arXiv — cs.AI preprintsInternational19 October 2026
CABAL: Multi-Agent Simulacra for Tracing the Effects of Collusive Bidding in Peer Review
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2609.05227v3 Announce Type: replace Abstract: Recent reports during the AAAI-27 review cycle highlight the risk of reviewers coordinating bids for reciprocal assignment advantage. Prior work treats bidding, reviewer assignment, and review manipulation as separate stages, leaving the lifecycle effects of collusive bidding unclear. Real-world analysis is further constrained by typically unobservable collusive intent and the lack of counterfactuals for the same conference. Motivated by this gap, we introduce \alg, an end-to-end multi-agent simulacra framework for studying reviewer assignmen
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- Structured Authority Injection: Persistent Human Control for LLM Agents via Typed MCP Overrides19 October 2026
- Rethinking User Encoders: Routing Diffusion States for Scenario-Adaptive User Representation19 October 2026
- From Events to Reasoning: Hierarchical Rule Composition over Explicit States19 October 2026
- ECHOES: Seeing Beyond the Current Image via Recursive History Compression and Privileged On-Policy Distillation19 October 2026
- Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model19 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.