A/B/U Review System - ORI Onboarding

Index

OpenSkill: Multiplayer Rating System. No Friction. In the multifaceted world of online gaming, an accurate multiplayer rating system plays a crucial role. A multiplayer rating system measures and c...

tuicr - TUI for Code Reviews

Review Signal Publishes 2016 WordPress Hosting Performance Benchmarks

Kevin Ohashi has published his annual Managed WordPress hosting performance benchmarks on Review Signal, an independent consumer review site. In past years (2014, 2015), Ohashi had every company an…

How well does AI peer review work?

A new evaluation benchmark

Attention Authors: Updated Practice for Review Articles and Position Papers in arXiv CS Category

arXiv’s computer science (CS) category has updated its moderation practice with respect to review (or survey) articles and position papers. Before being…

Rating all the things | Tynan Is Typing | Tynan is afk til 10/19 ⁂ | Offprint

All existing proposals are wrong because they are all right

AT Moderation Architecture | bryan newbold

The AT network is becoming more heterogeneous in practice, with independent PDS hosts, apps, and alternative bsky AppViews establishing themselves. This means that more complex inter-service moderation scenarios are starting to get attention. While the labeling system was released all the way back i...

CSU AI Survey Report | CSU

Performance Reviews Are the Scorecard of Capitalism (And Why That Should Free You)

Credit: Daniel Dalea / Unsplash Every review season is an emotional rollercoaster. Anxiety. Self assessments. Anticipation. Disappointment. As an IC, I felt like how much it impacted me was a perso…

AI-Assisted Peer Review at Scale: The AAAI-26 AI Review Pilot

Scientific peer review faces mounting strain as submission volumes surge, making it increasingly difficult to sustain review quality, consistency, and timeliness. Recent advances in AI have led the community to consider its use in peer review, yet a key unresolved question is whether AI can generate technically sound reviews at real-world conference scale. Here we report the first large-scale field deployment of AI-assisted peer review: every main-track submission at AAAI-26 received one clearly identified AI review from a state-of-the-art system. The system combined frontier models, tool use, and safeguards in a multi-stage process to generate reviews for all 22,977 full-review papers in less than a day. A large-scale survey of AAAI-26 authors and program committee members showed that participants not only found AI reviews useful, but actually preferred them to human reviews on key dimensions such as technical accuracy and research suggestions. We also introduce a novel benchmark and find that our system substantially outperforms a simple LLM-generated review baseline at detecting a variety of scientific weaknesses. Together, these results show that state-of-the-art AI methods can already make meaningful contributions to scientific peer review at conference scale, opening a path toward the next generation of synergistic human-AI teaming for evaluating research.

Open Evaluation: A Vision for Entirely Transparent Post-Publication Peer Review and Rating for Science

The two major functions of a scientific publishing system are to provide access to and evaluation of scientific papers. While open access (OA) is becoming a reality, open evaluation (OE), the other side of coin, has received less attention. Evaluation steers the attention of the scientific community and thus the very course of science. It also influences the use of scientific findings in public policy. The current system of scientific publishing provides only journal prestige as an indication of the quality of new papers and relies on a non-transparent and noisy pre-publication peer review process, which delays publication by many months on average. Here I propose an OE system, in which papers are evaluated post-publication in an ongoing fashion by means of open peer review and rating. Through signed ratings and reviews, scientists steer the attention of their field and build their reputation. Reviewers are motivated to be objective, because low-quality or self-serving signed evaluations will negatively impact their reputation. A core feature of this proposal is a division of powers between the accumulation of evaluative evidence and the analysis of this evidence by paper evaluation functions (PEFs). PEFs can be freely defined by individuals or groups (e.g. scientific societies) and provide a plurality of perspectives on the scientific literature. Simple PEFs will use averages of ratings, weighting reviewers (e.g. by H-factor) and rating scales (e.g. by relevance to a decision process) in different ways. Complex PEFs will use advanced statistical techniques to infer the quality of a paper. Papers with initially promising ratings will be more deeply evaluated. The continual refinement of PEFs in response to attempts by individuals to influence evaluations in their own favor will make the system ungameable. OA and OE together have the power to revolutionize scientific publishing and usher in a new culture of transparency, constructive criticism, and collaboration.

roostorg/coop

Review and moderation, your way. Online safety dashboard, queues, routing and automatic enforcement rules, and integrations.