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20192026
most citedKnowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey

31 citations · 198 across the 133 of their papers we have counts for

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cs.CL2026

EM^2Mem: Event-Centric Multimodal Memory for Large Language Models

Yijun Chen, Yaqi Zheng, Yanya Li +11

Multimodal memory offers a scalable interface for long-video question answering, but existing methods often retrieve captions, frames, transcripts, summaries, or graph facts as iso…

cs.CL2026

OneDayAgent: Towards a Long-Horizon Harness for Autonomous Agents

Jingsheng Zheng, Xinyuan Fang, Jintian Zhang +3

LLM agents are increasingly applied to open-ended everyday requests that span work, study, and life. These tasks are long-horizon, cross-environment, and multimodal, forcing the ag…

cs.CL2026

CORTEX: High-Quality Cross-Domain Organization of Web-Scale Corpora through Ontological Corpus Graph

Chengtao Gan, Xiaoke Guo, Yushan Zhu +5

The continuous evolution of large language models drives escalating demands on data scale and quality, and as different training stages impose increasingly tailored data requiremen…

cs.CL2026

Scaling LLM Knowledge Boundaries via Distribution-Optimized Synthesis

Songze Li, Yarong Lan, Zhongpu Bo +16

Knowledge injection via synthetic data is crucial for enhancing Large Language Models (LLMs). However, current synthesis methods simply stop at preset token counts or fixed data ra…

cs.CL2026

LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories

Baochang Ren, Xinjie Liu, Xi Chen +15

Scientific laboratories increasingly rely on AI systems to reason about experiments, but the physical act of doing science remains largely outside their reach. AI can help read lit…

cs.CL2026

Symbolic and Abstractive Reasoning with Complex Visual Queries

Yichi Zhang, Jingdian Lu, Zhuo Chen +4

Understanding and reasoning over abstract visual content remains a challenge for current multi-modal large language models (MLLMs). In this paper, we explore a novel abstract data…