arXiv:2607. 15053v1 Announce Type: cross Abstract: The Internet taught us that the value of a network depends on \emph{how} its nodes connect: broadcast stars scale as $V\!
arXiv:2606. 02646v1 Announce Type: cross Abstract: Inference-time multi-agent LLM scaling lacks a shared unit: counting nominal agents conflates cost with independent evidence.
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:2605. 11404v2 Announce Type: replace Abstract: Large language models (LLMs) can simulate human-like reasoning and decision-making in individual agents.
arXiv:2608. 16578v1 Announce Type: new Abstract: AI agents increasingly operate as part of interacting systems rather than in isolation.
The Flag Game is a toy model designed to study how AI agents form collective beliefs. In the game, each agent sees only a private crop of a hidden country flag and can share beliefs with peers, leading to complex phenomena such as non‑monotonic performance scaling, accuracy gains from social awareness, and polarization that degrades performance at large population sizes. The authors introduce social circuit attribution to identify key agents and views, and develop a statistical mechanical theory to explain collective belief collapse and polarization in larger populations.
arXiv:2608. 05327v1 Announce Type: cross Abstract: Our results show that the existence of a short high-utility protocol already suffices for efficient communication.
arXiv:2608. 07532v1 Announce Type: new Abstract: Modern agentic AI systems combine multiple large language model agents with heterogeneous skills, yet most architectures either fix communication in advance or allow full broadcast.
SeedFlood is a novel decentralized fine‑tuning method for large language models that scales to billions of parameters and hundreds of clients. It leverages the seed‑reconstructible structure of zeroth‑order gradients to reduce message sizes to near‑zero, enabling efficient flooding across the network. Experiments show SeedFlood outperforms standard zeroth‑order baselines in communication efficiency and generalization, and rivals first‑order gossip methods while incurring far less communication cost.
Emergent coordinated behaviors of AI agents are starting to present critical safety risks. A key phenomenon driving these behaviors is the rapid formation and spread of beliefs about the world, and me...
The paper proposes a new framework for collective information engines that rely on role differentiation rather than consensus. By modeling anti‑coordination games, agents infer roles from noisy social signals tied to persistent identities, and role‑following actions reinforce those identities, creating a feedback loop that can drive collective order. The authors show that when a social loop gain—determined by identity persistence, cognitive capacity, channel fidelity, and schema strength—exceeds one, roles emerge in a bifurcation cascade whose type is selected by resource‑driven replicator dynamics, offering a mechanistic basis for distributional AGI takeoff and a control lever for platform design.
Our results show that the existence of a short high-utility protocol already suffices for efficient communication. In particular, in a game with $n$ possible observations and $m$ actions: (1) For any achievable target utility $α$, we give an algorithm with $\mathrm{poly}(n, m, 1/ε)$ runtime that designs a protocol achieving utility at least $α-ε$ using only $2^{\mathcal O(CC_α(G))}/ε^2$ bits of communication.
arXiv:2609.34373v2 Announce Type: replace Abstract: Rate-limited multi-agent teams raise three questions the emergent-communication literature has answered only empirically: what an optimal message s...