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As LLM capabilities advance, agents are tackling increasingly complex tasks over longer horizons. Their growing interaction histories make memory compaction essential for staying within context windows and reducing prefill cost. Existing methods summarize the history or compress its KV cache, often adding model computa...
The Meeting Dynamic Forecasting Benchmark is introduced and GLARE is presented, an adaptation of adversarial imitation learning to conditional language generation and outperforming SFT and SPIN while remaining below the observed human continuation.
This paper reconsiders the evaluation of simulated conversations by explicitly recognizing that human conversations inherently involve inconsistent and uncollaborative behaviors, such as misunderstandings and interruptions, and argues that LLM-simulated conversations should reproduce them at frequencies comparable to t...
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