Uni-Mo generates 7,488 language-annotated quadruped motions via LLM prompts and video diffusion, lifts them to 3D trajectories, and trains policies achieving 96.7% real-robot success on 392 sampled motions.
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Seedance 2.0: Advancing Video Generation for World Complexity
Canonical reference. 100% of citing Pith papers cite this work as background.
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abstract
Seedance 2.0 is a new native multi-modal audio-video generation model, officially released in China in early February 2026. Compared with its predecessors, Seedance 1.0 and 1.5 Pro, Seedance 2.0 adopts a unified, highly efficient, and large-scale architecture for multi-modal audio-video joint generation. This allows it to support four input modalities: text, image, audio, and video, by integrating one of the most comprehensive suites of multi-modal content reference and editing capabilities available in the industry to date. It delivers substantial, well-rounded improvements across all key sub-dimensions of video and audio generation. In both expert evaluations and public user tests, the model has demonstrated performance on par with the leading levels in the field. Seedance 2.0 supports direct generation of audio-video content with durations ranging from 4 to 15 seconds, with native output resolutions of 480p and 720p. For multi-modal inputs as reference, its current open platform supports up to 3 video clips, 9 images, and 3 audio clips. In addition, we provide Seedance 2.0 Fast version, an accelerated variant of Seedance 2.0 designed to boost generation speed for low-latency scenarios. Seedance 2.0 has delivered significant improvements to its foundational generation capabilities and multi-modal generation performance, bringing an enhanced creative experience for end users.
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REGEN uses recurrent generative replays from World Action Models to cut catastrophic forgetting by up to 50% in continual imitation learning compared to sequential fine-tuning.
CoDMD adds a copula-matching regularizer to DMD for distilling 50-step video diffusion models to 4 steps, reporting VBench scores of 84.46/84.87 on 1.3B/14B Wan-2.1-T2V models.
Dream.exe evaluates 8 video generation models on 101 manipulation tasks by converting generated videos into executable robot trajectories in a simulator, finding measurable success rates that visual metrics do not predict.
EVID-Bench supplies 222 videos across nine manipulation types in three categories and shows that frontier multimodal models reach at most 61.43% point-level accuracy when forced to use web search to identify false information.
VLMs formulate differentiable rewards from task-specific rules to enable test-time online LoRA optimization of VGMs, delivering 16.7-point gains on symbolic and general video reasoning benchmarks over VLM-as-solver and Best-of-N baselines.
KIVI-Bench and new metrics reveal that seven state-of-the-art text-to-video models lag humans on factual accuracy, procedural clarity, and information presentation.
NAVA proposes native audio-visual alignment via Align-then-Fuse MMDiT and Timbre-in-Context Conditioning for joint audio-video generation with improved synchronization and timbre control.
AgenticVBench evaluates frontier VLMs on 100 real-world video post-production tasks across four families, with the best agent stack scoring just over 30% versus human experts.
What-If World is a new paired-prompt benchmark showing that nine state-of-the-art video generation models achieve at most 52% on causal intervention tests and cluster near 28% for open-source systems.
LongAV-Compass is a new benchmark and evaluation framework for minute-scale audio-visual generation across T2AV, I2AV, and V2AV with multi-dimensional assessment.
Adversarial Flow Distillation distills heterogeneous black-box video teachers into causal autoregressive students via on-policy Bradley-Terry discrimination converted to forward-process flow-matching updates.
WBench is a benchmark with 289 test cases and 1,058 turns for evaluating interactive world models using 22 automated metrics validated against human judgments.
VGenST-Bench is a new video benchmark for MLLM spatio-temporal reasoning built via generative synthesis, a multi-agent pipeline with human oversight, a 3x2x2 taxonomy, and hierarchical tasks separating perception from reasoning.
Anchored Tree Sampling converts horizon-compounding drift into anchor-bounded drift by organizing video generation as a sparse-to-dense tree of imputations instead of left-to-right autoregressive rollout.
MSAVBench is the first comprehensive benchmark for multi-shot audio-video generation featuring four dimensions, challenging scenarios, and an adaptive hybrid evaluation framework that achieves 91.5% Spearman correlation with human judgments.
Per-frame natural-language action prompts enable simultaneous multi-entity control and cross-entity action transfer in interactive video world models, outperforming discrete action-index interfaces.
CausalCine enables real-time causal autoregressive multi-shot video generation via multi-shot training, content-aware memory routing for coherence, and distillation to few-step inference.
RSPO interprets reward advantages as targets for relative log-ratios in dLLMs, calibrating noisy estimates to stabilize RLVR training and achieve strong gains on planning tasks with competitive math reasoning performance.
