# The Impact Market to Save Conference Peer Review: Decoupling Dissemination and Credentialing

arXiv: https://arxiv.org/abs/2512.14104 · HTML: https://mlsanity.com/paper/2512.14104/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2025.

## Built on by (most cited first, 2 of 2)

- [Can AI Review Improve Paper Drafting? An Empirical Study on 20 Computer Architecture Submissions](https://mlsanity.com/paper/2606.01013/) (2026)
- [Reimagining Peer Review Process Through Multi-Agent Mechanism Design](https://mlsanity.com/paper/2601.19778/) (2026)

## Abstract (arXiv)

Top-tier academic conferences are failing under the strain of two irreconcilable roles: (1) rapid dissemination of all sound research and (2) scarce credentialing for prestige and career advancement. This conflict has created a reviewer roulette and anonymous tribunal model - a zero-cost attack system - characterized by high-stakes subjectivity, turf wars, and the arbitrary rejection of sound research (the equivalence class problem). We propose the Impact Market (IM), a novel three-phase system that decouples publication from prestige. Phase 1 (Publication): All sound and rigorous papers are accepted via a PC review, solving the "equivalence class" problem. Phase 2 (Investment): An immediate, scarce prestige signal is created via a futures market. Senior community members invest tokens into published papers, creating a transparent, crowdsourced Net Invested Score (NIS). Phase 3 (Calibration): A 3-year lookback mechanism validates these investments against a manipulation-resistant Multi-Vector Impact Score (MVIS). This MVIS adjusts each investor's future influence (their Investor Rating), imposing a quantifiable cost on bad actors and rewarding accurate speculation. The IM model replaces a hidden, zero-cost attack system with a transparent, accountable, and data-driven market that aligns immediate credentialing with long-term, validated impact. Agent-based simulations demonstrate that while a passive market matches current protocols in low-skill environments, introducing investor agency and conviction betting increases the retrieval of high-impact papers from 28% to over 85% under identical conditions, confirming that incentivized self-selection is the mechanism required to scale peer review.