Discover the Best AI Resources
Curated essentials, no noise — just what matters
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.
Models long-horizon interactive literary simulation where characters and world co-evolve; introduces an open‑schema framework with a Character Agent and an LLM-based World Model, plus seven trainable tasks and a dataset from 57 books for benchmarking persistent narrative state.
Predicts variable-cardinality sets of evidence intervals in videos to temporally ground queries using multimodal large language models. Combines caption-derived multi-span supervision, a temporal Wasserstein matching-free reward, and temporal IoU, yielding strong mIoU gains across multiple benchmarks.
Provides newline-delimited JSON agent session traces (5 files) generated with Teich for moonshotai/kimi-k3, including recovered and embedded tool-schema snapshots so traces remain training-ready even when tools weren't invoked; includes guidance for Teich data preparation and conversion.
GGUF-quantized releases (NEO IMATRIX + MTP) of a multi-stage fine-tuned, uncensored Qwen3.5-9B model with vision enabled and a native 256k context window—optimized for instruction following, reasoning and image-text-to-text workflows; released under Apache-2.0.
Provides 2,000 hours of synchronized, high‑fidelity robot‑free bimanual manipulation demonstrations with multi‑view video, calibrated end‑effector trajectories, gripper states, and language annotations. Curated from a 20,000+ hour corpus; features 6 camera views, ~3 mm pose accuracy, <40 µs cross‑sensor sync, and LeRobot v3‑style Parquet+MP4 export under CC BY 4.0.
Turns Claude Code or Codex into an agent skill that storyboards, animates, and sound-designs cinematic product promos from your real screenshots using Remotion. Packs tuned shot recipe cards, 150+ motion previews, a production-ready template, Remotion components, and audio assets for web/desktop product films.
Prunes tool-output lines inside a coding LLM agent by turning the agent's own internal representations into per-line keep-or-prune labels. Implements a small classification head plus a length-aware embedding, saving up to 39% of tokens across benchmarks while preserving task quality.
Personalizes subject-driven videos to preserve human identity and accurate human–object interactions by integrating multimodal references and MLLM-derived semantics. Introduces global multimodal guidance in self-attention and modality-reference embeddings to align MLLM features with VAE tokens, supporting both inter- and intra-subject inputs (e.g., OCR, multi-view).
Generates interactive long-horizon 24-fps video worlds (540p/720p) from text, image, or video inputs. Uses a 15B video diffusion transformer with a bounded visual context (sink frame, compressed temporal history, geometry-aligned spatial memory, recent-frame conditioning) and a discrete autoregressive distillation that cuts inference to ~4 sampling steps per chunk.
Open preview checkpoint of a sparse Mixture-of-Experts causal LLM with ~314B total params (~13B active per token) and native 256K context for long-context multilingual text generation. Ships with custom modeling code (trust_remote_code) and a research/non-commercial use license.
Provides under-1K JSON agent-trace records documenting model refusal responses and forensic metadata — useful for evaluating refusal-detection, audit pipelines, and safety analysis; small size limits large-scale statistical studies.