11 related articles

Two papers flagged for fake authors still received oral presentation slots at top conferences, exposing systemic peer review failures in the AI era.

ICLR 2027's paper deadline falls 8 days before NeurIPS 2026 decisions, sparking debate over top conference timeline conflicts and their impact on researchers.

EMNLP 2026 introduces AI-generated reviews in ACL Rolling Review, exploring LLM-assisted academic peer review. Analysis of the experiment's background, core content, controversies, and implications.

EMNLP 2026 introduces AI-generated reviews in ACL Rolling Review, exploring LLM-assisted academic peer review. Analysis of the experiment's background, mechanics, controversies, and implications.

OpenReviewer is an open-source LLM for generating critical scientific paper reviews. This article analyzes its technical approach, use cases, and limitations.

OpenReviewer is an open-source LLM for generating critical scientific paper reviews. This article analyzes its technical approach, use cases, and limitations.

As NeurIPS and CVPR monopolize academic resources while niche venues like FG and ICASSP fade, quality research disappears into arXiv. A deep analysis of AI conference over-concentration.

A detailed breakdown of EMNLP/ACL review dimensions—Overall, Soundness, Excitement, Reproducibility—with an objective assessment of acceptance odds at 2.5 Overall, plus Rebuttal strategy and Findings advice.

Why doesn't the ML community cap submission counts? This deep dive explores the cultural roots, career pressures, and authorship complexities behind the peer review quality crisis, and examines viable solutions like quotas and mandatory reviewing.

An AI research engineer with 3 years of experience sent 50 applications to FAANG with zero replies. This article breaks down the hidden barriers of top-tech AI roles, the truth about LinkedIn ghost jobs, and the MLE vs. Research Engineer divide.

An exclusive look at the AI Engineer Summit dress rehearsals, decoding the paradigm shift from research to production. A deep dive into AI Engineer challenges, RAG, agent systems, and AI engineering as a distinct discipline.