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| // SPDX-License-Identifier: Apache-2.0 | |
| // © 2026 Lutar, Stephen P. — SZL Holdings · ORCID 0009-0001-0110-4173 · Doctrine v11 | |
| // | |
| // surfaces/nested.js — NESTED LEARNING / CONTINUUM MEMORY SYSTEM ("Hope") | |
| // organ for the holographic frontier ring (Behrouz, Razaviyayn, Zhong & | |
| // Mirrokni, Google Research, NeurIPS 2025 — "the illusion of deep learning | |
| // architectures"). Renders a THREE-TIER PLASTICITY LADDER of memory levels at | |
| // different update clocks (fast k=1, medium k=8, slow k=64), fed by a streaming | |
| // toy continual-learning task-block sequence. A HUD contrasts the FORGETTING | |
| // CURVE of task A for a single-clock baseline vs the multi-timescale CMS | |
| // schedule from the live snapshot at /api/killinchu/v1/nested/schedule. The | |
| // slow "core" (protected by a surprise gate) glows proof-teal as it retains | |
| // early tasks; the always-on baseline overwrites and forgets (grey). Honesty | |
| // label "MODELED" is read VERBATIM from the JSON and displayed as-is; never | |
| // upgraded. | |
| // | |
| // Surface export shape (mirrors titans.js / episodic.js exactly): | |
| // export default { id, title, endpoints, mount(ctx), unmount() } | |
| // ctx = { stage, container, live, label, THREE, szl3d } | |
| // | |
| // DATA SHOWN (all from live endpoint, inside payload): | |
| // num_tasks, block_len, n_tokens, k_fast, k_med, k_slow, gate, levels[], | |
| // taskA_baseline[], taskA_cms[], retainedA_baseline, retainedA_cms, | |
| // retention_delta, mean_retention_delta, slow_writes_cms, | |
| // slow_writes_baseline, mean_surprise, peak_surprise, momentum, forget_curve[] | |
| // | |
| // LEADERS ADOPTED & CITED (clean-room; NOT claimed as SZL's own): | |
| // Nested Learning: The Illusion of Deep Learning Architectures (the paradigm, | |
| // Continuum Memory System, and "Hope" proof-of-concept simulated here): | |
| // Behrouz, Razaviyayn, Zhong & Mirrokni 2025, Google Research, NeurIPS 2025 | |
| // https://arxiv.org/abs/2512.24695 | |
| // Google Research blog — Introducing Nested Learning (official announcement): | |
| // https://research.google/blog/introducing-nested-learning-a-new-ml-paradigm-for-continual-learning/ | |
| // | |
| // HONESTY LABELS: MODELED (deterministic re-implementation of the multi- | |
| // timescale update-scheduling + optimizer-as-memory arithmetic; NOT the Hope | |
| // architecture; NEVER-CLAIMED-AS a trained model). Independent-ablation | |
| // nuance: the NeurIPS paper was noted in public commentary to lack full | |
| // ablations in its first version, so this demonstrates the SCHEDULING | |
| // MECHANISM reduces forgetting on a controlled toy stream — NOT reproducing | |
| // Hope's benchmark wins. Read verbatim from JSON; never upgraded here. The | |
| // endpoint nests its fields under `payload`, with the label at the top level — | |
| // this surface handles the label at top-level OR inside payload.label | |
| // defensively. | |
| // COLOURS: lattice-blue 0x5b8dee (fast level / task stream / spine), violet-blue | |
| // 0x8a6bff (medium level / clock ring), proof-teal 0x3af4c8 (protected slow | |
| // core / retention / HUD accent), greys (forgotten / degraded state). Purple | |
| // BANNED. | |
| // 0 RUNTIME CDN. three.js via ctx.THREE (vendored by the page importmap). | |
