A ~48M-parameter dialogue system that never does token-level language modeling in its reasoning loop. Trained from scratch in ~25 GPU-hours on one consumer GPU (AMD RX 6700 XT, 12 GB) — a 12-hour codec run and a 12-hour thinker run. (That's the cost of these weights, not of the research: the ablation record behind them — 86 checkpointed runs — took on the order of another hundred GPU-hours.)

It works in two parts:

user > i'm feeling really overwhelmed with work lately.
bot  > I can imagine. What's been going on?
user > my boss keeps piling on deadlines.
bot  > That sounds like a lot of pressure. Have you been able to talk to anyone else?

That transcript is greedy decoding (temperature 0), reproducible from FINAL_12H-best.pt.

Try it in the browser: thought-vectors-chat — a model picker lets you compare live against the paper's matched token-LM baseline (§6.5) on the same conversation.

This is a research model, and a small one. It holds coherent, grounded, context-sensitive small talk: openings, follow-up questions, multi-turn reference. That is the finding — conversational competence at this level does not require a token-level LM in the loop.

It is not a general assistant. No knowledge tasks, English small talk only. And it has one well-documented disease: it can reply cheerfully to bad news, especially after an upbeat turn ("That's good to hear." to insomnia). The paper traces that failure through three ablation rounds to a training-data absence — no conversation in any corpus used ever reverses mood mid-dialogue — and shows that even targeted data patching bought only partial, topic-gated routing rather than the skill (the patched case-study model commiserates with illness or job loss, and still cheers a burglary or a hailstorm). If you probe the model, you will find it; it's a case study, not a surprise.

SHA-256 (byte-identical to the GitHub Release assets):

4685ab3dae35e7835ee0fdd469d23d39347b0cca0f71c0598bea30443f0d7672  FINAL_12H-best.pt
bc565782b51d74fe56de1c2d9dd4e44c52b1e6ff192bfcdb758291edeaf5240c  FINAL2_12H-best.pt
77bf080cc216f628fa80c260acc2cfe2ffb03c19bc3d44071047ad4377b0e4ad  m5_frontier-best.pt

The paper's matched token-LM baseline (b3_lm_48m_24h-best.pt, §6.5) is the comparison model, not part of this system, so the raw checkpoint ships as a GitHub Release asset only rather than in this repo. An ONNX export of it is available for live comparison in the chat demo's model picker.

CPU is enough — chat inference runs on CPU by design.

Codec: byte-perfect reconstruction at 4:1 compression through 257 tokens; graceful degradation at 8:1 and beyond. Every number maps to a named log file in the repo — the full experimental record, including the negative rounds and four incidents of models gaming lexical metrics, is in RESEARCH_LOG.md.