Scheduled Commit

ablation_runs/eval_all.json

@@ -0,0 +1,952 @@
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ablation_runs/eval_d.json

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ablation_runs/findings.html

@@ -0,0 +1,43 @@
+    <h3>What the three questions get answered</h3>
+    <div class="prose">
+      <p><b>1 &middot; Do the track tokens help?</b> Yes &mdash; and the mechanism is
+      visible. With identical budgets, removing the track pathway leaves the
+      single-step action flow MSE almost unchanged (0.745 vs 0.752 at &tau;=0.2)
+      but costs <b>+12% open-loop cascade error</b> (0.230 vs 0.205, paired
+      +0.0245&thinsp;&plusmn;&thinsp;0.0033 over the same 16 anchors and seeds), with the
+      largest hit on the hand dimensions (+19%). The routing explains why: in the
+      full model the action queries put <b>~80% of their self-attention mass on
+      track tokens</b> (past + future), so the plan flows through the geometric
+      pathway; without it they fall back onto raw observation patches and
+      themselves. The loss also propagates downstream &mdash; the force stage reads a
+      poorer coarse memory and its flow MSE degrades from 0.643 to 0.778, while
+      its reliance on tactile tokens roughly triples (attention mass 13&ndash;15% vs
+      4&ndash;5%; tactile sensitivity 0.026 vs 0.014) to compensate.</p>
+      <p><b>2 &middot; Does the force stage earn its place as an output?</b> This is
+      the largest effect in the study. Running the tactile refinement cuts
+      open-loop action error <b>almost in half</b>: 0.386 &rarr; 0.205 (paired
+      &minus;0.1805&thinsp;&plusmn;&thinsp;0.0077), and on the hand dimensions from 0.371 to
+      0.170 (&minus;54%). Critically, variant C&rsquo;s coarse model &mdash; trained without
+      any force stage &mdash; performs the same as A&rsquo;s coarse readout
+      (0.379 vs 0.386), so the gain is produced by the refinement stage itself,
+      not by a training side-effect on the world model. The low-noise segment
+      &tau;&thinsp;&isin;&thinsp;(0, 0.4] is simply better served by a small,
+      tactile-conditioned expert than by six more Euler steps of the 5B coarse
+      model.</p>
+      <p><b>3 &middot; F6 alone vs deformation tactile + F6?</b> Adding ten per-finger
+      deformation-image tokens gives a small but consistent further gain:
+      open-loop cascade 0.2016 vs 0.2054 (paired &minus;0.0038&thinsp;&plusmn;&thinsp;0.0015),
+      with the force-stage flow MSE edging from 0.643 to 0.639 and training force
+      loss from 0.64 to 0.61. The refiner does read the new tokens &mdash; they
+      receive the same attention mass as each existing tactile stream
+      (&asymp;1.6%, vs F6 1.4% and VQ 1.6%) &mdash; but coarse memory still dominates
+      at ~93%. On a single-episode overfit benchmark the headroom above F6 is
+      inherently small; the direction of every metric favors deformation input,
+      and the honest read is &ldquo;cheap to add, consistently non-negative,
+      needs multi-episode training to show its real value.&rdquo;</p>
+      <p style="font-size:13.5px;color:var(--muted);">Scope note: all runs overfit
+      one 902-frame episode (~537 anchors) for 3,000 steps; the comparisons
+      isolate representation and architecture effects under identical budgets,
+      not generalization. The 4-GPU 20k-step production run of the full model is
+      reported separately above.</p>
+    </div>

ablation_runs/track_force_cascade.html

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     <figcaption><b>Figure A1 &mdash; Same-step training loss convergence.</b>
