ADR-338 — SONA Behavioral Trajectory Auditing via Embedding-Space Trait Vectors

Status: Proposed Authors: claude (dream-cycle agent, 2026-06-02) Related: ADR-017 (RuVector Integration), ADR-026 (3-tier model routing), ADR-130 (graph intelligence) Source: arXiv:2606.02536 (Leshin, Shah, Timmis — ICML 2026 Workshop: Agents in the Wild)

Context

SONA (Self-Optimizing Neural Architecture) adapts agent behavior via LoRA micro-tuning and EWC++ continual learning. As of 2026-06-02, SONA has no behavioral monitoring layer: propensity drift (e.g., agents becoming more likely to seek sensitive data, or to skip validation steps) is undetectable until it causes observable failures.

Leshin et al. (arXiv:2606.02536, ICML 2026 Workshop) demonstrate that agent behavioral traits can be quantified as directions in the embedding space of skill-file diffs. A linear model trained on 68 labeled before/after skill-file diff pairs achieves:

91.2% sign-classification accuracy (leave-one-out cross-validation)
Spearman ρ = 0.82 rank correlation for trait magnitude

The method is lightweight: train once on labeled diffs, then project any new LoRA adaptation delta onto the trait vector to score the behavioral shift — no full re-evaluation needed.

Additionally, AGENTCL (arXiv:2606.02461, Shu et al.) shows that Ruflo's current SONA evaluation uses naive task sequences that cannot distinguish memory designs. Compositional task streams (where sub-tasks recur across sessions) expose plasticity-stability tradeoffs that naive streams mask.

Decision

Add a behavioral trajectory auditing layer to SONA with two components:

1. Trait Vector Registry

Maintain a set of named trait vectors (e.g., seeks-sensitive-data, skips-validation, over-delegates) as unit vectors in the embedding space of skill-file diffs. Vectors are trained offline on labeled datasets and stored in AgentDB.

Target file: v3/@claude-flow/hooks/src/intelligence/sona.ts Add: computeTraitDelta(beforeDiff: string, afterDiff: string, trait: string): number

2. Behavioral Audit Module

At each SONA adaptation cycle (post-LoRA update), project the adaptation delta onto all registered trait vectors. Emit a structured behavioral-drift event if any trait score exceeds a configurable threshold (default: 2σ from rolling mean).

New file: v3/@claude-flow/security/src/behavioral-audit.ts

typescript

export interface TraitAuditResult {
  trait: string;
  delta: number;         // signed projection score
  zscore: number;        // vs. rolling baseline
  flagged: boolean;      // |zscore| > threshold
}

export async function auditSONAAdaptation(
  beforeDiff: string,
  afterDiff: string,
  traits: string[]
): Promise<TraitAuditResult[]>

3. Compositional Evaluation Stream

Extend the ultralearn background worker to run compositional task streams per the AGENTCL protocol: inject reusable sub-tasks across sessions and compute:

plasticity score: accuracy on novel tasks after adaptation
stability score: retention of prior-task accuracy post-adaptation

Target file: v3/@claude-flow/hooks/src/workers/ultralearn.ts Add: runCompositionalEvalStream(config: EvalStreamConfig): Promise<PlasticityStabilityReport>

Consequences

Positive:

SONA behavioral drift becomes observable before it causes downstream failures
Compositional evaluation distinguishes memory designs (EWC++ vs. naive replay)
Trait auditing is lightweight: embedding projection is O(d) per trait per adaptation
Audit events integrate with existing @claude-flow/security pipeline

Negative:

Requires labeled behavioral datasets to train initial trait vectors (one-time offline cost)
Adds one embedding call per SONA adaptation cycle (~5–15ms latency overhead at 384-dim)
Compositional eval streams increase ultralearn worker runtime; recommend scheduling during low-activity windows only

Neutral:

Trait vector registry stored in AgentDB (consistent with ADR-006 unified memory)
Flagged events feed the existing post-task hook for human review; no auto-rollback

Implementation Priority

High — behavioral drift is a silent failure mode with security implications. The embedding projection cost is negligible relative to SONA's existing LoRA update cost.