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🚨 Thrilled to share that our lab will be presenting the 🏆 Best Paper at the NExT-Game Workshop at #ICML2026# today! 🎤 When Agents Lie: Premeditation, Persistence, and Exploitation in Repeated Games 🏆 Best Paper @ NExT-Game Workshop 📍 Conference Room S307 📅 Fri, Jul 10 🕐 13:00–13:20 KST Authors: @JerickShi @TerryJCZhang @bschoelkopf @conitzer @ZhijingJin 🤖 We introduce a three-stage endogenous promise protocol for repeated multi-agent games that asks not only whether LLM agents honor their public commitments when they can privately deviate, but also how model-on-model composition influences premeditated deception and persistent exploitation. 📊 Across six canonical games spanning binary and numerical action spaces, our evaluation of frontier models (GPT-5.2, Llama-4-Maverick, Claude-Opus-4.6) reveals: 🔹 Over 90% of promise-breaking instances are premeditated in agents' private plans. 🔹 Mixed-model groups with mismatched communication frameworks create systemic, persistent payoff gaps of up to 5.00 points from Round 0. 📄 Paper: #MultiAgentSystems# #LLMs# #GameTheory# #AI# #ICML2026#

Excited to share that #LatentMAS# has been accepted to ICML 2026 as a spotlight! 💻Code: 📄Paper: We push multi-agent collaboration into the latent space — beyond human language. Most multi-agent systems rely on text: agents reason in words, exchange messages, and repeatedly decode/re-encode information. But language can be slow, lossy, and unnecessarily constrained. 💡LatentMAS takes a different path: LLM agents reason and communicate directly through hidden embeddings. No text decoding. No extra training. No token-level message passing. Instead, agents collaborate through: 🧠 Autoregressive Latent Thoughts — hidden-state-level reasoning steps 🔁 Latent Communication — information sharing via KV-cache transfer 📌 Input-output Alignment — keeping latent representations in-distribution 🚀 Training-free Collaboration — plug-and-play with existing LLMs Why it matters: ✅ Up to +14.6% better accuracy on complex reasoning tasks ⚡ 4-4.6x faster end-to-end inference ✂️ 70.8%–83.7% reduction in output token usage A step toward multi-agent systems that collaborate not by speaking more, but by thinking together in latent space. #MultiAgentSystems# #ModelCollaboration# #LatentReasoning# #LLM# #AgenticAI# #ICML#