The engineers who should genuinely worry aren't the ones asking this question. They're the ones who haven't thought about it at all.
RESEARCH
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Code Quality Benchmarks Show Steep Trajectory Shifting AI Calculus
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The exciting version of this is a year or two away not a decade. The trajectory on code quality benchmarks is steep enough that the calculus will shift noticeably in a short window.
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Intelligent forgetting in AI systems memify strengthens useful paths
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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
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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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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 -
Efficient Cross-Domain Offline Reinforcement Learning with Data Filtering
By
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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