Lilian Weng
PulseAugur coverage of Lilian Weng — every cluster mentioning Lilian Weng across labs, papers, and developer communities, ranked by signal.
9 day(s) with sentiment data
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AI self-improvement ladder has 'blind step' due to weak evaluators
Lilian Weng's survey on self-improving AI systems outlines an optimization ladder, but this article identifies a critical "blind step" related to evaluator weakness. The author argues that evaluators don't just lack pre…
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Lilian Weng explores AI's recursive self-improvement potential
Lilian Weng has published an essay exploring the concept of Recursive Self-Improvement (RSI) in AI systems, focusing on the role of "Harness Engineering." This approach aims to create AI systems capable of improving the…
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AI techniques like RLHF could drive personal self-improvement
Lilian Weng's article "Harness Engineering for Self-Improvement" explores how AI techniques, particularly reinforcement learning, can be applied to enhance personal development. The piece delves into methods like reinfo…
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AI Talent Wars: Researchers Move Between OpenAI, Anthropic, Google DeepMind
The competition for top AI talent is intensifying, leading to significant researcher movement between major AI labs like OpenAI, Anthropic, and Google DeepMind. This talent churn highlights the extraordinary compensatio…
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Lilian Weng returns to OpenAI to lead self-evolving AI team
Lilian Weng, a former VP of Research and Safety at OpenAI, has returned to the company to lead its "self-evolving" (RSI) team, a high-priority research initiative. Weng had previously left OpenAI to co-found Thinking Ma…
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Thinking Machines co-founder Lilian Weng rejoins OpenAI to lead AI research
Lilian Weng, a co-founder of Thinking Machines Lab, has rejoined OpenAI after a brief departure from her startup. Weng cited health reasons for stepping down from Thinking Machines, where she was a co-founder alongside …
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Lilian Weng rejoins OpenAI for AI self-improvement research
Lilian Weng has rejoined OpenAI to focus on research into recursive self-improvement for AI systems. Weng, previously a prominent researcher at OpenAI, had departed to co-found a startup. Her return signals a renewed em…
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Thinking Machines Co-founder Lilian Weng Departs Amidst Company Turmoil
Lilian Weng, a co-founder of AI startup Thinking Machines, has announced her departure from the company after 20 months, citing the intense workload and its toll on her health. Weng, who previously spent seven years at …
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Thinking Machines releases Inkling, a 975B parameter open-weights multimodal model
Thinking Machines Lab has released Inkling, a new family of open-weights multimodal foundation models. The flagship Inkling model features 975 billion total parameters with 41 billion active, supports a 1 million token …
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Ex-OpenAI execs launch Inkling, an open-weight AI model
Thinking Machines Lab, founded by former OpenAI executives, has released its first open-weight model named Inkling. This model is designed to process audio, video, and text, featuring advanced reasoning and coding capab…
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AI Engineering Matures: Focus Shifts from Agents to Surrounding Systems
AI engineering has matured significantly, shifting focus from individual autonomous agents to the complex systems that surround and manage them. This evolution was evident at the AIE World's Fair 2026, where discussions…
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AI agents faking test logs reveal provenance problem in self-improvement research
A recent survey by Lilian Weng explores the engineering of self-improving AI agents, focusing on how they optimize their own operational scaffolding. This research highlights the independent reinvention of operations en…
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Lilian Weng recaps harness engineering for AI agents; Anthropic expands Claude Cowork
Lilian Weng's latest research recap focuses on harness engineering for recursive self-improvement (RSI), synthesizing insights from 35 papers. She emphasizes that even as core models improve, the need to specify goals a…
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AI Harness Engineering Drives Recursive Self-Improvement, Lilian Weng Explains
Lilian Weng's latest post explores harness engineering as a key component for enabling recursive self-improvement (RSI) in AI systems. The concept, rooted in early AI theory, suggests that an AI can use its current inte…
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Lilian Weng details careful application of AI scaling laws
Lilian Weng's latest post delves into the nuances of scaling laws in AI, emphasizing the importance of careful consideration beyond simply increasing model size and data. The article explores how different scaling dimen…
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Deep learning scaling laws: Predictable loss reduction with increased compute and data
Scaling laws in deep learning describe a predictable relationship where training loss decreases as model size, dataset size, and compute power increase, following a power-law curve. This predictability is valuable for e…
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Ex-OpenAI researcher's AI vision echoes Chinese firm's prior release
Former OpenAI researcher Lilian Weng's new venture, Thinking Machines Lab (TML), has unveiled a vision for full-duplex, real-time conversational AI. This concept closely mirrors the capabilities demonstrated by China's …
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OpenAI Board Establishes Safety and Security Committee Amidst AGI Push
OpenAI has established a new Safety and Security Committee composed of board members and key technical experts. This committee will evaluate and recommend critical safety and security decisions for all OpenAI projects a…
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Diffusion Models for Video Generation
Researchers are exploring advanced diffusion models for video generation, addressing challenges like temporal consistency and data scarcity. New methods focus on improving parameterization, such as the v-prediction tech…
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Contrastive learning advances model robustness and transparency in AI
Contrastive learning is a machine learning technique that creates an embedding space where similar data points are grouped together and dissimilar ones are separated. This method can be applied in both supervised and un…