Evaluates whether vision-language models can make actionable decisions for a physical body by decoupling decision-making from low-level motor execution. Introduces HumanCLAW-Bench with 1,218 long-horizon egocentric episodes across 41 indoor scenes and diagnoses a lack of embodied self-awareness in current VLMs.
Learns a discrete “physical language” from unlabeled videos and uses a reason-then-render pipeline: predict compact state-transition tokens, then decode them into future video. Separates dynamics inference from pixel synthesis to improve physical fidelity, controllable simulation, and zero-shot motion transfer.
AI Dataset2026
Provides 30,969 action-conditioned video episodes, each with source MP4, per-frame keyboard control logs, captions, and a COLMAP sparse pose model — intended for research on action-conditioned video prediction, controllable world models, and representation learning.
AI Agent Papers2026
Designs and evaluates a foundation GUI agent that performs cross-platform GUI and CLI actions on real devices to complete long-horizon workflows. Emphasizes a unified action space, a large-scale real-device mobile runtime, an AutoResearch-style data flywheel, and online RL training across 10,000+ concurrent environments.
Estimates the visually attributable portion of a privileged teacher’s next-token corrections and reconstructs student-anchored training targets for multimodal on-policy distillation. Uses counterfactual teacher queries and a signed proxy to raise supported tokens and suppress refuted ones, improving fine-grained visual knowledge transfer across model scales.
AI Agent Papers2026
Evaluates vision-language model judges on computer-using agent (CUA) trajectories to measure verifier reliability. Provides OSReward-Hard and OSReward-Multi challenge sets, the OS-Shepherd-100K reasoning-annotated corpus, and trained OS-Shepherd reward models that match commercial judges at ~30–60× lower cost.
AI Model2026
Provides GGUF-quantized, ComfyUI-ready MiniMax‑H3 model files (FL2VA/REF2VA, text encoder, audio/video VAEs) to enable local ComfyUI inference for short video + stereo audio generation; requires the official VAEs and sufficient VRAM.
Provides a unified multimodal framework for large-scale 3D understanding, text-to-3D generation, and instruction-guided 3D editing. Trains on an 87M-sample 3D multimodal corpus (25M understanding, 50M generation, 12M editing) and pairs a vision-language model with a diffusion-based 3D synthesizer to preserve structure and enable part-aware edits; suited for researchers building text-driven 3D asset pipelines but requires large compute and data.
AI Model2026
Provides ComfyUI-ready INT8 MiniMax‑H3 checkpoints (conditioning encoder plus optional generation tail) for a Heretic-edited Qwen3‑VL‑32B source; preserves the vision tower in BF16 and uses row-wise ConvRot INT8 quantization to reduce VRAM needs for ~32GB GPUs. Not a full Transformers generation repository.
AI Model2026
ComfyUI-ready H3 conditioning encoder builds for Qwen3-VL-32B: a BF16 full-precision checkpoint, an INT8 ConvRot quantized checkpoint, and an optional generation tail (layers 50–63). Retains vision tower in BF16 and targets H3 workflows and lower-VRAM systems.
AI Dataset2026
Installation-oriented dataset that packages ComfyUI-ready files and instructions for running MiniMax H3 locally — includes pruned/INT8/BF16 checkpoints, matching Qwen3-VL text encoders, video/audio VAEs, and official ComfyUI workflow templates for joint audio+video generation.
AI Dataset2026
Provides time-aligned simulated urban driving recordings that pair high-rate CSI/CIR with multi-view cameras, LiDAR, radar, IMU and GNSS for perception-to-channel research; contains 100 validated 1-second samples produced with CARLA and Sionna, but is limited in scene diversity and real-world fidelity.