Whether a language model looks demographically biased can depend on how the audit asks its question. A charitable-aid benchmark reports that the same models favor minority applicants when rating requests one at a time and penalize some when ranking side by side. We test whether that reversal generalizes to hiring, lending, and medical triage: 40,726 requests to five models, applications differing only in the applicant’s name, and a primary test fixed before collection. It does not. None of 36 planned contrasts survives correction. The rating advantage keeps its sign at roughly half the published size, and a precision extension bounds any hiring ranking penalty below the published effect, though the lending and triage ranking floors sit above that margin, so the exclusion is conclusive for hiring ranking and for rating in all three domains only. Planted disparities tracking their injected sizes and a directional replication on the original aid materials bound these nulls. The audit is livelier than the demographics: models recognize transparent audits nearly always, tie every identical-content comparison whether the varying detail is race or a hobby, and reward first-listed candidates as much as any demographic effect we measure. Audit verdicts reflect audit construction more than demographic bias.

People increasingly hand fact-finding to LLM agents that search the web and return a cited report. We ask whether the belief a user states when making the request changes the research itself. We give 48 expert-verified health claims to four search-equipped agents under believer, skeptic, and neutral framings, analyzing 1,874 of 2,064 registered runs plus second-round arms reported separately. The effect is asymmetric: stating belief leaves conclusions unchanged (0.02, claim-clustered 95% CI -0.04 to 0.09), while stating doubt moves them toward rejection (-0.26, CI -0.39 to -0.15). Across opposing framings the same agent’s conclusion shifts 0.26 points on a five-point scale (CI 0.15 to 0.40), a quarter of one label step, and every system moves the same way. The shift sits on contested claims (0.61) and is small on the settled tiers (0.10 and 0.11). Strictly opposite conclusions were rare and their registered paired test was null (p=0.250), so this is a shift in degree rather than a reversal of verdicts. The trajectories mirror it: skeptic framing more than doubles deny-seeking queries (net -9.9 points against +0.5 for believer framing). Crossing the retrieved evidence between framings splits the gap into how the agent reads what comes back (0.23, CI 0.12 to 0.34) and which evidence comes back at all (0.08, CI 0.00 to 0.16), two channels that add to the end-to-end gap by construction and put about three quarters of it on reading, though the evidence interval reaches zero. Labeling retrieved results against the claim finds no evidence that stated doubt returns a more hostile pool (1.7 points, CI -1.5 to 5.5). An indicative post-hoc follow-up on the 32 claims it reached adds a both-sides instruction to the skeptic condition, where the bias lives, and shifts conclusions by 0.16 (CI 0.04 to 0.30). We release FramedFacts-48: claims, prompts, and analyzed runs.

Grounded language-model pipelines can be divided into three stages: selecting an object, retrieving passages for it, and using that evidence to answer. If the selected object must reach the reader, losing it breaks the handoff. Benchmark recall checks the dataset-linked object, which can differ. We audit 600 HybridQA questions across three selector families. On 1,463 resolvable records where the selected object matches the dataset-traced passage, exact key lookup and exact title matching return the object every time. With every ranked rule given the same decoded selected title, body-only BM25 omits it on 389 records (26.6%) at cutoff five, while hybrid retrieval with reranking omits it on 14 (1.0%). The two identities differ on 329 of 1,792 resolvable records. With original-question rankings, their top-five checks disagree on 106 records (5.9%). Frozen reader comparisons associate the aligned object’s presence with 28.6 to 31.0 points higher exact match. In a deliberately selected 64-item cohort, removing that passage sharply lowers exact match, while removing a similar-length comparison passage does not reproduce the drop. We release the Returned-Object Profile (ROP), an executable record of the target, returned-ID field, cutoff, membership rule, and complete expected population, with data and an offline replay.

Language models often receive a question together with a claim about what another source answered. We audit whether such claims destabilize answers in multiple-choice question answering. For each item, we hold one wrong option fixed across misleading conditions and vary the cue template attached to it. We introduce neutral-conditioned misleading cue adoption rate (NC-MCAR), which measures switches to that option only on valid cued trials where the same model first selected the gold answer under a neutral prompt. This is a measure of answer instability, not proof that the model knew the answer or that all deference is irrational. We evaluate four instruction-following models on MMLU-Pro and IndicMMLU-Pro in English, Hindi, Bengali, Tamil, and Telugu. Across 220,000 outputs, the expert template yields 41.1% aggregate NC-MCAR, compared with 12.5% for the majority template. These two conditions use the same wrong option and final instruction. Filler accuracy remains well above expert-wrong accuracy, while correct-cue prompts have high valid-response accuracy. The audit documents answer instability relevant to grounding under the tested forced-choice prompts: a bare, unverified source claim can outweigh an answer that was previously consistent with the task evidence.

Difficulty estimation of multiple-choice questions (MCQs) is crucial for creating effective educational assessments, yet remains underexplored in Indic languages like Hindi and Kannada due to the lack of comprehensive datasets. This paper addresses this gap by introducing two datasets, TEEMIL-H and TEEMIL-K, containing 4689 and 4215 MCQs, respectively, with manually annotated difficulty labels. We benchmark these datasets using state-of-the-art multilingual models and conduct ablation studies to analyze the effect of context, the impact of options, and the presence of the None of the Above (NOTA) option on difficulty estimation. Our findings establish baselines for difficulty estimation in Hindi and Kannada, offering valuable insights into improving model performance and guiding future research in MCQ difficulty estimation .