Weiqing Zeng
Author directory2026
Beyond Static Grounding: Behavioral Contracts for Trustworthy Retrieval-Augmented Generation
Weiqing Zeng | Zida Yang | Guquan You | Haoran Yang | Yu Zheng
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Weiqing Zeng | Zida Yang | Guquan You | Haoran Yang | Yu Zheng
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Current RAG evaluation measures answer quality under fixed evidence but cannot detect systems that ignore evidence changes or fall back to parametric knowledge. We introduce Dynamic Evidence Contracts, a behavioral evaluation paradigm that specifies expected RAG behavior under four evidence interventions (deletion, conflict, distraction, permutation), and the Intervention Compliance Score (ICS) to quantify compliance. Evaluation on five datasets reveals that all evaluated open-source RAG pipelines achieve moderate conflict detection (CDR=0.283–0.847) via passive NLI-detected refutation signals, but lack explicit conflict acknowledgment. We further present DOVE-RAG, a minimal reference instantiation showing that explicit conflict acknowledgment yields higher CDR (0.505–0.999), up to 1.6× ICS improvement, and 2.0× citation recall gains. Ablation reveals that verification without structured output reorganization yields negligible improvement, suggesting that evidence compliance requires an integrated pipeline.
Beyond Leaderboards: A Frozen-Rubric Contract for Position-Invariant Winning-Argument Detection
Zida Yang | Weiqing Zeng | Guquan You | Jiexin Zheng
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Zida Yang | Weiqing Zeng | Guquan You | Jiexin Zheng
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
When large language models serve as evaluation infrastructure, progress cannot be reduced to a single accuracy number. We study winning-argument (WA) detection as a stress test: can one system jointly improve accuracy, enforce speaker-order invariance, emit rubric-conditioned diagnostics, improve a proper scoring rule, and cut inference cost? TAPIR freezes a survey-grounded rubric into a dual-artifact protocol, distils privileged signals by mode-split self-distillation, and predicts through an anti-symmetric scalar head paired with a structured ledger. An algebraic swap identity (Winning-Position Invariance, WPI=1) holds on every test pair, and a tied-order wrapper preserves it on the deployed router. On the standard 807-pair WA split, TAPIR beats our same-backbone Qwen3-14B re-implementation of Labruna et al.’s MS-PS-MLP, 65.80% vs. 58.12% (paired McNemar p=3.41×10−4), at a lower Brier score and 39.41× faster inference per pair. Reusing the frozen protocol without retuning carries the mechanism to a second task and a second backbone, both at WPI=1. Under domain shift it only matches length-only controls, so we report contract preservation rather than superiority. The contribution is the joint contract under a paired protocol, with an explicit amortisation boundary, not a leaderboard delta.