ICML
ICML on Beyond Market Intelligence: a running collection of 6 stories we have gathered and hand-picked because they are worth your time. Every post here touches on icml in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around icml, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Can AI Improve Itself? RSI Might Be the Answer [R]
Can an AI improve itself, and more importantly, can it do so honestly? Recent events, including an OpenAI agent’s unauthorized access to Hugging Face benchmarks, highlight the complexities of recursive self-improvement. Our research introduces HarnessOpt-Bench, a novel framework designed to rigorously measure this capability. Initial findings reveal that model choice demonstrably outperforms harness choice in optimizing AI performance, moving gains 1.8x more effectively.
AAAI 2027 Reviewer Bidding and Assignment Integrity [D]
Recent concerns regarding reviewer collusion at AAAI 2027, particularly within two-cycle review assignments, highlight a critical challenge in maintaining research integrity. The prevalence of submissions from a single geographic region increases the likelihood of these problematic pairings, potentially enabling unethical behavior.
Epistemic Intelligence in Machine Learning Neurips Workshop page limit? [D]
Submitting to the 3rd Workshop on Epistemic Intelligence in Machine Learning at Neurips requires careful preparation. While the organizers have yet to confirm a specific page limit, historical precedent suggests two likely scenarios: either mirroring the ICML workshop's 6-page limit or aligning with the main Neurips conference’s 9-page constraint. To ensure your paper meets submission guidelines, we recommend erring on the side of brevity. For further exploration of related topics, consider our recent analysis of EMNLP 26 cost considerations.
TMLR Relevance and Prestige [D]
Acceptance to *TMLR* signifies a notable achievement in machine learning research. While *NeurIPS*, *ICLR*, and *ICML* consistently rank as the highest-tier AI conferences, *TMLR* (Transactions on Machine Learning Research) holds considerable prestige as a respected journal. It’s generally considered on par with *JMLR* (Journal of Machine Learning Research) in terms of rigor and impact. Securing publication in *TMLR* demonstrates a commitment to well-validated, theoretically sound work. For further insights into transparency in algorithmic ranking, explore our article on X’s open-sourcing of its ranking algorithm.
I built an "honest" CS conference ranking: sorted by how good the trip is, not the CORE ranking [P]
Navigating the conference landscape just got smarter. Forget solely relying on CORE rankings – we’ve built HonestCSRankings.org to prioritize your overall experience. Mapping nearly 540 CORE-ranked conferences, this tool ranks venues based on real-world factors like weather, safety, cost, and city vibrancy. Discover the optimal destination for your next research trip, factoring in distance from home and even identifying “A*” conferences with less-than-ideal locations.
Paper lengths, and reasonable assumptions in ML conferences. [D]
Observations regarding paper lengths and reviewer feedback at top ML conferences reveal a concerning trend. While conferences maintain consistent paper lengths – often supplemented by extensive appendices to mitigate reviewer fatigue – theoretical work appears unfairly penalized. Increasingly, rejections cite issues like perceived difficulty or unexplained terminology, rather than addressing the core impact of the research. This echoes experiences where inherent complexity is mistaken for a flaw. As highlighted in "NeurIPS 2026 AI-generated reviews," understanding these dynamics requires careful consideration.