The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
arXiv:2509. 14474v3 Announce Type: replace Abstract: The debate around Artificial General Intelligence (AGI) remains open due to two fundamentally different goals: replicating human-level performance versus replicating human-like cognitive processes.
arXiv:2609.07741v1 Announce Type: new Abstract: Persistent AI assistants are intended to extend human attention, memory, and coordination across changing digital and physical environments. To be trul...
arXiv:2606. 23991v1 Announce Type: new Abstract: What is an agent?
arXiv:2610.18792v1 Announce Type: cross Abstract: Frontier language-model agents can now improve robots on their own: they write control code, test it, and keep what works as skills and memories for...
Harnesses for large language models have converged on a single-threaded shape: one agent, one history, one machine, one sitting. That shape fails work that spans many agents, outlives any one model ca...
The paper introduces Multimodal Embodied Agents (MMEAs), goal‑directed systems that combine multimodal task reasoning with physical action and adapt decisions based on feedback. It proposes the PAPAV framework, which defines five core capabilities—Perceive, Anticipate, Plan, Act, Verify—common to computer‑use agents, robotic systems, and MMEAs. The authors analyze 64 benchmarks, noting that most evaluations focus on Act while Anticipate and Verify are rarely assessed, highlighting gaps and open challenges in designing reliable physical agents.