The paper introduces CABAL, an end-to-end multi-agent simulation framework that models reviewer assignment in academic conferences using large language model-driven reviewer agents. It presents an affinity-guided collusive bidding strategy that forms collusion rings based on reviewer-paper affinities, leading to more effective target-paper capture and higher scores for colluding reviewers. Experiments show that while collusive bidding significantly increases target-paper capture and reviewer scores, overall conference-wide effects are modest, and existing bid-phase detectors offer limited detection capability.
arXiv:2607. 15388v1 Announce Type: new Abstract: Many math- and science-oriented agent systems use hierarchical designs with specialized reviewer roles, assuming that a dedicated review stage should help turn wrong candidates into correct ones.
Adversarial Review (AR) is a minimal cooperative code‑review protocol that employs a main coding agent, a reviewer, and a critic. The reviewer evaluates code while the critic audits the review through structured disagreement before the main agent edits. On multiple benchmarks (LiveCodeBench, SWE‑PRBench, SWE‑bench Verified), AR achieves higher pass rates or F1 scores than larger multi‑agent baselines, demonstrating that effective code review can be achieved with only three agents and minimal, evidence‑grounded disagreement.
MiniRep is a reputation‑based aggregation system designed for multi‑agent debate (MAD) that remains robust even when malicious agents are present. It evaluates agents on both their current task performance and historical reputation, while preventing groups of agents with highly similar responses from dominating the final decision. Experiments on the MATH benchmark show that MiniRep consistently outperforms conventional MAD aggregation and other reputation‑based approaches across a wide range of attack scenarios.
arXiv:2510. 18003v2 Announce Type: replace-cross Abstract: The convergence of LLM-powered research assistants and AI-based peer review systems creates a critical vulnerability: fully automated publication loops where AI-generated research is evaluated by AI reviewers without human oversight.
The paper introduces Verifiable Latent Alignments (VLA), a framework that monitors and steers hidden communication channels between language‑model agents. VLA links private latent states to public actions via event identifiers, enabling causal analysis. Experiments on a multi‑agent auction benchmark show high detection accuracy and effective mitigation of collusion, even without training on attack examples.
Empirical work on algorithmic collusion asks one question of the data: are prices supracompetitive? We show this can be answered "no" by a conspiracy that is nonetheless profitable.
arXiv:2607. 26385v1 Announce Type: cross Abstract: Empirical work on algorithmic collusion asks one question of the data: are prices supracompetitive?
arXiv:2606. 10159v1 Announce Type: cross Abstract: AI is increasingly used to support scientific peer review, from manuscript screening, reviewer assistance to editorial triage.
arXiv:2608.21374v1 Announce Type: new Abstract: Literature reviews are essential to scientific progress, but rigorously evaluating automatically generated reviews remains difficult because many aspec...
The paper studies how to safely delegate action approval to multiple AI reviewers when the reviewers themselves may be misaligned. It introduces a weaker condition—k‑robust coalitional alignment—under which a threshold rule that tolerates up to k disapprovals guarantees that the principal’s expected utility is at least as good as a baseline policy. The authors extend this characterization to sequential decision‑making in discounted MDPs and show that full‑panel coverage of reward functions ensures safety in Nash equilibria, while more permissive thresholds can lead to unsafe outcomes. Experiments demonstrate that collective review can remain sound even when individual reviewers are not fully aligned, provided some disapprovals are allowed.
arXiv:2603. 20450v2 Announce Type: replace-cross Abstract: A number of scientific conferences and journals have recently enacted policies that prohibit LLM usage by peer reviewers, except for polishing, paraphrasing, and grammar correction of otherwise human-written reviews.
arXiv:2608.30373v1 Announce Type: new Abstract: Multi-Agent Debate (MAD) has been widely adopted to improve LLM-based evaluation by prompting multiple agents to negotiate and reach a consensus. Howev...