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MAS-Shield is proposed, a secure and efficient defense framework designed with a coarse-to-fine filtering pipeline that achieves a 92.5\% recovery rate against diverse adversarial scenarios and reduces defense latency by over 70\% compared to existing methods.
This work introduces Router-Mem, an evidence-conditioned progressive execution framework for long-horizon agent memory that is trained with evidence-level supervision and rationale-conditioned representation distillation and achieves strong answer quality while maintaining low online latency.
HFlow is proposed, an evolutionary framework for generating agentic workflows through human-agent collaboration that produces cost-effective and adaptive solutions, better aligning automated agen-tic systems with dynamic human needs.
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