CoAgent: Collaborative Planning and Consistency Agent for Coherent Video Generation

2025-12-27 · Qinglin Zeng, Kaitong Cai, Ruiqi Chen, Qinhan Lv, Keze Wang arxiv

Maintaining narrative coherence and visual consistency remains a central challenge in open-domain video generation. Existing text-to-video models often treat each shot independently, resulting in identity drift, scene inconsistency, and unstable temporal structure. We propose CoAgent, a collaborative and closed-loop framework for coherent video generation that formulates the process as a plan-synthesize-verify pipeline. Given a user prompt, style reference, and pacing constraints, a Storyboard Planner decomposes the input into structured shot-level plans with explicit entities, spatial relations, and temporal cues. A Global Context Manager maintains entity-level memory to preserve appearance and identity consistency across shots. Each shot is then generated by a Synthesis Module under the guidance of a Visual Consistency Controller, while a Verifier Agent evaluates intermediate results using vision-language reasoning and triggers selective regeneration when inconsistencies are detected. Finally, a pacing-aware editor refines temporal rhythm and transitions to match the desired narrative flow. Extensive experiments demonstrate that CoAgent significantly improves coherence, visual consistency, and narrative quality in long-form video generation.

📄 PDF Abstract BibTeX arXiv:2512.22536

Code (0)

등록된 구현이 없습니다.

Tasks

Video Generation

EcoAgent: An Efficient Edge-Cloud Collaborative Multi-Agent Framework for Mobile Automation

2025-05-08 · Biao Yi, Xavier Hu, Yurun Chen, Shengyu Zhang 외

Cloud-based mobile agents powered by (multimodal) large language models ((M)LLMs) offer strong reasoning abilities but suffer from high latency and cost. While fine-tuned (M)SLMs enable edge deployment, they often lose g…

Coagent Networks: Generalized and Scaled

2023-05-16 · James E. Kostas, Scott M. Jordan, Yash Chandak, Georgios Theocharous 외

Coagent networks for reinforcement learning (RL) [Thomas and Barto, 2011] provide a powerful and flexible framework for deriving principled learning rules for arbitrary stochastic neural networks. The coagent framework o…

MuJoCo Reinforcement Learning (RL)

Coagent Networks Revisited

2020-01-28 · Modjtaba Shokrian Zini, Mohammad Pedramfar, Matthew Riemer, Ahmadreza Moradipari 외

Coagent networks formalize the concept of arbitrary networks of stochastic agents that collaborate to take actions in a reinforcement learning environment. Prominent examples of coagent networks in action include approac…

Hierarchical Reinforcement Learning reinforcement-learning Reinforcement Learning

Asynchronous Coagent Networks

2019-02-15 · ICML 2020 1 · James E. Kostas, Chris Nota, Philip S. Thomas

Coagent policy gradient algorithms (CPGAs) are reinforcement learning algorithms for training a class of stochastic neural networks called coagent networks. In this work, we prove that CPGAs converge to locally optimal p…

Hierarchical Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

TelcoAgent-Bench: A Multilingual Benchmark for Telecom AI Agents

2026-03-16 · Lina Bariah, Brahim Mefgouda, Farbod Tavakkoli, Enrique Molero 외 arxiv

The integration of large language model (LLM) agents into telecom networks introduces new challenges, related to intent recognition, tool execution, and resolution generation, while taking into consideration different op…

Intent Recognition