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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.
Safety-aware Contrastive Decoding (SafeCoDe) is introduced, a lightweight and model-agnostic decoding framework that dynamically adjusts token generation based on multimodal context that consistently improves context-sensitive refusal behaviors while preserving model helpfulness.
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