Large language models increasingly answer culturally situated questions whose surface form specifies a country but omits the subnational, community or situational context needed for a reliable answer. We study cultural answerability: whether the cultural scope supplied by a user supports a direct answer without presenting one situated practice as a country-wide norm. Using region-specific evidence from I N DICA , we construct a bilingual English–Hindi evaluation with 50 culturally stable and 50 culturally dependent held out questions. We evaluate two multilingual LLMs across three stages: explicit recognition of missing context, natural response policy, and paired regional counterfactual grounding. Models identify 89.5% of dependent conditions but correctly classify only 28.0% of stable conditions, revealing strong over detection of cultural dependence. Under natural assistance, the appropriate policy rate on dependent questions is 69.0%, while 30.5% of responses make an unscoped cultural commitment. Moreover in 28.0% of dependent model language conditions a model explicitly recognises missing context yet still makes such a commitment when answering naturally. Human adjudicated regional grounding succeeds in only 13.5% of paired conditions, showing that explicit regional context does not reliably produce grounded adaptation. These results show that cultural reliability is not primarily a knowledge problem: it is a calibration problem spanning recognition, response policy and grounded adaptation.

GoldenViewVQA asks a multimodal model to answer a driving-scene question and identify the synchronized camera view that supports its answer. Our first system, QAVEL, used option blind routing, local image inspection, pairwise view selection, and a final verifier. Tests with GPT-5.6 Luna and Terra showed a recurring problem: extra stages often replaced correct direct predictions, and higher reasoning effort did not repair the cascade. We therefore built Joint Answer-preserving Visual Re-grounding (JAVR) around a GPT-5.6 Sol joint prediction. JAVR freezes the answer, aligns the queried actor or condition across all six views, revisits disputed cameras with target-specific crops, and changes only the view when a restricted comparator certifies an exact, direct, and complete evidence source. On the 55-item development set, JAVR produced a descriptive one item increase over its Sol anchor, from 46 to 47 jointly correct predictions, without changing any answer. Our official 59-item test submission reached 79.66% joint accuracy, 81.36% view accuracy, and 94.92% answer accuracy

Agents grounded in heterogeneous sources often encounter conflicting evidence. We study an Audited Reasoning Pipeline (ARP): a forensic analyzer reads the raw sources and emits a sanitized diagnostic, while a clean-room synthesis stage receives the question and diagnostic but not the raw context. On 100 corrupted ConflictQA instances per model, ARP raises exact match from 15 to 30 for GPT-OSS-120B and from 19 to 29 for MiniMax-M2.5.CAT-Source changes from 40 to 49 and from 74 to 56, respectively; their paired model-by-condition interaction is 27 points (95% CI [13,41], p < .001), although two model/configuration pairs do not identify capability as its cause. The three diagnostic metrics are emitted before the clean-room boundary, and all items are corrupted, so conflict detection has no false-positive evaluation. Token-F1 also falls on multi-answer questions. Because the clean four-call control that gives Stage 4 both raw context and the diagnostic was not run, the air- gap’s causal effect remains unidentified.

We describe our system for the FinNLP 2026 Japanese Financial Implicit Communication Recognition (JF-ICR) shared task. The task requires assigning one of five ordinal commitment and refusal labels to a Japanese investor-relations question and company response. Our final system uses the 30.7B-parameter OpenRouter model ‘google/gemma-4-31b-it‘ in a two-stage, proposition-aware workflow. A label-blind first stage extracts tagged evidence for the target proposition, modality, actor and controllability, negation scope, conditionality, and institutional anchors. A second stage re-reads the original Japanese with this evidence and emits the final label before an optional rationale. On 253 labeled development examples, the system reaches 60.08% accuracy versus 56.92% for our earlier single-call control, with no invalid labels. The frozen submission scores 93.33% (14/15) publicly and 94.29% (33/35) privately. Deeper decision trees, latent-score mapping, and parallel judging all reduce accuracy, favoring compact decomposition with strict output control.