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Role-Specialized Model Routing (CONCEPT:ORCH-1.27)

Overview

Role-Specialized Model Routing binds functional roles (planner / generator / learner / judge) to model tiers + capability tags over the existing model registry, rather than hardcoding model ids. Assimilated from Quarq Agent's three-specialized-model pattern (agent-oss/agent.py:58-92), generalized so the same configuration runs on any provider pool and degrades gracefully. Extends ORCH-1.2 (Specialist Routing & Discovery).

How it works

  • Role binding. ModelRole = planner | generator | learner | judge. A RoleSpec (tier + tags) is resolved per role and delegated to the existing pick_for_task, inheriting its tier-fallback — so a role never hard-fails on a sparse pool (unless the registry is empty).
  • Default map. planner → light + tags[plan, json]; generator → heavy + tags[synthesis]; learner → heavy + tags[extraction]; judge → reasoning.
  • Overridable. Per-call RoleSpec, registry-level ModelRegistry.role_routing, AgentConfig.role_routing, or live via graph_configure(action="set_role_routing").
  • Consumers. The HyDE planner (KG-2.12), the background learner (KG-2.13), and the LongMemEval judge/generator (AHE-3.12) all request their model here.

Key files / API

Piece Location
Registry roles models/model_registry.py (ModelRole, RoleSpec, _DEFAULT_ROLE_ROUTING, pick_for_role, resolve_role)
Factory core/model_factory.py (create_model(role=...))
Config + MCP core/config.py (AgentConfig.role_routing), mcp/kg_server.py (graph_configure(action="set_role_routing"))

Wiring (≤3 hops)

graph_orchestrate/graph_configure → engine → pick_for_role (2 hops).

Research provenance

Quarq three specialized models — agent-oss/agent.py:58-92 (verified).

RLM-GEPA extension (CONCEPT:ORCH-1.28–1.31)

The registry also carries the RLM-GEPA roles — rlm-executor / rlm-sublm (cheap, run the skill) and rlm-proposer (strong, reflects on traces and rewrites the skill). A skill optimized with a cheap executor still lifts a strong one at eval (the AppWorld cost/quality trick), resolved via rlm/roles.py:rlm_role_model. The GEPA proposer (ORCH-1.30) binds to rlm-proposer.