SCENE is a new benchmark for testing LLMs on recognizing implicit social norms and adapting to sanctions in multi-party group chats.
Multi-party chat: Conversational agents in group settings with humans and models
3 Pith papers cite this work. Polarity classification is still indexing.
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CAVI framework uses character-guided token pruning, orthogonal feature modulation, and modality-adaptive role steering to resolve modality-role interference in multimodal RPAs.
M2CL trains per-agent context generators with a self-adaptive mechanism to maintain coherence and reduce output discrepancies in multi-LLM discussions, yielding 20-50% gains on reasoning, embodied, and mobile control tasks.
citing papers explorer
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SCENE: Recognizing Social Norms and Sanctioning in Group Chats
SCENE is a new benchmark for testing LLMs on recognizing implicit social norms and adapting to sanctions in multi-party group chats.
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Through the Lens of Character: Resolving Modality-Role Interference in Multimodal Role-Playing Agent
CAVI framework uses character-guided token pruning, orthogonal feature modulation, and modality-adaptive role steering to resolve modality-role interference in multimodal RPAs.
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Context Learning for Multi-Agent Discussion
M2CL trains per-agent context generators with a self-adaptive mechanism to maintain coherence and reduce output discrepancies in multi-LLM discussions, yielding 20-50% gains on reasoning, embodied, and mobile control tasks.