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Open problems in Multimodal

Lack of holistic video understanding benchmarks1 paper raises it · 1 addresses it
Existing benchmarks cover only partial evaluation dimensions, limiting systematic diagnosis of omni-modal video understanding.
- OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understandingraises · addresses
Long-horizon task execution and recovery1 paper raises it · 0 address it
Agents struggle with preserving progress, recovering from failures, and verifying physical states over long sequences.
- Has GPT-6-Astra Achieved Embodied Intelligence?raises
Efficient spatial reasoning for real-time interaction2 papers raise it · 2 address it
Current models use slow autoregressive decoding for compact output spaces, limiting interaction speed.
- VideoJev: System One Model for Video Understanding and Interactionraises · addresses
- MJev-Spatial: System One Spatial Intelligence via Native Attention Matchingraises · addresses
Cross-shot consistency in agentic video generation1 paper raises it · 1 addresses it
Maintaining spatial and temporal consistency across non-consecutive video shots is difficult.
- Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generationraises · addresses
Precise grounding for physical intelligence1 paper raises it · 1 addresses it
General-purpose backbones fail to precisely ground objects in clutter or for tiny targets at control speeds.
- GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitivesraises · addresses

Named by an AI from these papers' abstracts.

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Topics in this field

video understanding · video language · video generation291 papers47 this week
autoencoders · imagenet · autoencoder220 papers40 this week
visual question answering · vision language models · vision language151 papers39 this week
video generation · efficient video · autoregressive video178 papers33 this week
visual reasoning · multimodal reasoning · visual question answering166 papers31 this week
semantic segmentation · vision language · attention196 papers30 this week

