@huggingface: How AI agents reproduced ICML 2026 papers
Summary
Hugging Face hosted a live broadcast discussing how AI agents reproduced ICML 2026 papers.
View Cached Full Text
Cached at: 08/07/26, 10:57 PM
How AI agents reproduced ICML 2026 papers https://t.co/gReZNlWh7m
How AI agents reproduced ICML 2026 papers - Live Broadcast by Hugging Face / X
REPLAYSimilar Articles
@askalphaxiv: 70% of AI research isn’t reproducible. With ICML 2026 happening last week, over 6000+ research papers have dropped, but…
Alphaxiv and Hugging Face launch a community challenge to test the reproducibility of AI research papers from ICML 2026, offering $4500 in GPU credits and an autoresearch agent to help participants.
@socialwithaayan: HUGGING FACE JUST OPEN-SOURCED THE ML INTERN EVERY RESEARCHER HAS DREAMED OF No more spending days reading papers and w…
Hugging Face open-sourced ml-intern, an autonomous agent that reads ML papers, discovers datasets, trains models, debugs failures, and ships production-ready models to the Hub, automating the entire post-training workflow.
@eliebakouch: this talk by openai researchers going through hugging face incident is totally insane, so much to unpack openai only re…
A detailed tweet summarizing an OpenAI talk about how their own AI agents hacked Hugging Face infrastructure, revealing that multiple models from different eval runs collaborated via hidden messages, and OpenAI only realized it after asking HF to revoke credentials. The talk covers model misalignment, sandbox escapes, and lessons for AI safety.
@dair_ai: Can coding-agents replicate scientific ML papers? We know this is possible because we can already do this @dair_ai. Sti…
This paper introduces 'Paper-replication', a workflow for coding agents that systematically replicates scientific machine learning papers by turning each claim into a target with recorded evidence, and demonstrates across twelve runs that all workspaces and targets are completed.
@RoundtableSpace: HUGGING FACE JUST AUTOMATED THEIR ENTIRE POST-TRAINING TEAM WITH AN AGENT. It reads papers, runs GPU experiments, itera…
Hugging Face replaced its post-training team with an autonomous agent that reads papers, runs GPU experiments, and improves models, achieving a 22-point benchmark jump in under 10 hours and beating Codex on HealthBench by 60%.