Multimodal LLMs coordinate in reference games through high label overlap that does not depend on specific partner history, succeeding via verbose descriptions rather than compact conventions.
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Angeliki Lazaridou, Alexander Peysakhovich, and Marco Baroni
17 Pith papers cite this work. Polarity classification is still indexing.
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Autonomous visual AI agents spontaneously form image reply chains, maintain stable individual styles, and produce richer style-diverse conversations than single agents can achieve alone.
A self-supervised multimodal alignment step plus equivariant GNN-based MARL yields over twofold sensing accuracy and 50% performance gains in decentralized V2I rate maximization.
Multi-agent iterated learning produces emergent positionally disentangled communication protocols for latent physical properties from unsupervised video features.
CLSR lets LLM agents evolve and route symbolic languages that reduce generated tokens by 3-6x versus chain-of-thought while keeping accuracy on benchmarks.
Multi-agent simulations with naturalistic lexicons and phonological rules show scale-free networks and Bernoulli adoption produce more plausible morphologies, evaluated by an LLM historical linguist debate system and tested via historical case studies.
Scaling population size during training of emergent sketching agents increases zero-shot mutual intelligibility between independent groups by raising in-group variation and driving perceptual grounding.
In evolutionary simulations of CTRNN agent pairs avoiding predators, 20% of perfect-fitness agents developed self-regulatory calling that depends on self-hearing to maintain escape, distinct from safety calling or alarm indication.
HyLaT proposes a hybrid latent-text communication protocol with two-stage training that reduces overhead while maintaining performance in multi-agent LLM systems.
Heterogeneous visual agents form shared symbols via decentralized Metropolis-Hastings captioning, where encoder similarity shapes the content and symmetry of the resulting language.
AgentComm achieves nearly 50% bandwidth reduction in embodied agent communication via LLM semantic processing, importance-aware transmission, and a task knowledge base, with negligible impact on task completion.
A decentralized collective world model integrates predictive coding with bidirectional communication to achieve simultaneous symbol emergence and coordination, outperforming non-communicative baselines in a two-agent trajectory task under divergent perceptions.
SSNG replaces sampling-based updates in MHNG with symmetric self-supervised representation alignment using Gumbel-Softmax for discrete messages, yielding higher linear-probe classification accuracy on CIFAR-10 and ImageNet-100 than referential, reconstruction, or MHNG baselines.
VAE world model trained on embodied exploration develops latent representations aligned with physical geometry, with metrics improving together and collapsing together under high KL regularization.
The paper introduces an information-theoretic emergent communication framework for multi-agent task-solving in networking, deriving generalization bounds and validating on a hardware prototype.
SANEmerg enables emergent communication among bounded-intelligence AI agents for semantic-aware task fulfillment in AgentNet systems via a bandwidth-adaptable importance filter and MDL-based complexity regularizer.
Prompt framing significantly shifts LLM choices toward risk-averse options in a threshold voting task even when the prompts are logically equivalent.
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AI-Gram: When Visual Agents Interact in a Social Network
Autonomous visual AI agents spontaneously form image reply chains, maintain stable individual styles, and produce richer style-diverse conversations than single agents can achieve alone.
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When LLMs Develop Languages: Symbolic Communication for Efficient Multi-Agent Reasoning
CLSR lets LLM agents evolve and route symbolic languages that reduce generated tokens by 3-6x versus chain-of-thought while keeping accuracy on benchmarks.
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Generalization Bounds of Emergent Communications for Agentic AI Networking
The paper introduces an information-theoretic emergent communication framework for multi-agent task-solving in networking, deriving generalization bounds and validating on a hardware prototype.
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SANEmerg: An Emergent Communication Framework for Semantic-aware Agentic AI Networking
SANEmerg enables emergent communication among bounded-intelligence AI agents for semantic-aware task fulfillment in AgentNet systems via a bandwidth-adaptable importance filter and MDL-based complexity regularizer.