TBS is an interval-based multi-agent LLM simulation framework that separates structured internal evaluative states from public utterance generation and shows these states vary systematically with turn-allocation, silence, and memory conditions.
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OjaKV introduces hybrid full-rank storage for key tokens combined with online low-rank KV cache compression via Oja's algorithm to support memory-efficient long-context LLM inference.
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Think-Before-Speak: From Internal Evaluation to Public Expression in Multi-Agent Social Simulation
TBS is an interval-based multi-agent LLM simulation framework that separates structured internal evaluative states from public utterance generation and shows these states vary systematically with turn-allocation, silence, and memory conditions.
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OjaKV: Context-Aware Online Low-Rank KV Cache Compression
OjaKV introduces hybrid full-rank storage for key tokens combined with online low-rank KV cache compression via Oja's algorithm to support memory-efficient long-context LLM inference.