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A new MIT paper surveys 268 early-career mathematicians and finds nearly 70% expect AI to make tenure-track jobs harder to get, with fear of being scooped leading some to withhold unfinished work. Among those using AI in research, 54.8% report disliking it.
OpenAI 分享了与学术界合作、推动 AI 构建科学理解的举措，文章讨论 AI 在以太坊相关或高校学术水平测试中的表现，凸显 AI 在科研与教育领域的能力提升。
A discussion post about the rising trend of undergraduate students publishing at top-tier A* conferences without a supervisor, prompting reactions from the graduate student community about research progress and expectations.
研究者Peter Henderson在ICLR审稿中表达对AI冲击学术同行评审体系的担忧，指出大量疑似AI生成的投稿正导致审稿质量下降与评审系统的死亡螺旋。
OpenAI is expanding academic access to GPT-Rosalind through a new Rosalind Academic Plan, offering sponsored access to eligible U.S. university labs to support biological research and scientific discovery.
A University of Maryland-led academic collaboration introduces IdeaLens, a methodology trained on one million document outlines that can detect whether ideas originated with AI regardless of who wrote the final text, complementing existing AI prose detectors.
In a talk at ETH Zürich, Yann LeCun argued that academics should not work on LLMs or generative models, saying there is nothing new academia can bring to the table and that real progress lies in grounded, physical AI — a stance he admits makes him unpopular in Silicon Valley.
Sat researcher Mate Soos argues that academic incentives have drifted away from scientific integrity, citing cases where authors knowingly kept papers with incorrect evaluations rather than retracting them, and warning about a growing indifference toward correctness among PhD students.
Science Utopia introduces a persistent, closed-loop LLM-agent simulation framework for modeling academic research ecosystems, covering research choices, collaboration, peer review, funding, and attrition across 61 worlds with 40,000+ simulated researchers and 1.2M LLM-generated reviews. The study finds rejection-driven resubmission amplifies reviewer burden, cautious exploration balances citation impact with career success, and resource inequality can emerge without cumulative advantage from early funding wins.
Nicholas Polson has authored 258 academic papers in 2026 so far, marking a high level of research productivity.
A study by Northeastern University and Consumer Reports reveals that connected cars extensively share user data with advertisers and tech companies, with Tesla models showing high levels of tracking activity.
The paper introduces a Reliability-aware Cross-sample Enhancement (RCE) framework for robust multimodal sentiment analysis, addressing noise and missing modalities through adaptive information bottleneck and cross-sample enhancement, demonstrating superior performance across various settings.
RAZOR is a training-free method for pruning replaceable experts in Mixture-of-Experts LLMs by assessing functional replaceability, achieving superior performance on reasoning tasks compared to existing methods.
The paper introduces an algorithm allowing unfaithful LLM providers to artificially inflate reasoning paths for overcharging while evading detection, validated through experiments with models like Llama and Qwen.
A study in JAMA shows that pharmaceutical companies have tripled the number of patents per drug since 1990, creating 'patent thickets' that delay generic competition and maintain high drug prices.
This paper explores using instruction-tuned LLMs to analyze collaborative student sensemaking in science learning, finding that reasoning-enabled prompting and knowledge-state diagnostics improve alignment with expert annotations.
The paper proposes HasMem, an adaptive memory compression method for long-term LLM agents that adjusts memory widths while maintaining frozen LLM parameters, demonstrating improved performance on reconstruction and QA benchmarks.
This paper introduces ORCA, a comprehensive benchmark for evaluating Large Language Models on data science code translation tasks, highlighting challenges and proposing an intent-augmented method to improve performance.
ROSS introduces selective supervision on historical rollouts to improve language model post-training, enhancing performance in math, code, and software engineering tasks without additional policy rollouts.
The paper shows that deterministic PRM guidance in discrete diffusion language models underperforms simpler baselines when compute is matched, identifying two key failures: weak pruning signals and poor final judgment, and releases a corpus and toolkit for reproducible comparisons.