AV-Phys Bench shows that current joint audio-video models lack robust physical commonsense, with major drops on transitions and deliberate anti-physics prompts.
AniMatrix generates anime videos by structuring artistic production rules into a controllable taxonomy and training the model to prioritize those rules over physical realism, achieving top scores from professional animators on prompt understanding and artistic motion.
WNM decouples video generation into agent-built editable 4D physical narratives that condition frozen foundation models, reducing gacha-style resampling and improving layout, motion, and camera control.
Fine-tuning a video generator on a new 17K reasoning-video dataset improves Chain-of-Frame reasoning, and adding learnable visual/textual reasoning tokens yields further gains on external benchmarks.
Causal-rCM unifies teacher-forcing and self-forcing distillation for autoregressive video diffusion, delivering a 2-step model with VBench-T2V score 84.63 and enabling interactive world models on Cosmos 3 using only synthetic data.
citing papers explorer
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Unleashing Infinite Motion: Scaling Expressive Quadrupedal Motion via Generative Video Priors
Uni-Mo generates 7,488 language-annotated quadruped motions via LLM prompts and video diffusion, lifts them to 3D trajectories, and trains policies achieving 96.7% real-robot success on 392 sampled motions.
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World Action Models Enable Continual Imitation Learning with Recurrent Generative Replays
REGEN uses recurrent generative replays from World Action Models to cut catastrophic forgetting by up to 50% in continual imitation learning compared to sequential fine-tuning.
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CoDMD: Copula-aware Distribution Matching Distillation for Fast Video Generation
CoDMD adds a copula-matching regularizer to DMD for distilling 50-step video diffusion models to 4 steps, reporting VBench scores of 84.46/84.87 on 1.3B/14B Wan-2.1-T2V models.
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Dream.exe: Can Video Generation Models Dream Executable Robot Manipulation?
Dream.exe evaluates 8 video generation models on 101 manipulation tasks by converting generated videos into executable robot trajectories in a simulator, finding measurable success rates that visual metrics do not predict.
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When Seeing Is Not Believing -- A Benchmark for Search-Grounded Video Misinformation Detection
EVID-Bench supplies 222 videos across nine manipulation types in three categories and shows that frontier multimodal models reach at most 61.43% point-level accuracy when forced to use web search to identify false information.
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VLMs are Good Teachers for Video Reasoning via Adaptive Test-Time Optimization
VLMs formulate differentiable rewards from task-specific rules to enable test-time online LoRA optimization of VGMs, delivering 16.7-point gains on symbolic and general video reasoning benchmarks over VLM-as-solver and Best-of-N baselines.
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Knowledge-Intensive Video Generation
KIVI-Bench and new metrics reveal that seven state-of-the-art text-to-video models lag humans on factual accuracy, procedural clarity, and information presentation.
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Native Audio-Visual Alignment for Generation
NAVA proposes native audio-visual alignment via Align-then-Fuse MMDiT and Timbre-in-Context Conditioning for joint audio-video generation with improved synchronization and timbre control.
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AgenticVBench: Can AI Agents Complete Real-World Post-Production Tasks?
AgenticVBench evaluates frontier VLMs on 100 real-world video post-production tasks across four families, with the best agent stack scoring just over 30% versus human experts.
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What-If World: A Causal Benchmark for General World Models in Embodied Scenarios
What-If World is a new paired-prompt benchmark showing that nine state-of-the-art video generation models achieve at most 52% on causal intervention tests and cluster near 28% for open-source systems.
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LongAV-Compass: Towards Unified Evaluation of Minute-Scale Audio-Visual Generation Across T2AV, I2AV, and V2AV
LongAV-Compass is a new benchmark and evaluation framework for minute-scale audio-visual generation across T2AV, I2AV, and V2AV with multi-dimensional assessment.
-
On-Policy Adversarial Flow Distillation for Autoregressive Video Generation
Adversarial Flow Distillation distills heterogeneous black-box video teachers into causal autoregressive students via on-policy Bradley-Terry discrimination converted to forward-process flow-matching updates.
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WBench: A Comprehensive Multi-turn Benchmark for Interactive Video World Model Evaluation
WBench is a benchmark with 289 test cases and 1,058 turns for evaluating interactive world models using 22 automated metrics validated against human judgments.
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VGenST-Bench: A Benchmark for Spatio-Temporal Reasoning via Active Video Synthesis
VGenST-Bench is a new video benchmark for MLLM spatio-temporal reasoning built via generative synthesis, a multi-agent pipeline with human oversight, a 3x2x2 taxonomy, and hierarchical tasks separating perception from reasoning.
