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tinyvla2/RESULTS.md

@@ -219,3 +219,35 @@ learning needs exemplars in the attention stream where query tokens can attend t
 them selectively (RoboCat-style separate demo cross-attention, or explicit
 retrieval), not "placed alongside" where the network marginalizes them as constant
 noise. BEST CHECKPOINT OF THE SERIES REMAINS v2 (261mm on LeKiwi zero-shot).
+
+## DEMO-ONLY — all identity channels removed (5th and final in-context attempt)
+Removed embodiment-ID, morphology descriptor AND robot text; the K=4 demo exemplars
+in the LM attention stream were the ONLY cue for which robot this is (per-source
+action normalization creates the pressure: correct prediction requires knowing the
+robot's action scale, available only from demos). Held-out LeKiwi:
+
+| condition | step 5k | step 10k |
+|---|---|---|
+| REAL LeKiwi demos | 376.1 mm | 323.6 mm |
+| GARBAGE/noise demos | 393.4 mm | 322.1 mm |
+| OTHER-ROBOT (jaco) demos | 330.3 mm | 294.8 mm |
+
+At 5k a spread finally appeared (63mm — the first time any demo variant showed
+content-sensitivity) but by 10k it COLLAPSED back: real (323.6) ≈ garbage (322.1).
+Other-robot demos "win" only because they regress toward the training distribution,
+not because demos are read. Training stopped at 10k (no point burning 7 more hours).
+
+### In-context demo conditioning: FINAL VERDICT after 5 architectures
+1. MLP encoder, same-task exemplars — dead (real ≈ garbage)
+2. MLP encoder, cross-task exemplars — dead (but +regularization: 295→261mm)
+3. High-capacity encoder (24x params, spatial groups) — dead
+4. Demos inside the LM attention stream (rich-slow, dual-rate preserved) — dead
+5. Demos as the ONLY identity channel — brief flicker at 5k, dead by 10k
+Neither sampling, capacity, architectural placement, nor removing all alternatives
+makes this model read demonstration content. Plausible remaining explanation: the
+flow head predicts from the CURRENT observation, and nothing in the training
+objective ever *requires* comparing the current scene to an exemplar — the robot is
+visually identifiable in-frame (an identity leak we cannot remove), so the model
+always has a cheaper route than reading demos.
+**Working mechanism remains the numeric morphology descriptor (C-diverse: 150mm on
+held-out jaco). Best LeKiwi zero-shot: C-incontext-v2 at 261mm.**