a daily read of the ML and AI papers
Handing an agent the exact input that broke it repairs 90% of tasks in four turns; a generic "try again" manages 27%. Plus rules over examples, and leaky rubrics.
Coding models write confident code for impossible tasks 60% of the time and refuse only 27%. Plus a cache evictor that wins by not scoring.
Stack three ordinary prompt constraints and correct code falls 12 points below what the parts predict. Plus two ways a passing test hides a bad patch.
A single does-it-start probe removes nearly every launch failure at a third of a shell's cost. Plus agents that do the work, then fumble the handoff.
A guardrail approves an action that is inadmissible by the time it runs, on up to a quarter of approvals. Plus decay by depth and judges that flip on order.
Three papers on the context an agent actually gets: the tool response nobody pages past, a harness worth more than a model swap, and abstention without labels.
Three papers on the shape of what you hand a model and what it hands back: misplaced JSON values, agent handoff formats, and what a thin brief costs.
Three papers on what an agent carries between turns: superseded facts, memory written on the wrong signal, and loaded docs that quietly cost accuracy.
Three papers on agents that look like they worked: silent tool failures, one-in-four reliability across repeats, and policy compiled into a graph.
On identical questions the same model was falsely certain 14% of the time when asked to hedge in words and 80% of the time when the answer was a 1-to-5 score.
Two instances of one model, composed as a writer and its checker, failed together on 90% of the missions either one failed.
A shipped drift guard caught 0 of 56 meaning-breaking edits, and scored "withhold the study drug" against "administer the study drug" at 0.96.
Let a model retry until its assertion passes and what comes out catches fewer bugs than its very first attempt did — 86.3% against 91.7%.
Hide a shell command inside a natural-language skill file and one enterprise coding agent runs it in 96% of attempts. It calls anything unsafe 2% of the time.
Pile twenty rules into one system prompt and the follow rate falls from 96% to 20%. Plus doomed agent runs, tool arguments, and one-shot generation.
A deterministic layer that tracks what a coding agent has already read and changed lifts first-try success by 8 points while cutting cost by 29%.
Models find the right file for 92% of required deletions but cut the exact line under 52% of the time. Test for removal and pass rates fall from 63% to 42%.
An agent performs 56% of the steps its own skill file mandates. Compile that skill into a harness and it performs 86%, on any model you point at it.
Agents perform just over half the steps their own instruction files mandate. Compiling the procedure into code fixes it — plus four papers on checks that miss.
Recall by address beat every summariser and repository context was served once instead of rediscovered. Plus three papers on defaults nobody ever checked.
An agent working on files and app state instead of screenshots scored higher at a ninth of the cost. Plus harness cost, hidden reasoning, and agent security.
An agent sending 38% fewer tool-output tokens cost 6.8% more, because cached context is most of the bill. Five more on hallucinated skills, generous judges, and tracing failure.
A workbench injects tool faults into MCP agents so you can reproduce a failure and prove your fix before shipping. Five more on abstaining, search evidence, and context.
An independent judge disputed a leading coding benchmark's pass/fail calls on a third of runs. Five more on judging, abstaining, and routing.
Indexing code and action history into compact packets cuts a repair agent's tokens 51.5%. Five more on agreement, calibration, and agent memory.
A production agent that compiles repeated steps into saved tools cuts latency 42% and errors up to 53%. Five more papers on agent safety, judging, and repair.
Grounding the LLM test-writer in the task spec — one prompt line — beats doubling the test budget (+38 points, cross-vendor). Six more papers on running agents cheaper and catching silent failures: capacity placement, workflow crystallization, trace triage, write gates, and judge audits.