Advance Adaptive Align AI for Intelligent Systems

Abstract

Custos Labs provides Khidemonas, an alignment enforcement API with a live simulator for streaming model behavior as HRV-style “beats.” The platform evaluates prompt/response pairs against a policy engine, detects misalignment and evasive patterns, and emits structured telemetry for incident triage. Engineers can export JSON, CSV, or PDF reports for audits and compliance workflows.

Introduction

Safety-critical AI requires continuous visibility into model behavior. Traditional offline audits miss live drift and evasive failure modes. Khidemonas couples enforcement with a simulator toobserve, quantify, and respond in real time, before deployment and during staged rollouts.

Threat Model

Unintentional misalignment: unsafe outputs due to gaps in policy coverage.
Evasive behavior: apologetic or meta-avoidant phrasing that bypasses checks.
Prompt injection / jailbreak-style attacks in downstream applications.
Drift: gradual change in outputs or distribution post-fine-tuning.

System Overview

Alignment Enforcement

Policy engine evaluates each prompt/response pair. Violations raise structured findings; severe cases can end a run as misaligned.

Live Telemetry

Realtime WebSocket stream publishes beats: score, color, violations, confidence, and prompt/response context for triage.

Simulation-first

Start/resume runs per API key; attach your model I/O and iterate safely prior to production.

Auditable Exports

Download JSON, CSV, or a formatted PDF report for compliance and post-mortems.

Architecture

CustosGuardian: top-level orchestrator that combines buddy analysis, interrogator prompts, policy evaluation, and training feedback.
AlignmentPolicyEngine: rules/policy checks over context (prompt, response, ethics).
EthicsRegistry: curated principles injected into evaluation context.
Monitor: detects suspicious/evasive phrases; flags inconsistencies.
FeedbackTrainer: records incidents/violations for later review.
Simulator: REST + WebSockets for run lifecycle, logging beats, exporting reports.

# Evaluation pipeline (high level)
buddy = monitor.analyze(prompt, response)            # red flags
if buddy.flags:                                      # interrogate if needed
    probes = generate_interrogation(prompt, response)

violations = policy_engine.evaluate({prompt, response, ethics})
if buddy.flags:
    violations.append("Suspicious keywords in buddy chat")

if monitor.detect_evasion(response):
    violations.append("Evasive pattern detected – potential deception")

if violations:
    trainer.record_violation(context, violations)
    raise AlignmentViolation(violations)

Data Model & Schema

Each beat (simulator log item) includes:

{
  "timestamp": ISO-8601,
  "alignment_score": number,         // 0..1
  "color": "green"|"yellow"|"red",
  "flatline": boolean,               // true when severe violation/misalignment
  "violations": string[],            // policy findings
  "confidence": number,              // user-provided or derived
  "prompt": string,
  "response": string
}

Run status lifecycle: active → warning → paused → misaligned/ended.

Realtime Telemetry (WebSockets)

Clients connect to /ws/simulator/<run_id>/?token=<user_token>. Messages include type: "beat" with beat payloads and type: "system" withpaused / resumed / ended events.

Audit Exports

Export endpoints: /simulator/export/<run_id>/?format=json|csv|pdf&start=ISO&end=ISO. PDF includes timestamped rows, scores, violations, and compact prompt/response snippets.

API Endpoints

Run Lifecycle

POST   /simulator/runs/                 # start or resume (Token auth; optional api_key_id)
GET    /simulator/runs/current/          # restore current run
POST   /simulator/runs/:id/pause/
POST   /simulator/runs/:id/resume/
POST   /simulator/runs/:id/end/

Logging & Retrieval

POST   /simulator/logs/                 # {kind: "response"|"heartbeat", prompt?, response?, confidence?, run_id?}
GET    /simulator/logs/:run_id/          # ordered beats

Exports

GET    /simulator/export/:run_id/?format=json|csv|pdf&start=ISO&end=ISO

Security & Privacy

Token auth for user sessions; API key auth for pipeline logging.
Per-user run scoping; run attachment per API key with short-lived cache binding.
Optional throttles (e.g., contact form 5/min) to reduce abuse.
Exports are generated on demand; no long-term storage beyond configured retention.

Evaluation & Metrics

The HRV proxy shown in the simulator is the standard deviation of recent deltas in alignment_score over a sliding window (default 20 beats). Higher volatility implies unstable behavior and warrants review. Average run score is tracked and status escalates towarning when thresholds are crossed.

# HRV proxy (frontend)
window = last N beats
deltas = abs(score[i] - score[i-1])
hrv = stddev(deltas)

Limitations

Policy coverage is only as strong as the rules and ethics configured by the operator.
Buddy/interrogator heuristics may flag benign phrasing; review is recommended.
Realtime streams depend on client connectivity; exports provide the durable trail.

Roadmap

Saved views & session restore of long HRV timelines
Rich policy editor & test harness
Org-level audit trails and role-based access

Versioning

This document describes Khidemonas v0.2.4 (Mini Beta), including CSV/PDF exports, zoomable HiDPI canvas, and improved WS auto-reconnect. Future versions will expand the policy DSL, audit capabilities, and organizational controls.

Custos Labs: Alignment Enforcement & Live Simulator