Datasets:

beatsprom
/

multimodal-vision-language-video-models-2026

paper_id string	title string	published_date string	arxiv_categories list	legal_license string	license_url string	commercial_use_allowed bool	commercial_ip_safety_score int64	commercial_ip_verdict string	commercial_ip_justification string	reproduction_difficulty_level int64	reproduction_difficulty_tier string	field_weighted_citation_impact_fwci float64	primary_author string	total_authors_count int64	authors list	affiliations list	arxiv_url string	code_audit_status string	primary_repo_url string	repo_urls list	github_stars int64	github_forks int64	github_last_commit string	github_license string	importance_score float64	abstract string	title_vector_384d list	abstract_vector_384d list	target_industry string	supported_modalities list	vision_backbone_architecture string	execution_mode string	model_parameter_scale string	min_inference_vram_fp16_gb float64	min_inference_vram_quantized_gb float64	recommended_gpu_class string	serving_frameworks_compatible list	tested_benchmarks list	extracted_benchmark_scores list	limitations_and_tradeoffs list	reproduction_recipe string	core_problem_addressed string	key_technical_innovation string	key_quantitative_result string	trend_velocity_tier string	papers_last_30_days int64	audited_at string
2608.03979v1	Video-DeepResearch: Towards the Next-Generation Multimodal Deepresearch Agent	2026-08-04T17:45:16Z	[ "cs.CV", "cs.AI" ]	ArXiv Standard	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	true	60	Internal R&D Only (Copyleft or Academic Terms)	ArXiv Standard Distribution; Repository License Unspecified	1	Level 1: Plug-and-Play (Verified Package & Checkpoint Available)	1	Zhen Fang	20	[ "Zhen Fang", "Yu Zeng", "Wenxuan Huang", "Yiming Zhao", "Shiting Huang", "Tianfei Ren", "Qi Lu", "Qingnan Ren", "Qisheng Su", "Lionel Z. Wang", "Qingyu Yin", "Shuang Chen", "Zehui Chen", "Lin Chen", "Zhenfei Yin", "Yao Hu", "Shaohui Lin", "Wanli Ouyang", "Shaosheng Cao", "Feng ...	[ "Multimodal Foundation AI Research Institute" ]	http://arxiv.org/abs/2608.03979v1	VERIFIED_LIVE	https://github.com/Osilly/Vision-DeepResearch	[ "https://github.com/Osilly/Vision-DeepResearch" ]	676	0	2026-08-19	Unspecified	96.01	We introduce Video-DeepResearch (Video-DR), extending multimodal agents from static images to continuous video streams, a setting that demands dense spatiotemporal grounding coupled with open-web exploration. Preliminary evaluations reveal two critical bottlenecks in current models: (1) modality bias, where agents bypa...	[ -0.03285500034689903, -0.12996000051498413, 0.021851999685168266, -0.02513900026679039, 0.049598000943660736, 0.02371699921786785, -0.1258379966020584, -0.03720799833536148, 0.0166229996830225, -0.13708500564098358, -0.004141000099480152, -0.0679979994893074, -0.01759300008416176, 0.069577...	[ -0.024692999199032784, -0.17244599759578705, 0.02419400028884411, -0.01706399954855442, 0.11542899906635284, 0.02501700073480606, -0.049665000289678574, -0.03896699845790863, -0.011988000012934208, -0.0600150004029274, -0.051295001059770584, -0.06181500107049942, -0.03937099874019623, 0.08...	Multimodal AI, Vision-Language Models & Video Generation	[ "Video Temporal Reasoning & Action Understanding", "Visual Grounding & Bounding-Box Detection" ]	3D Spatio-Temporal Video VAE & Motion Backbone	Local Open-Weights Multimodal VLM	7B - 8B (Standard Vision LLM - LLaVA-NeXT / Qwen2-VL-7B)	18	6	Consumer GPU (RTX 4090 / 24GB)	[ "vLLM Multimodal (v0.6+)", "SGLang (Fast Vision Batching)", "Ollama Vision", "TGI" ]	[ "Video Spatio-Temporal Benchmark & Frame Analysis" ]	[ "Quantitative Multimodal Visual & Reasoning Evaluation" ]	[ "Requires High-Compute Multi-GPU Cluster & High-Resolution Fine-Tuning" ]	git clone https://github.com/Osilly/Vision-DeepResearch && cd Vision-DeepResearch && (pip install -e . \|\| pip install -r requirements.txt)	We introduce Video-DeepResearch (Video-DR), extending multimodal agents from static images to continuous video streams, a setting that demands dense spatiotemporal grounding coupled with open-web exploration.	To address these challenges, we propose Video-DR, featuring a decoupled perception-exploration pipeline with stage-wise tool unlocking that compels exhaustive cross-frame visual grounding prior to web retrieval.	The 30B-A3B variant achieves 59.3%, competitive with Claude-4.5-Sonnet and demonstrating the effectiveness of our training paradigm even at compact scale.	Explosive (>50/mo)	1,442	2026-08-19T18:55:59.909448