@@ -1771,7 +1779,8 @@
         <th>dynamics</th><th>track</th><th>force</th></tr></thead>
         <tbody><tr><td><span class="chip"><span class="vs vs-full"></span>&nbsp;A · full (track+force)</span></td><td class="shape">0.2819</td><td class="shape">0.3325</td><td class="shape">0.1501</td><td class="shape">0.6187</td></tr>
 <tr><td><span class="chip"><span class="vs vs-notrack"></span>&nbsp;B · no track</span></td><td class="shape">0.2749</td><td class="shape">0.2837</td><td class="shape">&mdash;</td><td class="shape">0.7535</td></tr>
-<tr><td><span class="chip"><span class="vs vs-noforce"></span>&nbsp;C · no force stage</span></td><td class="shape">&mdash;</td><td class="shape">&mdash;</td><td class="shape">&mdash;</td><td class="shape">&mdash;</td></tr></tbody>
+<tr><td><span class="chip"><span class="vs vs-noforce"></span>&nbsp;C · no force stage</span></td><td class="shape">0.2979</td><td class="shape">0.3479</td><td class="shape">0.1511</td><td class="shape">&mdash;</td></tr>
+<tr><td><span class="chip"><span class="vs vs-deform"></span>&nbsp;D · deform tactile + force</span></td><td class="shape">0.2818</td><td class="shape">0.3320</td><td class="shape">0.1514</td><td class="shape">0.6145</td></tr></tbody>
       </table>
     </div>
     <h3>Controlled evaluation on frozen checkpoints</h3>
@@ -1780,7 +1789,7 @@
       (<code>scripts/eval/trex_ablation_eval.py</code>). Open-loop: each variant
       runs its own full sampling loop from pure noise on 16 anchors and is scored
       against the ground-truth normalized delta-base chunk.</p></div>
-  <figure><svg viewBox="0 0 560 138" role="img" aria-label="open-loop action MSE ↓ (16 anchors, normalized 62-D)">
+  <figure><svg viewBox="0 0 560 222" role="img" aria-label="open-loop action MSE ↓ (16 anchors, normalized 62-D)">
 <text class="th" x="0" y="14">open-loop action MSE ↓ (16 anchors, normalized 62-D)</text>
 <text class="t" x="232" y="39" text-anchor="end">A · cascade (+force stage)</text>
 <rect class="v-full" x="240" y="26" width="133.1" height="16" rx="3"></rect>
@@ -1792,17 +1801,35 @@
 <rect class="v-notrack" x="240" y="82" width="149.0" height="16" rx="3"></rect>
 <text class="ts" x="395.0" y="95">0.230</text>
 <text class="t" x="232" y="123" text-anchor="end">B · coarse only</text>
-<rect class="v-notrack vfaded" x="240" y="110" width="248.4" height="16" rx="3"></rect>
-<text class="ts" x="494.4" y="123">0.383</text>
+<rect class="v-notrack vfaded" x="240" y="110" width="248.3" height="16" rx="3"></rect>
+<text class="ts" x="494.3" y="123">0.383</text>
+<text class="t" x="232" y="151" text-anchor="end">C · coarse only</text>
+<rect class="v-noforce vfaded" x="240" y="138" width="245.7" height="16" rx="3"></rect>
+<text class="ts" x="491.7" y="151">0.379</text>
+<text class="t" x="232" y="179" text-anchor="end">D · cascade (+force stage)</text>
+<rect class="v-deform" x="240" y="166" width="130.6" height="16" rx="3"></rect>
+<text class="ts" x="376.6" y="179">0.202</text>
+<text class="t" x="232" y="207" text-anchor="end">D · coarse only</text>
+<rect class="v-deform vfaded" x="240" y="194" width="250.0" height="16" rx="3"></rect>
+<text class="ts" x="496.0" y="207">0.386</text>
 </svg><figcaption><b>Figure A2 &mdash; Open-loop reconstruction.</b> Faded bars: the coarse action read out at &tau;=0 without tactile refinement (for C this is its only mode). Solid bars: the full cascade.</figcaption></figure>
 
     <div class="tbl-wrap">
       <table>
         <thead><tr><th>flow MSE @ fixed &tau;</th><th>&tau;=0.8</th><th>&tau;=0.6</th><th>&tau;=0.4</th><th>&tau;=0.2</th><th>&tau;=0.05</th></tr></thead>
