4 citations · 9 across the 18 of their papers we have counts for
8 papers · 1 filter
Reconstruction-Optimized Expert Pruning for Mixture-of-Experts Language Models
Jelena Markovic-Voronov, Petros Prastakos, Kayhan Behdin +7
Mixture-of-Experts (MoE) models reduce computation through sparse expert activation, but deployment still requires storing a large number of expert parameters. We study post-traini…
Budgeted KV-Cache Repair for Evolving Contexts
Han Yu, Wenhui Zhu, Jincheng Cao +15
Language-model agents revise their working context as information becomes obsolete, but deleting text does not erase its influence on cached computation. Later key-value (KV) state…
Maximum-modulus points on concentric circles
Yunpeng Li
We give a negative answer to the second question of Erdős Problem #1117: can the number of maximum-modulus points of a nonzero, non-monomial entire function on ten…
Composing What Each Teacher Learned: Delta Distillation for Multi-Teacher On-Policy Learning
Hejian Sang, Yuanda Xu, Zhengze Zhou +4
Multi-teacher on-policy distillation (MOPD) is used in two settings. In common-domain composition, several teachers score each student rollout from one prompt domain and their sign…
Hierarchical Reinforcement Learning with Stable Temporal Abstraction for Language Model Agents
Shayan Mohajer Hamidi, Yize Cheng, Yuanda Xu +2
Hierarchical reinforcement learning improves long-horizon control by organizing primitive actions around persistent subgoals and assigning credit at multiple temporal scales. Recen…
Smaller Models, Better Rejects: Preference Distillation Scaling
Rui Cai, Wenhui Zhu, Xiwen Chen +15
Preference distillation typically treats a teacher response as preferred and the student's own response as rejected. This assumes that self-generated failures are the most informat…
Regime-aware financial volatility forecasting via in-context learning
Saba Asaad, Shayan Mohajer Hamidi, Ali Bereyhi
This work introduces a regime-aware in-context learning framework that leverages large language models (LLMs) for financial volatility forecasting under nonstationary market condit…
ASMIL: Attention-Stabilized Multiple Instance Learning for Whole Slide Imaging
Linfeng Ye, Shayan Mohajer Hamidi, Zhixiang Chi +5
Attention-based multiple instance learning (MIL) has emerged as a powerful framework for whole slide image (WSI) diagnosis, leveraging attention to aggregate instance-level feature…