High aggregate scores can make language agents appear socially responsive even when they merely repeat a prompt-conditioned action. We introduce an identifiability-first framework that separates observed outcomes from evidence of adaptation. Across 3,465 released iterated-prisoner’s-dilemma simulations, 94.5% never change joint regime, leaving reciprocity largely unidentifiable. Controlled histories across nine models reveal opponent-dependent responses that vary substantially across equivalent interfaces. The released checkpoints have the smallest opponent-recency effects. Only the intervention cohort shows reliable propagation after a scripted defection. The diagnostic transfers to longer histories, a multiplayer game, and released-interface wrappers, although model rankings do not. Outcome-only evaluation conflates regime selection with adaptive behavior. Separating opportunity, response, and robustness prevents stable outcomes from being mistaken for social dynamics.

Pairwise LLM-as-judge evaluation should preserve response identity when presentation order changes. Across four judge models, short A/B prompts yield 70–100% second-position selection (72/80 comparisons overall). Targeted prompt ablations show format sensitivity: label changes alter parsed choices, and a neutral buffer produces complete first-position selection on 59 fixed-order comparisons, reversing rather than removing position preference. We use a position-swapped evaluation protocol, retaining a directional verdict only when both orders select the same response. In a new 16-condition GPT-4.1 factorial audit, analysis-first and a verdict prefix raise directional coverage by 5.6 and 6.5 percentage points, respectively. On 40 Arena-Hard queries with three generators, the 120 pairs comprise 62 directional results, no double ties, 14 swap failures, and 44 invalid pairs. Cross-Model Agreement (CMA) yields 25 unique query winners. A retrospective domain-routing check predicts 17 of these 25 labels using rules fitted outside each test fold. These labels and any oracle score remain judge-relative; human-preference validity and downstream training gains are not established.

Self-distillation for grounded question answering often filters tutor trajectories with agreement or confidence gates. For such gates to support faithful grounding, their signals should depend on the supplied document rather than on answers recoverable from parametric memory. We propose a four-mode randomization-control diagnostic that applies the same frozen gate to original, randomized, irrelevant, and empty documents for the same question. On HotpotQA and MuSiQue, using Qwen2.5-7B-Instruct with eight tutor traces, the joint gate changes sharply with document condition: kept fraction falls from 0.380 to 0.020 and from 0.330 to 0.015, respectively. Randomized and irrelevant documents lie between the endpoints, while a document-blind random gate stays flat. The protocol audits document sensitivity without additional student training; faithful evidence use and downstream gains require separate validation.