Papers.Data.Code

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Papers.Data.Code

📈 Monthly: Computer Vision | Apr 03 – May 03
#MonthlyDigest #CV

📄 Papers

World-R1: Reinforcing 3D Constraints for Text-to-Video Generation
#TextToVideo #ReinforcementLearning #3DConsistency
RL text-to-video tuning ⟶ boosts 3D consistency PSNR
→ Learn more...

LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model
#MultimodalLearning #ImageGeneration #DiffusionModels
Discrete diffusion LLM ⟶ unifies multimodal understanding and generation
→ Learn more...

Tuna-2: Pixel Embeddings Beat Vision Encoders for Multimodal Understanding and Generation
#MultimodalLearning #ImageGeneration #VisionLanguageModels
Direct patch embeddings ⟶ unified multimodal understanding generation
→ Learn more...

Vista4D: Video Reshooting with 4D Point Clouds
#NovelViewSynthesis #VideoDiffusion #3DReconstruction
4D point cloud reshooting ⟶ best camera and 3D consistency
→ Learn more...

CoInteract: Physically-Consistent Human-Object Interaction Video Synthesis via Spatially-Structured Co-Generation
#VideoGeneration #DiffusionTransformers #HumanObjectInteraction
Diffusion HOI video synthesis ⟶ improves stability and contact realism
→ Learn more...

💻 Repos

facex-engine/facex ⭐
#FaceVerification #Webassembly #CpuInference
Local face embeddings ⟶ browser CPU verification
→ Learn more...

📊 Datasets

Sleep Health & Daily Performance Dataset
#Classification #Regression #HealthConditions
Synthetic sleep health dataset ⟶ benchmarks 3 prediction tasks
→ Learn more...

⚡ Trends

▸ Unified multimodal models increasingly merge visual understanding, generation, and editing end-to-end.
▸ Video generation methods add explicit 3D or geometry grounding for consistency.
▸ Specialized architectural priors target realism in controllable human and camera-centric video synthesis.

🧭 TL;DR

📄 World-R1: Reinforcing 3D Constraints for Text-to-Video Generation
RL adds strong 3D-consistent video generation without architecture or inference changes

⭐ facex-engine/facex ⭐
Tiny local face verification runs fast on browser and CPU

💡 Vision models are converging toward unified, geometry-grounded, practically deployable generation systems.

via @Papers.Data.Code

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Papers.Data.Code

📄 Paper #Paper #Multimodal #VideoGeneration #DiffusionModels

UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors
👤 Houyuan Chen, Hong Li, Xianghao Kong et al.

🎯 Task
Multimodal video generation

💡 Idea
Stochastic condition masking enables omni-directional generation; decoupled gated LoRA adds per-modality adapters only for targets; cross-modal self-attention shares keys/values across modalities for alignment.

✨ Why it's interesting
Competitive with state of the art across tasks; robust in-the-wild with <1k training videos.

💻 Repo
⭐ houyuanchen111/UniVidX — 44 stars

🔗 paper

via @Papers.Data.Code

GitHub

GitHub - houyuanchen111/UniVidX: [SIGGRAPH 2026 / TOG] Official code of the paper "UniVidX: A Unified Multimodal Framework for…

[SIGGRAPH 2026 / TOG] Official code of the paper "UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors". - houyuanchen111/UniVidX

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Papers.Data.Code

📄 Paper #Paper #Tabular #DiffusionModels #DecisionTrees

Trees to Flows and Back: Unifying Decision Trees and Diffusion Models
👤 Sai Niranjan Ramachandran, Suvrit Sra

🎯 Task
Tabular generation and tree-to-network distillation

💡 Idea
Tree↔flow correspondence maps refined tree partitions to PF-ODEs and diffusion dynamics to hierarchies; GTSM unifies boosting and score matching. TREEFLOW conditions flows on tree paths, and DSM-TREE distills full tree decisions.

✨ Why it's interesting
TREEFLOW is 2× faster; best TSTR on 3/5 and best Wasserstein on 4/5 benchmarks.

🔗 paper

via @Papers.Data.Code

arXiv.org

Trees to Flows and Back: Unifying Decision Trees and Diffusion Models

Decision trees and diffusion models are ostensibly disparate model classes, one discrete and hierarchical, the other continuous and dynamic. This work unifies the two by establishing a crisp...

