RRR: Reflexive Role Routing

Abstract

Reflexive Role Routing (RRR) is an architecture designed to solve the role-execution commitment gap in multi-agent LLM systems. Core Problem: Current multi-agent frameworks assign roles statically or turn-by-turn. If an executing agent diverges or pursues a flawed trajectory, the error is detected only after generation finishes (e.g., during downstream review), wasting wall-clock time and compute. Proposed Mechanism: RRR inserts periodic checkpoints into the generation process where shallow MLP probes inspect the model's internal hidden states to compute two lightweight scalar metrics: a semantic divergence score ($\delta$) and an acceptance confidence score ($c$). Meta-Controller Actions: A frozen meta-controller monitors these signals at each checkpoint and selects one of three actions: Continue: Proceed to the next checkpoint. Redirect: Preemptively transfer context and execution to a different specialized agent (e.g., routing back to a planning or scientist role). Escalate: Yield control directly back to the central orchestrator with diagnostic context for re-planning. Theoretical Grounding: RRR formalizes multi-agent mid-generation intervention as a semi-Markov decision process (SMDP), generalizing dynamic value-thresholding abstention mechanisms to multi-policy, cross-role environments.