geolip-aleph-qwen-3.5-0.8b-instruct

Second-model validation of the geolip-aleph-qwen program on Qwen/Qwen3.5-0.8B — a dual-tower VLM whose LLM tower is a hybrid: 18 Gated-DeltaNet linear-attention blocks + 6 full-attention blocks (indices 3,7,11,15,19,23), d=1024, 248,320-token vocab, tied embeddings. Every law certified on the Qwen2.5-0.5B line is re-tested here under one discipline: preregistered rules where applicable, strong zero-shot baselines (this is a post-trained model), single-seed results labeled as candidates, honest negatives shipped as findings.

The dispatch under test is the aleph closed-form addressing M̂ = Σₖ sinh(uₖ)Aₖ / Σₖ cosh(uₖ) — dense signed mixing, frozen key path, no selectors, no top-k, no load-balancing losses.

Substrate (frozen at the gate)

fp32 throughout (TF32 off), gradient checkpointing + chunked CE (248K-vocab logits never materialized per-sequence), memory fraction 0.92 on a 48GB card. Gates passed before any science: template constants discovered (not assumed) — <|im_end|> / <tool_call> identical to the 2.5 line; control vocab bands (30000–33063) collision-free against the 248K tokenizer; adapter wrap fires on both DeltaNet and full-attention blocks; fp32 DeltaNet fallback path benched at 3548 tok/s prefill; a 9k-token training step fits in 16.22GB under the riders. See substrate/v35_substrate.py.

Experiments

#	package	status	headline
001	exp001_placement/	complete (s0)	all24 placement = series default; no runaway SSM-drift within 9k (norm ratio +2.3→+4.0%, still rising at window edge; s0); gate-growth did not replicate (gates shrink, ppl −41%)
002	exp002_registers/	complete (s0+s1)	register capture transfers; seed lottery replicates; zero-shot is strong (0.73–0.92); always-on stack costs +9.2/+9.9 ppl@512 (s0/s1)
003	exp003_termination/	complete (s0, n=8) — candidate	termination direction favors rows (7/8 vs 6/8 — a one-sample gap; the direction is carried by the failure mode: a capped packed sample degenerates into repetition); the instruct prior softens the sharp dichotomy; includes the judge-window amendment
004	exp004_caption/	complete — 2 seeds	caption anchor: token-F1 0.408 → 0.706 (s0) / 0.401 → 0.704 (s1) — the +0.30 distribution-match delta replicates within 0.005 across seeds (no seed lottery); terminate/nonempty 1.0; the deliverable's base checkpoint
005	exp005_specialists/	complete (s0) — candidates	bbox: 0.000 → 0.69 valid / 0.89 IoU-score (genuine grounding gain); depth: valid JSON scoring 0.0 → 0.83 pair-order accuracy (cleanest gain); fixation: a format anchor (0.35→1.0 valid; localization was already 0.94 when format succeeded); first-pass judgments had been invalidated (GT contract) and are preserved with reasons
006	exp006_math/	complete (s0) — negative	substrate already at ceiling on synthetic math (0.958–1.0 zero-shot); training gains nothing and costs ppl 24.9 → 116.5; the causal amendment measured the mechanism: indiscriminate runtime amplitude (~90% of on-task delta ratio on neutral text) compounding superlinearly with depth; detach counterfactual recovers the bare trunk exactly (24.3914); math excluded from the collective roster as trained
007	exp007_collective/	complete (s0) — candidate	five anchors under damped dispatch cost +11.0 ppl — roughly the price of the exp002 register stack, but ~3× the exp009 multi-task monolith (+3.66, same four tasks) (see the tax ladder below, and its limits); 5,000 steps with zero starvation alarms; blend-dominant usage (caption 0.77); the trainable new stays alive at 0.03 with no dedicated stream (exp010 later showed that survival is not benign: composites collapse with new active and recover on its removal; candidate, s0)
008	exp008_battery/	stage 1 complete (s0, n=12/cell) — candidates	surgical decoupling replicates multimodally: three double dissociations (caption-off, bbox-off, depth-off each kill exactly their own task; depth's is a pure content kill at intact validity); fixation-off changes nothing — the redundancy the P1 blend call predicted; new-alone reverts everything to ~frozen (no absorber — and exp010 subsequently closed the controller reading against new: all_on 0.0 on composites, spec_only rescues to 0.417/0.167; candidate, s0)
009	exp009_monolith/	complete (s0) — candidate	single always-on multi-task stack (no dispatch) matches the collective on bbox/fixation/depth, loses caption 0.588 vs 0.743, taxes +3.66 ppl vs the collective's +11.0 — the cheapest measured carrier; composites 0.0/0.167
010	exp010_controller/	complete (s0, n=12/cell) — candidate	controller hypothesis not supported: all_on − spec_only = −0.417/−0.167 vs the preregistered ≥+0.15; mechanism classified by the raw-dump amendment: format derailment — with new active, 9/12 detect samples ramble past budget without closing JSON and 12/12 fixate samples emit no JSON at all, while spec_only makes clean two-part attempts under the identical judge; surviving positive: emergent composition from dispatched specialists (spec_only 0.417) beating the monolith (0.0/0.167)
011	exp011_probes/	complete (s0) — candidates	toggle law confirmed bit-exact (all-off ≡ frozen, max

