arXiv:2606. 28781v1 Announce Type: new Abstract: Every existing vector database and agent memory framework treats memory as passive storage that agents query explicitly.
arXiv:2607. 26533v1 Announce Type: new Abstract: Graph Foundation Models (GFMs) aim to learn transferable knowledge from multi-domain graphs and adapt to unseen scenarios.
Graph Foundation Models (GFMs) aim to learn transferable knowledge from multi-domain graphs and adapt to unseen scenarios. As a fundamental source of relational semantics in graphs, the transferability of topological patterns has long been central to GFM research.
LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit model capabilities,...
arXiv:2608.21156v1 Announce Type: cross Abstract: LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms includi...
arXiv:2609.05774v1 Announce Type: new Abstract: Recent multi-agent LLM systems increasingly rely on graph-structured communication to coordinate specialized agents. We revisit multi-agent orchestrati...
Human collective intelligence depends on transmission processes: who shares what with whom, how, and when. While these processes emerge from individual cognition, they can also be directed by delibera...
The paper presents a method for designing state‑aware transmission protocols that guide how information and resources are shared among agents in a collective discovery task. Using LLM‑guided evolutionary search, the authors evolve protocols that outperform existing baselines by up to 37%, and show that the advantage stems from conditioning on content and agent states rather than just network topology. The evolved protocols also generalize across different domains and agent populations, indicating that such protocols can be discovered in silico and may inform AI‑assisted coordination systems for human collective intelligence.
The paper surveys self‑evolving agents, highlighting that their states—memories, tools, skills, workflows, and inter‑agent relations—are dynamic and can be modeled as evolving graphs. It critiques existing surveys for treating graphs merely as support structures and proposes a framework that views agent evolution as dynamic graph transformation, categorizing methods into node/feature, edge/topology, subgraph activation, and cross‑component co‑evolution. The authors further map dynamic‑graph learning subfields to agent capabilities, discuss potential failure modes, and outline graph‑aware evaluation and governance protocols to guide the design and oversight of self‑evolving agents.
Unified-MAS is a two-stage framework that decouples node implementation from orchestration in Automatic Multi-Agent Systems. It first searches external knowledge to synthesize domain‑specific node blueprints, then uses a perplexity‑guided reward to optimize bottleneck nodes. Experiments across four specialized domains show that adding Unified-MAS to existing baselines improves performance‑cost trade‑offs by up to 14.2% while lowering costs.
arXiv:2606. 24958v1 Announce Type: new Abstract: Collective behavior arises when locally interacting units produce coordinated global organization, from synchronization in dynamical systems to task-relevant information flow on graphs.
arXiv:2606. 16328v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate remarkable potential in dynamic graph reasoning, but suffer from a scaling bottleneck: current models can only handle graphs with tens of nodes, constrained by exponential reasoning overhead and finite context windows.
arXiv:2606. 12835v1 Announce Type: cross Abstract: The rapid emergence of autonomous AI agents is transforming artificial intelligence from isolated model inference into distributed systems of reasoning, communication, and action.