Discover the Best AI Resources
Curated essentials, no noise — just what matters
Proposes Monotonic Inference Policy Improvement (MIPI) and a two-step Monotonic Inference Policy Update (MIPU) to address training–inference probability mismatch in LLM reinforcement learning by constructing sampler-referenced candidate updates and accepting synchronized updates using an inference-gap proxy; shows improved reasoning accuracy and stability under FP8-quantized rollouts.
AI Model2026
An uncensored GGUF build of GLM-5.2 that applies weight “abliteration” to remove refusal filters and produce a locally runnable text-generation model; includes quantization conversions and shard-merge instructions, intended for experimental research rather than production use.
AI Dataset2026
Provides 300 adversarial "metacognitive-trap" problems to measure whether LLMs notice and recover from their own reasoning errors. Combines multiple-choice vulnerability tests with free-form adapter-gain evaluation and ships per-model metacognition adapters for frozen-base probing.
Treats hybrid layer selection as a budget-constrained subset optimization and introduces FlashMorph: a pipeline that equips each transformer layer with a linear-attention branch, jointly optimizes layerwise gates on synthetic long-context retrieval data, then discretizes, distills, and finetunes—achieving strong long-context recall using only 20M selection tokens.
AI Image2026
Applies or repositions directional sunlight in outdoor images by using a LoRA trained for Flux2Klein 9B to match a reference sun elevation and rotation. Workflow uses an overcast intermediate and a sphere (ball) reference; includes a ComfyUI node and Blender scene for rendering the reference.
AI Model2026
Performs zero-shot classification and regression on mixed numerical and categorical tabular data by treating training rows as in-context examples and predicting in a single forward pass. Uses alternating row/column attention and row compression; limited to 10 classes and model weights are non-commercial.
AI Dataset2026
Provides synchronized four-perspective Rocket League match recordings with per-frame H.264 video, player action streams, event logs, and privileged physics state — released as WebDataset shards in a ~4,000-hour slice (1,000 match-hours × 4 perspectives). Includes 720p@20fps video, multi-hot keyboard actions, and CC BY-NC-SA-4.0 license.
AI Model2026
Thinking-off fine-tune for coding-agent workflows that prioritizes fast next-step decisions, lower token usage and stable multi-turn tool calling. Highlights: MoE 35B base, MTP speculative decoding, SWE-bench 62.4% (300 cases). Best for local agent loops and automated debug cycles; requires disciplined harnessing and schema consistency.
AI Model2026
A large-scale MoE language model for agentic coding and long-context tasks, natively supporting 1M-token context and dynamically activating tens of billions of parameters per token. Uses sparse attention and zero-computation experts to allocate compute per-token; model weights planned for release.
AI Dataset2026
Provides ~494.7 hours of trimmed native PC/console gameplay screen recordings organized by game, with per-session clips plus input and per-frame event annotations. Each workflow includes clip.mp4, events.json, frame_events.json, and metadata — suitable for training vision-action, behavior-cloning, and gameplay understanding models.
AI Dataset2026
Provides 2,165 historical natural-history page scans paired with ~99.95% expert transcriptions and pixel-aligned PAGE XML layout ground truth for OCR and layout evaluation; multilingual (EN/FR/DE/LA), CC-BY 3.0.
AI Dataset2026
Provides ~50M multimodal annotations organized for unified training across structured visual understanding, segmentation, dense geometric prediction, and multi-view reconstruction — released as task-specific JSONL files that reference original image assets rather than redistributing raw images.