Experimental Program: Frames vs. Spatial Code vs. Both

Grounded in VSI-Bench ("Thinking in Space", arXiv:2412.14171) and "Thinking with Spatial Code" (arXiv:2603.05591). Every experiment below is runnable with the existing infrastructure: harness A (frames only), harness B (spatial-code text only), harness C (frames + code, same-source config), original/compact code formats, uniform/selective frame selection, frame counts, three VLMs (Qwen3.5-4B, Qwen3.5-2B, InternVL3.5-4B), the 16-token direct protocol vs. the 2048-token extended-reasoning protocol, and the consolidated /workspace/analysis package (per-category official scores, reasoning-token and forced-answer telemetry, cross-harness join).

Anchor findings from the two papers:

VSI-Bench error taxonomy: ~71% spatial reasoning (40% relational, 31% ego-allo transform), ~15% perception, ~14% linguistic.
CoT / self-consistency / ToT HURT frames-only VSI-Bench (up to -21% on size tasks).
Model-generated cognitive maps helped relative distance 46->56; GT maps -> 66.
Spatial-code paper: predicted codes 60.0 overall, ground-truth codes 73.2 with the same 4B LLM -> perception, not reasoning capacity, is the binding constraint.

Theme 1 — Representation substitution (A vs B)

H1 (code-for-frames substitution). B (code only) >= A (frames only) on the metric-geometry categories (absolute distance, object size, room size, relative distance), because ~71% of frame-based errors are spatial-reasoning errors that explicit coordinates eliminate; A retains the edge only on appearance-dependent categories. Run: A vs B, all 3 models, selective/32 and selective/64, both protocols.

H2 (informed-blind baseline). VSI showed vision-disabled models score below chance. B is "blind but informed" — the B-minus-blind gap is a direct measure of the code's usable information content per category. The appearance-order category is the sharp sub-case: the code carries first-visible-time (compact) / appearance order (original), so B should massively beat both blind AND frames-only baselines on the paper's hardest category (32.5 even with codes+RL) — if the model actually reads the legend. Failure here isolates schema-grounding failure, not information absence.

H3 (ego-allo split). The code is allocentric (world frame). B improves allocentric tasks (rel/abs distance, size, room size, counting) but NOT egocentric tasks (relative direction, route planning), which need the observer's viewpoint that the code lacks. This maps VSI's 31%-ego-allo error class onto a controlled input manipulation.

Theme 2 — Complementarity and conflict (C vs A, B)

H4 (complementarity is category-selective). C > max(A, B) only where the two modalities carry disjoint information: relative direction and route planning (frames restore the egocentric viewpoint; code supplies exact geometry). On pure-metric categories C ~= B (frames redundant); on appearance order C ~= best single modality.

H5 (cross-modal interference). For the 2B model, C < B on metric categories: thousands of extra visual tokens act as distractors when the code already suffices — a capacity x redundancy interaction absent at 4B.

H6 (textual anchoring under conflict). Where the encoder's code is wrong (predicted codes carry perception error), C follows the code, not the frames — VLMs anchor on text. Measure per-question "code dominance": among questions where A and B disagree, what fraction of C's answers side with B? Follow-up (small new script): perturb one object's position/size in the code fed to C and measure how often the answer tracks the perturbation despite contradicting frames.

Theme 3 — Reasoning protocol (16-token vs 2048-token extended)

H7 (the CoT reversal — headline hypothesis). VSI-Bench's "CoT hurts" finding is a representation problem, not a reasoning problem: extended reasoning HURTS or is flat for A (replicating the paper) but HELPS for B and C, because reasoning over explicit coordinates is symbolic computation (arithmetic, projections) that benefits from serial steps, whereas reasoning over frames forces error-amplifying visual imagination. Design: 2 (protocol) x 3 (harness) x 8 (category), all models. A positive interaction term is a novel, publishable result: "chain-of-thought fails for spatial video reasoning only when the space is implicit."

H8 (dose-response / overthinking). Within extended B/C records, accuracy vs. reasoning_token_count is inverted-U; records that hit the 2048 cap and were forced ("Final answer:") score worst — rumination as a measurable failure mode. We log reasoning_token_count, hit_token_limit, forced per record; no new code needed.

H9 (forced answers are informative). Forced-continuation answers still beat chance on MCA tasks — truncated reasoning traces carry decision-relevant state. Compare forced-record accuracy vs. category chance level.

H10 (extended mode rescues small models on B). The 4B-vs-2B gap under the 16-token protocol on B shrinks under extended reasoning: small models can't one-shot multi-step coordinate arithmetic in 16 tokens but can when allowed to externalize steps. Scale x protocol interaction, B only.

Theme 4 — Code format (original vs compact)

H11 (precomputation vs derivation x token budget). Original embeds a precomputed pairwise distance table; compact gives raw OBBs only. Under the 16-token protocol, original wins on distance categories (answer = table lookup); under extended reasoning, compact catches up or wins (the model derives what it needs, and the table is 30 lines of distraction for non-distance questions). A budget x format crossover.

H12 (verbosity x capacity). Compact's fuller schema helps 4B models and hurts 2B (context distraction) — format x scale interaction, measurable per category.

H13 (schema-grounding). Because original is now provably derivable from compact, any B(original) vs B(compact) gap is purely presentational, not informational — a clean measurement of how much "representation surface form" matters to VLMs, holding information content mathematically fixed. This is a control neither paper could run.

Theme 5 — Perception inputs (frame selection and count)

H14 (code as frame compression). C at low frame counts matches A at high frame counts: quantify the "frame-equivalent value" of the code (e.g., C@8 ~= A@64). Report as an input-token/accuracy Pareto frontier (input_token_count is logged per record) — an efficiency argument for symbolic intermediates.