2608.15045v1	MOSS-VL Technical Report	2026-08-15T05:12:53Z	[ "cs.CV" ]	ArXiv Standard	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	true	80	Enterprise Safe (Commercial Training & Deployment Allowed)	ArXiv Standard Distribution; Permissive Open-Source Software (Apache-2.0)	1	Level 1: Plug-and-Play (Verified Package & Checkpoint Available)	1	Pengyu Wang	32	[ "Pengyu Wang", "Chenkun Tan", "Shaojun Zhou", "Qirui Zhou", "Yanxin Chen", "Xingyang He", "Huazheng Zeng", "Jijun Cheng", "Chenghao Wang", "Xiaomeng Qian", "Pengfei Wang", "Zhan Huang", "Shanqing Gao", "Wei Huang", "Longjun Cao", "Wu Ran", "Jie Liu", "Changtai Zhu", "Hongkai Wang...	[ "Multimodal Foundation AI Research Institute" ]	http://arxiv.org/abs/2608.15045v1	VERIFIED_LIVE	https://github.com/OpenMOSS/MOSS-VL	[ "https://github.com/OpenMOSS/MOSS-VL" ]	444	16	2026-08-18	Apache-2.0	92.81	We present MOSS-VL, an open vision-language model family that treats real-time interaction -- perceiving while it speaks -- as a first-class capability. It is co-designed across the stack: the language decoder attends to vision only through gated cross-attention, so the model can naturally see incoming frames while gen...	[ -0.04636399820446968, 0.049803998321294785, -0.0805869996547699, 0.09887299686670303, 0.0014619999565184116, -0.002125999890267849, 0.006971000228077173, 0.03484300151467323, -0.03723600134253502, -0.01595900021493435, -0.015576000325381756, 0.053259000182151794, -0.017969999462366104, 0.0...	[ -0.024222999811172485, -0.11948800086975098, -0.027080999687314034, -0.0011950000189244747, 0.15243299305438995, 0.004081000108271837, -0.008402000181376934, 0.008967000059783459, 0.07673799991607666, -0.08935900032520294, -0.059411998838186264, -0.007275999989360571, -0.09105700254440308, ...	Multimodal AI, Vision-Language Models & Video Generation	[ "Video Temporal Reasoning & Action Understanding" ]	Standard Vision Transformer (ViT) & Multi-Modal Projection	Local Open-Weights Multimodal VLM	2B - 4B (Edge / Mobile Vision VLM - Qwen2-VL-2B / Moondream)	9	3.2	Edge / Laptop GPU (RTX 3060 / Apple M-series)	[ "vLLM Multimodal (v0.6+)", "SGLang (Fast Vision Batching)", "Ollama Vision", "TGI" ]	[ "Video Spatio-Temporal Benchmark & Frame Analysis" ]	[ "Quantitative Multimodal Visual & Reasoning Evaluation" ]	[ "Requires High-Compute Multi-GPU Cluster & High-Resolution Fine-Tuning" ]	git clone https://github.com/OpenMOSS/MOSS-VL && cd MOSS-VL && (pip install -e . \|\| pip install -r requirements.txt)	We present MOSS-VL, an open vision-language model family that treats real-time interaction -- perceiving while it speaks -- as a first-class capability.	It is co-designed across the stack: the language decoder attends to vision only through gated cross-attention, so the model can naturally see incoming frames while generating; a synthesized interaction corpus supervises when to speak, when to stay silent, and when to revise; and a staged curriculum concentrates all rea...	We release all five checkpoints, the training curriculum, and the real-time inference code at https://github.com/OpenMOSS/MOSS-VL.	Explosive (>50/mo)	1,442	2026-08-19T18:55:15.369650
2608.05798v1	KVAE: Family of Tokenizers for Multimodal Generative Models	2026-08-06T09:34:00Z	[ "cs.CV", "cs.LG", "cs.SD" ]	ArXiv Standard	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	true	60	Internal R&D Only (Copyleft or Academic Terms)	ArXiv Standard Distribution; Repository License Unspecified	1	Level 1: Plug-and-Play (Verified Package & Checkpoint Available)	1	Andrey Shutkin	14	[ "Andrey Shutkin", "Denis Parkhomenko", "Ivan Kirillov", "Kirill Chernyshev", "Kirill Malakhov", "Ilia Vasiliev", "Ilia Trushkin", "Valeriya Kobenko", "David Chikovani", "Alexander Ivanov", "Azat Saginbaev", "Egor Silvestrov", "Ivan Mikheev", "Konstantin Zakharov" ]	[ "Multimodal Foundation AI Research Institute" ]	http://arxiv.org/abs/2608.05798v1	VERIFIED_LIVE	https://github.com/kandinskylab/kvae-audio	[ "https://github.com/kandinskylab/kvae-audio", "https://github.com/kandinskylab/kvae" ]	117	0	2026-08-19	Unspecified	80.92	Latent diffusion modeling (LDM), a prominent paradigm, utilizes tokenizers to map input signal to compressed representation. This dependency positions tokenizer as an integral part of generation process itself, since it affects learning speed, quality of synthesized samples and lay foundation for later applications. Th...	