of a hidden road user, emitting a calibrated risk advisory rather than a control command. Recoverability of the hidden state is analyzed as an identifiability question separating shared from modality-unique information, and cueing is cast as Neyman-Pearson detection under an explicit false-alarm budget. On real occluded-approach recordings at blind junctions, the method warns a mean 1.7 seconds before line-of-sight entry, matches the sustained-window variant of the published acoustic baseline's detection rate with 42% fewer false alarms, localizes to 3.4 degrees median once in view, is well calibrated (expected calibration error 0.034), and keeps hazard awareness above 0.87 under staged vision degradation that collapses a vision-only channel to 0.03. We also measure the method's limits: calibration transfers to an unseen junction almost losslessly, the signature classifier does not, and moving-ego noise is the binding deployment constraint. Comments: 7 pages, 3 figures. Working draft prepared for journal submission Subjects: Robotics (cs.RO) ; Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP) Cite as: arXiv:2608.14952 [cs.RO] (or arXiv:2608.14952v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2608.14952 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Cong Xu [ view email ] [v1] Sat, 15 Aug 2026 00:38:17 UTC (944 KB) Full-text links: Access Paper: View a PDF of the paper titled Evidence of Absence: Cross-Modal Abductive Risk Perception to Sustain World Models When Vision Fails, by Cong Xu and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO < prev | next > new | recent | 2026-08 Change to browse by: cs cs.CV eess eess.SP References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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