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20242026
most citedSora as a World Model? A Complete Survey on Text-to-Video Generation

10 citations · 11 across the 23 of their papers we have counts for

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Showing 2026Show all

8 papers · 1 filter

cs.LG2026

Autoregression-Free Neural Operators for Time-Dependent PDEs

Jiaquan Zhang, Caiyan Qin, Haoyu Bian +7

Neural operators learn mappings from function-dependent inputs to solutions, providing an effective framework for solving partial differential equations (PDEs). For time-dependent…

cs.NE2026

Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models

Xudong Wang, Chaoning Zhang, Chenghao Li +10

Large Language Models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, while recent prompting strategies such as Chain-of-Thought (CoT) have further elevate…

cs.CL2026

Transforming External Knowledge into Triplets for Enhanced Retrieval in RAG of LLMs

Xudong Wang, Chaoning Zhang, Qigan Sun +7

Retrieval-Augmented Generation (RAG) mitigates hallucination in large language models (LLMs) by incorporating external knowledge during generation. However, the effectiveness of RA…

cs.CL2026

TDA-RC: Task-Driven Alignment for Knowledge-Based Reasoning Chains in Large Language Models

Jiaquan Zhang, Qigan Sun, Chaoning Zhang +11

Enhancing the reasoning capability of large language models (LLMs) remains a core challenge in natural language processing. The Chain-of-Thought (CoT) paradigm dominates practical…

cs.AI2026

Efficient and Interpretable Multi-Agent LLM Routing via Ant Colony Optimization

Xudong Wang, Chaoning Zhang, Jiaquan Zhang +8

Large Language Model (LLM)-driven Multi-Agent Systems (MAS) have demonstrated strong capability in complex reasoning and tool use, and heterogeneous agent pools further broaden the…

cs.CL2026

Optimizing Soft Prompt Tuning via Structural Evolution

Zhenzhen Huang, Chaoning Zhang, Haoyu Bian +8

Soft prompt tuning leverages continuous embeddings to capture task-specific information in large pre-trained language models (LLMs), achieving competitive performance in few-shot s…