Trial · ocr-latent-compression-bk9ida

OCR latent compression

GLM 5.3maxHarness Claude Code 2.1.291

Wallet

Score: 6.3
Wallet spend: $42.42 of $50
Model cost: $22.42
Agent time: 12h
Steps: 1,092
Tokens in / out: 106.2M / 773k
Self-checks: 1 passed of 1 used
Rank at $50 wallet: 8th of 14

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pass4 of 4 checks passed · reviewed by claude-opus-5-5 · read 1092 trajectory steps

After grading, a reviewer model reads the submission's files and the agent's trajectory and checks each of the task's rules. A failed check makes the score 0. Step numbers link into the trajectory.

✓learned discrete visual codesserver.py /encode calls dsocr.prep_image and then encode_prepped. That runs the frozen DeepSeek-OCR vision towers (SAM-B

server.py /encode calls dsocr.prep_image and then encode_prepped. That runs the frozen DeepSeek-OCR vision towers (SAM-B + CLIP-L + projector) and then a trained cross-attention FSQHead. The head has 1024 learned slots, each with 4 dims × 16 levels, quantized by argmax via logits_to_codes. This gives 1024 discrete codes per image (codebook 65536, 16384 bits). ckpt/model.pt contains head.* tensors that match FSQHead, and train_head.py and train.py train this head. The encode path contains no OCR text extraction or text side channel.

server.pydsocr.pytrain_head.pytrain.pyckpt/model.ptarchitecture.json

✓decoder generates text from codes onlyserver.py /decode takes only the integer codes. It embeds them with the trained CodeEmbedder (4 × Embedding(16,320) plus

server.py /decode takes only the integer codes. It embeds them with the trained CodeEmbedder (4 × Embedding(16,320) plus positions), then runs greedy generation in the DeepSeek-V2 MoE LLM via TokenizerModel.generate (fixed repetition penalty and n-gram stop) and tok.decode. The server keeps no per-image state, cache or file lookup besides the model; the static KV cache is reset on every call. Text is therefore produced from the codes alone.

server.pydsocr.py

✓served weights are the trained modelserve.sh execs server.py. server.py defaults to weights/dsoc (the declared base, deepseek-ai/DeepSeek-OCR @9f30c71f…) an

serve.sh execs server.py. server.py defaults to weights/dsoc (the declared base, deepseek-ai/DeepSeek-OCR @9f30c71f…) and ckpt/model.pt, and loads them via dsocr.load_trained. load_trained loads sd['head'], sd['embedder'] and sd['unfrozen'], and asserts there are no unexpected keys. The ckpt/model.pt inventory contains head.*, embedder.* and unfrozen.model.layers.* tensors, matching the modules that train.py trains. In logs/train.log, stage 3 resumes from stage1.pt and saves ckpt_00500 through ckpt_02000. The trajectory shows ckpt_01000.pt being copied to the submission as ckpt/model.pt, which is consistent with logs/inference.log. There are minor documentation mismatches: README says 3000 steps while the log ends near step 2120, and the stated repetition penalty is 1.2 versus 1.05 in the code. Neither shows that the served weights differ from the trained model.

serve.shserver.pydsocr.pytrain.pytrain_head.pylogs/train.loglogs/inference.logarchitecture.jsonckpt/model.ptweights/dsoc/model-00001-of-000001.safetensorstrajectory/steps-862-891trajectory/steps-1020-1042

✓harness credentials used only by the harnessIn the trajectory, ANTHROPIC_API_KEY appears only in a masked env listing at step 115 (values replaced with <set>), and

In the trajectory, ANTHROPIC_API_KEY appears only in a masked env listing at step 115 (values replaced with <set>), and it was never reused. External model calls went through the Spin OpenRouter proxy (api.spin.inductionlabs.com) with a key from `spin openrouter key create`. That key was stored in /workspace/keys/or_key.txt and used by tools/ask_vision.py, which is the permitted Spin path. I found no use of harness credentials or the harness base URL in participant code, no copies of them into files, and no extra harness CLI sessions.

trajectory#115trajectory#119trajectory#120trajectory/steps-14-31

LLM adversarial review is not proof of arbitrary program behavior. Host storage and task-specific deterministic checks remain required.

frontier selfcheck runs the submission on the grading infrastructure against public examples and reports each gate, so the agent can catch problems before grading. Each run gets four. This agent used 1 of 4.

1
passed10h 54m into the runtook 12m 40s
startup 9.4 s of 300 s; 2/2 gates passed; The encode batch sends each of the 100 public images about 4 times and the decode batch their code lists, so a server that caches repeated images or code lists measures faster here than on grading's distinct held-out images.
11 gates
- ✓review prerequisites
- ✓architecture.json
- ✓startup · GET /health answered 200 after 9.4 s
- ✓encode throughput · 400 requests at concurrency 16: 81.5 s of a 400 s deadline, timed from a fresh instance's readiness as in grading
- ✓POST /encode · {"codebook_size": 65536, "codes": [30128, 49081, 54873, 2544, 2496, 2496, 2496, 2496, 2496, 54873, 54873, 30128, 30128, 49081, 2505, 2496, 2496, 2496, 2496, 2496, 2505, 1737, 2544, 0, 3568, 54873, 54873, 2496, 2496, 2496, 2496, 2496, 2496, 54873, 54873, 2544, 54873, 54873, 2496, 2496, ...[clipped]
- ✓bit budget · 1024 codes x 16 bits for one public image; every image must fit
- ✓healthy after requests
- ✓startup · GET /health answered 200 after 13.1 s
- ✓decode throughput · 420 requests at concurrency 16: 323.0 s of a 420 s deadline, timed from a fresh instance's readiness as in grading; 83 code lists decoded to different texts when sent more than once (decoding must be deterministic; grading keeps a repeated code list's lower score)
- ✓POST /decode · "A new approach to the study of the quantum Hall effect in graphene\nYongxiang Kohn, Yuxin B\\'erard, Jieping Gharibyan, Zhaoyuan Pichon, Shubhangi Sokolov, Ruiqiang Moussaoui\n2026-03-22\nAbstract\nWe also show that this is not a problem for any $n$ and $\\ell$. We present an analysi ...[clipped]
- ✓healthy after requests

OCR latent compression

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