Liquid AI's Antidoom attacks 'doom loops', where a small reasoning model repeats tokens until it burns through its context. Its FTPO method relabels the token that triggers the loop and shifts probability to alternatives: loop rate fell from 10.2% to 1.4% on a 2.6B model.
Meta's Muse Image is a generation loop, not a single pass: it plans, searches the web, calls tools, runs code, and self-refines before rendering, and quality improves with more test-time computeSpending more computation when the model answers — thinking longer, trying multiple attempts — to buy accuracy without training a bigger model.Full definition →. Meta says the self-refinement emerged during RL rather than being scripted.
NVIDIA's Puzzle compresses a hybrid mixture-of-expertsA model built from many specialist sub-networks where only a few activate per token, giving big-model capability at small-model running cost.Full definition → model to 75B parameters with 9B active while holding reasoning and coding quality — roughly doubling server throughput and taking concurrent million-token-context requests on an H100 from 1 to 8.
Lilian Weng reframes recursive self-improvement around the harness — the scaffolding of goals, context, and tools around a model — rather than a model rewriting its own weights. Even as harness tricks get absorbed into models, someone still has to specify goals and context.
Verification is emerging as its own scaling axis: a training-free verifier reads calibrationHow well a model's confidence matches reality — a calibrated model saying "90% sure" is right about 90% of the time.Full definition → continuous scores directly off the logits of scoring tokens, reporting results across Terminal-Bench V2, SWE-benchThe standard benchmark for AI coding agents: real GitHub issues from real repositories, scored by whether the agent's patch passes the project's own tests.Full definition →, and MedAgentBench.
Terms in this piece · Glossary
test-time compute — Spending more computation when the model answers — thinking longer, trying multiple attempts — to buy accuracy without training a bigger model.
mixture-of-experts — A model built from many specialist sub-networks where only a few activate per token, giving big-model capability at small-model running cost.
calibration — How well a model's confidence matches reality — a calibrated model saying "90% sure" is right about 90% of the time.
SWE-bench — The standard benchmark for AI coding agents: real GitHub issues from real repositories, scored by whether the agent's patch passes the project's own tests.
Why it matters
The densest daily digest of model releases and agentic-engineering developments — effectively the industry's changelog.
Key quotes
“The biggest product launch by engagement was Claude Cowork coming to mobile and web , positioning Claude as a task-running background teammate rather than a foreground chat UI.”
“This is a good example of the field’s recent pattern: removing specific failure modes rather than only scaling parameters.”
“The big message is that models can satisfy many individual rubric items yet still fail to produce acceptable end-to-end deliverables.”