Topics in this field

Active contributors · 90 days

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Activity · 12 weeks

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arXiv cs.CVCodeOct 19▲ 26
RoAR-VAE: Improving Latent Diffusability through Robust Prediction
Yang Yue, Fangyun Wei, Yitong Wang, Jinjing Zhao, Henry Zheng +2
An effective visual tokenizer must preserve image fidelity while producing latents that downstream diffusion models can learn efficiently. Prior work improves latent diffusability through semantic alignment or joint tokenizer--diffusion training. Revisiting REPA-E, a representative approach, we find that removing its diffusion objective and dedicated generative blocks while retaining semantic prediction from corrupted latents yields comparable downstream generation performance under a matched setting. This observation motivates a general principle of robust prediction: generation-relevant information should remain recoverable from corrupted latents. We instantiate this principle with two complementary objectives. RobustAlign predicts pretrained semantic features from corrupted latents to preserve high-level structure, while RobustRecon reconstructs the clean image to retain fine-grained spatial and appearance information. Together, they form Robust Alignment and Reconstruction VAE (RoAR-VAE). Our analysis suggests that robust prediction improves diffusability by making useful information more accessible in high-variance latent directions, which diffusion models tend to learn…
PDF arXiv Code
arXiv cs.ROCodeOct 19▲ 3
Vision-Language-Delta-Action: Learning Compact Deltas as Actionable Transition Interfaces for Robotic Manipulation
Lingsen Zhang, Wei Li, Jie He, Henghua Zhang, Kai Mo +3
Robotic manipulation is not merely observation-to-action generation, but the realization of task-specified state changes in the physical world. Existing vision-language-action (VLA) methods directly learn observation-to-action mappings, imposing a substantial learning burden due to the gap between semantic understanding and action execution. Although world-action models (WAMs) improve control through future prediction, modeling complete future states wastes capacity on static appearance and action-irrelevant changes. We propose Vision-Language-Delta-Action (VLDA), a paradigm that reformulates full future prediction as compact actionable transition modeling. Its core is a compact Delta variable that captures latent world-state changes, serving as an interface between vision-language understanding and action generation. VLDA learns Delta through a unified objective composed of the Local Transition Filter (LT-Filter) and Global Transition Anchor (GT-Anchor). LT-Filter captures task-relevant manipulation-centric changes while suppressing static reconstruction biases. GT-Anchor provides coarse future-level regularization to preserve the overall direction of state changes. Delta-Driven…
PDF arXiv Code
arXiv cs.CVCodeOct 19▲ 4
ActionRoPE: action as coordinates for spatially consistent control in planar-camera game world models
Zhiyang Deng, Yuzhe Wu, Zijun Lin, Yeying Jin
Game world models have made rapid progress toward real-time interactive generation. Yet action control is still predominantly implemented as feature conditioning, leaving the model to learn from training data how each action should move the visual world. This formulation is poorly matched to a broad class of planar-camera worlds, such as top-down and isometric games. In these worlds, player motion induces a geometrically well-defined planar camera displacement. We observe that the key to spatially consistent control in such worlds is to represent the effect of each action directly in world coordinates. To this end, we introduce ActionRoPE, a parameter-free action interface that converts action-driven camera displacement into world-coordinate rotary positions. As a result, tokens corresponding to the same world location retain the same positional address across time. These world coordinates further expose a spatial asymmetry in generation: regions covered by the initial observation are geometrically known, while newly revealed regions remain to be synthesized. We therefore propose Coordinate-Conditioned Noise Scheduling (CNS), which assigns token-wise denoising schedules according…
PDF arXiv Code
arXiv cs.CVCodeOct 19▲ 15
Frechet Forcing: Training Long-Horizon Streaming World Models from Short Clips
Yian Zhao, Yu Yan, Hongcan Guo, Ruochong Zheng, Jian Zhang +1
Streaming video world models generate the future chunk by chunk, conditioned on their own past and a stream of actions. Trained on short clips, they drift once a rollout exceeds the training horizon, as prediction errors re-enter the context and compound. A common remedy is to train the model on its own rollouts via distribution matching distillation (DMD). For action-conditioned world models, the teacher becomes a bottleneck: an external video model is agnostic to the actions, whereas the model's own pretrained network drifts in the same manner as the student. We observe that a long rollout need not match a teacher, provided it remains a realistic continuation of its own scene that follows the commanded actions. Realism and scene fidelity can be measured against real-data statistics, which remain valid at any horizon, while action following is preserved by the model's conditional generation loss. We propose Fréchet Forcing, which fine-tunes a world model together with its deployed multi-step sampler under three constraints. Generative anchoring applies the flow-matching loss after a self-generated history ending on real data. Context fidelity, a Fréchet distance (FD) on latent…
PDF arXiv Code
arXiv cs.CVCodeOct 18▲ 36
MT Forcing: Towards Stable and Consistent Multi-Turn Image Editing with Autoregressive Diffusion
Houyuan Chen, Fanzhou Wang, Xiangyu Fan, Zesong Qiu, Yukun Wei +7
Multi-turn image editing aims to modify an image according to a sequence of instructions, enabling more effective interactive visual content creation for users. However, existing methods often exhibit noticeable drift after only a few editing turns, manifesting in two aspects: 1) unstable visual quality, with accumulating artifacts and degraded details; and 2) inconsistent image content, with unintended changes to preserved regions and prior edits. To address these challenges, we propose MT Forcing, a novel training paradigm for multi-turn image editing with unified multimodal models (UMMs) based on autoregressive diffusion. Specifically, we propose Single-Turn Distribution Matching Distillation (Single-Turn DMD), which supervises student self-rollouts with a teacher that applies cumulative edits to the source image in a single turn, providing visual-quality guidance without recursive error accumulation. Additionally, we introduce Teacher-Forcing Guidance, which supervises history-dependent transitions on reference trajectories during warmup and as a regularizer during distillation, encouraging cross-turn content consistency beyond what per-turn distribution matching alone can…
PDF arXiv Code
arXiv cs.LGCodeOct 18▲ 7
World Model Interpretability: From Decoding Representations to Understanding Latent Worlds
Jize Wang, Yongcheng Jing, Xinyi Le, Cailian Chen, Xinping Guan +1
Emerging Joint-Embedding Predictive Architectures (JEPAs) are moving beyond representation learning towards latent world models that support prediction, planning, and control. Yet their latent worlds naturally lack a human-interpretable interface. In this Perspective, we argue that as JEPA latents become internal states for prediction and action, interpretability must move beyond what they encode to how latent variables interact, evolve, and causally drive prediction. To this end, we envision a four-stage research agenda, involving Ground, Structure, Verify, and Act, to guide this shift toward actionable latent-world understanding. Within our framework, existing JEPA interpretability efforts provide early evidence for Ground, while systematic analysis of latent structure, causal verification, and intervention remains largely unexplored. Overall, this Perspective aims to call for a broader latent-world interpretability agenda that shifts attention from decoding representations toward understanding, validating, and acting on latent worlds.
PDF arXiv Code

