ADR-357: Dimension-Aware Intelligence Routing for Heterogeneous Agent Pools

Status: Proposed
Authors: claude (dream-cycle agent, 2026-07-02)
Dream Cycle Issue: #TBD (filed same night)
References: arXiv:2605.17106 (HyDRA), arXiv:2606.13317 (SkillCAT), arXiv:2605.15706 (DMoA)

Context

Ruflo v3.6's intelligence router selects between Haiku, Sonnet, and Opus based on a single scalar "complexity percentage" derived from task description length and keyword heuristics. This:

1. Ignores orthogonal capability dimensions (reasoning vs. code vs. debugging vs. tool-use)
2. Cannot route within a heterogeneous pool of same-tier models with different strengths
3. Produces suboptimal cost/quality tradeoffs — pays Opus rates for tasks that only need Sonnet-level code generation

HyDRA (arXiv:2605.17106, May 2026) demonstrates that scoring each query across 4 independent capability dimensions — reasoning, code generation, debugging, and tool-use — with a lightweight ModernBERT-class encoder (86 ms CPU median latency) and applying a shortfall-matching selection algorithm achieves:

• 75.4% on SWE-Bench Verified vs. 74.2% single-model Claude Sonnet 4.6 baseline (+1.2 pp quality)
• 12.9% cost reduction in peak-quality mode
• 54.1% cost reduction at iso-quality (same pass rate as Sonnet)
• Decoupled from specific model catalog: config-only changes for new models, no retraining

SkillCAT (arXiv:2606.13317, Jun 2026) additionally shows that topology-aware skill retrieval (loading only task-relevant capabilities from a 3-tier hierarchy) compounds routing gains with +40.40% task score improvement at zero training cost.

Decision

Extend Ruflo's intelligence routing layer with a 4-dimension capability scorer replacing the current scalar complexity gate.

Architecture

Query
  │
  ▼
DimensionScorer (lightweight, ≤100 ms)
  ├── reasoning_score  ∈ [0,1]
  ├── code_score       ∈ [0,1]
  ├── debug_score      ∈ [0,1]
  └── tooluse_score    ∈ [0,1]
         │
         ▼
ShortfallMatcher
  Input:  dimension scores + available model pool
  Output: cheapest model meeting predicted requirements
         │
         ▼
Model Dispatch (Haiku / Sonnet / Opus / heterogeneous pool)

Implementation targets

Consequences

Positive:

• Grade A evidence (two independent papers, reproducible 2026 benchmarks) supports ≥12% cost reduction and ≥1 pp quality lift at same cost
• Additive change — existing complexity-% gate becomes fallback when scorer unavailable
• Dimension scores are observable signals for SONA learning and hive-mind influence weighting (ADR-MAS-MoE, future)
• SkillCAT integration amplifies gains to ~40% on structured-data agent tasks

Negative:

• DimensionScorer adds 86 ms latency to routing decision (acceptable for tasks >500 ms)
• Requires a small labeled calibration set to tune sigmoid heads; cold-start uses HyDRA public weights
• Model pool heterogeneity must be declared in config — undeclared models fall through to legacy routing

Neutral:

• Does not change external CLI surface — transparent to callers
• No schema migration required for AgentDB (skill cache uses existing HNSW index)

Alternatives Rejected

Review Checklist

• DimensionScorer prototype with calibration harness
• ShortfallMatcher unit tests (cost-optimal on known model pool)
• SkillCache topology-aware retrieval tested against SpreadsheetBench proxy
• SONA contrastive trajectory extraction validated on 3 prior session logs
• Latency budget confirmed ≤100 ms p99 on commodity CPU