The content describes a perceived misalignment in the ICML review process, where reviewers feel pressured to homogenize scores to avoid prolonged discussions, potentially leading to positive papers being rejected. It highlights reviewers being reluctant to update scores even after concerns are addressed, creating distorted dynamics.
A researcher received a weak rejection from a reviewer suspected of using an LLM, whose points were irrelevant and unoriginal, contrasting with positive feedback from other reviewers. The author seeks advice on how to collect evidence and report the reviewer to the academic committee for low-quality or LLM-generated feedback, considering the challenge of proving AI usage.
A KDD 2026 submitter noticed that their paper's reviews and discussions have disappeared from their author view, while discussions for other papers are still visible in their reviewer view. They are inquiring if other users are experiencing the same technical glitch with the review platform.
The content discusses whether machine learning papers accepted between 2000 and 2021 would still get approved today, suggesting that the bar for publication has significantly risen. There's a debate on whether research standards have genuinely increased or if the field has simply become more crowded and competitive.
A Reddit post discusses significant score variance among paper batches for ICML 2026, with some batches having few high scores while others report higher averages. The user questions the reasons for this disparity, such as domain differences or harsher reviewers, and whether ICML accounts for it.
A user expresses shock regarding an ICLR 2025 Oral paper, criticizing its evaluation methodology for SQL code generation by LLMs. The paper reportedly used natural language metrics instead of execution metrics, leading to an approximately 20% false positive rate.
The author analyzed ICLR 2025 and 2026 review scores, finding a significantly lower correlation between human reviewers for 2026. This suggests a potential decline in the consistency of review quality at AI conferences.
An author's paper was rejected from a workshop for sounding more like a technical report than a research paper, despite following the usual computer vision format. They are seeking community opinion to understand common faux pas that lead to such an assessment.
The user queries whether there's increased pressure on Area Chairs (ACs) at ICML to ensure reviewers provide final justifications and reach consensus. They note a disparity, observing active AC engagement and final justifications for papers they reviewed, but complete silence and missing justifications for their own submission despite showing some disagreement in scores.
This is a place for UAI 2026 participants to share their reactions, whether rants or relief, once the conference reviews are released soon. Good luck to everyone with their results.
An ML researcher expresses discouragement with the current review system for top-tier AI/ML conferences, citing instances where papers are rejected despite authors addressing all reviewer concerns. The post seeks better methods to ensure a fair review process for the high volume of submissions.
Um autor de submissão na ICML 2026 relata frustração com o processo de revisão, onde alguns revisores não forneceram o reconhecimento esperado às refutações ou ajustaram as pontuações. Isso gerou uma desvantagem percebida, com falta de comunicação após o prazo e impacto nas chances de aceitação do artigo.
Um autor descreve enfrentar um avaliador extremamente antiprofissional no ICML 2026, que utilizou referências falsas, ataques pessoais e argumentos sem sentido para desqualificar seu trabalho. O autor busca orientação sobre como intervir contra um avaliador que emprega citações fraudulentas e ataques ad hominem no processo de revisão por pares.
The content questions the effectiveness of the NeurIPS 2026 "AC-Pilot" system, which aims to focus reviewers on listed concerns. It raises doubts about whether reviewers will disregard unlisted issues or still prioritize raw scores over sufficiently addressed concerns, despite guidelines.
An ICML paper author is concerned because a reviewer increased their score after rebuttal but then reduced it back to the original score. The decrease in the paper's average score makes them fear an early rejection.
Um pesquisador em transição da física teórica para IA busca feedback sobre as chances de aceitação de seu artigo de teoria de deep learning no ICML 2026, com base em pontuações de revisão. Ele descreve a evolução das avaliações e as condições para melhorar o resultado, enquanto também busca financiamento para a conferência.
The author criticizes the decision to extend the deadline for reviewers' final justifications at ICML 2026 without extending for authors to contact ACs, deeming it a significant mistake. A reviewer raised new, critical issues in their final justification, not previously mentioned, which could jeopardize a paper with otherwise strong reviews.
This content addresses the perception that the quality of the review process in major conferences and journals is declining, leading to nearly random acceptance decisions. The author seeks recommendations for a conference or journal with a more fair and accurate review process.
Sem-Detect is a novel method for distinguishing between human-written and AI-generated peer reviews, combining textual features with claim-level semantic analysis. It leverages the observation that AI models tend to converge on similar points, while human reviewers introduce more unique ideas, enabling the detection of fully AI reviews and human reviews refined by LLMs.
Este conteúdo questiona se os revisores da ICML 2026 ainda podem enviar suas justificativas finais até o término do período de discussão entre AC e revisor.