| // DOCTRINE v11: degrades gracefully (grey) on 404/error; honesty label still shown. | |
| // Nothing here is in the locked-8. Λ stays Conjecture 1. Trust never 100%. | |
| const ID = "nested"; | |
| const TITLE = "Nested Learning · Continuum Memory System (live)"; | |
| // Endpoint is hosted on the dedicated killinchu Space (isolated compute), reached | |
| // cross-origin (killinchu returns access-control-allow-origin: https://a-11-oy.com). | |
| // This keeps the nested organ's rebuilds/faults isolated from the flagship. | |
| const EP = "https://szlholdings-killinchu.hf.space/api/killinchu/v1/nested/schedule?seed=42&num_tasks=6&k_fast=1&k_med=8&k_slow=64&gate=0.15"; | |
| // data-viz hues — purple BANNED | |
| const C_FAST = 0x5b8dee; // lattice-blue (fast level / task stream / spine) | |
| const C_MED = 0x8a6bff; // violet-blue (medium level / clock ring) | |
| const C_SLOW = 0x3af4c8; // proof-teal (protected slow core / retention / HUD accent) | |
| const C_FORGET = 0x5a6570; // grey (forgotten / overwritten baseline point) | |
| const C_DIM = 0x42505d; // grey (degraded / no-live-data) | |
| const C_GRID = 0x1b3a44; // floor / link colour | |
| // layout geometry | |
| const LEVEL_Y = [0.7, 2.2, 3.7]; // y of fast / med / slow tiers (bottom -> top) | |
| const LEVEL_R = [1.4, 2.6, 3.8]; // ring radius of each tier | |
| const RING_SEG = 64; // ring outline segments | |
| const MAX_BLOCKS = 96; // cap on forgetting-curve markers rendered | |
| const CURVE_SPAN = 12.0; // world-units the forgetting curve spans along X | |
| let _stage = null, _THREE = null, _ctx = null, _group = null, _overlay = null; | |
| let _frameReg = false, _polls = [], _el = {}, _badge = null; | |
| let _plain = false; | |
| // geometry handles | |
| let _floor = null; | |
| let _rings = []; // Array<THREE.LineLoop> — one clock ring per level | |
| let _tiers = []; // Array<THREE.Mesh> — the three memory-level nodes | |
| let _baseMarks = []; // Array<THREE.Mesh> — baseline forgetting-curve markers | |
| let _cmsMarks = []; // Array<THREE.Mesh> — CMS forgetting-curve markers | |
| let _core = null; // THREE.Mesh — central slow-core "retention" node | |
| // live state | |
| const S = { | |
| label: null, | |
| numTasks: null, // num_tasks | |
| blockLen: null, // block_len | |
| nTokens: null, // n_tokens | |
| kFast: null, // k_fast | |
| kMed: null, // k_med | |
| kSlow: null, // k_slow | |
| gate: null, // gate | |
| taskABase: null, // taskA_baseline[] | |
| taskACms: null, // taskA_cms[] | |
| retainBase: null, // retainedA_baseline | |
| retainCms: null, // retainedA_cms | |
| retDelta: null, // retention_delta | |
| meanDelta: null, // mean_retention_delta | |
| slowWritesCms: null, // slow_writes_cms | |
| slowWritesBase: null, // slow_writes_baseline | |
| meanSurprise: null, // mean_surprise | |
| peakSurprise: null, // peak_surprise | |
| momentum: null, // momentum | |
| curve: null, // forget_curve[] | |
| state: "init", | |
| }; | |
| // ============================================================================= | |
| // mount(ctx) | |
| // ============================================================================= | |
| export function mount(ctx) { | |
| _ctx = ctx; _stage = ctx.stage; _THREE = ctx.THREE; | |
| _group = new _THREE.Group(); | |
| _stage.scene.add(_group); | |
| _stage.camera.position.set(0, 8, 20); | |
