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ML Papers Accepted Without Code

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๐Ÿค–Read original on Reddit r/MachineLearning

๐Ÿ’กReveals reproducibility crisis in ML conferencesโ€”check papers you cite

โšก 30-Second TL;DR

What Changed

Papers claim SOTA performance without code for verification

Why It Matters

Erodes trust in ML research; pushes for mandatory code release policies in conferences.

What To Do Next

Always search for and test GitHub code before replicating ML paper claims.

Who should care:Researchers & Academics

๐Ÿง  Deep Insight

Web-grounded analysis with 9 cited sources.

๐Ÿ”‘ Enhanced Key Takeaways

  • โ€ขMajor conferences like AISTATS 2026 explicitly ban hidden LLM prompts in submissions, with policies to reject and report violators, including retroactive rejection post-acceptance[1].
  • โ€ขNeurIPS 2025 proceedings included over 100 papers with AI-hallucinated citations that survived peer review despite a 24.52% acceptance rate[7].
  • โ€ขICML 2026 introduced a new policy capping reciprocal reviewers per person and regulating LLM use in reviewing to enhance integrity[3].
  • โ€ขTMLR journal paused new submissions from December 2025 to January 2026, amid broader efforts to manage rising ML paper volumes[9].

๐Ÿ”ฎ Future ImplicationsAI analysis grounded in cited sources

ML conferences will mandate code and data availability by 2027
Trends in AISTATS and ICML calls show escalating reproducibility requirements, building on open science policies already enforced in related venues like MSR 2026[1][3][2].
Peer review rejection rates for non-reproducible papers will rise 20%
Explicit bans on LLM misuse and hallucinated content in NeurIPS 2025 signal stricter verification, as conferences prioritize rigorous, verifiable claims[1][7].

โณ Timeline

2025-12
NeurIPS 2025 publishes papers with 100+ AI-hallucinated citations surviving peer review
2025-11
TMLR announces submission pause into 2026 amid ML publication surge
2026-01
ICML 2026 releases call for papers with new LLM reviewing policy and optional attendance
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Original source: Reddit r/MachineLearning โ†—