acceptodds: Which papers will get accepted at ICLR 2027?
Abstract
A prediction market on peer review. Each paper has a market on its decision (accept or reject), priced by researchers who stake $rep, an in app currency, on what they expect. Everyone who signs up receives 1,000 $rep to trade with. Prices are probabilities, and every market settles when the venue publishes its decisions. It is a game made for fun, and has no connection to ICLR, OpenReview or any other organisation. acceptodds is an open source project, open to contributions, at github.com/benedict-armstrong/acceptodds.1
- ETH Zurich
- University of Maryland, College Park
- Birla Institute of Technology and Science, Pilani – Pilani Campus
- Pohang University of Science and Technology
- University of Illinois Urbana-Champaign
- Amazon
- École Polytechnique Fédérale de Lausanne
- Harvard University
- Los Alamos National Laboratory
- Mila
- Roche
- Technical University of Munich
- The Chinese University of Hong Kong
- University of California, Irvine
- University of Tübingen
- West Virginia University
- Aalto University
- Delhi Technological University
- ELLIS Institute Tübingen
- Emory University
- Indian Institute of Technology Delhi
- Industrial University of Santander
- INHA University
- Massachusetts Institute of Technology
- New York University
- Polytechnique Montréal
- University of California, Los Angeles
- University of California, Santa Barbara
- University of Moratuwa
- University of Pennsylvania
- University of Southern California
- University of Surrey
- Uppsala University
- Virginia Tech
[PDF]Self-Play Evaluation Under Population Exposure Mismatch: Unidentified Verdicts on Defensive Skill in Competitive Mahjong
[PDF]Retrieval Gains, Similarity Losses: Controllable Specialisation for Entity-Specific Few-Shot Adaptation
[PDF]TAMRec: Time-Aware and Monotonic Reinforcement Learning for On-Demand Embedding Dimensions in Streaming Recommendation
[PDF]Latent Workflow Discovery for Enterprise World Models: Grounding General-Purpose Agents in Organizational Context
[PDF]Adapt the Score or Localize the Calibration? A Finite-Sample Comparison for Conditional Conformal Prediction
[PDF]Explaining and Calibrating Dynamically Orchestrated LLM MAS through Stepwise Uncertainty Decomposition
[PDF]SQUARE: Structured Quantum Representation Adapters as Compact Quadratic Feature Maps for Frozen Language Models
[PDF]A Lightweight Velocity Correction Framework for Improving Instruction Following in Few-Step Text-to-Image Generation
[PDF]They Know When They're Unsure: Confidence-Gated Acceptance as a Two-Sided Defense against Sycophancy in Video Language Models
1 If this project is interesting to you, reach out at [email protected].