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This work proposes ASPIRE, a non-synchronized batched self-speculative decoding framework built on three components, which achieves speedup in decoding throughput over autoregressive baselines and improves average speedup by approximately $27\% over the strongest prior self-speculative baselines.
This work introduces Agent Harness Distillation (AHD), a framework for studying the security risks arising from inference-time harness extraction in AMAS and proposes a deception-based defense that reduces harness extraction effectiveness while preserving the utility of the protected agent.
This work proposes BCSD (Bidirectional Context Self-Distillation), a framework that combines self-distillation with reinforcement learning to train LLM agents to use external skills more effectively, enabling agents to utilize external skills more effectively.
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