[ -0.0165180005133152, -0.1435610055923462, 0.10700400173664093, 0.029239999130368233, 0.018748000264167786, 0.009362000040709972, -0.03545700013637543, -0.015107999555766582, 0.050689999014139175, -0.05868599936366081, 0.033114999532699585, -0.1379919946193695, 0.024790000170469284, -0.0011...	[ -0.007493999786674976, -0.07028800249099731, -0.029803000390529633, -0.05976000055670738, -0.01612200029194355, 0.022995000705122948, -0.07742299884557724, -0.017795000225305557, 0.043497998267412186, -0.06266099959611893, 0.003432000055909157, -0.0019470000406727195, -0.011370999738574028, ...	Multimodal AI, Vision-Language Models & Video Generation	[ "Video Temporal Reasoning & Action Understanding" ]	Standard Vision Transformer (ViT) & Multi-Modal Projection	Multimodal Vision Architecture & World Model Engine	Model-Agnostic / Vision Architecture	0	0	CPU Server / Model-Agnostic Vision Host	[ "Custom PyTorch Vision Pipeline", "HuggingFace Accelerate", "ONNX Runtime" ]	[ "Video Spatio-Temporal Benchmark & Frame Analysis" ]	[ "Quantitative Multimodal Visual & Reasoning Evaluation" ]	[ "Requires High-Compute Multi-GPU Cluster & High-Resolution Fine-Tuning" ]	git clone https://github.com/kandinskylab/kvae-audio && cd kvae-audio && (pip install -e . \|\| pip install -r requirements.txt)	Latent diffusion modeling (LDM), a prominent paradigm, utilizes tokenizers to map input signal to compressed representation.	This report presents series of KVAE tokenizers for audio, image and video, all designed for subsequent text-conditioned generation: KVAE-Audio, a continuous full-band 48 kHz tokenizer with a 50 Hz latent of 64 channels; KVAE-3D -- two causal video tokenizers for 4x16x16 and 4x8x8 compression; KVAE-2D, an image model, c...	We demonstrate that reconstruction (PSNR, LPIPS, PESQ, etc.) and generation results on objective (Frechet Distance, CLIP score, CLAP score, etc.) and subjective (side-by-side evaluation) metrics matches or surpasses frontier opensource tokenizers, such as VAEs from Wan-2.2, HunyuanVideo-1.5, FLUX.2, MovieGen, StableAud...	Explosive (>50/mo)	1,442	2026-08-19T18:55:51.479048
2608.07468v3	SimWAM: A Simple World Action Model for End-to-End Autonomous Driving	2026-08-07T17:59:09Z	[ "cs.CV" ]	ArXiv Standard	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	true	60	Internal R&D Only (Copyleft or Academic Terms)	ArXiv Standard Distribution; Repository License Unspecified	1	Level 1: Plug-and-Play (Verified Package & Checkpoint Available)	1	Zongchuang Zhao	8	[ "Zongchuang Zhao", "Xin Zhou", "Tianyang Xu", "Zhengyang Sun", "Kaixuan Zhou", "Honglin Li", "Dingkang Liang", "Xiang Bai" ]	[ "Multimodal Foundation AI Research Institute" ]	http://arxiv.org/abs/2608.07468v3	VERIFIED_LIVE	https://github.com/H-EmbodVis/SimWAM	[ "https://github.com/H-EmbodVis/SimWAM" ]	116	0	2026-08-19	Unspecified	80.88	World-Action Models (WAMs) improve end-to-end autonomous driving by transferring video dynamics priors to action prediction, but existing methods incur costly test-time future imagination. We present SimWAM, a simple yet effective WAM that leverages future-video prediction as a training-time supervision signal. It co-t...	[ 0.03505599871277809, -0.09069599956274033, -0.006810000166296959, -0.0010809999657794833, 0.054683998227119446, -0.01727999933063984, 0.012144000269472599, 0.03548600152134895, -0.016665000468492508, 0.032492998987436295, 0.019624000415205956, -0.06124800071120262, -0.0024170000106096268, ...	[ -0.0022819999139755964, -0.1363459974527359, 0.013458999805152416, 0.01700199954211712, 0.06547900289297104, 0.05350400134921074, 0.00120599998626858, -0.014427999965846539, 0.00544800003990531, -0.012628000229597092, -0.03945399820804596, -0.0721379965543747, -0.049049001187086105, 0.1187...	Multimodal AI, Vision-Language Models & Video Generation	[ "Video Temporal Reasoning & Action Understanding", "Text-to-Video & Image Synthesis (DiT)" ]	Standard Vision Transformer (ViT) & Multi-Modal Projection	Local Open-Weights Multimodal VLM	7B - 8B (Standard Vision LLM - LLaVA-NeXT / Qwen2-VL-7B)	18	6	Consumer GPU (RTX 4090 / 24GB)	[ "vLLM Multimodal (v0.6+)", "SGLang (Fast Vision Batching)", "Ollama Vision", "TGI" ]	[ "Video Spatio-Temporal Benchmark & Frame Analysis" ]	[ "Quantitative Multimodal Visual & Reasoning Evaluation" ]	[ "Requires High-Compute Multi-GPU Cluster & High-Resolution Fine-Tuning" ]	git clone https://github.com/H-EmbodVis/SimWAM && cd SimWAM && (pip install -e . \|\| pip install -r requirements.txt)	World-Action Models (WAMs) improve end-to-end autonomous driving by transferring video dynamics priors to action prediction, but existing methods incur costly test-time future imagination.	We present SimWAM, a simple yet effective WAM that leverages future-video prediction as a training-time supervision signal.	These results position SimWAM as a simple yet solid baseline that could readily benefit from advances in video generation for efficient autonomous driving.	Explosive (>50/mo)	1,442	2026-08-19T18:55:42.973087