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Goodbye Drift: Anchored Tree Sampling for Long-Horizon Video-to-Video Generation
Anchored Tree Sampling converts horizon-compounding drift into anchor-bounded drift by organizing video generation as a sparse-to-dense tree of imputations instead of left-to-right autoregressive rollout.
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MSAVBench: Towards Comprehensive and Reliable Evaluation of Multi-Shot Audio-Video Generation
MSAVBench is the first comprehensive benchmark for multi-shot audio-video generation featuring four dimensions, challenging scenarios, and an adaptive hybrid evaluation framework that achieves 91.5% Spearman correlation with human judgments.
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Incantation: Natural Language as the Action Interface for Multi-Entity Video World Models
Per-frame natural-language action prompts enable simultaneous multi-entity control and cross-entity action transfer in interactive video world models, outperforming discrete action-index interfaces.
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CausalCine: Real-Time Autoregressive Generation for Multi-Shot Video Narratives
CausalCine enables real-time causal autoregressive multi-shot video generation via multi-shot training, content-aware memory routing for coherence, and distillation to few-step inference.
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Relative Score Policy Optimization for Diffusion Language Models
RSPO interprets reward advantages as targets for relative log-ratios in dLLMs, calibrating noisy estimates to stabilize RLVR training and achieve strong gains on planning tasks with competitive math reasoning performance.
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Do Joint Audio-Video Generation Models Understand Physics?
AV-Phys Bench shows that current joint audio-video models lack robust physical commonsense, with major drops on transitions and deliberate anti-physics prompts.
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AniMatrix: An Anime Video Generation Model that Thinks in Art, Not Physics
AniMatrix generates anime videos by structuring artistic production rules into a controllable taxonomy and training the model to prioritize those rules over physical realism, achieving top scores from professional animators on prompt understanding and artistic motion.
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World Narrative Model for Highly Controllable Video Generation: A Paradigm Shift from Pixel Sampling to Physical World Orchestration
WNM decouples video generation into agent-built editable 4D physical narratives that condition frozen foundation models, reducing gacha-style resampling and improving layout, motion, and camera control.
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OpenCoF: Learning to Reason Through Video Generation
Fine-tuning a video generator on a new 17K reasoning-video dataset improves Chain-of-Frame reasoning, and adding learnable visual/textual reasoning tokens yields further gains on external benchmarks.
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Causal-rCM: A Unified Teacher-Forcing and Self-Forcing Open Recipe for Autoregressive Diffusion Distillation in Streaming Video Generation and Interactive World Models
Causal-rCM unifies teacher-forcing and self-forcing distillation for autoregressive video diffusion, delivering a 2-step model with VBench-T2V score 84.63 and enabling interactive world models on Cosmos 3 using only synthetic data.
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Wan-Streamer v0.1: End-to-end Real-time Interactive Foundation Models
Wan-Streamer is a unified end-to-end Transformer for low-latency streaming audio-visual interaction using block-causal attention on interleaved multimodal tokens.
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Navigating User Behavior toward Personalized Multimodal Generation
NaviGen encodes user behavior via dual collaborative-textual identifiers and applies SFT+RL to produce personalized multimodal outputs and better instructions from interaction history.
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SteerVTE: Seamless Video Text Editing with Style and Glyph Control
SteerVTE adds lightweight style and dual-granularity glyph adapters to a frozen video diffusion model, introduces a glyph-aware loss and progressive training, and releases a 1M synthetic dataset to enable accurate video text editing.
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DataClaw0: Agentic Tailoring Multimodal Data from Raw Streams
A 9B multimodal model learns to tailor raw video/GUI streams into schema-aligned training data, matching a proprietary annotator on downstream tasks; the abstract's capacity-scaling claims are not supported by the body.
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OmniDirector: General Multi-Shot Camera Cloning without Cross-Paired Data
OmniDirector introduces a grid-based camera representation and hierarchical prompt agent for multi-shot camera cloning in video diffusion models trained on million-scale unpaired data.
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GenHOI: Contact-Aware Humanoid-Object Interaction by Imitating Generated Videos without Task-Specific Training
A humanoid robot can carry out diverse manipulation tasks in a zero-shot way by imitating one AI-generated video, using contact-aware trajectory optimization instead of task-specific policy training.
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SpecLoR: Spectral Lookahead Rectification for Motion-Coherent Text-to-Video Generation
SpecLoR rectifies the amplitude spectrum of lookahead-estimated clean latents to natural-video priors during early ODE sampling steps, cutting physical artifacts with only four extra NFEs.