| try { if (_stage.controls && _stage.controls.target) { _stage.controls.target.set(0, 2.0, 0); _stage.controls.update(); } } catch (_) {} | |
| try { _stage.setBloom(true); } catch (_) {} | |
| _buildFloor(); | |
| _buildLadder(); | |
| _buildCurve(); | |
| _buildCore(); | |
| if (!_frameReg) { _stage.onFrame(_onFrame); _frameReg = true; } | |
| _badge = ctx.live.createBadge(); | |
| _polls.push(ctx.live.poll(EP, 5000, _onNested, { badge: _badge, onState: (m) => { S.state = m.state; _paintOverlay(); } })); | |
| _buildOverlay(); | |
| return { id: ID, started: true }; | |
| } | |
| // ============================================================================= | |
| // builders | |
| // ============================================================================= | |
| function _buildFloor() { | |
| const THREE = _THREE; | |
| const grid = new THREE.GridHelper(44, 44, C_GRID, 0x0f2027); | |
| grid.material.opacity = 0.18; grid.material.transparent = true; grid.position.y = -0.01; | |
| _group.add(grid); | |
| _floor = grid; | |
| } | |
| // The plasticity LADDER: three stacked clock rings (fast/med/slow) each with a | |
| // central level node. Higher rings = slower clocks = more protected memory. | |
| function _buildLadder() { | |
| const THREE = _THREE; | |
| const cols = [C_FAST, C_MED, C_SLOW]; | |
| for (let lvl = 0; lvl < 3; lvl++) { | |
| const r = LEVEL_R[lvl]; | |
| const y = LEVEL_Y[lvl]; | |
| // clock ring outline | |
| const pts = []; | |
| for (let i = 0; i <= RING_SEG; i++) { | |
| const a = (i / RING_SEG) * Math.PI * 2; | |
| pts.push(new THREE.Vector3(Math.cos(a) * r, y, Math.sin(a) * r)); | |
| } | |
| const geo = new THREE.BufferGeometry().setFromPoints(pts); | |
| const mat = new THREE.LineBasicMaterial({ color: cols[lvl], transparent: true, opacity: 0.4 }); | |
| const ring = new THREE.LineLoop(geo, mat); | |
| _group.add(ring); | |
| _rings.push(ring); | |
| // level node (the memory level itself) | |
| const node = new THREE.Mesh( | |
| new THREE.OctahedronGeometry(0.34, 0), | |
| new THREE.MeshStandardMaterial({ color: cols[lvl], emissive: cols[lvl], emissiveIntensity: 0.3, transparent: true, opacity: 0.85 }), | |
| ); | |
| node.position.set(0, y, 0); | |
| _group.add(node); | |
| _tiers.push(node); | |
| } | |
| } | |
| // Two rows of forgetting-curve markers streaming along X: task-A accuracy after | |
| // each task block for the single-clock baseline (front row) and the multi- | |
| // timescale CMS schedule (back row). Height encodes retained accuracy. | |
| function _buildCurve() { | |
| const THREE = _THREE; | |
| const mGeo = new THREE.SphereGeometry(0.13, 8, 8); | |
| for (let i = 0; i < MAX_BLOCKS; i++) { | |
| const x = -CURVE_SPAN / 2 + (i / (MAX_BLOCKS - 1)) * CURVE_SPAN; | |
| const b = new THREE.Mesh( | |
| mGeo, | |
| new THREE.MeshStandardMaterial({ color: C_FORGET, emissive: C_FORGET, emissiveIntensity: 0.2, transparent: true, opacity: 0.0 }), | |
| ); | |
| b.position.set(x, 0.2, 2.4); | |
| b.visible = false; | |
| _group.add(b); | |
| _baseMarks.push(b); | |
| const c = new THREE.Mesh( | |
| mGeo, | |
| new THREE.MeshStandardMaterial({ color: C_SLOW, emissive: C_SLOW, emissiveIntensity: 0.2, transparent: true, opacity: 0.0 }), | |
| ); | |
| c.position.set(x, 0.2, -2.4); | |
| c.visible = false; | |
| _group.add(c); | |
| _cmsMarks.push(c); | |
| } | |
| } | |
