That's a fair reframe. Knowing what to forget is arguably harder than knowing what to remember. memify() is exactly aimed at that, strengthening useful paths and letting stale ones decay. The title optimizes for the hook, but you're right that intelligent forgetting is the
RESEARCH
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Graph Traversal Enables Multi-Hop Queries Beyond Vector Search
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Good questions. Graph traversal adds a small overhead but makes multi-hop queries possible that vector search alone simply can't answer.
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Retrieval vs Behavioral Learning: The Real Gap in AI Agents
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Really well articulated. The distinction between "finds the right fact when asked" vs "already changed behavior from experience" is the real gap. Retrieval is table stakes. Consolidation turning episodic traces into behavioral defaults is where agents actually start learning.
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LLM Training Data Overlap and Legal Penalties Across AI Models
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ils se prendront des amendes. Parcque toutes les IA sont parties d'un même jeu de données, ou parti de version distilled, même mistral et si y'a suspiscion qu'un contenu est dans un LLM, il y a des fortes chances qu'il soit dans les autres.
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Thinking Machines acquires AI research lab Workshop Labs
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Thinking Machines acquired Workshop Labs. > Workshop Labs is an AI research lab with a mission to make people irreplaceable. Thinking Labs
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Factual Sycophancy: Selection Bias Harder to Detect
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factual sycophancy is the hardest kind to catch, the facts check out but the selection itself is the bias
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Philosophy of Mind Researcher Joins DeepMind
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huge for philosophy of mind to actually land inside deepmind, congrats henry!
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Efficient Cross-Domain Offline Reinforcement Learning with Data Filtering
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Efficient Cross-Domain Offline Reinforcement Learning with Dynamics- and Value-Aligned Data Filtering Paper: https://
arxiv.org/pdf/2512.02435
Code: https://
github.com/zq2r/DVDF.git Our report: https://
mp.weixin.qq.com/s/ztE8GofcssuI
1PdkHx_kLg
… #PapersAccepted by Jiqizhixin -
AI Agents Learning Across Different Environments
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How can AI agents learn effectively when their training data comes from environments vastly different from where they'll operate? Researchers from City University of Hong Kong, UIUC, Tencent, and Tsinghua University present DVDF, a new method for cross-domain offline
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DeepMind Hires Philosopher to Explore AI Consciousness
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Henry Shevlin – a philosopher of mind and AI ethics from Cambridge, just got hired as an in-house philosopher at Google DeepMind. He'll be focusing on machine consciousness, human-AI interaction, and the ethical governance of increasingly autonomous systems. What's significant here: DeepMind is treating philosophy as a discipline on par with computer science and neuroscience, embedding it directly into core research rather than just keeping ethicists as external advisors. The labs are starting to think about the consciousness, agency, and moral reasoning question. Whereas, I am working at the applied human level – what happens when a mid-level manager doesn't trust the AI their company just deployed, or when a team's workflows break because no one designed the adoption path. That's not the philosophy angle but rather organisational and psychological infrastructure. Both matter. [Translated from EN to English]
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