Tag

OmniEcho: Audio-Visual Spatial Understanding for Omni-Modal Embodied Agents

Provides a unified benchmark and a spatially aware omni-modal model for spatial audio-visual reasoning in embodied agents — OmniEchoBench (197 scenes, 2,972 QA pairs; 900 FOA-based navigation tasks) plus OmniEcho, which adds an FOA spatial encoder to a pretrained semantic audio pathway to enable spatial QA and sound-guided navigation.

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.

MiMo-V2.6-Flash-RL

A sparse-MoE multimodal LLM fine-tuned with large-scale mixed reinforcement learning, supporting text/image/video/audio and a 1M-token context window; designed for long-horizon agentic tasks, coding and visual reasoning with groupwise grading and on-policy distillation.

LensVLM-9B

Scans long documents rendered as compressed page-images, locates relevant pages, and selectively expands only those pages to full text for question answering; built on Qwen3.5-9B, supports 5x/10x/15x compression and is released under Apple’s research-only model license.

MiMo-V2.6-Pro-RL

Open-weights multimodal LLM checkpoint designed for 1M-token long-context agent runs that combines large-scale on-policy RL with groupwise grading for iterative self-improvement. Key traits: sparse MoE backbone (≈1.02T total / 42B active), text-only output with image/video/audio inputs, MIT-licensed weights on HuggingFace—suited for long-horizon agents and research at significant infra cost.

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.

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.

MiMo-V2.6-Distill-Qwen-9B

A 9B agentic multimodal SFT checkpoint distilled from Qwen3.5-9B for coding, general agent tasks, visual coding and cybersecurity. Provided by Xiaomi MiMo as a research seed (77.4B-token SFT mix) to bootstrap agentic RL and tool-use experiments.

Honey-Data-V2

A multimodal supervised fine‑tuning corpus for training MLLMs: 44.3M samples (100.3M QA pairs) over 19.7M image groups, annotated with short/long chain‑of‑thought and produced by rerunning the HoneyPipe curation pipeline with multi‑model rollouts and verifications.

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.