Papers

Topics in this field

video understanding · video language · video generation291 papers47 this week
autoencoders · imagenet · autoencoder220 papers40 this week
visual question answering · vision language models · vision language151 papers39 this week
video generation · efficient video · autoregressive video178 papers33 this week
visual reasoning · multimodal reasoning · visual question answering166 papers31 this week
semantic segmentation · vision language · attention196 papers30 this week

Active contributors · 90 days

Recent papers

All in Research →

arXiv cs.CVCodeOct 19▲ 26
RoAR-VAE: Improving Latent Diffusability through Robust Prediction
Yang Yue, Fangyun Wei, Yitong Wang, Jinjing Zhao, Henry Zheng +2
An effective visual tokenizer must preserve image fidelity while producing latents that downstream diffusion models can learn efficiently. Prior work improves latent diffusability through semantic alignment or joint tokenizer--diffusion training. Revisiting REPA-E, a representative approach, we find that removing its diffusion objective and dedicated generative blocks while retaining semantic prediction from corrupted latents yields comparable downstream generation performance under a matched setting. This observation motivates a general principle of robust prediction: generation-relevant information should remain recoverable from corrupted latents. We instantiate this principle with two complementary objectives. RobustAlign predicts pretrained semantic features from corrupted latents to preserve high-level structure, while RobustRecon reconstructs the clean image to retain fine-grained spatial and appearance information. Together, they form Robust Alignment and Reconstruction VAE (RoAR-VAE). Our analysis suggests that robust prediction improves diffusability by making useful information more accessible in high-variance latent directions, which diffusion models tend to learn…
PDF arXiv Code
arXiv cs.ROCodeOct 19▲ 3
Vision-Language-Delta-Action: Learning Compact Deltas as Actionable Transition Interfaces for Robotic Manipulation
Lingsen Zhang, Wei Li, Jie He, Henghua Zhang, Kai Mo +3
Robotic manipulation is not merely observation-to-action generation, but the realization of task-specified state changes in the physical world. Existing vision-language-action (VLA) methods directly learn observation-to-action mappings, imposing a substantial learning burden due to the gap between semantic understanding and action execution. Although world-action models (WAMs) improve control through future prediction, modeling complete future states wastes capacity on static appearance and action-irrelevant changes. We propose Vision-Language-Delta-Action (VLDA), a paradigm that reformulates full future prediction as compact actionable transition modeling. Its core is a compact Delta variable that captures latent world-state changes, serving as an interface between vision-language understanding and action generation. VLDA learns Delta through a unified objective composed of the Local Transition Filter (LT-Filter) and Global Transition Anchor (GT-Anchor). LT-Filter captures task-relevant manipulation-centric changes while suppressing static reconstruction biases. GT-Anchor provides coarse future-level regularization to preserve the overall direction of state changes. Delta-Driven…
PDF arXiv Code
arXiv cs.CVCodeOct 19▲ 4
ActionRoPE: action as coordinates for spatially consistent control in planar-camera game world models
Zhiyang Deng, Yuzhe Wu, Zijun Lin, Yeying Jin
Game world models have made rapid progress toward real-time interactive generation. Yet action control is still predominantly implemented as feature conditioning, leaving the model to learn from training data how each action should move the visual world. This formulation is poorly matched to a broad class of planar-camera worlds, such as top-down and isometric games. In these worlds, player motion induces a geometrically well-defined planar camera displacement. We observe that the key to spatially consistent control in such worlds is to represent the effect of each action directly in world coordinates. To this end, we introduce ActionRoPE, a parameter-free action interface that converts action-driven camera displacement into world-coordinate rotary positions. As a result, tokens corresponding to the same world location retain the same positional address across time. These world coordinates further expose a spatial asymmetry in generation: regions covered by the initial observation are geometrically known, while newly revealed regions remain to be synthesized. We therefore propose Coordinate-Conditioned Noise Scheduling (CNS), which assigns token-wise denoising schedules according…
PDF arXiv Code
arXiv cs.CVCodeOct 19▲ 15
Frechet Forcing: Training Long-Horizon Streaming World Models from Short Clips
Yian Zhao, Yu Yan, Hongcan Guo, Ruochong Zheng, Jian Zhang +1
Streaming video world models generate the future chunk by chunk, conditioned on their own past and a stream of actions. Trained on short clips, they drift once a rollout exceeds the training horizon, as prediction errors re-enter the context and compound. A common remedy is to train the model on its own rollouts via distribution matching distillation (DMD). For action-conditioned world models, the teacher becomes a bottleneck: an external video model is agnostic to the actions, whereas the model's own pretrained network drifts in the same manner as the student. We observe that a long rollout need not match a teacher, provided it remains a realistic continuation of its own scene that follows the commanded actions. Realism and scene fidelity can be measured against real-data statistics, which remain valid at any horizon, while action following is preserved by the model's conditional generation loss. We propose Fréchet Forcing, which fine-tunes a world model together with its deployed multi-step sampler under three constraints. Generative anchoring applies the flow-matching loss after a self-generated history ending on real data. Context fidelity, a Fréchet distance (FD) on latent…
PDF arXiv Code
arXiv cs.CVCodeOct 18▲ 36
MT Forcing: Towards Stable and Consistent Multi-Turn Image Editing with Autoregressive Diffusion
Houyuan Chen, Fanzhou Wang, Xiangyu Fan, Zesong Qiu, Yukun Wei +7
Multi-turn image editing aims to modify an image according to a sequence of instructions, enabling more effective interactive visual content creation for users. However, existing methods often exhibit noticeable drift after only a few editing turns, manifesting in two aspects: 1) unstable visual quality, with accumulating artifacts and degraded details; and 2) inconsistent image content, with unintended changes to preserved regions and prior edits. To address these challenges, we propose MT Forcing, a novel training paradigm for multi-turn image editing with unified multimodal models (UMMs) based on autoregressive diffusion. Specifically, we propose Single-Turn Distribution Matching Distillation (Single-Turn DMD), which supervises student self-rollouts with a teacher that applies cumulative edits to the source image in a single turn, providing visual-quality guidance without recursive error accumulation. Additionally, we introduce Teacher-Forcing Guidance, which supervises history-dependent transitions on reference trajectories during warmup and as a regularizer during distillation, encouraging cross-turn content consistency beyond what per-turn distribution matching alone can…
PDF arXiv Code
arXiv cs.LGCodeOct 18▲ 7
World Model Interpretability: From Decoding Representations to Understanding Latent Worlds
Jize Wang, Yongcheng Jing, Xinyi Le, Cailian Chen, Xinping Guan +1
Emerging Joint-Embedding Predictive Architectures (JEPAs) are moving beyond representation learning towards latent world models that support prediction, planning, and control. Yet their latent worlds naturally lack a human-interpretable interface. In this Perspective, we argue that as JEPA latents become internal states for prediction and action, interpretability must move beyond what they encode to how latent variables interact, evolve, and causally drive prediction. To this end, we envision a four-stage research agenda, involving Ground, Structure, Verify, and Act, to guide this shift toward actionable latent-world understanding. Within our framework, existing JEPA interpretability efforts provide early evidence for Ground, while systematic analysis of latent structure, causal verification, and intervention remains largely unexplored. Overall, this Perspective aims to call for a broader latent-world interpretability agenda that shifts attention from decoding representations toward understanding, validating, and acting on latent worlds.
PDF arXiv Code

