Yu Yao

Zhuomin He, Yizhen Yao, Pengfei Zuo et al.

Long-context large language models (LLMs) inference is increasingly critical, motivating a number of studies devoted to alleviating the substantial storage and computational costs in such scenarios. Layer-wise skipping methods are promising optimizations but rarely explored in long-context inference. We observe that existing layer-wise skipping strategies have several limitations when applied in long-context inference, including the inability to adapt to model and context variability, disregard for sublayer significance, and inapplicability for the prefilling phase. This paper proposes \sysname, an adaptive sublayer skipping method specifically designed for long-context inference. \sysname adaptively identifies less important layers by leveraging on-the-fly similarity information, enables sublayer-wise skipping, and accelerates both the prefilling and decoding phases. The effectiveness of \sysname is demonstrated through extensive experiments on various long-context benchmarks and models, showcasing its superior inference performance over existing baselines.

Yu Yao, Jiayi Dong, Ju Li et al.

Large language models (LLMs) have demonstrated remarkable capabilities not only in language generation but also in advancing scientific discovery. A growing body of work has explored ways to improve their reasoning, from self-consistency and chain-of-thought to multi-agent debate. Inspired by the dynamics of scientific committees and the "Society of Mind," we introduce Roundtable Policy, a complementary inference-time reasoning framework that performs inference through the weighted consensus of multiple LLMs. Our findings indicate that this approach significantly enhances reasoning in complex heterogeneous scientific tasks and improves scientific narratives in terms of creativity, rigor, and logical coherence, while reducing hallucinations that single models are prone to. Our approach emphasizes structured and interpretable consensus rather than opaque convergence, while requiring only black-box access and uniform procedures, making it broadly applicable to multi-LLM reasoning.