SARA is a unified RAG framework that balances local factual precision with global coverage by combining natural-language spans with compact semantic compression vectors, achieving consistent gains under strict context budgets.
Jul 1, 2026
UniSD unifies the fragmented landscape of self-distillation for large language models, providing a principled framework that supports systematic comparison and new combinations across data, representation, and decoding levels.
May 30, 2026
AgentArk distills the collaborative behavior of multi-agent systems into a single LLM agent, decomposing trajectories into role-conditioned skills and recovering most of the ensemble's performance at a fraction of the cost.
Feb 4, 2026
An efficient approach to probing LLM knowledge that adapts pre-trained embeddings to query model knowledge with substantially reduced compute.
Feb 1, 2026
A survey of efficient LLM training organized around data-centric techniques — selection, mixing, ordering, and synthesis — and their trade-offs with compute and downstream performance.
Jul 31, 2025