Post-training with internal feedback can improve a targeted knowledge-grounded behavior while silently degrading complementary abstraction and logical-consistency capabilities. We propose a conjunctive evaluation protocol that accepts a method only when a designated target improves and complementary probes remain non-inferior (with a stricter secondary criterion requiring complementary equivalence), while also reporting retention and predictive drift. We instantiate the protocol in DION-Bench, combining two lexical/ontological probes (same-category exceptions; abstraction recovery) with a synthetic SAT-checked logical-consistency probe. On Qwen3-8B-Base, a task-specific self-judged DPO adaptation improves OntoNeg-WN by 5.27 percentage points but reduces ConceptShift by 37.50 points—despite humans reaching 82.7% and frontier LLMs 44–73% on OntoNeg—whereas mean change across five external accuracy suites is only −1.70 points. A development-tuned decoding rule partially mitigates ConceptShift regression to 12.93/14.60 points on the original/holdout splits while retaining most of the target gain. These results show that faithful post-training claims require joint reporting of gain, complementary abstraction regression, retention, drift, and equivalence—not proof that a single shared knowledge item degraded.

Retrieval-augmented generation treats citation as a default proxy for reliability, yet a cited passage can support a claim and still be obsolete, superseded, contradicted, or invalid at the user’s target time. We name this failure a citation-supported lifecycle error and define lifecycle faithfulness: a claim-level criterion that requires semantic support, target-time validity, non-supersession, and provenance. We release Vera-Life-Eval, an 8,500-question benchmark with 41,203 versioned evidence pieces across six lifecycle settings (current, past, stale-only, update, conflict, and multi-hop), and Vera-Life, a low-training modular controller that builds an evidence-state graph, selects claim-evidence-time contracts, and chooses among answer, warn_stale, explain_conflict, retrieve_more, and abstain, emitting a claim-level lifecycle ledger. On a shared Qwen3-8B generator, Vera-Life raises LifeEM from .374 (best prompt-only baseline) to .623, and remains effective under timestamp-only (.584) and text-only (.504) regimes. Same-action controls, metadata isolation, and source-disjoint transfer across product, policy, scientific, and news evidence show that the gains are not an artefact of output format or Wikipedia revision cues. The next mission for RAG is not merely to cite evidence, but to know whether that evidence is still alive.

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.