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20242026
most citedNeural Network Optimization Reimagined: Decoupled Techniques for Scratch and Fine-Tuning

9 citations · 9 across the 22 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

RareLens: Towards End-to-End Rare Disease Care via Aligning Divergent Large Language Model Reasoning

Xi Chen, Hongru Zhou, Shiyu Feng +24

Rare diseases represent one of the most challenging settings for clinical decision-making, where heterogeneous presentations, sparse evidence and limited expertise create persisten…

cs.AI2026

Reasoning with Memory: A Temporal Granularity-Adaptive Framework for Training-Free Long Video Understanding

Linghao Meng, Qiankun Li, Junyuan Mao +7

While Multimodal Large Language Models (MLLMs) demonstrate superior generalization in fundamental video tasks, restricted context windows limit their long video understanding. To a…

cs.AI2026

PathMem: Toward Cognition-Aligned Memory Transformation for Pathology MLLMs

Jinyue Li, Yuci Liang, Qiankun Li +7

Computational pathology demands both visual pattern recognition and dynamic integration of structured domain knowledge, including taxonomy, grading criteria, and clinical evidence.…

cs.AI2026

Reallocating Attention Across Layers to Reduce Multimodal Hallucination

Haolang Lu, Bolun Chu, WeiYe Fu +7

Multimodal large reasoning models (MLRMs) often suffer from hallucinations that stem not only from insufficient visual grounding but also from imbalanced allocation between percept…

cs.AI2026

Diagnosing Knowledge Conflict in Multimodal Long-Chain Reasoning

Jing Tang, Kun Wang, Haolang Lu +7

Multimodal large language models (MLLMs) in long chain-of-thought reasoning often fail when different knowledge sources provide conflicting signals. We formalize these failures und…

cs.AI2026

RareAlert: Aligning heterogeneous large language model reasoning for early rare disease risk screening

Xi Chen, Hongru Zhou, Huahui Yi +10

Missed and delayed diagnosis remains a major challenge in rare disease care. At the initial clinical encounters, physicians assess rare disease risk using only limited information…