Discover the Best AI Resources
Curated essentials, no noise — just what matters
AI Dataset2026
Provides 50 ARC‑AGI‑3 gameplay trajectories (GPT‑5.6 Sol and Claude Opus/Fable) plus a dependency‑free scorer and event logs; includes sanitized session data, snapshots, and utilities to recompute RHAE scores for reproducible agent evaluation and cross-model comparison.
AI Agent Papers2026
Turns fragile, implicit search progress into explicit, persistent, shared state for multi-agent information seeking — externalizes progress as Frontier Task, Evidence Graph, Coverage Map and Failure Memory, and uses pipeline-parallel scheduling plus a middleware harness to avoid repeated failed searches and improve utilization and throughput.
Enables RL post-training with million-token prompts under a fixed GPU budget by evaluating shared prompt state without autograd, retaining only minimal model state, and replaying short response branches; instantiated as GRPO and demonstrated on Qwen3.6-27B and GLM-5.2 up to multi-million token execution.
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 Model2026
Policy-adaptive multimodal safety classifier that evaluates text and images against free-form natural-language policies and returns a continuous yes/no safety score. Produces a single-token verdict from a 3B-parameter model, supports multiple languages, and is designed for lightweight real-time moderation.
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.
AI Video Papers2026
Evaluates whether video models reason according to physical laws by treating generated videos as visible reasoning traces and using a three-stage Perception–Formulation–Deduction protocol. Includes Orchard (400 mechanics videos), chain-of-frames prompting on annotated first frames, and a hybrid MLLM-plus-objective scoring suite for stage-resolved diagnostics.
Quantifies active visual observation in multimodal LLMs with ActiveVision, a 17-task benchmark that forces repeated perception rather than one-shot description. Finds frontier MLLMs fail badly (top model 10.6% vs humans 96.1%) and that model-generated vision code does not close the gap.
AI Model2026
Provides GGUF-format fine-tuned Qwen3.6-27B weights optimized for consumer hardware, offering NEO IMATRIX and MTP quant variants, vision support, 256k native context, and uncensored 'heretic' traces with published benchmark improvements over the base model.
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.
Embodied AI2026
Generates robot manipulation actions from visual observations and text instructions using a 1.5B vision-language-action model. Uses streaming context and visual-token compression to cut per-step compute, runs a unified policy across tasks, and is open-sourced on Hugging Face under Apache-2.0.