MIRAGE: Benchmarking Intent Identifiability under Partial Observation through Counterfactual Multi-Agent Reasoning

Intent inference from partial observations underpins autonomous systems, multi-agent interaction, and situation awareness, yet existing formulations assume that an observed trajectory determines a unique latent intent. This assumption fails in settings where different intentions can generate similar behaviors: a movement toward a region may correspond to an attack, a reconnaissance pass, or a deliberate feint, and single-label evaluation cannot distinguish poor reasoning from intrinsically ambiguous evidence. We introduce MIRAGE, a counterfactual benchmark for intent identifiability. MIRAGE generates matched counterfactual trajectories by intervening on latent intent while keeping scenario conditions fixed. We formalise the counterfactual equivalence set (CES), the set of intents compatible with an observation prefix at each step, using an explicit observation distance $D_{\mathrm{obs}}$ and a calibrated indistinguishability threshold ${ϵ}_o$, released as a criterion-indexed family and reported under its strictest member. We further introduce ShenJi, an evidence-aware set-valued reasoner that maintains multiple hypotheses and contracts them as discriminative evidence emerges, with contraction enforced by a cardinality regulariser rather than observed post hoc. Across ten runs, 38.7% of trajectory steps remain ambiguous under CES (88.5% within the evaluation window, a lower bound). ShenJi preserves the commanded intent in 94.5% of ambiguous windows while reducing premature exclusion to 0.006, compared with 0.090 and 0.157 for deep-ensemble and conformal baselines. However, discrimination on resolved windows remains below chance level. Selective abstention and adaptive conformal prediction cannot recover the missing evidence, indicating that the observation contract, rather than estimator capacity alone, limits identifiability.