arXiv:2606. 30668v1 Announce Type: cross Abstract: What happens when LLM agents operate with no context outside a turn, minimal prompting, and simple tools?
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2310. 06555v3 Announce Type: replace-cross Abstract: Emergent communication enables agents to develop bespoke languages that improve communication efficiency.
arXiv:2606. 31046v1 Announce Type: new Abstract: Artificial life has explored life-like behavior on many computational substrates, but mostly in researcher-designed closed worlds.
arXiv:2603. 03555v3 Announce Type: replace-cross Abstract: As multi-agent Large Language Model (LLM) systems scale, evaluating their emergent coordination dynamics becomes increasingly critical.
arXiv:2607. 26637v1 Announce Type: cross Abstract: Deployed LLM agents increasingly keep their long-term memory as a filesystem: a directory tree of markdown files that the agent itself reads, writes, and reorganizes through generic file tools.
arXiv:2506. 12078v2 Announce Type: replace-cross Abstract: Understanding the dynamic evolution of complex social phenomena requires both high-fidelity modeling of human behavior and large-scale simulations.
arXiv:2510. 16392v3 Announce Type: replace Abstract: Personalized and continuous interactions are critical for LLM-based conversational agents, yet finite context windows and static parametric memory hinder the modeling of long-term, cross-session user states.