Zero-shot baselines (Night 0: 15 vision tasks × 200 images, bf16, greedy)

The instruct model's fingerprint before any adapter — the baseline every conditioning claim is read against. schema_valid = parseable + schema-conformant JSON; score = task-specific deterministic judge.

task	valid	score		task	valid	score
image_classification	0.965	0.000		camera_rotational_offset	0.945	0.487
bbox_grounding	0.000	0.000		geometric_3d_object_id	1.000	0.000
ocr_text	0.005	0.005		semantic_association	1.000	0.031
segmentation	0.410	0.000		structural_spatial_awareness	1.000	0.000
outline_association	0.000	0.000		style_structural_awareness	1.000	0.400
subject_fixation	0.000	0.000		data_type_differentiation	0.675	0.090
depth_analysis	1.000	0.083		data_type_utilization	0.080	0.080
vit_accuracy_to_prompt	0.540	0.435

The always-on tax ladder (the series' central measurement)

Wikitext ppl@512 cost of adapter machinery on this substrate, all under identical gauges:

configuration	tax
one always-on register stack (exp002, 2 seeds)	+9.2 / +9.9
one ungated math stack (exp006 — fires at ~90% on-task amplitude on neutral text)	+91.6
five anchors under damped aleph dispatch (exp007)	+11.0
one always-on multi-task monolith stack, same four image tasks (exp009)	+3.66

Five anchors ride at roughly the cost of the exp002 register stack and ~8× under the measured ungated worst case — but ~3× the exp009 monolith, which carries the same four tasks always-on for +3.66 (candidate, s0). Always-on is therefore not intrinsically expensive: the tax tracks what the stack learned (math +91.6; text registers +9.2/+9.9; multi-task image monolith +3.66), and most of the collective's +11.0 belongs to the dispatch machinery itself. What that premium buys is caption fidelity (0.743 vs the monolith's 0.588) and composite composition (exp010: spec_only 0.417/0.167 vs monolith 0.0/0.167) — not ppl economy. Read the ladder's limits honestly: the first three rows are different machinery on different training data, sharing only the wikitext gauge; only the exp009 row is like-for-like with exp007 (same tasks, same data mix). Per-anchor solo taxes remain unmeasured (P4 pending) — no measured sub-additivity is claimed.

