When Models Decide and When They Bind: A Two-Stage Computation for Multiple-Choice Question-Answering

Abstract: Multiple-choice question answering (MCQA) is easy to evaluate but adds a meta-task: models must both solve the problem and output the symbol that represents the answer, conflating reasoning errors with symbol-binding failures. We study how LLMs implement MCQA internally using representational analyses (PCA, linear probes) as well as causal interventions. We find that option-boundary (newline) residual states often contain strong linearly decodable signals related to per-option correctness. Winner-identity probing reveals a two-stage progression: the winning content position becomes decodable immediately after the final option is processed, while the output symbol is represented closer to the answer emission position. Tests under symbol and content permutations support a two-stage mechanism in which models first select a winner in content space and then bind or route that winner to the appropriate symbol to emit.