most citedBeyond End-to-End Video Models: An LLM-Based Multi-Agent System for Educational Video Generation

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cs.AI2026

MapAgent: An Industrial-Grade Agentic Framework for City-scale Lane-level Map Generation

Deguo Xia, Zihan Li, Haochen Zhao +6

Lane-level maps are critical infrastructure for autonomous driving and lane-level navigation, yet constructing and maintaining standardized lane networks for hundreds of cities rem…

cs.AI20261 cited

Beyond End-to-End Video Models: An LLM-Based Multi-Agent System for Educational Video Generation

Lingyong Yan, Jiulong Wu, Dong Xie +3

Although recent end-to-end video generation models demonstrate impressive performance in visually oriented content creation, they remain limited in scenarios that require strict lo…

cs.AI2026

Woodpecker Distillation: Weak Models Diagnose Reasoning Bugs in Strong Models

Dayu Wang, Jiaye Yang, Weikang Li +4

Large language models often fail on reasoning tasks despite possessing the capability to solve them. We argue that many such failures arise from localized reasoning bugs in interme…

cs.AI2026

Stay in Character, Stay Safe: Dual-Cycle Adversarial Self-Evolution for Safety Role-Playing Agents

Mingyang Liao, Yichen Wan, shuchen wu +6

LLM-based role-playing has rapidly improved in fidelity, yet stronger adherence to persona constraints commonly increases vulnerability to jailbreak attacks, especially for risky o…

cs.AI2025

Probabilistic Modeling of Intentions in Socially Intelligent LLM Agents

Feifan Xia, Yuyang Fang, Defang Li +5

We present a probabilistic intent modeling framework for large language model (LLM) agents in multi-turn social dialogue. The framework maintains a belief distribution over a partn…