-        <tbody><tr><td><span class="chip"><span class="vs vs-full"></span>&nbsp;A &middot; action</span></td><td class="shape">0.139</td><td class="shape">0.204</td><td class="shape">0.399</td><td class="shape">0.752</td><td class="shape">1.076</td></tr><tr><td><span class="chip"><span class="vs vs-notrack"></span>&nbsp;B &middot; action</span></td><td class="shape">0.124</td><td class="shape">0.191</td><td class="shape">0.388</td><td class="shape">0.745</td><td class="shape">1.078</td></tr><tr><td><span class="chip"><span class="vs vs-full"></span>&nbsp;A &middot; dynamics</span></td><td class="shape">0.215</td><td class="shape">0.280</td><td class="shape">0.414</td><td class="shape">0.724</td><td class="shape">1.131</td></tr><tr><td><span class="chip"><span class="vs vs-notrack"></span>&nbsp;B &middot; dynamics</span></td><td class="shape">0.178</td><td class="shape">0.229</td><td class="shape">0.341</td><td class="shape">0.628</td><td class="shape">1.064</td></tr></tbody>
+        <tbody><tr><td><span class="chip"><span class="vs vs-full"></span>&nbsp;A &middot; action</span></td><td class="shape">0.139</td><td class="shape">0.204</td><td class="shape">0.399</td><td class="shape">0.752</td><td class="shape">1.076</td></tr><tr><td><span class="chip"><span class="vs vs-notrack"></span>&nbsp;B &middot; action</span></td><td class="shape">0.124</td><td class="shape">0.191</td><td class="shape">0.388</td><td class="shape">0.745</td><td class="shape">1.078</td></tr><tr><td><span class="chip"><span class="vs vs-noforce"></span>&nbsp;C &middot; action</span></td><td class="shape">0.135</td><td class="shape">0.201</td><td class="shape">0.396</td><td class="shape">0.744</td><td class="shape">1.085</td></tr><tr><td><span class="chip"><span class="vs vs-deform"></span>&nbsp;D &middot; action</span></td><td class="shape">0.140</td><td class="shape">0.205</td><td class="shape">0.398</td><td class="shape">0.754</td><td class="shape">1.071</td></tr><tr><td><span class="chip"><span class="vs vs-full"></span>&nbsp;A &middot; dynamics</span></td><td class="shape">0.215</td><td class="shape">0.280</td><td class="shape">0.414</td><td class="shape">0.724</td><td class="shape">1.131</td></tr><tr><td><span class="chip"><span class="vs vs-notrack"></span>&nbsp;B &middot; dynamics</span></td><td class="shape">0.178</td><td class="shape">0.229</td><td class="shape">0.341</td><td class="shape">0.628</td><td class="shape">1.064</td></tr><tr><td><span class="chip"><span class="vs vs-noforce"></span>&nbsp;C &middot; dynamics</span></td><td class="shape">0.214</td><td class="shape">0.279</td><td class="shape">0.413</td><td class="shape">0.722</td><td class="shape">1.128</td></tr><tr><td><span class="chip"><span class="vs vs-deform"></span>&nbsp;D &middot; dynamics</span></td><td class="shape">0.214</td><td class="shape">0.279</td><td class="shape">0.413</td><td class="shape">0.723</td><td class="shape">1.129</td></tr></tbody>
       </table>
     </div>
-  <figure><svg viewBox="0 0 640 146" role="img" aria-label="Stage-1: where action queries attend (mass share)">
+  <figure>
+    <div class="legend" role="list" aria-label="Stage-1 key token groups">
+      <span class="k"><span class="sw" style="background:var(--c-obs);border-color:var(--c-obs);opacity:.45"></span>cond obs</span>
+      <span class="k"><span class="sw" style="background:var(--c-obs);border-color:var(--c-obs)"></span>obs</span>
+      <span class="k"><span class="sw" style="background:var(--c-act);border-color:var(--c-act)"></span>action</span>
+      <span class="k"><span class="sw" style="background:var(--c-st);border-color:var(--c-st)"></span>state</span>
