CORTEXA
← Browse
arxivcs.LGcs.AIcs.IReess.SY2026-07-14

Certified Domain Consistency for Multi-Domain Retrieval: Label-Free Per-Domain Contamination Control with Conformal Risk Guarantees

Jayakumar Manoharan

Retrieval over corpora that mix several domains often returns relevant but wrong-domain evidence that ranking metrics miss and that conformal risk control bounds only marginally, under-covering the worst domains. This work introduces C3R, a drop-in control layer that, from an inferred domain posterior and no query-time label, certifies a per-domain contamination budget where feasible and otherwise abstains rather than silently violating; on the hardest domains it guarantees a reduction, not a tight bound. The core is a two-split scheme built on risk-controlling prediction sets, whose finite-sample transfer bound crosses from the inferred to the true domain with fully estimable slack, supports heterogeneous budgets, and inverts for deployment. Population validity rests on this bound and a controlled simulation; across a thousand resampled calibrations the certificate never violates (a stability result) while marginal control violates the most-contaminated domain in every draw, and soft demotion retains more recall than the strongest calibrated cascade at equal certified contamination. The method replicates across open testbeds including an independent one from public federal regulations, and an LLM-judged downstream probe indicates wrong-authority grounding rises with contamination and falls under control. The layer is frozen-stack and reranker-agnostic.

View free PDFSource page

Related papers

arxivcs.LGcs.AI2026-07-31

TFGformer: Multivariate Time Series Forecasting via Time-Frequency Graph Learning and Covariate Fusion

Yu Sun, Yuan Chang, Xiaohou Shi, Yan Sun

Large-scale multivariate time series from heterogeneous IoT sensors demand accurate long-term forecasting for resource scheduling and predictive maintenance. While recent time series foundation models exhibit strong generalization, they rely on static parametric knowledge and lac…

View free PDFSource page
arxivcs.CVcs.AIcs.LG2026-07-24

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography

Dominik Bernard Lau, Hubert Malinowski, Jerzy Szyjut, Adam Brzeski, Tomasz Dziubich, Radosław Targoński, et al.

Accurate pixel-level classification of coronary angiograms is critical for cardiovascular disease assessment, yet the field lacks standardized evaluation protocols. In this work we demonstrate a new benchmark for the assessment of deep learning models which densely classify pixel…

View free PDFSource page
arxivcs.LGcs.AIcs.CV2026-07-31

A Human-Centered Validation of the Explainability-Performance Coefficient

Christian Oliva, Luis F. Lago-Fernández

The rapid adoption of deep learning models in high-risk domains has intensified the need for trustworthy Explainable Artificial Intelligence (XAI). However, objectively evaluating explanation fidelity and aligning XAI metrics with human-centered understanding remain critical open…

View free PDFSource page
arxivcs.DBcs.AIcs.CLcs.LG2026-07-24

DBA-Bench: A Production-Fidelity Benchmark for LLM-Based Database Operations Agents

Junming Chen, Junyang Jiang, Xu Chen, Zibo Liang, Kai Zheng

LLM-based database agents show promise, but differing task scopes, testbeds, and metrics hinder comparison. We identify four gaps between evaluation and production operations: live-environment fidelity (multi-turn read-write interaction with a running database); observation-space…

View free PDFSource page
arxiveess.AScs.AIcs.LGcs.SD2026-07-31

Stable Autoregressive Speech Generation with Low-Frame-Rate High-Dimensional Continuous Tokens

Yi Luo, Rongzhi Gu, Jixun Yao

Balancing sequence length, representational capacity, and long-horizon stability is a central problem in autoregressive (AR) speech and audio generation. Representations with higher frame rates or greater capacity can preserve more signal detail, but they also make streaming gene…

View free PDFSource page
arxivcs.LGcs.AI2026-07-31

DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search

Jiayang Niu, Yan Wang, Jie Li, Ke Deng, Azadeh Alavi, Muhammad Usman, et al.

Reinforcement-learning-based quantum architecture search (RL-QAS) repeatedly optimizes a variational quantum eigensolver (VQE) after extending a circuit, although circuit construction and action legality are deterministic and known. We introduce DreamQAS, a model-based RL framewo…

View free PDFSource page