| function _buildCore() { | |
| const THREE = _THREE; | |
| _core = new THREE.Mesh( | |
| new THREE.IcosahedronGeometry(0.6, 1), | |
| new THREE.MeshStandardMaterial({ color: C_SLOW, emissive: C_SLOW, emissiveIntensity: 0.45, wireframe: true, transparent: true, opacity: 0.85 }), | |
| ); | |
| _core.position.set(0, LEVEL_Y[2], 0); | |
| _group.add(_core); | |
| } | |
| // ============================================================================= | |
| // live data handler | |
| // ============================================================================= | |
| function _onNested(j) { | |
| // The endpoint nests its metrics under `payload`; the honesty label may sit at | |
| // the TOP LEVEL or (defensively) inside payload.label. Read it VERBATIM from | |
| // wherever it is — never upgrade. | |
| const p = (j && typeof j.payload === "object" && j.payload) ? j.payload : j; | |
| const rawLabel = (j && j.label) || (p && p.label) || "MODELED"; | |
| S.label = String(rawLabel).toUpperCase(); | |
| S.numTasks = typeof p.num_tasks === "number" ? p.num_tasks : null; | |
| S.blockLen = typeof p.block_len === "number" ? p.block_len : null; | |
| S.nTokens = typeof p.n_tokens === "number" ? p.n_tokens : null; | |
| S.kFast = typeof p.k_fast === "number" ? p.k_fast : null; | |
| S.kMed = typeof p.k_med === "number" ? p.k_med : null; | |
| S.kSlow = typeof p.k_slow === "number" ? p.k_slow : null; | |
| S.gate = typeof p.gate === "number" ? p.gate : null; | |
| S.retainBase = typeof p.retainedA_baseline === "number" ? p.retainedA_baseline : null; | |
| S.retainCms = typeof p.retainedA_cms === "number" ? p.retainedA_cms : null; | |
| S.retDelta = typeof p.retention_delta === "number" ? p.retention_delta : null; | |
| S.meanDelta = typeof p.mean_retention_delta === "number" ? p.mean_retention_delta : null; | |
| S.slowWritesCms = typeof p.slow_writes_cms === "number" ? p.slow_writes_cms : null; | |
| S.slowWritesBase = typeof p.slow_writes_baseline === "number" ? p.slow_writes_baseline : null; | |
| S.meanSurprise = typeof p.mean_surprise === "number" ? p.mean_surprise : null; | |
| S.peakSurprise = typeof p.peak_surprise === "number" ? p.peak_surprise : null; | |
| S.momentum = typeof p.momentum === "number" ? p.momentum : null; | |
| S.taskABase = Array.isArray(p.taskA_baseline) ? p.taskA_baseline : null; | |
| S.taskACms = Array.isArray(p.taskA_cms) ? p.taskA_cms : null; | |
| S.curve = Array.isArray(p.forget_curve) ? p.forget_curve : null; | |
| _updateScene(); | |
| _paintOverlay(); | |
| } | |
| // ============================================================================= | |
| // geometry updater — drives the ladder + forgetting curve from live data | |
| // ============================================================================= | |
| function _updateScene() { | |
| const live = S.state === "live"; | |
| const cols = [C_FAST, C_MED, C_SLOW]; | |
| // clock rings + level nodes degrade to grey when not live | |
| for (let lvl = 0; lvl < 3; lvl++) { | |
| const ring = _rings[lvl]; | |
| if (ring) { | |
| ring.material.color.setHex(live ? cols[lvl] : C_DIM); | |
| ring.material.opacity = live ? 0.4 : 0.12; | |
| } | |
| const node = _tiers[lvl]; | |
| if (node) { | |
| const c = live ? cols[lvl] : C_FORGET; | |
| node.material.color.setHex(c); | |
| node.material.emissive.setHex(c); | |
