Search Results for "multi-threaded"

LatentMAS is an advanced framework for multi-agent reinforcement learning (MARL) that uses latent variable modeling to bridge perception and decision-making in environments where agents must coordinate under uncertainty. It provides mechanisms for agents to learn high-level latent representations of states, which simplifies complex sensory inputs into compact, actionable embeddings that facilitate both individual policy learning and inter-agent coordination.

TREK is a system designed to explore task execution, reasoning, or knowledge traversal in structured environments, likely focusing on guiding agents through complex multi-step processes. The project appears to emphasize traceability, allowing users to follow how decisions are made and how tasks progress through different stages. Its architecture suggests a focus on modular reasoning, where each step in a process is clearly defined and can be analyzed independently. This makes it particularly useful for debugging, experimentation, and understanding agent behavior in complex workflows. ...

...Facilitators drive each stage through a live timer and state machine. Participants build on breakout-room canvases with live cursors, presence, per-brick reactions, and threaded comments. Rooms auto-compose each member's individual model into one shared view. A library of 20 scenario templates covers every stage, and AI session reports run on the facilitator's own Anthropic key. Apache 2.0, free forever, no paid tier. It ships original brick assets and uses no LEGO trademark assets.