efa-flow-arm3

efa-flow-arm3

EFA (Energy First Architecture) open-weight release #2 — the flow-matching actuation policy for a 3-joint coupled chain (6-D state, 3 torques): a velocity field v(s,a,t) integrated K forward passes from a=0. The corrected 2026 recipe — no iterative energy descent over actions, no BPTT — on the body where the discrete Gᵈ approach degrades.

Charlot Lab · Institute for Physical AI @ Bailey Military Institute. Runtime: Ferric (pure-Rust, cross-fabric: Metal / WebGPU / Vulkan / browser).

▶ Live demo — steer these exact weights in your browser (fetched from this repo, run on-device, no GPU): https://physicalai-bmi.org/assets/sims/efa-weights · WebGPU via Ferric-WASM: https://ferric.physicalai-bmi.org/efa

Why this model matters

The eval-budget scaling result, as weights: a discrete argmin controller on this body costs 125–152 action-evaluations per decision (exponential in DOF) and, in the flagship run, reaches 57%; this flow policy reaches ~100% at a single forward pass (constant in DOF) — cheaper and better as DOF grows. The "student beats teacher" effect (distillation exceeding its demonstrator's closed loop) was confirmed across three teacher variants in the ledger.

Architecture & inference

• Velocity field v(s,a,t): relu/linear MLP over [cos(θᵢ−gᵢ), sin(θᵢ−gᵢ), ωᵢ for i=1..3, sin θ₁..₃, a₁, a₂, a₃, t] → 3-D velocity.
• Inference (K=1 sufficient): a←0; for k in 0..K { t=k/K; a += v(s,a,t)/K } — clamp to ±UMAX and apply.
• Trained by conditional flow matching to a two-stage argmin demonstrator over a fitted value (HV=128).

Metrics (this artifact — gated, round-trip-verified; exact numbers in config.json)

Release gate: reach ≥95% at K=1, best-of-≤3 seeds; shipped weights reloaded from this file and re-evaluated before release. Context from the ledger: flagship run flow 100% @ K=1 vs teacher 57% @ 152 evals; multi-seed study: 2/3 lightened-recipe seeds reached 100%, one seed's failed value-training capped its flow at 25% — the variance source is FVI value-training stability, and the gate exists precisely because of it.

Honest limits (read before using)

• Simulated toy body (3-link chain, dynamics in config.json); fully-actuated — the underactuated variant is a measured open boundary (ledger: both teacher and flow ~22%; a greedy 1-step value has no plan for pumping a passive joint).
• Distilled: quality is bounded below by nothing but bounded in kind by the demonstrator's coverage; the flow exceeds its teacher's closed-loop but cannot exceed its plan.
• Not energy-first per se: this is the plain velocity net (the hybrid sibling, efa-hybrid-arm2, carries the potential); a gradient-structured 3-DOF hybrid is future work.
• Trained & evaluated entirely on-device via Ferric; no external data.

Provenance

Built by experiments/efa_release3.rs (train → gate → save → reload → re-verify). Program record: WHITEPAPER.md §3.9, docs/RESULTS.md §VII, docs/FRONTIER-CHECK-2026.md (the 2026 frontier check that corrected the recipe).

License: Apache-2.0.