Most of the time. Sometimes the agent assume the wrong invariants, and the real fix is text in agents .md to explain system constraints.
AGENTS
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GPT-5.6 improvement and token efficiency in agentic workflows
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It’s reasonable to expect that the next iteration will be better. It would be surprising if GPT-5.6 wasnt an improvement over GPT-5.5. But the more interesting part is token efficiency. As models move into more complex, longer-running, agentic workflows, every wasted token
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LeCun: Intelligence equals trained world model plus optimal control
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Essentially. Or rather: Trained world model + optimal control = intelligence
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HiF-VLA: Robots remembering past and anticipating future via motion
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What if your robot could remember the past and anticipate the future to master long tasks? Researchers from Westlake University and Zhejiang University introduce HiF-VLA. It uses motion as a compact representation to give robots hindsight, insight, and foresight—learning from
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Could AgenticAI topple grant-funding systems? Nature article
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Could #AgenticAI topple grant-funding systems?
by Geraint Rees James Wilsdon @Nature Learn more: https://
bit.ly/4eexZBK #AI #GenerativeAI #ArtificialIntelligence #MachineLearning #MI -
Microsoft developing super app and always-on agent Scout
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COPILOT : Microsoft is working on its own super app along with a new always-on agent called Scout according to Sources. This UI looks familiar
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EverOS: redesigned interface with long memory for AI agents
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我们新设计了 EverOS 的界面， 现在主要想突出两个点。
— 艾略特 (@elliotchen100) 30 mai 2026
第一，它是面向 AI Agent 的“长程记忆操作系统”。
传统 LLM 应用更多依赖短期上下文，而 EverOS 试图把 agent… pic.twitter.com/mwLmJLrFXjWe have redesigned the EverOS interface, and we now wish to highlight two main points. First, it is a "long memory operating system" designed for AI agents. Traditional LLM applications rely more on context to
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Theory of Agent: When AI Agents Should Use External Tools
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When should AI agents actually reach for external tools? Researchers from Edinburgh, CUHK, UIUC, Northwestern & Princeton present Theory of Agent (ToA) at ICML 2026. Their answer: only when epistemically necessary — meaning the agent cannot complete the task reliably using its
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Chat tools hit ceiling when context window full; become CI pipeline
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That's a good way to frame it. Chat tools hit a ceiling when the task outgrows what one context window can hold. Once the plan lives in executable code with parallel agents, resumability, and convergence loops, it's closer to a CI pipeline than a conversation.
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Adversarial verification loop prevents organized wandering
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Both, actually. The adversarial verification loop is what prevents organized wandering. Agents don't just fan out and report back. Other agents actively try to refute findings. The system keeps iterating until answers converge, not until agents run out of things to do. So scope