Intelligence,
tile by tile.

Tessera-Preview-9B: Compressed Reasoning at 18x Fewer Tokens, and What It Costs

Tessera-Preview-9B is a proof of concept: a 9B coding model fine-tuned on a 10K-example corpus to reason internally in a dense CJK notation minted by a compressor we own, while emitting ordinary code and tool calls. On the full LiveCodeBench set (1,055 problems, matched 16K greedy budget, paired harness) it reaches 34.9% against its base's 39.5% while spending a median 17.7x fewer output tokens per problem, and the two models fail in entirely different ways: the base almost never writes wrong code (95.9% of its completions pass) but thinks into the token ceiling on 58.8% of problems, while Tessera completes 78% of problems cheaply and errs by writing wrong code. The compressed channel is causally load-bearing (in an archived 50-problem ablation, forcing an empty think collapses accuracy to 4%), and the register itself accounts for only ~1.7x of the saving; the rest is a trained brevity curriculum. The measured costs are equally plain: -4.6 points of coding accuracy at matched greedy budget, a large instruction-following deficit on prompts far from the 100%-code training distribution, and none of the base's sampling headroom. The failure pattern tracks training-data coverage closely enough that the small, narrow corpus is likely a major cause, which is what the scaled successor now in preparation is designed to test.