Activity · 12 weeks

Coming with more sources: repos

Fields · AI · updated 31m ago

Multimodal

Models that combine modalities such as vision-language models, video-language and any-to-any models.

492papers this week−34.4% vs last week

All papers · 3.2k With code · 201

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3k papers this month

Open problems in Multimodal

Lack of holistic video understanding benchmarks1 paper raises it · 1 addresses it
Existing benchmarks cover only partial evaluation dimensions, limiting systematic diagnosis of omni-modal video understanding.
- OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understandingraises · addresses
Long-horizon task execution and recovery1 paper raises it · 0 address it
Agents struggle with preserving progress, recovering from failures, and verifying physical states over long sequences.
- Has GPT-6-Astra Achieved Embodied Intelligence?raises
Efficient spatial reasoning for real-time interaction2 papers raise it · 2 address it
Current models use slow autoregressive decoding for compact output spaces, limiting interaction speed.
- VideoJev: System One Model for Video Understanding and Interactionraises · addresses
- MJev-Spatial: System One Spatial Intelligence via Native Attention Matchingraises · addresses
Cross-shot consistency in agentic video generation1 paper raises it · 1 addresses it
Maintaining spatial and temporal consistency across non-consecutive video shots is difficult.
- Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generationraises · addresses
Precise grounding for physical intelligence1 paper raises it · 1 addresses it
General-purpose backbones fail to precisely ground objects in clutter or for tiny targets at control speeds.
- GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitivesraises · addresses

Named by an AI from these papers' abstracts.