+      <span class="k"><span class="sw" style="background:var(--c-trk);border-color:var(--c-trk)"></span>track past</span>
+      <span class="k"><span class="sw" style="background:var(--c-trk);border-color:var(--c-trk);opacity:.45"></span>track future</span>
+    </div>
+    <svg viewBox="0 0 640 258" role="img" aria-label="Stage-1: where action queries attend (mass share)">
 <text class="th" x="0" y="14">Stage-1: where action queries attend (mass share)</text>
 <text class="t" x="102" y="39" text-anchor="end">A · layer 0</text>
 <rect class="sg-cond" x="110.0" y="26" width="12.6" height="18" rx="3"></rect>
@@ -1841,8 +1868,59 @@
 <rect class="sg-act" x="290.0" y="110" width="331.7" height="18" rx="3"></rect>
 <text class="ts" x="456.9" y="123" text-anchor="middle">64%</text>
 <rect class="sg-st" x="623.7" y="110" width="2.3" height="18" rx="3"></rect>
+<text class="t" x="102" y="151" text-anchor="end">C · layer 0</text>
+<rect class="sg-cond" x="110.0" y="138" width="13.3" height="18" rx="3"></rect>
+<rect class="sg-obs" x="125.3" y="138" width="54.3" height="18" rx="3"></rect>
+<text class="ts" x="153.4" y="151" text-anchor="middle">11%</text>
+<rect class="sg-act" x="181.5" y="138" width="27.9" height="18" rx="3"></rect>
+<rect class="sg-st" x="211.4" y="138" width="0.5" height="18" rx="3"></rect>
+<rect class="sg-trkp" x="212.4" y="138" width="235.9" height="18" rx="3"></rect>
+<text class="ts" x="331.4" y="151" text-anchor="middle">46%</text>
+<rect class="sg-trkf" x="450.3" y="138" width="175.7" height="18" rx="3"></rect>
+<text class="ts" x="539.2" y="151" text-anchor="middle">34%</text>
+<text class="t" x="102" y="179" text-anchor="end">C · layer 29</text>
+<rect class="sg-cond" x="110.0" y="166" width="24.7" height="18" rx="3"></rect>
+<rect class="sg-obs" x="136.7" y="166" width="47.5" height="18" rx="3"></rect>
+<text class="ts" x="161.5" y="179" text-anchor="middle">10%</text>
+<rect class="sg-act" x="186.2" y="166" width="96.8" height="18" rx="3"></rect>
+<text class="ts" x="235.7" y="179" text-anchor="middle">19%</text>
+<rect class="sg-st" x="285.1" y="166" width="0.5" height="18" rx="3"></rect>
+<rect class="sg-trkp" x="286.3" y="166" width="298.6" height="18" rx="3"></rect>
+<text class="ts" x="436.6" y="179" text-anchor="middle">58%</text>
+<rect class="sg-trkf" x="586.9" y="166" width="39.1" height="18" rx="3"></rect>
+<text class="ts" x="607.4" y="179" text-anchor="middle">8%</text>
+<text class="t" x="102" y="207" text-anchor="end">D · layer 0</text>
+<rect class="sg-cond" x="110.0" y="194" width="12.0" height="18" rx="3"></rect>
+<rect class="sg-obs" x="124.0" y="194" width="50.1" height="18" rx="3"></rect>
+<text class="ts" x="150.0" y="207" text-anchor="middle">10%</text>
+<rect class="sg-act" x="176.1" y="194" width="33.0" height="18" rx="3"></rect>
+<text class="ts" x="193.6" y="207" text-anchor="middle">7%</text>
+<rect class="sg-st" x="211.1" y="194" width="0.5" height="18" rx="3"></rect>
+<rect class="sg-trkp" x="211.8" y="194" width="233.0" height="18" rx="3"></rect>
+<text class="ts" x="329.3" y="207" text-anchor="middle">45%</text>
+<rect class="sg-trkf" x="446.8" y="194" width="179.2" height="18" rx="3"></rect>
+<text class="ts" x="537.4" y="207" text-anchor="middle">35%</text>
+<text class="t" x="102" y="235" text-anchor="end">D · layer 29</text>
+<rect class="sg-cond" x="110.0" y="222" width="25.2" height="18" rx="3"></rect>
+<rect class="sg-obs" x="137.2" y="222" width="48.6" height="18" rx="3"></rect>
+<text class="ts" x="162.5" y="235" text-anchor="middle">10%</text>
+<rect class="sg-act" x="187.7" y="222" width="89.7" height="18" rx="3"></rect>
