Can agents learn inside of their own dreams?
Agentic AI gets lost
On the failure of AI to develop world models
Memory Models: Towards Agents That Learn
Agents that truly learn from experience will be powered by memory models: models that create and curate token-space memory across model generations, trained with memory-native RL.
[Keynote 04] AgentSociety: Exploring Large Language Model Agents for Piloting Social Experiments
Letta
Making machines that learn. Create stateful agents that remember everything, learn continuously, and improve themselves over time.
Why I believe current AI models can really think and understand - Part 1 of 2
How the human mind is different from our conception of it
Will we have a theory for in-the-wild AI agents? - undisciplinary
Thoughts after writing a sociology-meets-physics-meets-agents paper I just put out a preprint out on a theory of role emergence in multi-agent systems (soci…
Agentic AI Needs a Systems Theory
The endowment of AI with reasoning capabilities and some degree of agency is widely viewed as a path toward more capable and generalizable systems. Our position is that the current development of agentic AI requires a more holistic, systems-theoretic perspective in order to fully understand their capabilities and mitigate any emergent risks. The primary motivation for our position is that AI development is currently overly focused on individual model capabilities, often ignoring broader emergent behavior, leading to a significant underestimation in the true capabilities and associated risks of agentic AI. We describe some fundamental mechanisms by which advanced capabilities can emerge from (comparably simpler) agents simply due to their interaction with the environment and other agents. Informed by an extensive amount of existing literature from various fields, we outline mechanisms for enhanced agent cognition, emergent causal reasoning ability, and metacognitive awareness. We conclude by presenting some key open challenges and guidance for the development of agentic AI. We emphasize that a systems-level perspective is essential for better understanding, and purposefully shaping, agentic AI systems.
EMERGENCE WORLD: A Laboratory for Evaluating Long-horizon Agent Autonomy — Emergence AI
Most evaluations of AI agents look like exams: a discrete task, a clean environment, a score in minutes or hours. Emergence World is built for the opposite question—what happens when you let agents run continuously, in a shared environment with real-world signals, for weeks. It is a research platfor
On AI as model organism for human learning
I've a pretty taste for paradox
The Self-Evidencing Agent: Mind, Existence, and Predictive Processing
How the concept of self-evidencing offers a philosophical principle for understanding mind and behavior, consciousness, value, wisdom, and meaning.What is
Introducing LM Studio Bionic: the AI agent for open models
The AI agent made for open models, built to get things done.
pguso/ai-agents-from-scratch
Demystify AI agents by building them yourself. Local LLMs, no black boxes, real understanding of function calling, memory, and ReAct patterns.
Equipping agents for the real world with Agent Skills
Discover how Anthropic builds AI agents with practical capabilities through modular skills, enabling them to handle complex real-world tasks more effectively and reliably.
