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The author describes spending seven days straight using the AI models GPT 5.6 Sol and Fable 5 to solve a 25-year-old open problem in wireless communication theory, noting that verification was the biggest bottleneck.
An analysis piece discussing why persona depth in LLM systems fails to improve output diversity, and how isolating readers with broad personas yields better convergence evidence.
An opinion piece expressing skepticism about Discovery Loop, an AI research startup founded by former Google AI leaders, arguing that automating ML research differs fundamentally from automating scientific/engineering tasks and questioning their unique advantage while acknowledging they deservedly raised funding.
This arXiv paper investigates whether simple linear transformations can translate representations across nine heterogeneous text embedding models, finding that shared structure and transferability depend jointly on architecture, training objective, pooling, and data distribution, challenging the notion of universal latent compatibility.
OpenAI released AI-generated math breakthroughs that experts are calling research misconduct due to lack of academic rigor.
A tweet highlights that top young talent in SF is shifting from AGI/alignment to brain-computer interfaces, noting Naomi Bashkansky's resignation from OpenAI to join Conduit as a founding researcher working on non-invasive mind-reading models.
Tencent reports that its Hy3-powered Hyra research agent assisted in settling a 50-year-old sum-difference problem in mathematics, with the paper published on arXiv.
This paper studies self-distillation with privileged information (PI) as a lone post-training objective for LLMs, reproducing reported gains on easy tasks but showing it fails on difficult reasoning tasks: per-token loss drops while validation accuracy stagnates or degrades. The authors trace the failure to PI bias, which pulls teacher targets toward a reference trajectory and trains students to be flatter and less decisive without improving reasoning.
It explains the evolution path of RAG technology from basic RAG in 2020 to autonomous agents + reinforcement learning in 2025-2026, covering retrieval precision improvement, reflection and correction, graph enhancement, routing optimization, and Agentic RAG, and summarizes representative works and pain points of each stage.
Jeff Dean宣布在谷歌工作27年后离职，引发外界对Gemini项目未来走向的关注。
A blog post analyzing Anthropic's recent LLM-assisted cryptanalytic attacks on HAWK and reduced-round AES, arguing that LLMs will not break established symmetric cryptographic schemes.
This paper surveys the evolution of continual learning from parameter-centric methods to system-level adaptation, proposing a tri-axial framework (When, How, Where) to characterize learning across pre-training, post-training, and inference stages.
Hank Green apologizes for overreliance on AI as a research aid, highlighting gaps in YouTube's AI disclosure rules that don't require labeling for AI-assisted research, outlining, and voice cloning.
Jeff Dean and several other top Google AI researchers are leaving to launch Discovery Loop, a public benefit startup aimed at using AI to automate and massively scale scientific experimentation, with backing from Alphabet and major VC firms.
Jeff Dean, Google's chief scientist, is leaving after 27 years to co-found Discovery Loop, an AI research company aiming to automate the research cycle. The startup has seed funding from Radical Ventures and Khosla Ventures, with Alphabet as a founding investor and cloud partner.
Google and Alphabet CEO Sundar Pichai announces leadership changes at Google DeepMind: Demis Hassabis becomes Chair of GDM and Chief Scientist of Alphabet, Koray Kavukcuoglu steps up as SVP of Google DeepMind, and Jeff Dean and Sanjay Ghemawat launch an independent public benefit corporation for ML and science research.
A tweet speculates that SSI is developing AI that learns rapidly from its own experience, potentially true online learning, and suggests GPT-6 might incorporate a version of it. Both models are expected to be shown this month.
An AI researcher reflects on six years in the field and questions whether the concept of 'understanding' can be meaningfully distinguished from advanced pattern matching in LLMs, citing personal experiences and examples from child learning.
Google DeepMind CSO Jasjeet Sekhon discusses how AI infrastructure spending is driven by hopes for recursive self-improvement, citing AlphaEvolve's bounded gains like reducing Gemini training time by 1%.
A position paper arguing that large language models have fundamental limitations, using the metaphor 'can't jump' to highlight gaps in reasoning or generalization.