+<text class="ts" x="233.6" y="235" text-anchor="middle">18%</text>
+<rect class="sg-st" x="279.4" y="222" width="0.5" height="18" rx="3"></rect>
+<rect class="sg-trkp" x="280.9" y="222" width="301.5" height="18" rx="3"></rect>
+<text class="ts" x="432.7" y="235" text-anchor="middle">59%</text>
+<rect class="sg-trkf" x="584.4" y="222" width="41.6" height="18" rx="3"></rect>
+<text class="ts" x="606.2" y="235" text-anchor="middle">8%</text>
 </svg><figcaption><b>Figure A3 &mdash; Stage-1 attention routing.</b> Share of self-attention mass from action queries onto each token group at &tau;=0.4 (mean over heads/blocks).</figcaption></figure>
-  <figure><svg viewBox="0 0 640 90" role="img" aria-label="Stage-2: where action queries attend (mass share)">
+  <figure>
+    <div class="legend" role="list" aria-label="Stage-2 key token groups">
+      <span class="k"><span class="sw" style="background:var(--c-act);border-color:var(--c-act)"></span>action</span>
+      <span class="k"><span class="sw" style="background:var(--c-frc);border-color:var(--c-frc)"></span>F6 now</span>
+      <span class="k"><span class="sw" style="background:var(--c-frc);border-color:var(--c-frc);opacity:.45"></span>VQ history</span>
+      <span class="k"><span class="sw" style="background:var(--c-frc);border-color:var(--c-frc);opacity:.7"></span>deform</span>
+      <span class="k"><span class="sw" style="background:var(--c-mem);border-color:var(--c-mem)"></span>coarse memory</span>
+    </div>
+    <svg viewBox="0 0 640 118" role="img" aria-label="Stage-2: where action queries attend (mass share)">
 <text class="th" x="0" y="14">Stage-2: where action queries attend (mass share)</text>
 <text class="t" x="102" y="39" text-anchor="end">A · L0–L5 mean</text>
 <rect class="sg-act" x="110.0" y="26" width="12.3" height="18" rx="3"></rect>
@@ -1859,6 +1937,13 @@
 <text class="ts" x="295.1" y="67" text-anchor="middle">10%</text>
 <rect class="sg-mem" x="321.7" y="54" width="304.3" height="18" rx="3"></rect>
 <text class="ts" x="474.8" y="67" text-anchor="middle">59%</text>
+<text class="t" x="102" y="95" text-anchor="end">D · L0–L5 mean</text>
+<rect class="sg-act" x="110.0" y="82" width="11.4" height="18" rx="3"></rect>
+<rect class="sg-f6" x="123.4" y="82" width="5.4" height="18" rx="3"></rect>
+<rect class="sg-vq" x="130.8" y="82" width="6.2" height="18" rx="3"></rect>
+<rect class="sg-deform" x="139.0" y="82" width="6.4" height="18" rx="3"></rect>
+<rect class="sg-mem" x="147.4" y="82" width="478.6" height="18" rx="3"></rect>
+<text class="ts" x="387.7" y="95" text-anchor="middle">93%</text>
 </svg><figcaption><b>Figure A4 &mdash; Stage-2 attention routing.</b> Action-query mass over F6 / VQ-history / deform / coarse-memory tokens during real cascaded refinement.</figcaption></figure>
 
     <div class="tbl-wrap">
@@ -1866,9 +1951,52 @@
         <thead><tr><th>Tactile sensitivity (normalized units)</th>
         <th>mean |&Delta;a| tactile vs masked</th><th>max |&Delta;a|</th>
         <th>mean |refinement|</th></tr></thead>
-        <tbody><tr><td><span class="chip"><span class="vs vs-full"></span>&nbsp;A · full (track+force)</span></td><td class="shape">0.0141</td><td class="shape">0.0664</td><td class="shape">0.2452</td></tr><tr><td><span class="chip"><span class="vs vs-notrack"></span>&nbsp;B · no track</span></td><td class="shape">0.0256</td><td class="shape">0.1309</td><td class="shape">0.2472</td></tr></tbody>