| node.material.emissiveIntensity = live ? 0.35 : 0.08; | |
| node.material.opacity = live ? 0.9 : 0.25; | |
| } | |
| } | |
| // forgetting-curve markers: height = retained task-A accuracy per block. | |
| const base = live && S.taskABase && S.taskABase.length ? S.taskABase.slice(0, MAX_BLOCKS) : []; | |
| const cms = live && S.taskACms && S.taskACms.length ? S.taskACms.slice(0, MAX_BLOCKS) : []; | |
| const n = Math.max(base.length, cms.length); | |
| for (let i = 0; i < MAX_BLOCKS; i++) { | |
| const bm = _baseMarks[i]; | |
| const cm = _cmsMarks[i]; | |
| if (!live || i >= n) { bm.visible = false; cm.visible = false; continue; } | |
| if (i < base.length) { | |
| const a = base[i]; | |
| bm.visible = true; | |
| bm.position.y = 0.2 + a * 3.2; | |
| // baseline that has forgotten (low retention) fades to grey | |
| const c = a >= 0.5 ? C_FAST : C_FORGET; | |
| bm.material.color.setHex(c); | |
| bm.material.emissive.setHex(c); | |
| bm.material.emissiveIntensity = 0.2 + 0.5 * a; | |
| bm.material.opacity = 0.35 + 0.55 * a; | |
| bm.scale.setScalar(0.7 + 0.7 * a); | |
| } else { bm.visible = false; } | |
| if (i < cms.length) { | |
| const a = cms[i]; | |
| cm.visible = true; | |
| cm.position.y = 0.2 + a * 3.2; | |
| cm.material.color.setHex(C_SLOW); | |
| cm.material.emissive.setHex(C_SLOW); | |
| cm.material.emissiveIntensity = 0.25 + 0.6 * a; | |
| cm.material.opacity = 0.4 + 0.55 * a; | |
| cm.scale.setScalar(0.7 + 0.7 * a); | |
| } else { cm.visible = false; } | |
| } | |
| // slow core: size/brightness reflect how much of task A the protected core | |
| // retains (retainedA_cms), and pulse brighter with the retention advantage. | |
| if (_core) { | |
| if (live && S.retainCms != null) { | |
| _core.material.color.setHex(C_SLOW); | |
| _core.material.emissive.setHex(C_SLOW); | |
| _core.material.opacity = 0.85; | |
| _core.scale.setScalar(0.6 + S.retainCms * 1.0); | |
| } else { | |
| _core.material.color.setHex(C_DIM); | |
| _core.material.emissive.setHex(C_DIM); | |
| _core.material.opacity = 0.3; | |
| _core.scale.setScalar(0.6); | |
| } | |
| } | |
| } | |
| // ============================================================================= | |
| // per-frame animation | |
| // ============================================================================= | |
| function _onFrame() { | |
| const t = performance.now(); | |
| if (_group) _group.rotation.y = Math.sin(t * 0.00008) * 0.14; | |
| // rings spin at their own clock rate — fast ring spins fastest | |
| if (_rings[0]) _rings[0].rotation.y += 0.010; | |
| if (_rings[1]) _rings[1].rotation.y += 0.0035; | |
| if (_rings[2]) _rings[2].rotation.y += 0.0009; | |
| if (_core) { | |
| _core.rotation.y += 0.02; | |
| _core.rotation.x += 0.009; | |
| const pulse = 1.0 + 0.12 * Math.sin(t * 0.0035); | |
| const base = (S.state === "live" && S.retainCms != null) ? (0.6 + S.retainCms * 1.0) : 0.6; | |
| _core.scale.setScalar(base * pulse); | |
| } | |
| } | |
| // ============================================================================= | |
| // overlay | |
| // ============================================================================= | |
| function _buildOverlay() { | |
| const ctx = _ctx; | |
| _overlay = document.createElement("div"); | |
| Object.assign(_overlay.style, { | |
| position: "absolute", left: "14px", top: "14px", zIndex: "6", | |
| display: "flex", flexDirection: "column", gap: "8px", | |
