Whether it's generated at scale by our expert-designed pipelines or verified by a specialist sample by sample, every Ancest dataset clears the same high-quality bar — source-traced, leakage-checked, and built for the research problem you're solving. Every stage, every modality.
Recaptioned, deduplicated, provenance-traced web-scale corpora — text, image-text, and multimodal — cleaned for signal, not volume.
Expert-written instructions and demonstrations, plus reasoning traces with verified intermediate steps for chain-of-thought and tool use.
Preference pairs, process-reward labels, and verifiable reward signals authored by people who know the right answer in their field.
Curated safety-alignment and red-team sets that harden reasoning models without dulling their capability.
Leakage-sealed benchmarks scored on held-out data, with measured anchors so a number means what it claims to mean.
Long-horizon, closed-loop rollouts in RL/SFT-ready format — including our AutoResearch task bank — archived uniformly to Hugging Face.
Experts design every pipeline and the checks that gate it — automated quality controls at scale, hand-verification where it counts — so every dataset clears the same bar. Provenance is recorded, leakage paths are cut, and benchmarks are scored from scratch on sealed held-out data, checked against four gates before anything ships.
Value — does this measure something a real researcher cares about?
Measurability — is success defined by an objective, reproducible metric?
Real data, no leakage — sourced, anonymized, leakage paths cut.
Metrics + measured anchors — every number tied to a verified baseline.
Our open-source agents have tens of thousands of GitHub stars, and our datasets train models across the industry — published at CVPR, ICML, ICLR, NeurIPS, and ECCV. The long-horizon data we sell comes from the same lab.
Chat an idea, get a paper — fully autonomous, self-evolving research.
Measures AutoResearch agents — improve a real method from a weak baseline, scored on sealed hidden data.
Efficient lifelong memory for LLM agents — text and multimodal.
Talk to your agent; it learns and turns conversation into training data.
Two agents co-evolve from zero data via tool-integrated reasoning.
RL agents distill trajectories into a reusable, co-evolving skill library.
Real-time self-evolving VLM agent — frame-gating and skill banks cut API cost dramatically.
200-scenario benchmark pairing video clips with a persistent workspace and executable checkers.
Lightweight runtime harness adding risk control, cost tracking, and audit trails to any LLM client.
Visual chain-of-thought: models must draw intermediate images to reason.
Benchmarking AI agents in evolving information environments.
Executable interactive benchmarks for command-line agents.
Safety benchmark — 88 attacks probing capability, identity, and knowledge poisoning of personal agents.
Modular GUI-automation agent that knows when to stop, recover, and search.
Complexity-controllable image-editing benchmark with a Chain-of-Edit evaluation pipeline.
500 original physics problems, high-school to Olympiad — best model 37% vs humans 62%.
Tests whether video LVLMs truly reason over time — 3,269 videos, 4,342 human-crafted questions.
1.5M+ GPT-4o-refined image-edit triplets for training instruction-based editors.
1.3B web images recaptioned with LLaMA-3 to train CLIP and diffusion models.
25M+ medical images across ten modalities with multigranular annotations.
32,682 medical QA pairs with knowledge-graph reasoning paths for training clinical reasoners.
~150K image-question-answer reasoning traces for training R1-style reasoning VLMs.
73K vision-language process-reward samples for training VL reward models.
Safer alignment of reasoning LLMs (e.g. DeepSeek-R1) from just 1K curated examples.
Capability-staged RLVR curriculum — perception, visual-reasoning, and text-reasoning data for VLM post-training.
13.9K medical problems with DAG-structured, knowledge-grounded reasoning traces for training reasoners.
Large-scale video-preference data scoring generation across five aspects and 28 fine-grained criteria.
Selected releases · HF downloads (trailing 30 days) and GitHub stars verified June 2026.
Massively parallel cloud sandboxes, native RL / SFT rollout export, two-stage anti-cheat verifier, and trajectories archived uniformly to Hugging Face — born out of the Terminal-Bench ecosystem.
We build the expert-verified data your pipeline is missing — from pretraining corpora to RL trajectories, in any field and any modality.