We present ACAR (Adaptive Complexity the mechanism is modelagnostic and requires no learned components. What does not hold: (1) Retrievalaugmentation decreased accuracy by 3.4 percentage points—median retrieval similarity was only 0.167, demonstrating that experience injection without semanticalignment introduces harmful noise rather than grounding. (2) When models agreeon incorrect answers (σ=0), no downstream ensemble can recover; this “agreementbut-wrong” failure mode is intrinsic to self-consistency and bounds achievableaccuracy at 8pp below full ensembling. (3) Attribution estimates based on proxysignals (response similarity, entropy) showed weak correlation with ground-truthleave-one-out values; practical attribution requires explicit counterfactual computation. This paper documents what assumptions fail in practice, providing falsifiablebaselines for future work on routing, retrieval, and multi-model attribution.
Ramchand Kumaresan (Fri,) studied this question.