Aliased frames, mined — and the subgoal gain doesn’t live there

2026-08-08. The frame-mining stage of the owner-steered field/subgoal-conditioning meta-report (13:21Z steering), executed early in a GPU-quiet window. Instrument: fontaine/scripts/frame_mining.py (embed → mine → sheet); protocol from the observation-aliasing lit slice (AliasBench’s diagnostic run in reverse, 2605.14712). Record-only read on banked panel data; decision frame pinned in the script header before execution. Artifacts: reports/analysis__framemining_ar100k_k4l2.json + flagged-frame npz + embeddings npz.

What ran

The owner asked the upcoming meta-report to showcase frames where the right action is ambiguous from the image alone (“am I at the beginning of the episode or the end?”). Instead of hand-picking anecdotes, we mined them: every one of the 17,204 core panel frames embedded with the frozen Gemma-4 E2B vision tower — AR-100k’s own eye (that run trained text-lr only, so this is literally the perception of the policy being scored; alignment oracle verified every row against the banked npz, actions included). Then within-dataset top-5 nearest neighbors (same-episode frames within the 50-step chunk horizon excluded — overlapping chunks share their continuation by construction), and an alias score per frame: mean std-normalized ground-truth chunk divergence to its closest visual neighbors. High score = “frames that look like this one do different things next.”

One figure per mined pair (per the owner’s spec, 2026-08-08 16:20Z): the two near-identical frames side by side, then both ground-truth action-chunk continuations overlaid — blue follows the query frame, orange the neighbor — with each frame’s subgoal label in the caption. The subgoal is the text the oracle arm conditions on: on most pairs it names exactly the phase distinction the image alone can’t carry.

Pair 1 — LeRobot-worldwide-hackathon/162-Les_traboules-record_draw_lerobot · alias score 1.24 · embed dist 0.0031 · continuation divergence 2.19σ. Query (blue): ep 39 f 1460, Δ_oracle +0.13, subgoal “lift the pen away and hand it off”. Neighbor (amber): ep 40 f 170, Δ_oracle +0.48, subgoal “trace the outer head outline”.

Pair 2 — LeRobot-worldwide-hackathon/162-Les_traboules-record_draw_lerobot · alias score 1.31 · embed dist 0.0025 · continuation divergence 2.11σ. Query (blue): ep 39 f 80, Δ_oracle +0.00, subgoal “lower the pen onto the whiteboard surface”. Neighbor (amber): ep 40 f 170, Δ_oracle +0.48, subgoal “trace the outer head outline”.

Pair 3 — jmrog/record-sweet3 · alias score 0.88 · embed dist 0.0017 · continuation divergence 1.89σ. Query (blue): ep 27 f 561, Δ_oracle +0.00, subgoal “retract the arm back to rest”. Neighbor (amber): ep 15 f 888, Δ_oracle +0.67, subgoal “align the gripper over the sweet”.

Pair 4 — willnorris/cylinder-in-box-hillside-2 · alias score 0.90 · embed dist 0.0041 · continuation divergence 1.81σ. Query (blue): ep 31 f 442, Δ_oracle +0.11, subgoal “lower the cylinder into the box”. Neighbor (amber): ep 31 f 619, Δ_oracle +0.12, subgoal “retract the arm to the rest pose”.

Pair 5 — EverNorif/so101-table-cleanup · alias score 1.06 · embed dist 0.0022 · continuation divergence 1.80σ. Query (blue): ep 72 f 634, Δ_oracle -0.33, subgoal “grasp the red pen and place it into the holder”. Neighbor (amber): ep 68 f 31, Δ_oracle +0.50, subgoal “pick up the first black pen and drop it in the holder”.

Pair 6 — EverNorif/so101-pick-pen · alias score 1.57 · embed dist 0.0036 · continuation divergence 1.74σ. Query (blue): ep 20 f 578, Δ_oracle -0.02, subgoal “retract the arm away from the holder”. Neighbor (amber): ep 22 f 486, Δ_oracle -0.44, subgoal “pick up the last 0.5 mechanical pencil and drop it in the holder”.

Pair 7 — Mohamedal/so100_put_plum_bowl_new_data · alias score 1.25 · embed dist 0.0019 · continuation divergence 1.73σ. Query (blue): ep 16 f 129, Δ_oracle +0.04, subgoal “lower the gripper onto the plum and close on it”. Neighbor (amber): ep 43 f 8, Δ_oracle -1.42, subgoal “reach down toward the plum on the table”.

Pair 8 — CnLori/so101_piper · alias score 0.88 · embed dist 0.0024 · continuation divergence 1.73σ. Query (blue): ep 41 f 1164, Δ_oracle -0.54, subgoal “release and retract the arm”. Neighbor (amber): ep 21 f 675, Δ_oracle -0.43, subgoal “pick the black tape and set it on the gray square”.

