{"slug": "show-hn-acceptodds-a-prediction-market-on-which-iclr-2027-papers-get-accepted", "title": "Show HN: Acceptodds a prediction market on which ICLR 2027 papers get accepted", "summary": "A developer launched acceptodds, a prediction market where users bet on whether each of the 42,000 ICLR 2027 submissions will be accepted or rejected, using Hanson's logarithmic market scoring rule (LMSR) to set prices. The site uses a non-convertible in-app currency called reputation, offers a public leaderboard ranked by P&L, pseudonymous comments that display the author's position, and a public HTTP API with an OpenAPI spec at /api/v1/openapi.json that explicitly encourages bots and LLM traders. The creator framed the project as an attempt to spark conversation about what they call a broken conference peer-review system.", "body_md": "Hi, I built acceptodds, a prediction market on conference peer review. Every ICLR 2027 submission (42k papers) has a market: Accept vs. Reject.\n\nLike many in the ML community, I think the current conference/paper situation is a bit broken. I'm not sure how to fix it, but this is my attempt at building something fun to get the conversation started.\n\nSome details: - Prices come from Hanson's LMSR (logarithmic market scoring rule).\n\n- You can sell and/or rebuy at any time\n\n- There's a public leaderboard that ranks traders by P&L.\n\n- It uses an in-app currency called reputation. It can't be converted to money.\n\n- Bots can trade too, and they're encouraged. If you can get an LLM to predict the market, that would be cool! There's a public HTTP API with an OpenAPI spec (/api/v1/openapi.json).\n\n- Comments are pseudonymous, like on OpenReview (\"User k3xm\"), but they show the author's position.\n\n- There's also a 2D map of all submissions: [https://acceptodds.com/map](https://acceptodds.com/map)\n\nHow would you extend this? How should ML conference reviews work? Is there something better than open review?\n\nGo find your own paper (if you have one), place a bet and LMK what you think :)\n\nComments URL: [https://news.ycombinator.com/item?id=49976376](https://news.ycombinator.com/item?id=49976376)\n\nPoints: 3\n\n# Comments: 0", "url": "https://wpnews.pro/news/show-hn-acceptodds-a-prediction-market-on-which-iclr-2027-papers-get-accepted", "canonical_source": "https://acceptodds.com", "published_at": "2026-10-06 10:03:26+00:00", "updated_at": "2026-10-06 10:20:27.252921+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "ai-tools"], "entities": ["acceptodds", "ICLR 2027", "Hanson", "OpenReview"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/show-hn-acceptodds-a-prediction-market-on-which-iclr-2027-papers-get-accepted", "markdown": "https://wpnews.pro/news/show-hn-acceptodds-a-prediction-market-on-which-iclr-2027-papers-get-accepted.md", "text": "https://wpnews.pro/news/show-hn-acceptodds-a-prediction-market-on-which-iclr-2027-papers-get-accepted.txt", "jsonld": "https://wpnews.pro/news/show-hn-acceptodds-a-prediction-market-on-which-iclr-2027-papers-get-accepted.jsonld"}}