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20242026
most citedA Closer Look at Time Steps is Worthy of Triple Speed-Up for Diffusion Model Training

1 citations · 1 across the 9 of their papers we have counts for

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cs.LG2026

Adapting Context Compression for Long-Horizon Agents with Counterfactual Continuations

Guanghui Min, Liang Wu, Mingjia Shi +4

Long-horizon agents require context compression to manage growing interaction histories. Compression quality, however, is ultimately determined by downstream execution. Existing pr…

cs.CL2026

Grounding Memory Summarization in Utility Intent

Zhenyu Lei, Mingjia Shi, Xingbo Fu +3

Existing summarizers for memory systems are typically optimized for human-facing criteria such as faithfulness, which misaligns with their true objective: preserving the evidence n…

cs.CV2026

Dive Into the Implicit Biases of Low-rank Vision-language Alignment

Mingjia Shi, Shuo Wang, Xiaobo Wang +7

Vision-language alignment, the stage that bridges pretrained vision encoders and large language models, is widely treated as a form of pretraining requiring full-parameter updates.…

cs.CV2026

Saliency-Aware Multi-Route Thinking: Revisiting Vision-Language Reasoning

Mingjia Shi, Yinhan He, Yaochen Zhu +1

Vision-language models (VLMs) aim to reason by jointly leveraging visual and textual modalities. While allocating additional inference-time computation has proven effective for lar…

cs.CL2026

IAPO: Information-Aware Policy Optimization for Token-Efficient Reasoning

Yinhan He, Yaochen Zhu, Mingjia Shi +5

Large language models increasingly rely on long chains of thought to improve accuracy, yet such gains come with substantial inference-time costs. We revisit token-efficient post-tr…