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20202026
most citedEfficient Prompting Methods for Large Language Models: A Survey

18 citations · 23 across the 15 of their papers we have counts for

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5 papers · 1 filter

cs.CL2026

Can Terminal Agents Trust Their Own Verification? Diagnosing and Improving Self-Verification

Yingfeng Luo, Shaowei Wei, Daixin Wang +7

Terminal agents rely on self-verification to assess and correct their solutions as they solve tasks through interaction with command-line environments. Yet how trustworthy such sel…

cs.CV2026

Reading Right, Answering Wrong: How Visual Configuration Changes Affect Evidence Use in VLMs

Dingyang Lin, Yingfeng Luo, Chenglong Wang +4

Vision-language models (VLMs) have achieved strong performance on tasks such as visual question answering, yet small image resizes can turn correct answers into errors. We investig…

cs.LG2026

SPRI: SVD-Partitioned Residual Initialization for Data-Constrained MoE Upcycling

Weiqiao Shan, Ruixiang Mao, Yuang Li +10

Mixture-of-Experts (MoE) models enable efficient scaling, but training them from scratch remains prohibitively expensive. MoE upcycling mitigates this cost by converting pretrained…

cs.CL2026

RouteLMT: Learned Sample Routing for Hybrid LLM Translation Deployment

Yingfeng Luo, Hongyu Liu, Dingyang Lin +6

Large Language Models (LLMs) have achieved remarkable performance in Machine Translation (MT), but deploying them at scale remains prohibitively expensive. A widely adopted remedy…

cs.CL2026

APR: Penalizing Structural Redundancy in Large Reasoning Models via Anchor-based Process Rewards

Kaiyan Chang, Chenwei Zhu, Yingfeng Luo +7

Test-Time Scaling (TTS) has significantly enhanced the capabilities of Large Reasoning Models (LRMs) but introduces a critical side-effect known as Overthinking. We conduct a preli…