| maxWidth: "min(94%,460px)", | |
| font: "12px ui-sans-serif,system-ui,Segoe UI,Roboto,Arial", | |
| color: "#eef3f6", | |
| }); | |
| const h = document.createElement("div"); | |
| h.style.cssText = "font:600 13px ui-sans-serif,system-ui;letter-spacing:.4px"; | |
| h.textContent = TITLE; | |
| _overlay.appendChild(h); | |
| const sub = document.createElement("div"); | |
| sub.style.cssText = "color:#9fb1bf;font-size:11px;line-height:1.55"; | |
| sub.innerHTML = | |
| 'A model as a stack of memory levels updating at <b>different clocks</b> (fast/medium/slow), with ' + | |
| 'the <b>optimizer itself as associative memory</b>. On a toy continual-learning stream, the slow ' + | |
| '<b>protected core</b> (a surprise-gated timescale) <b>retains earlier tasks</b> where a single-clock ' + | |
| 'baseline overwrites and <b>forgets</b> them. ' + | |
| 'Honesty label <b>MODELED</b> (deterministic scheduling simulation; NOT the Hope architecture). 0 runtime CDN.'; | |
| _overlay.appendChild(sub); | |
| const brow = document.createElement("div"); | |
| brow.style.cssText = "display:flex;gap:8px;align-items:center;flex-wrap:wrap"; | |
| if (_badge && _badge.el) brow.appendChild(_badge.el); | |
| _overlay.appendChild(brow); | |
| const card = document.createElement("div"); | |
| card.style.cssText = "background:#0a1117;border:1px solid #1d2a36;border-radius:9px;padding:9px 10px;display:flex;flex-direction:column;gap:6px"; | |
| const chead = document.createElement("div"); | |
| chead.style.cssText = "display:flex;align-items:center;gap:8px;flex-wrap:wrap"; | |
| const dot = document.createElement("span"); | |
| dot.style.cssText = "width:9px;height:9px;border-radius:50%;background:#3af4c8;box-shadow:0 0 7px #3af4c8"; | |
| const nm = document.createElement("b"); | |
| nm.style.cssText = "font-size:12px;color:#3af4c8;letter-spacing:.3px"; | |
| nm.textContent = "nested learning / continuum memory system"; | |
| chead.appendChild(dot); chead.appendChild(nm); | |
| card.appendChild(chead); | |
| const grid = document.createElement("div"); | |
| grid.style.cssText = "display:grid;grid-template-columns:1fr;gap:4px"; | |
| function kpiRow(id, label) { | |
| const r = document.createElement("div"); | |
| r.style.cssText = "display:flex;justify-content:space-between;gap:10px;font-size:11px"; | |
| const l = document.createElement("span"); l.style.cssText = "color:#9fb1bf"; l.textContent = label; | |
| const v = document.createElement("b"); | |
| v.id = id; | |
| v.style.cssText = "font-variant-numeric:tabular-nums;color:#eef3f6;text-align:right;max-width:58%"; | |
| v.textContent = "\u2014"; | |
| _el[id] = v; | |
| r.appendChild(l); r.appendChild(v); return r; | |
| } | |
| grid.appendChild(kpiRow("ne-tasks", "continual-learning tasks")); | |
| grid.appendChild(kpiRow("ne-clocks", "clocks k_fast / k_med / k_slow")); | |
| grid.appendChild(kpiRow("ne-gate", "slow-core surprise gate")); | |
| grid.appendChild(kpiRow("ne-retbase", "task-A retained \u2014 single-clock")); | |
| grid.appendChild(kpiRow("ne-retcms", "task-A retained \u2014 CMS (MODELED)")); | |
| grid.appendChild(kpiRow("ne-delta", "retention gain (CMS \u2212 baseline)")); | |
| grid.appendChild(kpiRow("ne-writes", "slow-core writes CMS / baseline")); | |
| grid.appendChild(kpiRow("ne-surprise","mean / peak surprise")); | |
| grid.appendChild(kpiRow("ne-momentum","momentum (optimizer-as-memory)")); | |
