Dataset distillation (DD) condenses a large original dataset into a small distilled dataset with high training utility. Decoupled statistical matching methods substantially reduce distillation time and memory overhead while achieving strong performance. However, they typically supervise all distilled samples using runn...
Hong-Xu Ma, Guang Li, Shi-Jie Wang et al.· 0 citations
Existing deep joint source-channel coding (JSCC) methods typically employ uniform resource allocation across spatial regions, which may lead to spatially imbalanced reconstruction quality under limited channel resources. To address this, this paper proposes AgentJSCC, a difficulty-aware transmission framework. Specific...
Jiafu Hao, Chen-Tao Yue, Suorong Yang et al.· IEEE Transactions on Cogniti...· 0 citations
TailSieve, a partial-rollout-guided framework that jointly controls tail routing and replica allocation for LLM rollouts, and shows that makespan-optimal routing in the long-tail regime combines tail isolation with load balancing, and that a simple top-k policy closely approximates this offline optimum.
Tianqi Xu, Lu Lv, Hao-Yang Huang et al.· 3 citations· ⚡1
Memory is central to long-horizon LLM agents, yet existing memory systems primarily preserve interaction content rather than modeling which agents can be trusted and under what conditions. This limitation is particularly important in multi-agent systems, where a central model may be unable to directly verify plausible...
TAILS resolves cross-task ambiguity at the representation level, while leaving the original PTM, method-specific modules, and classifier unchanged, and can improve classification and task-inference performance with modest parameter overhead and negligible inference cost.
Zhiming Xu, Huiyu Yi, Zheng-He Xie et al.· 0 citations
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