4 citations · 17 across the 25 of their papers we have counts for
9 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…
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…
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…
TREK: Distill to Explore, Reinforce to Refine
Yuanda Xu, Zhengze Zhou, Kayhan Behdin +10
Group Relative Policy Optimization (GRPO) is effective when the current policy already samples useful reasoning trajectories, but it stalls on hard prompts whose correct solution m…
Sampling for Quality: Training-Free Reward-Guided LLM Decoding via Sequential Monte Carlo
Jelena Markovic-Voronov, Wenhui Zhu, Bo Long +5
We introduce a principled probabilistic framework for reward-guided decoding in large language models, addressing the limitations of standard decoding methods that optimize token-l…
Closed-Form Formulas for Designing Ultra-Low Phase-Noise Cross-Coupled Dynamically Body-Biased Only-NMOS LCVCOs
Naser Khatti Dizabadi, Peter LoPresti
This paper presents a system-level analytical framework for modeling and minimizing phase noise in body-biased cross-coupled LC-tank voltage-controlled oscillators (LC-VCOs). Build…
Robust Batch-Level Query Routing for Large Language Models under Cost and Capacity Constraints
Jelena Markovic-Voronov, Kayhan Behdin, Yuanda Xu +3
We study the problem of routing queries to large language models (LLMs) under cost, GPU resources, and concurrency constraints. Prior per-query routing methods often fail to contro…
Semantic Search At LinkedIn
Fedor Borisyuk, Sriram Vasudevan, Muchen Wu +71
Semantic search with large language models (LLMs) enables retrieval by meaning rather than keyword overlap, but scaling it requires major inference efficiency advances. We present…
MixLM: High-Throughput and Effective LLM Ranking via Text-Embedding Mix-Interaction
Guoyao Li, Ran He, Shusen Jing +21
Large language models (LLMs) excel at capturing semantic nuances and therefore show impressive relevance ranking performance in modern recommendation and search systems. However, t…