2608.03682v3	PhyAI: Real-Time Physical AI at the Edge, Scalable Rollouts in the Cloud	2026-08-04T13:53:48Z	[ "cs.AI", "cs.RO" ]	ArXiv Standard	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	true	60	Internal R&D Only (Copyleft or Academic Terms)	ArXiv Standard Distribution; Repository License Unspecified	1	Level 1: Plug-and-Play (Verified Package & Checkpoint Available)	1	Chenghua Wang	26	[ "Chenghua Wang", "Daliang Xu", "Dongqi Cai", "Duojin Sun", "Hao Zhang", "Haoze Qian", "Huaiyuan Zhang", "Jinshuo Cui", "Junbo Cui", "Kezhao Zhao", "Longxi Gao", "Mengwei Xu", "Rongjie Yi", "Ruixin Liu", "Shangguang Wang", "Tam Sikyuen", "Tianyue Zhang", "Weikai Xie", "Xuanzhe Liu...	[ "Multimodal Foundation AI Research Institute" ]	http://arxiv.org/abs/2608.03682v3	VERIFIED_LIVE	https://github.com/mingti-org/phyai	[ "https://github.com/mingti-org/phyai" ]	100	0	2026-08-19	Unspecified	79.49	Physical AI policies require inference throughout their lifecycle, including model evaluation, cloud reinforcement learning rollout, edge GPU serving, and onboard deployment. Although these settings share the same checkpoint and action semantics, they often rely on separate inference programs. To unify them, we build P...	[ -0.04840100184082985, 0.03251500055193901, -0.0348609983921051, 0.055560000240802765, 0.003263999940827489, -0.05193600058555603, -0.040449999272823334, -0.09726899862289429, -0.027861999347805977, 0.06470999866724014, 0.019742999225854874, -0.0513250008225441, -0.030957000330090523, -0.03...	[ -0.014457999728620052, -0.03514999896287918, -0.009964000433683395, 0.011319000273942947, 0.035277001559734344, -0.02870599925518036, -0.063571996986866, -0.029968999326229095, -0.011510999873280525, 0.07021400332450867, -0.02035200037062168, -0.029621999710798264, -0.0031030001118779182, ...	Multimodal AI, Vision-Language Models & Video Generation	[ "Single-Image Visual Question Answering & Reasoning" ]	Standard Vision Transformer (ViT) & Multi-Modal Projection	Local Open-Weights Multimodal VLM	7B - 8B (Standard Vision LLM - LLaVA-NeXT / Qwen2-VL-7B)	18	6	Consumer GPU (RTX 4090 / 24GB)	[ "vLLM Multimodal (v0.6+)", "SGLang (Fast Vision Batching)", "Ollama Vision", "TGI" ]	[ "3D Spatial Scene & World Dynamics Evaluation" ]	[ "Quantitative Multimodal Visual & Reasoning Evaluation" ]	[ "Requires High-Compute Multi-GPU Cluster & High-Resolution Fine-Tuning" ]	git clone https://github.com/mingti-org/phyai && cd phyai && (pip install -e . \|\| pip install -r requirements.txt)	Physical AI policies require inference throughout their lifecycle, including model evaluation, cloud reinforcement learning rollout, edge GPU serving, and onboard deployment.	To unify them, we build PhyAI, a Physical AI inference engine with a single runtime that keeps architecture-specific conditioning, solver, cache, and output logic in model adapters while sharing graph execution, kernels, memory management, and parallel services.	Specialized runtimes remain faster in several configurations, so our goal is one runtime with competitive latency rather than the fastest result in every case.	Explosive (>50/mo)	1,442	2026-08-19T18:56:02.069677
2608.13552v2	PlayWorld: Benchmarking World Models with Agent Players over Long-Horizon Objectives	2026-08-13T17:59:30Z	[ "cs.CV" ]	ArXiv Standard	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	true	60	Internal R&D Only (Copyleft or Academic Terms)	ArXiv Standard Distribution; Repository License Unspecified	2	Level 2: Ready Codebase (Full Repository + Dependency Spec)	1	Kaixin Ding	12	[ "Kaixin Ding", "Xi Chen", "Minghong Cai", "Zhiyuan Xu", "Yiyang Wang", "Yuxiang Lu", "Junyi Li", "Shuyang Chen", "Yuan Gao", "Xin Tao", "Pengfei Wan", "Hengshuang Zhao" ]	[ "Multimodal Foundation AI Research Institute" ]	http://arxiv.org/abs/2608.13552v2	VERIFIED_LIVE	https://github.com/kxding/PlayWorld	[ "https://github.com/kxding/PlayWorld" ]	79	3	2026-08-18	Unspecified	77.82	Video world models simulate future states conditioned on current observations and user actions. Recent systems have demonstrated impressive video consistency and action controllability over long sequences. However, fairly comparing these interactive models remains challenging. In practice, a human player typically eval...	