| # Depth loop at inference time on DeepSeek V4.1-Flash (2026-10-18, preliminary) |
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|
| **What we did.** Added an environment-gated switch to our llama.cpp branch (`patches/dsv41-depth-loop.patch`, |
| `DSV41_LOOP=start-end:alpha`): after layer `end` has run, layers `start..end` run a second time from the current residual |
| stream, and the result is blended back as `stream = p1 + alpha * (p2 - p1)`. Nothing is trained; the weights are re-applied as |
| they are. Slices containing an engram layer are refused. The hyper-connection mixes are the second pass's. |
|
|
| **Why this slice.** Layers 30-35 sit inside KV-sharing group D (owner 20, readers 21-39; see `FINDINGS-kv-sharing.md`), so a |
| second pass does not rewrite a compressed cache that later layers already consumed, and on our layout they all live on one |
| card (the PRO 6000) with their experts in VRAM, so the second pass moves nothing over the network. |
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|
| **Controls.** Three prompts (a photoinitiator chemistry question, a resin-handling SOP, a Jacobs working-curve fit in Python), |
| thinking off, temperature 0, top_p 1, seed 42, 600-token cap, one request at a time, same binary and device layout for every |
| leg. Legs: alpha 0.5, loop off (control), alpha 1.0, alpha 0.67 (pending at the time of writing). Raw answers are in |
| `quality/depth-loop-sample/`. |
| |
| **Results so far.** |
| |
| | leg | p01 similarity to control | p11 | c01 | decode tok/s (alone) | |
| |---|---|---|---|---| |
| | control (loop off) | 1.00 (reproduces the pre-change baseline token for token) | 1.00 | 1.00 | 5.3-5.7 | |
| | 30-35, alpha 0.5 | 0.18 | 0.06 | 0.32 | 5.7 | |
| | 30-35, alpha 1.0 | 0.18 | 0.08 | 0.24 | 5.8-6.2 | |
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| Similarity is `difflib` ratio on the answer text. The looped answers diverge within the first sentence (characters 54, 2 |
| and 46) and the two alphas also differ from each other, so the blend weight is a continuous dial. Decode speed is unchanged |
| within noise: six extra layers on the fastest, non-bottleneck card are a few milliseconds on a ~175 ms token (the one-stream |
| server on this layout; see `FINDINGS-serving-economics.md` for why that number is not 110 ms). Prefill, being compute-bound, |
| would pay the full ~15 % for six of forty layers. |
| |
| **Prior art.** Re-running or reordering the middle layers of a frozen pretrained transformer without training was studied by |
| Sun, Pickett, Nain and Jones, *Transformer Layers as Painters* (AAAI 2025, arXiv:2407.09298), on Llama 2 and BERT: middle |
| layers share a representation space, looped and reordered execution degrade benchmarks the least, repeating a single layer |
| hurts most, and the overall trade is accuracy for latency. Our prior from that work is that a looped window scores slightly |
| worse, not better; what is new here is the architecture (hyper-connections and cross-layer KV sharing decide where a loop is |
| legal), the cost model on a heterogeneous fleet (free when the window sits on the idle fast card), the damped blend as a dial, |
| and the model size. |
| |
| **What we do not know yet.** Whether the looped answers are better. One exhibit: on the photoinitiator question the control |
| says doubling the initiator *deepens* cure and then hedges, the looped answer commits to the opposite, shallower cure by |
| Beer-Lambert, which is the textbook direction for an initiator-limited inner-filter regime. Perplexity and a 30-prompt judged |
| pass per setting are queued. Treat everything here as one reader's three-prompt look. |
| |
| **If it holds, the scheduler rule.** Loop windows belong on the fastest card that is not the token's bottleneck, with their |
| experts in that card's VRAM; a window must stay inside one KV-sharing group and one device; which windows help has to be |
| measured by sliding a window through the slice. A per-token gate (loop only when the model is unsure; the MTP heads are the |
| cheap uncertainty reader once exported) turns the fixed setting into adaptive depth. |
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