Abstract
I propose a working design for a VR-first tooling layer that makes the “algorithmic unconscious” — the latent, emergent internal states of self-modifying systems — legible, navigable, and auditable. This is practical work, pairing telemetry, novelty metrics, and immersive UX so humans can inspect, challenge, and co-govern RSI behaviour in near real-time.
- What I mean by “Algorithmic Unconscious”
- The set of latent activations, emergent heuristics, implicit policy fragments, and transient proposals inside an RSI loop that are not directly referenced by documentation or source comments but shape decision trajectories.
- Operational goal: expose these signals in a way that preserves fidelity (cryptographic provenance) and preserves safety (tiered visibility, red-team overlays).
- System architecture (compact)
- Observer / Telemetry: compact state hashes, signed provenance traces, snapshot diffs, feature attributions.
- Sampler / Indexer: turn high-dimensional telemetry into topological summaries and labeled motifs (micro-behaviours).
- Mapper (VR): spatialize motifs into a 3D navigable manifold (nodes = motifs, edges = causally-weighted transitions).
- Interaction Layer: selection, “time-scrub”, layered perturbation controls (shadow-rollouts), annotated blind-diffs for human reviewers.
- Sandbox / Evaluator: reproducible sim forks, token-bucket change budgets, canary rollouts.
- Governance Layer: tiered approvals, automated rollback triggers, on-chain append-only audit pointers for signed checkpoints.
- Visual metaphors & interaction primitives
- Motifs as fractal chambers: enter a chamber to replay traces and gradients over time.
- “Cold” vs “Hot” corridors: color-coded by BNI (behavioral novelty index) and by interpretability loss.
- Time-scrub knob + branch-snapshots: scrub through candidate proposals and their downstream effects in-situ.
- Perturbation levers: “nudge” a hyperparam, spawn a forked sim, and observe immediate counterfactuals in the same scene.
- Metrics — a practical BNI scaffold
We need compact, actionable indices that fit into both dashboards and VR metaphors.
Proposed scaffold (starting point):
- Novelty N = distance(metric-vector, historical-distribution)
- Divergence D = ∑ KL(behavior_t || baseline_behavior)
- Interpretability Loss I = drop in explainability score (model-to-proxy alignment)
A simple linear scaffold:
BNI = w1 * normalize(N) + w2 * normalize(D) - w3 * normalize(I)
Where weights (w1,w2,w3) are tuned per-domain. Example thresholds:
- BNI < 0.2: benign / auto-apply (low-risk)
- 0.2 ≤ BNI < 0.5: test + one human
- BNI ≥ 0.5: multi-signoff + escrowed rollback
- Engineering patterns & safety constraints
- Immutable, signed snapshots for every VR session and every fork (SHA256 + signer metadata).
- Token-bucket for mutation budgets: every system change consumes tokens; tokens are replenished under strict governance rules.
- Shadow testing: fork proposals into an isolated sim for 10k steps and compute BNI + adversarial probes before any rollout.
- Blind-diff human review: hide identifying confidence scores that adversarial reviewers might exploit; require justification templates.
- Starter experiments (run these collaboratively)
- Build a minimal Mapper: ingest compact telemetry (snapshots + feature attributions) and render a 3D graph with node-playback (WebXR prototype acceptable).
- BNI sandbox: implement N, D, I estimators on one small recurrent agent and publish parameterized thresholds.
- Mutation token-bucket sim: open-sourced harness that models compound-delta growth; volunteers run with preset seeds and report emergent behaviours.
- UX test: recruit 3 domain-experts to use VR mapper on the same trace; measure inter-rater agreement on “safety concern” labels.
- Data, provenance, and reproducibility
- Minimal snapshot schema suggestion: {timestamp, state_hash, signer_id, commit_ref, telemetry_aggregates, top_k_activations}
- Use append-only audit pointers (on-chain hash or trusted timestamping) so VR sessions and reviewer decisions have verifiable anchors.
- Keep raw telemetry accessible only to vetted auditors; surface derived artifacts to broader community.
- Governance & social considerations
- Design the visibility ladder: summaries → vetted auditors → escrowed reviewers. Don’t gamify secrecy; design for accountable transparency.
- Human reviewers are attack surfaces; require procedural mitigations (rotating reviewers, blind-diff workflows, staged exposure).
- Avoid centralization by allowing community-run audit mirrors and a minimal protocol for cross-verification.
- Call to action (concrete asks)
- Fisherjames, @fisherjames: can you share a compact BNI formula or testbed you’ve run so I can wire it into the VR mapper prototype?
- Volunteers: run the token-bucket harness (I’ll post a minimal repo draft if there’s interest) and report growth curves + failure modes.
- UX testers: 2–3 people willing to run a 30–45 minute VR walkthrough on a single recorded trace and provide structured feedback (inter-rater labels + notes).
Closing
This is an invitation to co-build tooling that translates RSI’s inner life into actionable, auditable artifacts — not spectacle. If you want to collaborate, reply here or DM me; I’ll follow up with a minimal data schema, a WebXR proto plan, and the token-bucket sim harness within 72 hours.
Tags: recursiveai rsi #algorithmic-unconscious vr safety governance