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SCOUT introduces a recovery-aware agentic framework with an adaptive exploration-exploitation policy for ultra-long egocentric video reasoning, trained via UPS-GRPO, a uncertainty-prioritized RL method with turn-level advantage decomposition. It achieves state-of-the-art results on ultra-long egocentric benchmarks and remains competitive on shorter long-video settings.
Proposes Self Gradient Forcing (SGF), a two-pass training strategy for autoregressive video diffusion models that provides missing supervision for writing useful context memory, enabling strong long-video extrapolation even from short training windows.
This tweet explains LingBot-World-Infinity, an open-weight video generation model that uses a training technique to recover from errors, enabling coherent hour-long videos across multiple scenes.
OPSD-V improves few-step autoregressive video diffusion models by using real long-video data as temporal context during training, providing dense trajectory-level supervision that enhances visual quality and motion dynamics without altering inference mechanisms.
HKU open-sourced ViMax, a multi-agent collaborative long video generation tool that can generate a coherent video with script, storyboard, voiceover, and consistent characters from a single sentence, solving issues like short video fragmentation and character inconsistency. The developer also introduced Taste-Skill, a frontend framework that improves the aesthetics of AI-generated interfaces.
Seedance 2.5 enables consistent AI-generated videos up to 3 minutes in one go, marking a significant leap in AI video generation that could soon lead to TV episodes and feature films.
This paper introduces V-RAGBench, a benchmark for evaluating retrieval-augmented generation over long egocentric videos, and CARVE, a method that adaptively selects retrieval configurations per chunk to improve VideoRAG performance.
OmniMem introduces a modality-aware memory allocation and perturbation-aware selection strategy for streaming audio-visual LLMs, achieving 2-4% absolute accuracy gains over compression baselines on long-video benchmarks.
This blog from NVIDIA Research discusses how sequence parallelism can scale long-video training systems for both understanding and generation, addressing the challenge of fitting very long video sequences across multiple GPUs.
JD Open Source releases JoyAI-Echo (Echo-LongVideo), a text-to-audio-video diffusion model capable of generating minute-level multi-shot videos with consistent character identity and voice, using DMD distillation for 7.5x speedup.
LongLive-RAG formulates long video generation as a retrieval-augmented generation problem, using a dynamic memory of previously generated latents to reduce error accumulation and identity drift, achieving improved quality across multiple autoregressive backbones.
LVSA introduces a training-free sparse attention mechanism for video diffusion models, reducing compute up to 3.17x while enabling generation beyond training horizons without quality loss.
Kwai releases Keye-VL-2.0-30B-A3B, a 30B-class multimodal base model that introduces DSA attention to multimodality for the first time, targeting long-video understanding and agent capabilities.
NVlabs releases LongLive 2.0, a parallel infrastructure for real-time long video generation using NVFP4 quantization, supporting both training and inference. It achieves 45.7 FPS and is accepted at ICLR 2026.
The article introduces A²RD, a novel architecture for generating consistent long videos using agentic autoregressive diffusion. It proposes a Retrieve–Synthesize–Refine–Update cycle and a new benchmark, LVBench-C, to address semantic drift in long-horizon video synthesis.