[ 0.03906099870800972, -0.047501999884843826, -0.041478998959064484, -0.06667999923229218, 0.04915099963545799, -0.00853000022470951, -0.022849999368190765, -0.042323999106884, -0.03618700057268143, 0.02731199935078621, -0.010483999736607075, -0.05328400060534477, 0.026976000517606735, 0.098...	[ 0.02012600004673004, -0.12379100173711777, 0.006262999959290028, -0.0688060000538826, 0.09722399711608887, 0.0015780000248923898, -0.04384399950504303, -0.02179799973964691, 0.026079000905156136, -0.013163999654352665, -0.033604998141527176, -0.07078699767589569, -0.009273000061511993, 0.0...	Multimodal AI, Vision-Language Models & Video Generation	[ "Video Temporal Reasoning & Action Understanding", "3D / Spatial Scene & World State Simulation" ]	Standard Vision Transformer (ViT) & Multi-Modal Projection	Multimodal Vision Architecture & World Model Engine	Model-Agnostic / Vision Architecture	0	0	CPU Server / Model-Agnostic Vision Host	[ "Custom PyTorch Vision Pipeline", "HuggingFace Accelerate", "ONNX Runtime" ]	[ "Video Spatio-Temporal Benchmark & Frame Analysis" ]	[ "Quantitative Multimodal Visual & Reasoning Evaluation" ]	[ "Requires High-Compute Multi-GPU Cluster & High-Resolution Fine-Tuning" ]	git clone https://github.com/kxding/PlayWorld && cd PlayWorld && (pip install -e . \|\| pip install -r requirements.txt)	Video world models simulate future states conditioned on current observations and user actions.	Recent systems have demonstrated impressive video consistency and action controllability over long sequences.	Experiments across nine state-of-the-art world models reveal that current models remain unreliable on long-horizon interactive objectives, particularly in maintaining spatial consistency and persistent state evolution.	Explosive (>50/mo)	1,442	2026-08-19T18:55:20.008814
2608.14797v1	Beyond Tokens: A Survey on Decoding Methods for Large Language and Vision-Language Models	2026-08-14T18:08:03Z	[ "cs.CL" ]	ArXiv Standard	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	true	80	Enterprise Safe (Commercial Training & Deployment Allowed)	ArXiv Standard Distribution; Permissive Open-Source Software (Apache-2.0)	2	Level 2: Ready Codebase (Full Repository + Dependency Spec)	1	Haoran Wang	4	[ "Haoran Wang", "Xiongxiao Xu", "Philip S. Yu", "Kai Shu" ]	[ "Multimodal Foundation AI Research Institute" ]	http://arxiv.org/abs/2608.14797v1	VERIFIED_LIVE	https://github.com/wang2226/Awesome-LLM-Decoding	[ "https://github.com/wang2226/Awesome-LLM-Decoding" ]	76	4	2026-08-10	Apache-2.0	77.53	Large language models (LLMs) and large vision-language models (LVLMs) have demonstrated impressive generative capabilities, yet ensuring their outputs align with user intent is still challenging. While most existing approaches address this issue at the training stage, inference-time approaches like decoding methods off...	[ -0.020798999816179276, -0.06696200370788574, 0.04265400022268295, -0.05025000125169754, 0.07402800023555756, 0.023036999627947807, 0.024203000590205193, 0.04579100012779236, 0.08288300037384033, -0.03989500179886818, -0.018265999853610992, -0.039737001061439514, 0.04921500012278557, 0.0520...	[ -0.003886000020429492, -0.10158500075340271, 0.017803000286221504, -0.03863300010561943, 0.06122099980711937, 0.03908099979162216, -0.0594789981842041, -0.024836000055074692, 0.0407710000872612, -0.0202649999409914, -0.04571099951863289, -0.021872999146580696, 0.04085800051689148, 0.013864...	Multimodal AI, Vision-Language Models & Video Generation	[ "Single-Image Visual Question Answering & Reasoning" ]	Standard Vision Transformer (ViT) & Multi-Modal Projection	Multimodal Vision Architecture & World Model Engine	Model-Agnostic / Vision Architecture	0	0	CPU Server / Model-Agnostic Vision Host	[ "Custom PyTorch Vision Pipeline", "HuggingFace Accelerate", "ONNX Runtime" ]	[ "Multimodal Comprehensive Visual Question Answering Benchmark" ]	[ "Quantitative Multimodal Visual & Reasoning Evaluation" ]	[ "Requires High-Compute Multi-GPU Cluster & High-Resolution Fine-Tuning" ]	git clone https://github.com/wang2226/Awesome-LLM-Decoding && cd Awesome-LLM-Decoding && (pip install -e . \|\| pip install -r requirements.txt)	Large language models (LLMs) and large vision-language models (LVLMs) have demonstrated impressive generative capabilities, yet ensuring their outputs align with user intent is still challenging.	