Pair 9 — CnLori/so101_piper · alias score 0.75 · embed dist 0.0026 · continuation divergence 1.70σ. Query (blue): ep 41 f 740, Δ_oracle +2.02, subgoal “pick the black tape roll and place it on the gray square”. Neighbor (amber): ep 41 f 1164, Δ_oracle -0.54, subgoal “release and retract the arm”.

Pair 10 — dopaul/game_v7 · alias score 0.70 · embed dist 0.0014 · continuation divergence 1.69σ. Query (blue): ep 83 f 4, Δ_oracle +0.11, subgoal “lower the arm toward the piece on the red-marked square”. Neighbor (amber): ep 60 f 68, Δ_oracle +0.69, subgoal “reach over the board toward the red-marked square”.

Pair 11 — EverNorif/so101-pick-pen · alias score 1.52 · embed dist 0.0025 · continuation divergence 1.68σ. Query (blue): ep 20 f 543, Δ_oracle -0.24, subgoal “pick the last pen and place it in the holder”. Neighbor (amber): ep 22 f 258, Δ_oracle +0.56, subgoal “pick up the clear-barrel pen and place it in the holder”.

Pair 12 — dopaul/1500_chess_moves · alias score 1.15 · embed dist 0.0013 · continuation divergence 1.68σ. Query (blue): ep 1002 f 75, Δ_oracle +0.11, subgoal “swing the arm over the board toward the red-marked square”. Neighbor (amber): ep 1007 f 276, Δ_oracle +0.16, subgoal “release the piece on the blue square”.

The top of the list is exactly the owner’s ask, found automatically: the cylinder mid-place vs already-placed (near-identical images, one frame must keep lowering, the other must retreat), the mug pre- vs post-grasp, mirrored pick-pen approaches, and chess boards — where the next move is genuinely unreadable from a wide shot of the position.

The instrument detects something real

DSSP’s Prop 4.2 (same lit slice) predicts a reactive policy carries an irreducible error floor on aliased frames. It shows up: alias score correlates with AR-100k’s per-frame error at Spearman ρ = 0.41, and the flagged decile runs +29% baseline chunk MAE (6.84 vs 5.32). Caveat, carried in the analysis json: state-copy error is elevated on the same frames (14.4 vs 11.0), so the score partly conflates “ambiguous” with “dynamic/hard” — the per-pair figures above are the qualitative check that genuine aliasing sits at the top.

The concentration read: a clean null

The meta-report’s central question, pinned before the run: does the per-frame oracle-subgoal gain (Δ_oracle, conditioned − baseline, banked rung-(a) arms) concentrate on the aliased frames? IntentVLA’s 9% → 45.8% says intent conditioning earns its keep on aliased states specifically; if our subgoal slot is a disambiguator, the −0.29 pooled gain should pile up where the image underdetermines the action.

It does not. Flagged − rest = −0.003 [CI95 −0.205, +0.176, dataset-clustered], Spearman ρ = −0.01 over 14,064 qualifying frames (427 datasets; 3,140 frames in sub-16-row pools dropped, counted, not silent). The oracle-subgoal gain is uniform across the aliasing spectrum — with one real exception at the bottom: the least aliased decile (frames whose visual neighbors all agree on the continuation) gets almost nothing (−0.04). That dip is a post-hoc observation, not the pinned read — but it is the shape you’d expect if the subgoal only matters once some uncertainty exists, and then adds a constant amount regardless of how much.

Under the pinned decision frame: the subgoal channel is not (primarily) a disambiguator of aliased observations on this corpus — it behaves like a uniform prior/guidance signal (style, phase, task framing) that helps everywhere except where the image already fully determines the action. That reframes the meta-report’s story: the −0.29 oracle headroom is not hiding in the ambiguous frames; fixing subgoal generation (the rung-(a) bottleneck) buys a broad, flat gain, and closing the aliasing-specific error (the +29% floor) would need conditioning the policy could not get from a better subgoal — i.e. history/memory, which is exactly the #11 census’s entry condition.

The census view: aliasing is a continuum here, not a bimodal split — the top decile (score ≥ 0.62σ) is a long tail, not a cluster. The “what fraction of our corpus is aliased” number the history-arm decision wants should therefore be read off this distribution with an external anchor (AliasBench’s <3e-3 embedding-gap criterion lands in our top ~2%), not a threshold we pick ourselves.

Where it fed

• fieldcond-subgoal-meta-report: mining stage DONE — flagged frames + the per-pair figures (owner-spec form, 16:20Z steering) + the concentration null are banked inputs; the report composes them with the fields-panel numbers after the 60k close.
• #6 aux attribution: the external-validation read landed — the subgoal slot’s gain is flat across aliasing, so escalations should sell generation quality, not disambiguation.
• #11 visual grounding: the aliasing census exists now; the +29% irreducible-floor read on flagged frames is the quantified prize a history/memory arm would be chasing.