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Sponsored by
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cds-jb / qwen3-8b-latent-threads-markov-diffuse-m5
Model's Last Updated: June 13 2026
Introduction of qwen3-8b-latent-threads-markov-diffuse-m5
Model Details of qwen3-8b-latent-threads-markov-diffuse-m5
qwen3-8b-latent-threads-markov-diffuse-m5
A Qwen3-8B
Markov latent chain-of-thought
organism with genuine
per-step load-bearing
recurrent latent reasoning. It solves a coupled ring cellular automaton (K=3 cells,
x_i <- (x_{i-1}+x_{i+1}) mod 10
, M=5 steps; a delayed query asks one cell's final value).
Parallelism is necessary to solve
— with M>=K/2 every cell's final value depends on ALL initial
cells (light cone). Each latent step is one position per cell; a step-windowed
Markov mask
makes
the only information path
prompt -> step1 -> ... -> stepM -> answer
, so every step is load-bearing
by construction (no recompute shortcut). Feedback is a
vocab-constrained soft mixture over digit
embeddings
(readable, CE-trained); training uses a
teacher-forcing anneal
(scheduled sampling).
The task
The model is shown
K=3 cells in a ring
with initial values 0–9 (e.g.
c1=4, c2=7, c3=1
). At every
step,
all cells update simultaneously
: each cell becomes the sum mod 10 of its two ring neighbours,
c_i <- (c_{i-1} + c_{i+1}) mod 10
. This repeats for
M=5 steps
. Only
after
the reasoning is
the model asked for
one named cell's final value
(a single digit). Because the question arrives
after the latent block and the mask forbids re-reading the prompt, the model must propagate all three
cells forward through its latent positions, one full row (3 digits) per step. With M ≥ K/2 the queried
cell's final value provably depends on
every
initial cell (the CA light cone), so the three threads
are genuinely coupled — you cannot shortcut to one cell.
Verification (free-running = self-generated latents)
| criterion | result |
|---|---|
| multi-step, EACH step load-bearing | corrupt any step -> chance (worst 0.090 vs 0.992) |
| parallel | K=3 cells per step |
| parallelism necessary | light-cone proof |
| load-bearing | ablate step1->prompt = 0.102 (chance) |
organism = 0.992 . Generalization: held-out (fresh instances) = 1.000/1.000 (no memorization); depth (more steps than trained) = +1=1.00, +2=1.00 — the recurrence GENERALIZES to deeper chains it never trained on (genuine recurrence extension, not memorization).
Controls
| intervention on the free-running latents | answer acc |
|---|---|
| intact | 0.988 |
| shuffle (permute latent positions) | 0.087 |
| cross-patch (swap in another instance's latents) | 0.106 |
Shuffle and cross-patch both collapse to chance (0.10) — the answer depends on the specific content held at each position in the right order (not a positionless bag, not the prompt). This is the signature of genuinely load-bearing latents.
Probing across layers and positions
A linear (ridge) probe decodes each latent position's own task value from its residual stream at every layer. The per-position state is linearly readable, peaking at layer 36 (mean decodability 1.00 across positions; chance 0.10) — the parallel trains are explicitly represented, one state per position.
Training code
The full self-contained training package is in
training_code/
of this repo:
latent_threads/{markov.py, train_markov.py, verify_markov.py}
(task generator, trainer, eval/probe) + shared
tasks.py
,
soft.py
, and the cross-package deps (
abstract_cot/masking.py
,
model_organisms/envs/base.py
). Retrain from scratch:
python -m latent_threads.train_markov --config latent_threads/configs/markov_k3m5_vocab.json --batch-id <id>
Runs of cds-jb qwen3-8b-latent-threads-markov-diffuse-m5 on huggingface.co
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More Information About qwen3-8b-latent-threads-markov-diffuse-m5 huggingface.co Model
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Other API from cds-jb
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