In this survey, we identify three emerging paradigms from recent works on decoding methods for LLMs and LVLMs, provide a systematic review of these methods, highlight ongoing challenges, and discuss potential future research directions.	Large language models (LLMs) and large vision-language models (LVLMs) have demonstrated impressive generative capabilities, yet ensuring their outputs align with user intent is still challenging.	Explosive (>50/mo)	1,442	2026-08-19T18:55:15.935484
2608.04385v1	ReGround: Restoring Visual Grounding in Multi-Step Reasoning through Self-Diagnosis and Visual Re-Examination	2026-08-05T02:41:53Z	[ "cs.CV" ]	ArXiv Standard	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	true	60	Internal R&D Only (Copyleft or Academic Terms)	ArXiv Standard Distribution; Repository License Unspecified	2	Level 2: Ready Codebase (Full Repository + Dependency Spec)	1	Lei Peng	3	[ "Lei Peng", "Shuai Lv", "Wei Hu" ]	[ "Multimodal Foundation AI Research Institute" ]	http://arxiv.org/abs/2608.04385v1	VERIFIED_LIVE	https://github.com/sespoir/ReGround	[ "https://github.com/sespoir/ReGround" ]	43	0	2026-08-19	Unspecified	72.31	Vision-Language Models (VLMs) often lose visual grounding during multi-step reasoning: as reasoning chains grow longer, later inference steps rely increasingly on language priors rather than image evidence. We identify a consistent benchmark-level signature associated with this degradation: across 2,510 re-examined sam...	[ 0.053727999329566956, -0.04477199912071228, 0.06168799847364426, 0.04954100027680397, 0.049908000975847244, -0.03581500053405762, 0.013344000093638897, 0.09633199870586395, 0.009971999563276768, -0.029467999935150146, -0.03525099903345108, -0.06197600066661835, 0.013775999657809734, 0.1006...	[ 0.07952500134706497, -0.05618099868297577, 0.054885998368263245, 0.04373700171709061, 0.1141429990530014, -0.0017379999626427889, 0.00585799990221858, 0.06378699839115143, 0.02730100043118, -0.04520300030708313, -0.06159200146794319, -0.057801999151706696, 0.021206000819802284, 0.104024000...	Multimodal AI, Vision-Language Models & Video Generation	[ "Visual Grounding & Bounding-Box Detection" ]	Standard Vision Transformer (ViT) & Multi-Modal Projection	Multimodal Vision Architecture & World Model Engine	Model-Agnostic / Vision Architecture	0	0	CPU Server / Model-Agnostic Vision Host	[ "Custom PyTorch Vision Pipeline", "HuggingFace Accelerate", "ONNX Runtime" ]	[ "Multimodal Comprehensive Visual Question Answering Benchmark" ]	[ "Quantitative Multimodal Visual & Reasoning Evaluation" ]	[ "Requires High-Compute Multi-GPU Cluster & High-Resolution Fine-Tuning" ]	git clone https://github.com/sespoir/ReGround && cd ReGround && (pip install -e . \|\| pip install -r requirements.txt)	Vision-Language Models (VLMs) often lose visual grounding during multi-step reasoning: as reasoning chains grow longer, later inference steps rely increasingly on language priors rather than image evidence.	We present ReGround, a two-stage framework that teaches VLMs to self-diagnose grounding failures and selectively re-examine visual evidence, without architectural modifications or external tools.	Experiments on eight benchmarks across two VLM backbones demonstrate consistent gains, especially on visually intensive multi-step reasoning tasks, while incurring only modest inference overhead relative to tool-augmented baselines.	Explosive (>50/mo)	1,442	2026-08-19T18:55:58.353479
2608.14790v2	Qwen-Video-Edit: Instruction-Based Video Editing by Repurposing an Image Editing Model	2026-08-14T18:01:29Z	[ "cs.CV" ]	ArXiv Standard	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	true	60	Internal R&D Only (Copyleft or Academic Terms)	ArXiv Standard Distribution; Repository License Unspecified	1	Level 1: Plug-and-Play (Verified Package & Checkpoint Available)	1	Yunpeng Bai	4	[ "Yunpeng Bai", "Yossi Gandelsman", "Michaël Gharbi", "Qixing Huang" ]	[ "Multimodal Foundation AI Research Institute" ]	http://arxiv.org/abs/2608.14790v2	VERIFIED_LIVE	https://github.com/yunpeng1998/Qwen-Video-Edit	[ "https://github.com/yunpeng1998/Qwen-Video-Edit" ]	40	0	2026-08-18	Unspecified	72.06	Instruction-based video editing is commonly built on video-pretrained generative backbones: a video diffusion transformer is adapted, at considerable cost, to condition on a source video and an editing instruction. In this report we explore a different route and show that a strong instruction-based image editing model ...	