Google DeepMind 宣布与游戏开发商合作，用 AI 原型化全新游戏体验，并回顾了从 DQN 玩 49 款 Atari 游戏、AlphaGo、AlphaZero、MuZero、AlphaStar 到 SIMA 的游戏 AI 研究历程。

City University and Kuaishou Kling proposed VLM-as-Teacher, which uses VLM feedback to optimize VGM LoRA at test time, reporting a 16.7-point average gain and raising VBVR-Bench from 0.666 to 0.781.

PhysX-Omni models rigid, deformable, and articulated objects in one simulation-ready 3D generation framework, while PhysXVerse contains over 8.7K physical 3D assets across more than 2.9K categories.

Southeast University and Peking University researchers introduced MetaFine, a diagnostic meta-evaluation framework that tests fine-grained robot manipulation across understanding, perception, and behavior, and the article says traditional binary success metrics can overestimate fine-manipulation capability by up to 70%.

Xpeng presented its world-model stack at CVPR 2026, covering X-World, X-Foresight, and X-Cache; the article says X-Cache cuts about 70% of repeated computation, the second-generation VLA used over 4 trillion training tokens, and the in-car stack reduced inference latency to 80 ms.

Ant Lingbo and HKUST introduced LingBot-VA, an autoregressive video-action world model that unifies visual dynamics prediction and action inference, and the paper reports fine-tuning with 50 real-world demonstrations per task plus 92.0% and 91.1% success on RoboTwin 2.0 Easy and Hard settings.

Daxiao Robot and NTU introduced PhysX-Omni, a unified simulation-ready physical 3D generation framework for rigid, deformable, and articulated objects, with PhysXVerse covering 8.7K assets across 2.9K categories and PhysX-Bench evaluating six dimensions including geometry, scale, material, affordance, kinematics, and description.

Apple’s team published PICO, a perceptual image codec that uses 57%-70% fewer bits than AV1, VVC, and JPEG AI at the same subjective visual quality, while encoding a 12MP photo in 230 ms and decoding it in 150 ms on an iPhone 17 Pro Max.

NVIDIA, Tsinghua, University of Toronto, and Vector Institute released Gamma-World, a multi-agent world model using simplex-based positional encoding and hub tokens to cut interaction cost from quadratic to linear, with 8-player latency dropping from 17.6 ms to 4.5 ms.

NVIDIA Research presented 8 ICRA papers on sim-to-real robotics: ScheduleStream delivered a 3x speedup for multi-arm planning, COMPASS reached about 80% success across 20 real-world navigation trials, and Grasp-MPC achieved about 75% real-robot grasping success.

The title says Nvidia LocateAnything-3B performs vision-language grounding with parallel box decoding and runs 10x faster than Qwen3-VL; the post body only provides Hugging Face, GitHub, demo, and project links, and does not disclose benchmark setup or accuracy numbers.

NTU AutoMan Lab, Harvard, and Xiaomi Auto proposed AutoMoT, a unified VLA driving model using a 4B Qwen3-VL Understanding Expert and a 1.6B Action Expert with asynchronous inference, reaching 89.42 DS and 74.09% SR on Bench2Drive with AutoMoT+.

Shanghai Innovation Institute’s LeapQuest and three universities released Ophiuchus and MedScope, applying Think with Images and Think with Videos to medical AI; Ophiuchus-7B scored 68.0 on eight VQA benchmarks, above OpenAI-o3 at 62.2, Gemini 2.5 Pro at 61.8, and GPT-5 at 59.9.

Runway released Project Luxo, showing AI shorts and ad samples including The Rogue; each work was made by a single-person team, with production times ranging from three weeks to four hours.

Nanjing University LAMDA and Alibaba Intelligent Engine proposed DAR, a timestep-aware cross-layer routing method that replaces fixed residual accumulation in DiT; on ImageNet 256x256, it reduced SiT-XL/2 FID from 9.67 to 7.56 and reached baseline convergence quality with 8.75x fewer training iterations.

Zhejiang University and Alibaba introduced Unified Thinker, an independent planning module trained with 40,000 HieraReason-40K samples and a two-stage GRPO reinforcement-learning setup that turns structured reasoning traces into executable visual instructions for image generation and editing.

Visual Para-Thinker introduces a parallel thinking framework for vision-language models, using Pa-Attention and LPRoPE to isolate four visual reasoning paths and training on 163,000 question-answer pairs.

Zhejiang University and the University of Adelaide introduced FlashAR, using 0.05% of the original training data to reduce Emu3.5-Image-34B 512×512 generation latency from 130.10 seconds to 5.68 seconds, while GenEval changed from 80.48 to 80.29.

Westlake University and collaborators introduced HiF-VLA, a motion-centric VLA framework that extracts compact Motion vectors with codecs such as H.264 and uses a joint expert to predict future visual motion and generate action sequences, reporting 31.4GB peak memory and 117.7ms latency under the cited history-window setting.

Meta AI and the Chinese University of Hong Kong proposed ATLAS, a visual reasoning method that uses one Functional Token to connect Agentic and Latent Visual Reasoning, with ATLAS-178K, a two-stage SFT+RL pipeline, and LA-GRPO to train sparse visual-operation tokens.