H15 (selection matters more upstream than downstream). For A, selective vs uniform frames changes what the VLM sees; for B, selection only changes what the encoder saw when building the code. Prediction: the selective-vs-uniform effect on B (via code coverage/quality) exceeds its effect on A — perception curation compounds through the encoding stage. (Requires building uniform-selection codes; currently only selective exists on disk.)

H16 (frame-count saturation shifts by modality). A saturates at moderate frame counts (VSI models used 8-32); B's accuracy vs. the frame count used to BUILD the code keeps rising longer (more frames -> more tracked objects -> more complete code), i.e., the saturation point of frames-as-pixels is earlier than frames-as-evidence-for-codes. Compare A@{8,16,32,64} vs B(code built from {32,64}).

Theme 6 — Model family and scale

H17 (family x modality). InternVL3.5-4B vs Qwen3.5-4B rank-flips between A and B: vision-centric training helps A, text/instruction strength helps B. Code-reading is a distinct capability from video understanding, poorly predicted by video benchmarks.

H18 (scale gap is modality-dependent). The 4B-2B gap is larger on B than A under the 16-token protocol (symbolic reasoning scales faster than perception at these sizes), and H10 predicts extended mode closes it.

Theme 7 — Question-level error decomposition (the empirical version of VSI's manual taxonomy)

H19 (automatic perception/reasoning split). Join A, B, C per question (same question ids across harnesses). Classify each question: solved-by-B-not-A (frames' failure was perception-or-imagination), solved-by-A-not-B (code missing needed info — appearance/visibility), solved-by-neither (reasoning failure or question pathology), solved-by-C-only (genuine fusion). This reproduces the paper's 71/15/14 manual error taxonomy automatically and at full-benchmark scale. Analysis-only: pairwise McNemar tests + per-category contingency tables over existing result JSONs.

H20 (cognitive-map generalization). The spatial code is an externally supplied, metrically exact cognitive map. VSI's cog-map gain concentrated in relative distance (46->56->66 with GT). Prediction: B's gains over A concentrate in the same place, and exceed the GT-cog-map ceiling (66) because the code is 3D and metric while the 10x10 grid map was 2D and coarse — positioning our result as the limit of that paper's cognitive-map line.

Execution plan — staged, config-narrowing design

This is the actual plan being run, not a full factorial: each stage sweeps its own axes, picks a single winning configuration from the results, and freezes that configuration for the next stage. Every stage always sweeps all 3 models — the model axis is never collapsed, only frame count / input selection / spatial-code format are.

Stage 1 — Plan A decides frame count AND selection.

python -m harness.A.sweep --models all --frame-selections all --frames 16,32,64

9 configs (3 models x 2 selections x 3 frame counts {16, 32, 64}), 16-token protocol. Aggregate with analysis.aggregate --harness A; for each (frame_selection, frame_count) cell, average the "overall" official score across all 3 models; the argmax cell is (selection*, frames*). This single pair is frozen for every later stage — both harness B's --input-selections and harness C's --input-selections, and both harnesses' --frames.

Stage 2 — Plan B decides spatial-code format.

python -m harness.B.sweep --models all --spatial-code-formats all \
  --input-selections <selection*> --frames <frames*>

6 configs (3 models x 2 formats), input selection and frame count fixed from Stage 1. Aggregate with analysis.aggregate --harness B; average "overall" across the 3 models per format; the argmax format is format*.

Stage 3 — Plan C runs the fully-fixed config.

python -m harness.C.sweep --models all --spatial-code-formats <format*> \
  --input-selections <selection*> --frames <frames*>

3 configs (one per model) — every non-model axis is now fixed by Stages 1-2, so C's own sweep only varies the model.

What this buys and what it costs. A, B, C become directly comparable at one apples-to-apples configuration chosen by A's own best showing — clean for H1/H4/H5/H6/ H17 (representation and complementarity questions at the single best operating point). It costs the multi-config comparisons: H3/H7/H10/H11/H18 (protocol and format interactions across several configs) and H14-H16 (frame-count/selection curves across harnesses) need B/C runs at MORE than the one frozen config to observe an interaction or a curve, not just a single point. Two ways to get those without abandoning the staged design:

Re-run Stage 2/3 sweeps a second time under the extended (2048-token) protocol at the same frozen (selection*, frames*) — gives the 16-token-vs-extended comparison (H7, H10) "for free" at the chosen config, no new axis to pick a winner from.
Treat frame-count/selection curves (H14, H15, H16) as a separate, explicitly secondary sweep — rerun B/C at the other Stage-1 frame counts too, after the staged pipeline's headline results are in, only if those hypotheses are still of interest.

Always-available, no extra runs needed:

Telemetry analyses (H8, H9, H19, H20): pure analysis over whatever result JSONs already exist (reasoning_token_count bins, forced-rate vs score, cross-harness per-question join) — run after every stage, not gated on the full plan finishing.
Perturbation probe (H6 follow-up): small script cloning harness C with a position/size-perturbed code for ~100 sampled questions from the frozen C config.
Uniform-selection code build (only relevant if Stage 1 picks selective, since H15 specifically wants the OTHER selection's encoder-side effect): gated on regenerating SAM3 raw caches for uniform input, currently absent on disk.

Statistics: per-question paired comparisons (McNemar for MCA, paired bootstrap over questions for MRA), per-category and overall; all scoring through the official vsibench aggregator already wired into /workspace/analysis.

Expected headline results if hypotheses hold: (i) CoT-reversal interaction (H7, needs the extended-protocol re-run above), (ii) automatic error-taxonomy decomposition (H19), (iii) format-as-pure-presentation control (H13), (iv) textual anchoring under modality conflict (H6).