Aurora introduces a VLM-based agent that converts raw user video edit requests into structured conditioning inputs for a unified diffusion transformer, improving performance on underspecified tasks via a new benchmark.
Mora: Enabling generalist video generation via a multi-agent framework
8 Pith papers cite this work. Polarity classification is still indexing.
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DIRECT uses a three-level multi-agent framework to solve video mashup creation as a multimodal coherency problem, outperforming baselines on a new benchmark.
A collaborative Planning-Perception agent framework using MLLMs constructs a holistic cognitive map through iterative viewpoint supplementation and achieves reported SOTA gains on six 3D benchmarks.
Introduces a commercial-model contrastive AIGC video dataset and a hybrid contrastive-MLLM detection framework claiming SOTA performance on realistic video forgery detection.
Parallel, role-specialized prompt agents improve cultural relevance in text-to-video generation, with a new cross-cultural benchmark showing the largest gains for location cues.
SWIFT introduces a semantic injection cache with head-wise updates and an adaptive dynamic window plus segment anchors to achieve efficient multi-prompt long video generation at 22.6 FPS while preserving quality in causal diffusion models.
An SNN-based detector combining multi-channel pseudo-event residuals with frozen semantic features reaches 93.14% mean accuracy on unseen generators under the Pika-trained GenVideo protocol.
CiteAudit supplies a human-validated benchmark and multi-agent verification system that outperforms existing LLMs and commercial tools at detecting hallucinated scientific references.
citing papers explorer
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Aurora: Unified Video Editing with a Tool-Using Agent
Aurora introduces a VLM-based agent that converts raw user video edit requests into structured conditioning inputs for a unified diffusion transformer, improving performance on underspecified tasks via a new benchmark.
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DIRECT: Video Mashup Creation via Hierarchical Multi-Agent Planning and Intent-Guided Editing
DIRECT uses a three-level multi-agent framework to solve video mashup creation as a multimodal coherency problem, outperforming baselines on a new benchmark.
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Agentic Collaborative Cognition for Zero-Shot 3D Understanding
A collaborative Planning-Perception agent framework using MLLMs constructs a holistic cognitive map through iterative viewpoint supplementation and achieves reported SOTA gains on six 3D benchmarks.
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CoCoVideo: The High-Quality Commercial-Model-Based Contrastive Benchmark for AI-Generated Video Detection
Introduces a commercial-model contrastive AIGC video dataset and a hybrid contrastive-MLLM detection framework claiming SOTA performance on realistic video forgery detection.
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When Cultures Move: Measuring and Improving Multicultural Text-to-Video Generation
Parallel, role-specialized prompt agents improve cultural relevance in text-to-video generation, with a new cross-cultural benchmark showing the largest gains for location cues.
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SWIFT: Prompt-Adaptive Memory for Efficient Interactive Long Video Generation
SWIFT introduces a semantic injection cache with head-wise updates and an adaptive dynamic window plus segment anchors to achieve efficient multi-prompt long video generation at 22.6 FPS while preserving quality in causal diffusion models.
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Detecting AI-Generated Videos with Spiking Neural Networks
An SNN-based detector combining multi-channel pseudo-event residuals with frozen semantic features reaches 93.14% mean accuracy on unseen generators under the Pika-trained GenVideo protocol.
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CiteAudit: You Cited It, But Did You Read It? A Benchmark for Verifying Scientific References in the LLM Era
CiteAudit supplies a human-validated benchmark and multi-agent verification system that outperforms existing LLMs and commercial tools at detecting hallucinated scientific references.