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Prisma-World: Camera-Controllable Multi-Agent Video World Model
Prisma-World is a diffusion-based multi-agent video model that uses joint full-attention, multi-agent RoPE, and relative camera geometry injection plus curriculum training to produce consistent cross-view videos from flexible agent counts.
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VideoWeaver: Evaluating and Evolving Skills for Agentic Long Video Generation
Introduces VideoWeaver benchmark (16 categories, 285 cases) plus agent-as-judge and skill-evolution algorithm to assess and improve agentic long video generation across frameworks.
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Self-Evolving Deep Research via Joint Generation and Evaluation
SCORE is a shared-parameter co-evolutionary framework coupling generation and evaluation of deep research reports with a meta-harness to adapt evaluation standards as performance improves.
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Foley-Omni: A Unified Multimodal Generation Model from Task-Level Audio Synthesis to Complete Video Soundtrack Generation
Foley-Omni extends isolated audio synthesis to joint generation of full video soundtracks across speech, effects, and music, with a new V2ST-Bench for evaluation showing competitive single-task results and gains in mixed-track consistency.
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StreamChar: Long-Horizon Streaming Character Audio-Video Generation with Decoupled Orchestration
StreamChar decouples LLM-based orchestration from DiT denoising to achieve real-time long-horizon streaming character audio-video generation with reduced drift and misalignment.
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DySink: Dynamic Frame Sinks for Autoregressive Long Video Generation
DySink replaces static early-frame anchors in autoregressive video generation with dynamically retrieved, visually relevant history, filtered by an inter-head consensus gate, improving long-video temporal scores.
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Lance: Unified Multimodal Modeling by Multi-Task Synergy
Lance presents a dual-stream mixture-of-experts model with modality-aware positional encoding and staged multi-task training that outperforms prior open-source unified models on image and video generation while keeping strong understanding performance.
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RAVEN: Real-time Autoregressive Video Extrapolation with Consistency-model GRPO
RAVEN aligns training and inference for causal autoregressive video diffusion via interleaved rollout repacking and introduces CM-GRPO for direct RL on consistency-model kernels, claiming better quality than recent baselines.
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SARA: Semantically Adaptive Relational Alignment for Video Diffusion Models
SARA introduces semantic saliency to guide relational alignment in video diffusion models, improving text following and motion quality over prior alignment methods.
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D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models
D-OPSD formulates supervised fine-tuning of step-distilled diffusion models as on-policy self-distillation by having the model act as both teacher (with multimodal context) and student (with text-only context) on its own roll-outs.
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ExoActor: Exocentric Video Generation as Generalizable Interactive Humanoid Control
ExoActor uses exocentric video generation to implicitly model robot-environment-object interactions and converts the resulting videos into task-conditioned humanoid control sequences.
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Leveraging Verifier-Based Reinforcement Learning in Image Editing
Edit-R1 builds a CoT-based reasoning reward model (RRM) via SFT and GCPO, then applies it with GRPO to improve image editing models such as FLUX.1-kontext.
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Scaling Mixture-of-Experts Video Pretraining for Embodied Intelligence
LingBot-Video is an open-source MoE video foundation model for embodied intelligence that scales to 120B parameters, integrates robot data, and uses multi-dimensional RL to improve physical plausibility.
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A Definition and Roadmap for World Models
A perspective article defining world models as finite-resource compression of physical state transitions and outlining a roadmap toward physical AGI via unified representations and interactive simulators.
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World Value Models for Robotic Manipulation
World Value Model (WVM) integrates world models with value estimation to achieve SOTA Value-Order Correlation on expert and suboptimal robotic data and improves downstream policy performance.
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TailorMind: Towards Preference-Aligned Multimodal Content Generation
TailorMind links hypergraph collaborative filtering and textual gradient descent with multimodal generation to produce user-tailored content, showing gains in novelty, aesthetics, and reranking recall on a new benchmark from three platforms.
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Kairos: A Regret-Aware Native World-Action Model Stack for Physical AI
Kairos learns and maintains control-sufficient world states via a cross-embodiment curriculum, hybrid linear temporal attention, and deployment-aware co-design for Physical AI.
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AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing
AHA-WAM is a dual-DiT asynchronous world-action model with horizon-adaptive offset training and OVCR routing that reports 92.8% success on RoboTwin and 78.3% on real tasks at 24.17 Hz without robot pretraining.
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CineDance: Towards Next-Generation Multi-Shot Long-Form Cinematic Audio-Video Generation
Introduces CineDance-1M dataset for multi-shot long-form text-to-audio-video generation along with CineBench and a model adaptation.