Researchers released the Taste-Bench benchmark, which measures long-term decision "taste" by freezing agent runs at decision forks in their trajectories and asking models to choose between two paths; it contains 502 questions drawn from coding and AI research tasks such as SWE-bench Pro and METR's MALT.
Researchers from Columbia University, Google DeepMind, and Google have published a paper proposing Back-to-the-Future (BTTF), an end-to-end agentic framework that automates post-simulation waveform debugging in chip verification.
Vals AI tested GPT-6 Sol and Claude Opus 5.5 in single-agent versus team setups on Vibe Code Bench, finding teams cost 1.8x to 5.1x as much as single agents, and of four comparisons only GPT-6 Sol at the medium reasoning tier improved 7.3 points with statistical significance.
Epoch AI used its InnovationEval benchmark to test whether AI agents can independently invent new training methods: GPT-5.6 Sol reached only about 35% of the human reference method SDPO's relative improvement over GRPO under lenient scoring, only about 15% for changes within the rules, while Claude Fable 5 showed no measurable gain.
A study found that after AI coding agents were introduced, pull request review time from submission to merge rose 49% on average, the share of pull requests requiring changes nearly doubled, comments per pull request rose 35%, and the share of employees participating in code review increased 14%.
GitHub has released ReviewBench, an open offline benchmark for evaluating AI code review agents. The benchmark is modeled on the distribution of 103.9 million GitHub pull requests.
Epoch and METR released the MirrorCode benchmark, testing AI's ability to reimplement complete software with CLI access alone; 17 of 25 target programs had at least one perfect run, and Claude Opus 4.7 completed a task humans estimated at 2-17 weeks in 14 hours with $251 in inference costs.
Berkeley AI Research proposes the ABBEL framework, which replaces full interaction history with a natural-language belief state as the agent's working context and introduces belief grading to supervise summary content. On the CollabBench collaborative coding benchmark, ABBEL-rec-BG closes roughly 50% of the performance gap with full-context models, cuts training steps from 100 to 50, and uses significantly shorter peak context token lengths than the full-context setting.
Hugging Face launched the open-source benchmark ScarfBench (Self-Contained Application Refactoring Benchmark), which evaluates AI agents on enterprise Java cross-framework migration tasks across the Spring, Jakarta EE and Quarkus ecosystems.