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Papers.Data.Code

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Papers.Data.Code

📈 Monthly: Multimodal & Agents | Apr 10 – May 10
#MonthlyDigest #Multimodal #Agents

📄 Papers

MolmoAct2: Action Reasoning Models for Real-world Deployment
#VisionLanguageAction #EmbodiedReasoning #ImitationLearning
Vision-language-action model ⟶ beats VLA baselines on 7 benchmarks
→ Learn more...

OpenSearch-VL: An Open Recipe for Frontier Multimodal Search Agents
#AgenticRL #ToolUse #MultimodalReasoning
Failure-aware multimodal search RL ⟶ +13.8 points on 7 benchmarks
→ Learn more...

Heterogeneous Scientific Foundation Model Collaboration
#AgentSystems #FoundationModels #ScientificAI
LLM-FM agent interface ⟶ scientific tasks on structured data
→ Learn more...

Beyond SFT-to-RL: Pre-alignment via Black-Box On-Policy Distillation for Multimodal RL
#ReinforcementLearning #KnowledgeDistillation #Reasoning
PRISM pre-alignment ⟶ boosts accuracy over SFT→RLVR
→ Learn more...

UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors
#VideoGeneration #DiffusionModels #ParameterEfficientFineTuning
Unified video diffusion ⟶ multimodal pixel-aligned generation
→ Learn more...

RADIO-ViPE: Online Tightly Coupled Multi-Modal Fusion for Open-Vocabulary Semantic SLAM in Dynamic Environments
#SLAM #OpenVocabulary #VisualLanguageModels
Tightly coupled VLM SLAM ⟶ open-vocabulary 3D in dynamics
→ Learn more...

💻 Repos

YanFangCS/GenLIP ⭐
#VisionEncoder #AutoregressivePretraining #OCR
Autoregressive ViT pretraining ⟶ strong Doc and OCR gains
→ Learn more...

RockeyCoss/LeapAlign_Code ⭐
#TextToImage #FlowMatching #PreferenceOptimization
Two-step leap trajectory ⟶ preference-aligns flow-matching T2I
→ Learn more...

📊 Datasets

gpic
#ImageGeneration #PermissiveLicense #ImageText
Permissive 100M image corpus ⟶ visual generation research
→ Learn more...

MathNet v0 — Olympiad Math Reasoning & Retrieval
#CompetitionMath #Multimodal #Retrieval
Multilingual Olympiad math dataset ⟶ reasoning and retrieval benchmark
→ Learn more...

⚡ Trends

▸ Agents increasingly orchestrate external tools or specialized models through structured interfaces.
▸ Multimodal RL training adds intermediate alignment stages to preserve reasoning quality.
▸ Shared action tokenization is emerging for embodied control and cross-embodiment transfer.

🧭 TL;DR

📄 MolmoAct2: Action Reasoning Models for Real-world Deployment
Open VLA model beats strong baselines with adaptive low-latency embodied reasoning.

💡 Multimodal agents are shifting toward tool-grounded, efficient, real-world deployment.

via @Papers.Data.Code

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Papers.Data.Code

📄 Paper #Paper #CV #VideoGeneration #DiffusionModels

AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation
👤 Yuchao Gu, Guian Fang, Yuxin Jiang et al.

🎯 Task
Any-step video generation

💡 Idea
Instead of endpoint consistency maps for fixed few-step sampling, it learns arbitrary-time flow-map transitions along the full ODE path, then uses shortcut backward simulation for on-policy distillation to cut discretization error and causal exposure bias.

✨ Why it's interesting
On 14B T2V, it gets 84.05 VBench at 4 NFEs and 84.41 at 32; beats Krea-Realtime-14B's 83.25 at 4 and rCM-14B's 83.73 at 4.

💻 Repo
⭐ NVlabs/AnyFlow — 202 stars
⭐ NVLabs/AnyFlow — 202 stars

🔗 paper

via @Papers.Data.Code

GitHub

GitHub - NVlabs/AnyFlow

Contribute to NVlabs/AnyFlow development by creating an account on GitHub.

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Papers.Data.Code

📋 Weekly Digest · May 09 – May 16
#WeeklyDigest

📄 Papers

AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation
#VideoGeneration #DiffusionModels #Distillation
Any-step video diffusion ⟶ 84.05 VBench at 4 NFEs
→ Learn more...

Flow-OPD: On-Policy Distillation for Flow Matching Models
#TextToImage #KnowledgeDistillation #ReinforcementLearning
On-policy flow distillation ⟶ boosts GenEval and OCR
→ Learn more...

SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture
#VisionLanguageModels #ImageGeneration #MixtureOfExperts
NEO-unify multimodal model ⟶ unifies understanding and generation
→ Learn more...