USTC researchers released MMEVOKE and KORE: MMEVOKE contains 9,422 samples across 159 subcategories, while KORE uses knowledge-tree augmentation and null-space constrained fine-tuning to reduce catastrophic forgetting during multimodal knowledge injection.

VAST and Tsinghua propose DeG, a 3D Gaussian generation method that samples Gaussian centers from a learned density distribution and trains density control with a render loss contribution gradient; in some settings, it reaches TRELLIS-like visual quality with less than half the Gaussian count.

Xie Saining’s team, Adobe Research, and the Australian National University released RAEv2, which reaches gFID 1.06 after 80 epochs on ImageNet-256 and reduces EPFID@2 from 177 epochs to 35 epochs while keeping compute at 189 GFLOPs.

ByteDance released Lance as a research project for image and video generation and understanding in one model; the RSS snippet states 3B active parameters, fewer than 128 GPUs used for training, and links to a homepage, arXiv paper, and Hugging Face model, while the post does not disclose benchmark results or licensing terms.

NVIDIA released the Nemotron-Labs-Diffusion 3B, 8B, and 14B dense model family with AR decoding, diffusion parallel decoding, and self-speculation; the 8B model reaches 850 tok/s on GB200 at concurrency 1, compared with 253 tok/s for AR and 360 tok/s for Eagle3.

NUS proposed Threshold-Guided Optimization, which converts scalar feedback into positive or negative updates through a score-distribution threshold and was accepted by ICML 2026; experiments cover Stable Diffusion v1.5, FLUX, Wan 1.3B, and Meissonic across image and video generation settings.

NTU MARS Lab and collaborators released a 43-page survey on robot world models, covering definitions, architectures, applications, benchmarks, and challenges around action-conditioned consistency, inference efficiency, and physical grounding.

Zhejiang University and Microsoft introduced World-R1, training Wan 2.1 with about 3,000 text-only prompts, Flow-GRPO, and a four-part reward; the 1.3B version improves PSNR over the baseline by 10.23 dB.

Google DeepMind released a Gemini-powered AI-enabled pointer and opened two demos in Google AI Studio: image editing and place finding on maps, while the post says Chrome pointer selection and a Googlebook Magic Pointer are planned product paths.

PsiBot says it uses 100,000 hours of human operation data to train robot policies, with the W0 world model acting only as a training-time transfer module while deployment runs R2 alone.

ByteDance’s commercial technology team proposed GRN, a visual generation architecture using HBQ, global refinement, and complexity-aware sampling to address quantization loss, error accumulation, and fixed-step inference; on a 130M model, adaptive sampling reduced inference from 50 steps to an average of 24, while gFID changed from 3.56 to 3.79.

SenseTime released the SenseNova-U1 technical report, covering six-stage training, RL post-training, and distillation; the open-source SenseNova-U1-A3B-MoT uses an MoE architecture and activates only 3 billion parameters.

Thinking Machines released TML-Interaction-Small, a 276B-parameter MoE model with 12B active parameters, and the post says it advances realtime voice through 200ms time-aligned microturns, encoder-free early fusion for audio and images under 200ms, and benchmark wins over GPT-Realtime-2 and Gemini 3.1-Flash.

Qwen-Image-2.0 uses a Qwen3-VL condition encoder and multimodal diffusion transformer for image generation and precise editing, with instruction inputs up to 1K tokens and reported gains in multilingual text rendering, layout quality, and human-rated generation and editing tasks.

NVIDIA Cosmos Lab and Purdue University proposed Scenethesis, a language-and-vision agentic framework for text-to-3D scene generation that uses visual grounding, SDF-based physical constraints, and a judge module; experiments report about 72% first-pass success, 91% after self-checking, and collision rate reduction from 6.1% to 0.8%.

Zhejiang University and Alibaba proposed MetaCompress, a learned token-compression framework that generates a compression mapping from the input image alone for multi-turn VQA. The article says it can remove 90% of visual tokens while preserving accuracy, and reports only 1.71% overlap between optimally retained tokens and high-attention tokens.

Zhejiang University and Harvard released UniGeo with code, a report, a project page, and an HF Space. UniGeo injects geometry guidance into representation, architecture, and loss layers; it reports SOTA on DL3DV, RE10K, and Tanks against five methods. The key is video priors plus geometry-anchor attention, not just using a video model.

PolyU and Sichuan University introduced SignThought, accepted to ACL 2026 Main and slated for oral recommendation. It uses latent thoughts, plan-then-ground, and dual-stream decoding, reaching top gloss-free BLEU-4 on five SLT benchmarks. The team also built LC-HKSLT with 1,311 hours, 432K clips, and 14 signers.

DeepSeek, Peking University, and Tsinghua released the Thinking with Visual Primitives paper and repository. The framework inserts coordinate points and bounding boxes into chain-of-thought; the post does not disclose benchmark scores.

Google DeepMind announced an AI co-clinician research program, exploring how AI agents can assist patient care under a doctor's clinical supervision. In a blinded evaluation of 98 real primary care queries, the system made no critical errors in 97 cases, and doctors preferred its answers over mainstream evidence synthesis tools. On 140 consultation skills, it matched or beat primary care physicians on 68, but expert physicians were still better overall at spotting red flags and key physical exams.