AI Coding2023
A statically typed, parallel programming language that prevents AI-introduced bugs by making correctness claims (laws) require machine-checked proofs. Compiles to native code and targets CPU/GPU parallelism with a fast proof checker.
AI Dataset2026
50,000 distilled conversational traces (≈120M tokens) generated from GLM-5.2 for high-reasoning text generation and QA, covering STEM, programming, creative and support dialogues; Apache-2.0 licensed.
AI Dataset2026
Provides live Codex-CLI agent run traces from GPT-5.6 Sol capturing coding, debugging, security reviews, and harness/seed workflows in cumulative next-action prefixes — suitable for supervised fine-tuning and analysis of tool-using coding agents.
AI Dataset2026
Provides a near-deduplicated, quality-filtered 15.9 TB training subset of GitHub source code grouped by repository, with inline UTF‑8 file contents and repo metadata for pre-training and analysis of code LLMs; cutoff Aug 7, 2025, ODC-By license.
AI Dataset2026
Contains 5,000 coding and chain-of-thought reasoning traces generated by Fable 5.1 — ~150M tokens of step-by-step programming CoT. Deduplicated and filtered for high quality; intended for supervised fine-tuning and distillation to improve reasoning in smaller models.
AI Dataset2026
A curated set of 1,000 high-quality chain-of-thought coding and reasoning traces generated by Fable 5.1, totaling ~30M tokens (109 MB). Designed for SFT/distillation to teach smaller models step-by-step programmatic reasoning and debugging.
AI Dataset2026
Provides layered code pretraining corpora (L2 ~400B tokens, L3 ~150B tokens) across 11 languages by filtering ~192M public GitHub repositories into standardized files, algorithmically relevant selections, and implementation-grounded programming exercises. Includes per-file metadata (role, algo relevance, quality) and serialized task records; released under Apache-2.0.
AI Dataset2026
Selected ~245B-token corpus of algorithmically relevant code across 11 languages from The Stack v3, produced by file-level language-adaptive selection (UltraData-Code-L2-Classifier). Concentrates problem-solving implementations for LLM code pretraining while preserving broad language coverage; released under ODC-By.
AI Video Papers2026
Translates natural-language instructions into executable programs that maintain an explicit, persistent global world state and compiles state-augmented 3D oriented bounding boxes into pixel-aligned conditioning signals for pretrained video generators. The approach decouples state evolution from rendering, enabling programmable entity control, off-screen state, and long-horizon interactive scenarios.
AI Agent Papers2026
Extracts replayable behaviors from working web apps and converts them into verifiable, reference-guided software-engineering tasks to evaluate and diagnose coding agents; includes an automated pipeline that scales to thousands of tasks.
AI Agent Papers2026
Chengyi Yang, Renxu Bao +11Affiliation: Xiamen University, Affiliation: Washington University in St. Louis +2
Converts real repositories into verifiable, sandboxed training environments with black-box tests to train coding agents on long-horizon program reproduction. Provides Program-Env for automated environment construction and Program-Agent for a role-isolated development workflow, yielding 1,648 C/C++/Go/Rust tasks and improving benchmark performance.