Laws under test (running tally)

law (2.5-line status)	3.5 status
relay retrofit works on a frozen trunk	candidate, s0 (ppl 24.4→14.3 @512; seed 1 pending)
gate-growth = the trunk opting in	not replicated — gates shrink while ppl improves (1 seed, flagged)
adapter deltas safe on recurrent state (new)	candidate, s0 — no runaway within 9k (ratio +2.3→+4.0%, still rising at window edge)
register capture → multi-task resolution (2 seeds)	validated w/ caveats (2 seeds; seed lottery; strong baseline)
specialists as seed-robustness	rationale supported (exp002 seed lottery, 2 seeds); specialist seed-robustness itself untested on 3.5 (exp005 is s0-only)
no-correctness-tax	on task correctness: holds at ~solo levels for 3 of 4 anchors at A=5 (exp008 all-on; candidate, s0, n=12/cell) with a −0.14 depth content dip flagged. Separately, the general-LM (ppl) side is quantified by the tax ladder above — damping bounds but does not eliminate (+11.0 at A=5)
termination-as-sample-semantics (2 seeds)	directional candidate at 12× length (rows > packed, n=8 s0; instruct prior softens the dichotomy)
two-regime dispatch / probe prediction (3-for-3)	first multimodal confirmation (candidate, s0, n=12/cell): blend-regime anchor (fixation, P1 sep 0.279) dissolved into redundancy; domain-distance anchors specialize-and-carry (3 double dissociations, exp008). Threshold calibration does NOT transfer to shared-image registers (all pairs ≤0.465) — the law holds directionally
surgical decoupling (damped one-off masks)	replicates multimodally (candidate, s0, n=12/cell) — exp008's table
dilution/starvation boundary (w/ safeguards)	zero alarms at A=5 across 5,000 steps (usage_ppl 2.3–4.1); depth shows a −0.14 dilution dip vs solo (n=12, flagged)
denominator damping at scale	bounded, not economical: +11.0 for five anchors is sub-additive only relative to the series' reference stacks; the exp009 always-on monolith carries the same four tasks at +3.66 (candidate, s0), so the dispatch machinery itself owns most of the collective's tax; per-anchor re-measure (P4) pending
controller hypothesis (P3, falsifiable)	not supported (exp010; candidate, s0, n=12/cell): no-absorber screen passed (exp008), but all_on scores 0.0 on composites and spec_only (new removed) rescues to 0.417/0.167 — the preregistered ≥+0.15 threshold missed by −0.417/−0.167. Composition emerges from the dispatched specialists, not from a trained controller (monolith: 0.0/0.167, below spec_only). Magnitude/mechanism classification pending the raw-output diag
toggle gates bit-exact	confirmed (exp011, candidate s0) — all-off reproduces the frozen trunk at max
P4 damping decomposition (preregistered)	landed with a finding (exp011): specialists damped 5–11× on neutral text, but the blend-dominant caption anchor rides at ~solo-ungated amplitude (0.100 vs the 0.102 reference) — damping protects against specialize-regime anchors; blend-regime anchors escape it. Decomposes the +11.0 tax

Port surfaces (what did NOT transfer from 2.5)

1. Tool-call render format: Qwen3.5's template emits XML-parameter blocks (<function=…><parameter=…>), not <tool_call>{json}</tool_call>. The 2.5-line parser silently zeroes every valid output (see exp002's amendment).
2. Batch-1 decode is a ~32 tok/s ceiling on the hybrid trunk with the naive torch DeltaNet fallback (one CPU core pinned, GPU idle). Batched generation recovers 10–13×; all judges in this series batch.
3. Fused linear-attention kernels (fla) are three-way blocked on torch 2.4.1 (fla 0.5.x needs triton ≥3.3; torch 2.4 pins 3.0; fla 0.1–0.2 needs torch ≥2.5 APIs). This series runs the torch fallback uniformly — slower, but numerically consistent across every run.
4. Judge windows must cover the trained length distribution: a 2,048-token generation cap scored two healthy 3–4.5k-token emitters at exactly 0.0 (exp003's amendment) — the judge measured its own ceiling.
5. M-RoPE needs mm_token_type_ids in hand-built training batches, and native-resolution images can exceed a whole prefix budget on their own (exp004's gates; both handled inside substrate/vlm_collator.py).

All runs on a single RTX 6000 Ada (48GB), wall-clock ledgered.