| grid.appendChild(kpiRow("ne-label", "honesty label")); | |
| card.appendChild(grid); | |
| const fn = document.createElement("div"); | |
| fn.style.cssText = "font-size:9.5px;color:#6b7a86;line-height:1.5"; | |
| fn.textContent = "Behrouz, Razaviyayn, Zhong & Mirrokni 2025 (Google Research) \u00b7 Nested Learning: The Illusion of Deep Learning Architectures \u00b7 NeurIPS 2025 \u00b7 arXiv:2512.24695 \u00b7 research.google/blog Nested Learning. MODELED \u00b7 not claimed-as."; | |
| card.appendChild(fn); | |
| _overlay.appendChild(card); | |
| const pl = document.createElement("button"); | |
| pl.textContent = "\u25d1 what this means"; | |
| pl.title = "Toggle plain-language explanation for investors & consumers."; | |
| pl.style.cssText = "font:11px ui-monospace,monospace;padding:5px 11px;border-radius:7px;border:1px solid #3af4c8;background:#08140f;color:#3af4c8;cursor:pointer;width:fit-content"; | |
| pl.addEventListener("click", () => { | |
| _plain = !_plain; | |
| pl.style.background = _plain ? "#0f2a20" : "#08140f"; | |
| _applyPlain(); | |
| }); | |
| _overlay.appendChild(pl); | |
| const pd = document.createElement("div"); | |
| pd.id = "ne-plain"; | |
| pd.style.cssText = "font-size:10.5px;color:#c9d6df;line-height:1.55;border:1px dashed #26333f;border-radius:7px;padding:7px 9px;display:none"; | |
| _el["plain"] = pd; | |
| _overlay.appendChild(pd); | |
| (ctx.container || document.body).appendChild(_overlay); | |
| _paintOverlay(); | |
| } | |
| function _applyPlain() { | |
| const pd = _el["plain"]; | |
| if (!pd) return; | |
| pd.style.display = _plain ? "block" : "none"; | |
| if (!_plain) return; | |
| const rBase = S.retainBase != null ? (S.retainBase * 100).toFixed(0) + "%" : "loading\u2026"; | |
| const rCms = S.retainCms != null ? (S.retainCms * 100).toFixed(0) + "%" : "loading\u2026"; | |
| const dPct = S.retDelta != null ? "+" + (S.retDelta * 100).toFixed(0) + " points" : "loading\u2026"; | |
| pd.innerHTML = | |
| "<b>What this means:</b> When an AI learns a new skill it often <b>forgets the old one</b> " + | |
| "(\u201ccatastrophic forgetting\u201d). Nested Learning treats the model as a <b>stack of memories that " + | |
| "update at different speeds</b> \u2014 a fast one that chases whatever it is learning right now, and " + | |
| "slower ones that only commit knowledge once it has <b>settled</b>. The slowest \u201ccore\u201d is " + | |
| "<b>protected</b>: it refuses to overwrite itself during the noisy middle of learning something new. " + | |
| "Here we teach it several small tasks in a row and then check how much of the <b>first</b> task it still " + | |
| "remembers. A plain single-speed model keeps only about <b>" + rBase + "</b> of task A, while the " + | |
| "multi-speed schedule keeps about <b>" + rCms + "</b> \u2014 a gain of roughly <b>" + dPct + "</b> less " + | |
| "forgetting on this toy stream. " + | |
| "<b>Honest caveat:</b> this is a <b>MODELED</b> toy simulation of the scheduling idea (tiny linear " + | |
| "tasks, hand-set clocks), <b>not the \u201cHope\u201d model</b>. The paper was noted publicly to lack full " + | |
| "ablations in its first version, so this only shows the <b>scheduling mechanism</b> reduces forgetting " + | |
| "on a controlled stream \u2014 it does <b>not</b> reproduce Hope's language-modeling or needle-in-haystack " + | |