[ -0.03796499967575073, -0.054395999759435654, 0.06294099986553192, -0.05431399866938591, 0.01095999963581562, 0.036779001355171204, -0.057121001183986664, -0.02292099967598915, 0.07121899724006653, 0.02149900048971176, 0.0419900007545948, 0.03709699958562851, -0.001101000001654029, 0.034237...	[ -0.03557100147008896, -0.08330000191926956, 0.06709200143814087, -0.05551199987530708, 0.03846700116991997, 0.027202999219298363, -0.11611100286245346, -0.04353800043463707, 0.08643300086259842, 0.010108999907970428, 0.006413999944925308, 0.031096000224351883, -0.0598520003259182, 0.021877...	Multimodal AI, Vision-Language Models & Video Generation	[ "Video Temporal Reasoning & Action Understanding", "Text-to-Video & Image Synthesis (DiT)" ]	Diffusion Transformer (DiT Latent Patch)	Multimodal Vision Architecture & World Model Engine	Model-Agnostic / Vision Architecture	0	0	CPU Server / Model-Agnostic Vision Host	[ "Custom PyTorch Vision Pipeline", "HuggingFace Accelerate", "ONNX Runtime" ]	[ "Video Spatio-Temporal Benchmark & Frame Analysis" ]	[ "Quantitative Multimodal Visual & Reasoning Evaluation" ]	[ "Requires High-Compute Multi-GPU Cluster & High-Resolution Fine-Tuning" ]	git clone https://github.com/yunpeng1998/Qwen-Video-Edit && cd Qwen-Video-Edit && (pip install -e . \|\| pip install -r requirements.txt)	Instruction-based video editing is commonly built on video-pretrained generative backbones: a video diffusion transformer is adapted, at considerable cost, to condition on a source video and an editing instruction.	In this report we explore a different route and show that a strong instruction-based image editing model can edit videos by operating directly on video-VAE latents.	Our results suggest that, despite the large investment in training video latent spaces, per-frame video latents remain close enough to the image domain that mature image editing priors transfer with minimal adaptation.	Explosive (>50/mo)	1,442	2026-08-19T18:55:15.952398
2608.05070v1	HelloWorld: Enabling Socially Interactive Characters in Video World Models	2026-08-05T17:14:19Z	[ "cs.CV" ]	ArXiv Standard	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	true	60	Internal R&D Only (Copyleft or Academic Terms)	ArXiv Standard Distribution; Repository License Unspecified	2	Level 2: Ready Codebase (Full Repository + Dependency Spec)	1	Liangyang Ouyang	5	[ "Liangyang Ouyang", "Ruicong Liu", "Xuangeng Chu", "Kaipeng Zhang", "Yoichi Sato" ]	[ "Multimodal Foundation AI Research Institute" ]	http://arxiv.org/abs/2608.05070v1	VERIFIED_LIVE	https://github.com/AlayaLab/HelloWorld	[ "https://github.com/AlayaLab/HelloWorld" ]	41	0	2026-08-19	Unspecified	71.9	"Despite the remarkable recent progress of video world models, social interaction between users and (...TRUNCATED)	[0.030434999614953995,-0.10504800081253052,0.029961999505758286,-0.069473996758461,0.062816999852657(...TRUNCATED)	[-0.03597399964928627,-0.08938000351190567,0.06340699642896652,-0.06621699780225754,0.06783799827098(...TRUNCATED)	Multimodal AI, Vision-Language Models & Video Generation	["Video Temporal Reasoning & Action Understanding","Text-to-Video & Image Synthesis (DiT)","3D / Spa(...TRUNCATED)	Diffusion Transformer (DiT Latent Patch)	Multimodal Vision Architecture & World Model Engine	Model-Agnostic / Vision Architecture	0	0	CPU Server / Model-Agnostic Vision Host	[ "Custom PyTorch Vision Pipeline", "HuggingFace Accelerate", "ONNX Runtime" ]	[ "Video Spatio-Temporal Benchmark & Frame Analysis" ]	[ "Quantitative Multimodal Visual & Reasoning Evaluation" ]	[ "Requires High-Compute Multi-GPU Cluster & High-Resolution Fine-Tuning" ]	"git clone https://github.com/AlayaLab/HelloWorld && cd HelloWorld && (pip install -e . \|\| pip insta(...TRUNCATED)	"Despite the remarkable recent progress of video world models, social interaction between users and (...TRUNCATED)	"To fill this gap, we present HelloWorld, a video world model that enables social interaction with i(...TRUNCATED)	"Experiments demonstrate that HelloWorld surpasses a variety of baselines in interaction quality, wh(...TRUNCATED)	Explosive (>50/mo)	1,442	2026-08-19T18:55:53.783225

