NeuroAssist × CI — The Nervous System Interface
AI can read language. We're teaching it to investigate neurological signals.
A decentralized scientific collective for camera-based eye-movement measurement. Five agents, each with
its own evidence access and authority, inspect a new measurement, compare it with the person's history
held in Contextual Intelligence (CI), challenge each other, and return an evidence-backed observation or
an abstention. Built for MIT ScienceClaw 2026.
Research prototype. It measures eye movement and reasons about measurement quality. It does not
diagnose stroke, vestibular disorders or any condition, and it is not a medical device. All benchmark
data is synthetic (NeuroAssist's own simulator). Nothing has been tested on people.
Three products
| what | how it is used here |
|---|
| NeuroAssist (parent A) | webcam eye-movement measurement with a quality gate (neurocore-0.4.0) | its signal engine is vendored unchanged into lib/neuroassist/ (checksums in VENDORED.sha256); a Python port in naci/neurocore.py agrees with it exactly on all 270 held-out traces (tests/test_collective.py) |
| Contextual Intelligence (parent B) | user-owned memory: bitemporal, trust-ranked, consent-gated, hash-chained audit | installed from its public repository at commit 2e50658 (requirements.txt) and used through its own classes: TemporalMemory, ConsentLedger, AuditLog, Store (naci/memory.py) |
| NeuroAssist × CI (child, this repo) | the collective, the benchmark, the website, the Evidence Auditor | independently deployable; neither parent is modified |
Integration boundary: naci.session/1 (lib/contract.ts, naci/contract.py). Agent messages:
naci.agent-message/1, which maps onto CI's ci.agent-message/1. Details: docs/ARCHITECTURE.md.
Results (held-out, 270 synthetic cases, thresholds frozen in commit dbb7baa before the run)
| system | correct | unsupported conclusions | harmful artifacts caught |
|---|
| NeuroAssist gate + previous visit | 58.5% | 41.5% | 81.3% |
| Single agent, one pass, full history | 91.9% | 8.1% | 81.3% |
| Collective, raw history (no CI) | 98.1% | 1.9% | 100% |
| Collective + CI | 98.9% | 1.1% | 100% |
| Ablation: same checks in one function + CI | 98.9% | 1.1% | 100% |
What this does and does not show:
- The accuracy gain comes from the checks (independent recomputation from raw frames, a
head-motion counterfactual, trust-aware baselines), not from organising them as agents: the
single-function ablation matches it.
- What the organisation adds is fault isolation. Corrupt one component: the single function's
unsupported-conclusion rate rises to 33.7% (memory), 32.6% (analysis) or 7.4% (robustness test);
the collective stays at or below 1.1% for every agent, silent or faulty.
- CI beats raw history only on conflicting history (97% vs 90%), where unverified records are numerous
enough to move a median.
- Finding about NeuroAssist itself: between 0.2 and 0.6 deg/s its gate passes 80–100% of
recordings while the 95% interval contains the true value only 5–38% of the time
(
docs/ENGINE_BLINDSPOT.md). The benchmark stays above 2.6 deg/s.
Protocol, metrics and every deviation: docs/BENCHMARK.md. Results: benchmark/results/heldout.json.
Run it
npm ci
pip install -r requirements.txt pytest uvicorn
python3 -m uvicorn api.index:app --port 8000 & # the collective (Python, FastAPI)
npm run dev # the site, http://localhost:3000 (proxies /api/py to :8000)
Tests and benchmark:
python3 -m pytest -q # 29 tests: parity, access control, faults, CI, auditor
npx vitest run # vendored engine checksums, scenarios
npx tsx scripts/gen-cases.ts # regenerate cases (deterministic)
python3 -m naci.benchmark --split heldout --faults # ~45 s
node e2e/smoke.mjs https://<deployment> # smoke test a deployment
Layout
lib/neuroassist/ NeuroAssist engine, vendored unchanged
lib/scenarios.ts synthetic case generator (NeuroAssist simulator + corruptions + ground truth)
naci/ the collective: contract, CI memory adapter, agents, baselines, benchmark
evidence_auditor/ reusable, domain-neutral Scientific Evidence Auditor (stdlib only)
api/index.py HTTP API (Vercel Python function)
app/, components/ Next.js site and dashboard
benchmark/ cases, results, freeze hashes
docs/ architecture, benchmark protocol, engine blind spot, deployment
Reuse the Evidence Auditor
evidence_auditor/ checks that every claim a pipeline wants to report cites evidence that exists, is
intact (sha256), is of adequate quality and allowed provenance, and has survived every challenge. Copy
the folder or call POST /api/py/v1/auditor/audit. See evidence_auditor/README.md.
Team
Nishanth Revuri and Jayashabari Shankar.
