An undergraduate researcher interested in Information Retrieval (IR) is seeking advice on less competitive academic conferences for submission. They specifically ask about SIGIR-AP, a new conference backed by SIGIR, and for other suitable options.
An author shares their frustration with the ICML review process, highlighting an average score of 3.5 and a reviewer's changed evaluation. They note that a score of 4.2, according to "Paper Co-Pilot," still only places a paper in the top 40%.
The content questions whether publishing solely in ML journals (e.g., TMLR, JMLR) instead of conferences negatively impacts job prospects for corporate ML research scientist positions, considering the perceived fairness of journal review processes. The discussion focuses on post-PhD corporate careers.
The content criticizes the new generation of deep learning researchers for following trends. The author seeks discussion on the complex term 'post-agentic AI', which they are struggling with.
This content discusses the submission of code alongside papers for NeurIPS 2026, with the author torn between the boost in credibility and concerns about plagiarism. The post seeks community opinions on this practice.
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.
The user proposes creating a separate discussion thread for the ICML 2026 position track, fearing that discussions about this niche track would be submerged in the main discussion. The aim is to facilitate decisions regarding this specific track.
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 paper proposes a novel technique, Token-to-Mask (T2M) remasking, to refine masked diffusion language models like LLaDA2.1. The method addresses the shortcomings of Token-to-Token (T2T) editing by resetting suspect tokens to a mask state, enabling more accurate re-prediction.
The Multi-Persona Debate System (MPDS) is a literature-grounded framework designed to automate scientific hypothesis generation, specifically addressing the challenge of synthesizing fragmented knowledge in areas like battery materials research. It combines literature retrieval, large language model reasoning, and multi-agent debate to enable negotiation between personas while preserving evidence traceability.
This content introduces the "Density-aware Chamfer Distance" as a new comprehensive metric for evaluating point cloud completion tasks. It aims to provide a more robust and accurate assessment of completed 3D models.
Recent research indicates that "hallucination neurons" (H-neurons) predicting LLM hallucinations do not generalize across different knowledge domains. This suggests that hallucination may not be a single mechanism with a universal neural signature, but rather context-dependent.
This content explores a novel approach to improve Reinforcement Learning for Large Language Model (LLM) reasoning by focusing on "high-entropy minority tokens". It proposes that these less frequent yet highly informative tokens are key drivers for effective learning, challenging the conventional 80/20 rule.
This research introduces Self-Verified Distillation, an algorithm enabling large language models (LLMs) to improve themselves using only unlabeled prompts. It involves generating, self-verifying through multi-stage checks, and then training on self-curated datasets, without external teachers.
This research investigates whether real-data scaling laws are governed by a progressive coverage of a latent predictive contribution spectrum, rather than solely by token-frequency. Using a suffix-automaton and a global-KL predictive contribution spectrum, the study finds a strong correlation between the spectrum's tail slope and the data-scaling exponent of GPT learners, showing that effective truncation rank scales logarithmically.
This paper introduces Structured Progressive Knowledge Activation (SPARK) to address the challenge of integrating architectural knowledge in LLM-driven Neural Architecture Search (NAS). SPARK mitigates "functional entanglement" by enabling factor-conditioned editing, leading to more targeted and reliable architecture modifications.
This content provides an evidence-backed analysis of a research paper titled "Remember to be Curious: Episodic Context and Persistent Worlds for 3D Exploration." It highlights a significant shift in AI, supported by data and expert analysis.
This paper presents a geometric framework to analyze the instability of feature unions in Sparse Autoencoders (SAEs), particularly concerning compositional steering. It derives an asymptotic compositional-collapse threshold under a spherical dictionary model.
This research introduces a "finite-answer preference stabilization" method to determine when a language model's answer preference becomes stable before its final output. It shows that this stabilization often occurs before the answer is parseable, with a significant lead time.
This paper introduces RateQuant, a method for optimal mixed-precision KV cache quantization in large language models to address memory bottlenecks. It tackles the challenge of distortion model mismatch, where applying one quantizer's distortion model to another degrades performance compared to uniform quantization.