Discover the Best AI Resources
Curated essentials, no noise — just what matters
AI Dataset2026
Provides labeled prompts with full-reference answers (including chain-of-thought and code blocks) and per-example metadata to train edge routing/orchestrator models that decide whether to handle inputs locally or route them to larger models. Includes complexity scores, coding/math flags, routing justifications, and an automated override rule; suited for fine-tuning small models (50M–1.5B) for edge deployment.
AI Video Papers2026
Converts an academic paper into reusable extracted assets and then produces editable poster, synchronized talk video, and bilingual blog via modular generator skills. Key differentiator: a single Paper2Assets extractor shared by three editable generators plus an interactive Paper2Reel viewer that links slides, video, captions and blog while preserving factual consistency and round-tripable PPT/DOCX output.
AI Dataset2026
Provides re-annotated academic video instruction data for captioning, video QA, and fine-grained motion understanding; rewrites short answers and concise captions into evidence-grounded, instruction-following responses and supplies JSONL annotation files (original videos not included).
AI Agent Papers2026
Trains cross-platform GUI agents by combining a Uni-GUI cross-platform dataset with platform-conditioned multi-teacher on-policy distillation, enabling a shared policy to adapt to new platforms while retaining platform-specific behaviors; suitable for research on continual GUI agent learning and cross-platform adaptation.
Provides a reusable skill suite for evidence-grounded research ideation: Paper-Search for multi-source literature retrieval, Scoop-Check for prior-art collision checking, and IdeaSpark for pattern-guided idea generation, evidence auditing, and idea-card rendering.
Stabilizes on-policy policy distillation by dynamically constructing a proximal teacher that controls gradient variance. Provides theoretical global convergence and monotonic improvement bounds, and shows improved training stability, sample efficiency, and final performance on mathematical reasoning tasks with zero extra compute overhead.
AI Model2026
Fine-tuned variant of Qwen3.6-27B that cuts internal reasoning (‘thinking’) token usage by roughly 46% on average while preserving benchmark accuracy and safety behavior. Targets lower latency and inference cost; ships on Hugging Face with GGUF quantizations for local use.
AI Model2026
Deployment-optimized hybrid MoE LLM (75B total / 9.3B active) produced via Iterative Puzzle compression and Multi-Token Prediction to double server throughput and raise single-GPU concurrency; designed for multilingual reasoning, long-context generation, and high-volume agentic/chat deployments.
Transfers RL-induced policy shifts from a smaller 'weak' teacher to a stronger target by using the teacher's post-/pre-RL log-ratio as a dense implicit reward applied on the student's on-policy states. Enables reuse of RL supervision without running RL rollouts on the target, improving sample/time efficiency.
AI Video Papers2026
Provides a reflexive agentic framework for long-horizon video understanding that replaces costly iterative reasoning with dual contextual states: a consolidated global multimodal script and parametric latent states for fast retrieval and response, improving speed and memory efficiency.
Trains a single diffusion model that unifies 3D scene reconstruction and generative modeling by operating directly in pixel/rendered-image space. Supervises diffusion on rendered views and adds a geometry-perception loss from a pretrained 3D foundation model, reducing latent information loss and improving 3D fidelity.
AI Model2026
GGUF-format quantized release of DeepSeek‑V4‑Flash for local inference — compatible with llama.cpp and Unsloth runtimes, with guidance for FP4/FP8 mixed precision and Q4/Q8 quantization; tuned for million-token long-context usage.