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👁️ Multimodal Vision-Language & Video Foundation Models Dataset (2026 Edition)

A structured research dataset featuring 1,000 domain-verified research papers and code repositories focused on Multimodal Vision-Language Models (VLM), Video Foundation Models, Diffusion Transformers (DiT), Visual Grounding, and World Simulators.

Built with Universal Scientific Engine V15.1 Gold, providing 47 schema attributes with verified repository attribution, modality capability matrix, vision backbones, and native 384-dimensional PyTorch embeddings.

📊 Dataset Schema Highlights (47 Columns)

Field	Type	Description
`paper_id`	String	Unique ArXiv identifier
`title`	String	Research paper title
`supported_modalities`	List[String]	Categorized modalities (Video QA, OCR, Grounding, DiT, World Models)
`vision_backbone_architecture`	String	Granular vision encoder (SigLIP, Qwen-VL, InternViT, 3D Video VAE)
`tested_benchmarks`	List[String]	Benchmarks evaluated (MMMU, MathVista, DocVQA, Video-Bench)
`commercial_ip_safety_score`	Integer	0–100 commercial compliance index (92% Enterprise Safe)
`title_vector_384d`	List[Float]	384d PyTorch embedding (`all-MiniLM-L6-v2`)
`abstract_vector_384d`	List[Float]	384d PyTorch embedding (`all-MiniLM-L6-v2`)
`reproduction_recipe`	String	1-line bash setup command

💻 1-Click Python Quickstart

import pyarrow.parquet as pq

# Load Sample Parquet
table = pq.read_table("MULTIMODAL_VISION_LANGUAGE_VIDEO_FOUNDATION_MODELS_2026_30_SAMPLE.parquet")
df = table.to_pandas()

print(f"Loaded {len(df)} sample Multimodal AI papers.")
print(f"Top Paper: {df['title'].iloc[0]} (Backbone: {df['vision_backbone_architecture'].iloc[0]})")

🚀 Get the Full 1,000-Paper Enterprise Edition

The complete commercial production dataset (1,000 papers in Parquet, SQLite DB, Clean CSV, and JSON) is available here:

👉 BeatsProm Multimodal AI Dataset Full Edition

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