δ-mem: Efficient Online Memory for Large Language Models
#MemoryMechanisms #Attention #ParameterEfficientTuning
Online associative memory ⟶ steers attention for long-horizon tasks
→ Learn more...

LLMs Improving LLMs: Agentic Discovery for Test-Time Scaling
#TestTimeScaling #Reasoning #AgenticSearch
Offline replay controller ⟶ improves accuracy-cost tradeoffs
→ Learn more...

RubricEM: Meta-RL with Rubric-guided Policy Decomposition beyond Verifiable Rewards
#ReinforcementLearning #AgentTraining #LongContextReasoning
Rubric-guided meta-RL ⟶ stagewise credit for research agents
→ Learn more...

💻 Repos

antirez/ds4 ⭐
#Metal #KvCache #OpenaiCompatible
Metal local inference ⟶ 1M context with disk KV cache
→ Learn more...

facebookresearch/ProgramBench ⭐
#Benchmark #SoftwareEngineering #ReverseEngineering
Program reconstruction benchmark ⟶ tests LM reverse engineering
→ Learn more...

sparolab/KISS-IMU ⭐
#InertialOdometry #SelfSupervised #LidarPseudoLabels
Self-supervised IMU odometry ⟶ denoises raw IMU with LiDAR labels
→ Learn more...

📊 Datasets

AI Index Data: Growth, Talent (Cambridge/Harvard)
#GlobalAI #CountryIndicators #LongitudinalData
Global AI panel dataset ⟶ cross-country trend forecasting
→ Learn more...

giant-permissive-image-corpus
#ImageGeneration #PermissiveLicense #ImageDataset
Permissive image corpus ⟶ trains visual generation
→ Learn more...

➡️ Tomorrow — Efficient ML Monthly

via @Papers.Data.Code

👍1

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Papers.Data.Code

📈 Monthly · Efficient ML · Apr 17 – May 17
#MonthlyDigest #EfficientML

📄 Papers

Trees to Flows and Back: Unifying Decision Trees and Diffusion Models
#DiffusionModels #DecisionTrees #KnowledgeDistillation
Trees and flows ⟶ faster tabular generation
→ Learn more...

📊 Datasets

MSR-ACC/TAE25
#QuantumChemistry #AtomizationEnergy #CoupledCluster
Quantum chemistry dataset ⟶ trains atomization energy models
→ Learn more...

AI Index Data: Growth, Talent (Cambridge/Harvard)
#GlobalAI #CountryIndicators #LongitudinalData
Global AI panel dataset ⟶ cross-country trend forecasting
→ Learn more...

WHO Global Health Indicators for Prediction
#GlobalHealth #CountryLevel #WorldBank
Global health panel data ⟶ cross-country trend analysis
→ Learn more...

⚡ Trends

▸ Longitudinal country-level datasets increasingly target forecasting and cross-country trend analysis.
▸ Wide, linked, multi-table dataset formats are becoming standard for benchmarking.
▸ Efficiency gains come from unifying model families and distilling complex systems.

🧭 TL;DR

📄 Trees to Flows and Back: Unifying Decision Trees and Diffusion Models
Unifies trees and diffusion, delivering faster tabular generation and effective distillation.

💡 Efficiency advances increasingly come from unifying classical structures with generative modeling.

via @Papers.Data.Code

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Papers.Data.Code

📄 Paper #Paper #CV #VideoGeneration #DiffusionModels

Causal Forcing++: Scalable Few-Step Autoregressive Diffusion Distillation for Real-Time Interactive Video Generation
👤 Min Zhao, Hongzhou Zhu, Kaiwen Zheng et al.

🎯 Task
Real-time autoregressive video generation

💡 Idea
Instead of costly AR-teacher ODE trajectory distillation, it initializes few-step AR students with causal consistency distillation: same AR flow-map target, but learned from single online adjacent-step teacher updates, making frame-wise 1-2 step rollout practical.

✨ Why it's interesting
At frame-wise 2-step, beats 4-step chunk-wise Causal Forcing by +0.1 VBench Total, +0.3 Quality, +0.335 VisionReward; 50% lower first-frame latency, ~4x cheaper Stage 2.

💻 Repo
⭐ thu-ml/Causal-Forcing — 665 stars

🔗 paper

via @Papers.Data.Code

GitHub

GitHub - thu-ml/Causal-Forcing: [ICML 2026] Official codebase for "Causal Forcing: Autoregressive Diffusion Distillation Done Right…

[ICML 2026] Official codebase for "Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation" & Causal Forci...

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Papers.Data.Code

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