Expresses diverse computer-vision tasks as instruction-driven text, image, or mixed generation from a single unified multimodal model, producing outputs for detection, segmentation, depth, pose, OCR and more. Trained on a converted SenseNova‑Vision instruction–response corpus and requires no task-specific prediction heads.
Performs native structural reasoning for proteins, small molecules and inorganic crystals by tokenizing coordinates, topologies and periodic connectivities into a unified structure-aware vocabulary. Treats structural tokens as addressable evidence to produce interpretable prediction traces and improves accuracy across biology, chemistry and materials benchmarks.
AI Video Papers2026
Pretrains a DiT-based Mixture-of-Experts video foundation model for embodied intelligence by augmenting internet videos with robot-centric footage and using a multi-dimensional reward system to prioritize physical realism and task completion while scaling MoE for better capacity vs. inference trade-offs.
Creates an open-ended interactive world simulator with an unbounded interaction horizon via causal pretraining, a distilled real-time runtime that drives 720p@60fps, a wider action/event repertoire, and a pilot–director agent split for behavior planning and environment synthesis.
AI Video Papers2026
Uses large-scale text-to-video generative pretraining to create GenCeption, a feed-forward perception model that performs diverse vision tasks from text instructions—depth, surface normals, camera pose, referring segmentation, and 3D keypoints—often matching or surpassing specialized models while requiring far less task-specific data.
Explores unsupervised visual pretraining on visually rich documents to improve language-model intelligence; shows visual-pretrained models outperform text-only counterparts on the same corpora. Key aspects: direct use of images/layouts (no OCR-only pipeline), scalable across backbones and benchmarks.
AI Agent Papers2026
Unifies high-level visual-language reasoning and low-level control for visual navigation by decoupling cognition and control: a slow vision-language reasoner produces pixel goals with explicit chain-of-thought, and a fast action expert converts those anchors into continuous waypoints for robust urban and indoor navigation.
AI Model2026
GGUF conversions of Laguna S 2.1 for llama.cpp, including quantized builds (Q4_K_M, Q8_0, F16) and a small DFlash drafter for speculative decoding; configured for a 256K default context window and intended for local inference and serving with Poolside's llama.cpp fork.
Expands a Transformer’s residual stream into many parallel streams and introduces xHC to scale Hyper-Connections beyond N=4. Combines temporal feature augmentation with sparse residual updates (update k=4 of N=16) and xHC-Flash memory optimizations to raise downstream scores while cutting effective compute and memory traffic.
AI Agent Papers2026
A vision-language-action foundation model trained on 100k+ hours of real-world robot manipulation trajectories to follow natural-language instructions and adapt to downstream tasks with minimal fine-tuning. Uses a two-stage (pre-/post-) training recipe and a scalable auto-labeling pipeline; shows clear scaling benefits and state-of-the-art sim-to-real transfer on standard benchmarks.
A looped-Transformer LLM series using Mixture-of-Experts (20B with 2B active; 6B with 0.6B active) that trades extra pretraining compute for repeated looping. Shows superior compute-efficiency versus matched-compute vanilla baselines and attains gold-medal performance on 2025 IMO and IPhO after a post-training pipeline.
AI Dataset2026
Provides intermediate pretraining checkpoints for the Aether-7B-5Attn base model to enable reproducible training-dynamics research. Includes three raw checkpoints (110k, 115k, 162k steps) packaged with model.safetensors, config, and tokenizer; uses a custom aether_v2_7way architecture requiring the aether_pkg loader.