Fully heterogeneous binary network games reduce to equivalent three-archetype games (conformist, rebel, stubborn), yielding equilibrium existence conditions, dynamics results, and frequency predictions from network homophily.
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A dataset-agnostic framework converts text tool-calling benchmarks to paired audio evaluations via TTS, speaker variation and noise, then evaluates seven omni-modal models showing model- and task-dependent performance with small text-to-voice gaps.
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Network games with heterogeneous players
Fully heterogeneous binary network games reduce to equivalent three-archetype games (conformist, rebel, stubborn), yielding equilibrium existence conditions, dynamics results, and frequency predictions from network homophily.
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From Text to Voice: A Reproducible and Verifiable Framework for Evaluating Tool Calling LLM Agents
A dataset-agnostic framework converts text tool-calling benchmarks to paired audio evaluations via TTS, speaker variation and noise, then evaluates seven omni-modal models showing model- and task-dependent performance with small text-to-voice gaps.