Category

RULER: Instance-aware Rubric Rewards for SVG Generation

Converts natural-language SVG instructions into instance-aware six-item rubrics and uses a vision–language judge to score rendered SVGs item-by-item as dense RL rewards. Produces stronger human alignment and lifts benchmark rubric scores substantially on MMSVG datasets.

All-in-One Multilingual Scene Text Recognition with Script-aware Mixture-of-Experts

An all-in-one multilingual scene text recognition approach that pairs a shared visual encoder with a script-aware Mixture-of-Experts (ScriptMoE) decoder to route each image to top-2 script experts. Introduces TextMuSS-10M, a synthetic dataset covering 10 scripts and 229 languages, and reports state-of-the-art accuracy and large end-to-end OCR F1 gains while remaining parameter-efficient.

WorldCrafter: Consistent Video World Model with Implicit 3D-aware Memory

Learns a camera-queryable implicit 3D-aware memory that compresses multi-view history into target-view tokens to enable long-horizon, camera-controllable video generation. Improves revisit consistency and camera-control accuracy and supports streaming exploration from a single image or text prompt.

GameHorizon Suite: Multi-Horizon Data and Evaluation in Gameplay

Provides a unified data and evaluation suite for measuring gameplay capabilities across short, medium, and long temporal horizons. Includes an automated annotator, a 5,000-hour AAA gameplay dataset with temporally aligned frames, actions and multi-horizon instructions, and a reproducible offline + stepwise online benchmark.

HappyWorld-Bench

Evaluates whether generative world models maintain consistent, controllable, and physically plausible simulated environments under exploration, interaction, and intervention. Introduces a six-level W1–W6 capability taxonomy across three tracks (video, spatial, embodied) with human A/B Arena and automated metrics to measure behavioral correctness.

GAE: Learning a Geometry-Native Latent Space for 3D-Consistent World Generation

Compresses a geometry-foundation model’s multi-level features into a compact latent that decodes jointly to RGB, depth, cameras and point maps — enabling a conditional flow to generate 3D-consistent video and novel views with measurably improved coherence.

FoMo: Forking Moment in Generative Trajectory as a Perceptual Distance

Uses diffusion-model forking moments as a proxy for perceptual distance to automatically generate pointwise reference-grounded labels, enabling annotation-free training of reference-based image quality assessment metrics.

FuseReg: Regularizing Layer Fusion Mitigates the Reconstruction-Generation Gap in Representation Autoencoders

Trains decoders and diffusion generators to be robust to random subsets of pretrained encoder layers by regularizing layer-fusion during training, narrowing the reconstruction–generation gap in representation autoencoders and improving ImageNet-256 PSNR and gFID.

InternW0-$Δ$: A World Action Model Bridging Predictive Dynamics and Actions with 20K+ Hours of Open Data

Learns joint predictive visual dynamics and action generation for generalist robot manipulation, translating future-relevant visual representations into actions. Integrates a Mixture-of-Transformers coupling a video expert and action expert, a frozen vision-language model for semantics, 4D distillation, and Causal Imprint; pretrained on a 20K+ hour heterogeneous corpus.

PanoVLN: Towards Effective Panoramic Vision-and-Language Navigation

Uses 360° panoramic observations to improve vision-and-language navigation by predicting longer action sequences, confidence-guided execution, and combined semantic–geometric panorama features; yields large SR gains on R2R-CE and RxR-CE Val-Unseen.

Learning Native Reflection in Unified Models with Interleaved Reinforcement Learning

Trains a single unified multimodal model with reinforcement learning to perform end-to-end self-reflection and iterative image repair — jointly learning the diagnostic (textual) reflection and the flow-based image revisions so credit propagates across rounds without an external verifier.

MotorMind: Scaffolding General Vision Language Models for Zero-Shot Robot Manipulation

Lets general-purpose vision-language models directly command robots via a compact mid-level action interface and asynchronous monitoring, enabling zero-shot manipulation without task-specific policy training; demonstrates strong sim benchmarks and real xArm6 transfer.