Counterfactual Witness Exchange for Evidence-Grounded Multi-Agent Reasoning

Repeated model calls can produce agreement without adding evidence for an answer. In evidence-grounded multi-hop reasoning, several passages may be useful only in combination, while different supporting sets can overlap. We hypothesize that accounting for this structure can improve answer selection and inference allocation. We propose Counterfactual Witness Exchange (CWE), which queries evidence subsets by replaying the complete answering process, including subsequent peer exchanges. CWE measures support capacity as the maximum number of pairwise disjoint nonempty subsets that reproduce an answer under a fixed response rule, assigning zero capacity to answers supported without evidence. Lower and upper capacity bounds guide queries toward unresolved comparisons, while newly observed answers enter the candidate set. A separate bound for undiscovered answers prevents certification based solely on an incomplete candidate menu. The resulting certificate concerns capacity under a fixed replay function, not factual correctness. Existing communication and matched-text diagnostics motivate this distinction; the nominal-label effect is concentrated in one tested backbone. Our evaluation framework separates answer quality, support structure, and query efficiency through matched-budget comparisons, shared-observation aggregation controls, and structural interventions. It is designed to test when alternative support discovery makes additional reasoning useful.