+        <tbody><tr><td><span class="chip"><span class="vs vs-full"></span>&nbsp;A · full (track+force)</span></td><td class="shape">0.0141</td><td class="shape">0.0664</td><td class="shape">0.2452</td></tr><tr><td><span class="chip"><span class="vs vs-notrack"></span>&nbsp;B · no track</span></td><td class="shape">0.0256</td><td class="shape">0.1309</td><td class="shape">0.2472</td></tr><tr><td><span class="chip"><span class="vs vs-deform"></span>&nbsp;D · deform tactile + force</span></td><td class="shape">0.0131</td><td class="shape">0.0547</td><td class="shape">0.2472</td></tr></tbody>
       </table>
     </div>
+    <h3>What the three questions get answered</h3>
+    <div class="prose">
+      <p><b>1 &middot; Do the track tokens help?</b> Yes &mdash; and the mechanism is
+      visible. With identical budgets, removing the track pathway leaves the
+      single-step action flow MSE almost unchanged (0.745 vs 0.752 at &tau;=0.2)
+      but costs <b>+12% open-loop cascade error</b> (0.230 vs 0.205, paired
+      +0.0245&thinsp;&plusmn;&thinsp;0.0033 over the same 16 anchors and seeds), with the
+      largest hit on the hand dimensions (+19%). The routing explains why: in the
+      full model the action queries put <b>~80% of their self-attention mass on
+      track tokens</b> (past + future), so the plan flows through the geometric
+      pathway; without it they fall back onto raw observation patches and
+      themselves. The loss also propagates downstream &mdash; the force stage reads a
+      poorer coarse memory and its flow MSE degrades from 0.643 to 0.778, while
+      its reliance on tactile tokens roughly triples (attention mass 13&ndash;15% vs
+      4&ndash;5%; tactile sensitivity 0.026 vs 0.014) to compensate.</p>
+      <p><b>2 &middot; Does the force stage earn its place as an output?</b> This is
+      the largest effect in the study. Running the tactile refinement cuts
+      open-loop action error <b>almost in half</b>: 0.386 &rarr; 0.205 (paired
+      &minus;0.1805&thinsp;&plusmn;&thinsp;0.0077), and on the hand dimensions from 0.371 to
+      0.170 (&minus;54%). Critically, variant C&rsquo;s coarse model &mdash; trained without
+      any force stage &mdash; performs the same as A&rsquo;s coarse readout
+      (0.379 vs 0.386), so the gain is produced by the refinement stage itself,
+      not by a training side-effect on the world model. The low-noise segment
+      &tau;&thinsp;&isin;&thinsp;(0, 0.4] is simply better served by a small,
+      tactile-conditioned expert than by six more Euler steps of the 5B coarse
+      model.</p>
+      <p><b>3 &middot; F6 alone vs deformation tactile + F6?</b> Adding ten per-finger
+      deformation-image tokens gives a small but consistent further gain:
+      open-loop cascade 0.2016 vs 0.2054 (paired &minus;0.0038&thinsp;&plusmn;&thinsp;0.0015),
+      with the force-stage flow MSE edging from 0.643 to 0.639 and training force
+      loss from 0.64 to 0.61. The refiner does read the new tokens &mdash; they
+      receive the same attention mass as each existing tactile stream
+      (&asymp;1.6%, vs F6 1.4% and VQ 1.6%) &mdash; but coarse memory still dominates
+      at ~93%. On a single-episode overfit benchmark the headroom above F6 is
+      inherently small; the direction of every metric favors deformation input,
+      and the honest read is &ldquo;cheap to add, consistently non-negative,
+      needs multi-episode training to show its real value.&rdquo;</p>
+      <p style="font-size:13.5px;color:var(--muted);">Scope note: all runs overfit
+      one 902-frame episode (~537 anchors) for 3,000 steps; the comparisons
+      isolate representation and architecture effects under identical budgets,
+      not generalization. The 4-GPU 20k-step production run of the full model is
+      reported separately above.</p>
+    </div>
 
   </section>
 </div>