| "results or its claimed wins over Titans / Transformers."; | |
| } | |
| function _tok(s) { | |
| if (s === "live") return null; | |
| if (s === "missing") return "NO-LIVE-DATA"; | |
| if (s === "degraded") return "DEGRADED"; | |
| if (s === "error") return "OFFLINE"; | |
| return "\u2026"; | |
| } | |
| function fx(v, d) { return typeof v === "number" ? v.toFixed(d) : "\u2014"; } | |
| function pct(v, d) { return typeof v === "number" ? (v * 100).toFixed(d) + "%" : "\u2014"; } | |
| function _set(id, v) { if (_el[id]) _el[id].textContent = v; } | |
| function _paintOverlay() { | |
| const t = _tok(S.state); | |
| _set("ne-tasks", t || (S.numTasks != null ? String(S.numTasks) : "\u2014")); | |
| _set("ne-clocks", t || (S.kFast != null || S.kMed != null || S.kSlow != null | |
| ? (S.kFast != null ? String(S.kFast) : "\u2014") + " / " + | |
| (S.kMed != null ? String(S.kMed) : "\u2014") + " / " + | |
| (S.kSlow != null ? String(S.kSlow) : "\u2014") | |
| : "\u2014")); | |
| _set("ne-gate", t || fx(S.gate, 3)); | |
| _set("ne-retbase", t || pct(S.retainBase, 1)); | |
| _set("ne-retcms", t || pct(S.retainCms, 1)); | |
| _set("ne-delta", t || (S.retDelta != null ? "+" + (S.retDelta * 100).toFixed(1) + "%" : "\u2014")); | |
| _set("ne-writes", t || (S.slowWritesCms != null || S.slowWritesBase != null | |
| ? (S.slowWritesCms != null ? String(S.slowWritesCms) : "\u2014") + " / " + | |
| (S.slowWritesBase != null ? String(S.slowWritesBase) : "\u2014") | |
| : "\u2014")); | |
| _set("ne-surprise", t || (S.meanSurprise != null || S.peakSurprise != null | |
| ? fx(S.meanSurprise, 3) + " / " + fx(S.peakSurprise, 3) | |
| : "\u2014")); | |
| _set("ne-momentum", t || fx(S.momentum, 2)); | |
| // honesty label verbatim — never upgraded | |
| _set("ne-label", t || (S.label || "MODELED")); | |
| if (_plain) _applyPlain(); | |
| } | |
| // ============================================================================= | |
| // unmount — clean up everything; must not affect other organs | |
| // ============================================================================= | |
| export function unmount() { | |
| _polls.forEach((p) => { try { p.stop(); } catch (_) {} }); _polls = []; | |
| try { if (_overlay && _overlay.parentNode) _overlay.parentNode.removeChild(_overlay); } catch (_) {} | |
| try { | |
| if (_group && _stage) { | |
| _group.traverse((o) => { | |
| if (o.geometry && o.geometry.dispose) o.geometry.dispose(); | |
| if (o.material) { | |
| const ms = Array.isArray(o.material) ? o.material : [o.material]; | |
| ms.forEach((m) => { if (m.dispose) m.dispose(); }); | |
| } | |
| }); | |
| _stage.scene.remove(_group); | |
| } | |
| } catch (_) {} | |
| _group = _overlay = null; | |
| _floor = null; _rings = []; _tiers = []; _baseMarks = []; _cmsMarks = []; _core = null; | |
| _el = {}; _badge = null; _plain = false; _frameReg = false; | |
| _stage = _THREE = _ctx = null; | |
| S.label = S.numTasks = S.blockLen = S.nTokens = null; | |
| S.kFast = S.kMed = S.kSlow = S.gate = null; | |
| S.taskABase = S.taskACms = null; | |
| S.retainBase = S.retainCms = S.retDelta = S.meanDelta = null; | |
| S.slowWritesCms = S.slowWritesBase = null; | |
| S.meanSurprise = S.peakSurprise = S.momentum = null; | |
| S.curve = null; | |
| S.state = "init"; | |
| } | |
| export default { id: ID, title: TITLE, endpoints: [EP], mount, unmount }; | |