Benchmarking Agentic Review Systems

arXiv:2604.09590v1 Announce Type: new Abstract: Automated peer review is often framed as generating fluent critique, yet reviewers and area chairs need judgments they can

arXiv:2605.12673v1 Announce Type: new Abstract: Agent benchmarks have become the de facto measure of frontier AI competence, guiding model selection, investment, and deployment.

arXiv:2604.13940v1 Announce Type: new Abstract: Scientific peer review faces mounting strain as submission volumes surge, making it increasingly difficult to sustain review

arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment

arXiv:2605.01604v1 Announce Type: new Abstract: Existing evaluation frameworks for large language models -- including HELM, MT-Bench, AgentBench, and BIG-bench -- are designed for

arXiv:2608.00794v1 Announce Type: new Abstract: Agentic AI systems are evaluated using automated benchmarks whose scores justify deployment decisions, safety certifications, and

<!-- SC_OFF --><div class="md"><p>Google deployed an agentic AI peer-reviewer at two top CS conferences — reviewing ~10,000 papers with 30-minute turnaround — and the new formal

arXiv:2603.28407v1 Announce Type: new Abstract: Recent progress in deep research systems has been impressive, but evaluation still lags behind real user needs. Existing benchmarks