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Turing at NeurIPS 2025
From research acceleration to proprietary intelligence. Explore reproducible environments, coding evaluation systems, and datasets powering frontier labs.
Trusted by AI leaders and enterprises
RL Environments
Reproducible environments for agent training and evaluation. Each environment includes prompts, verifiers, and seed data for controlled experimentation and reproducible results.
1
DashDoor (DoorDash-style)
Interactive cart and checkout flows for testing transactional agents.
2
DeskZen (Zendesk-style)
Ticket triage and resolution tracking with verifier-scored responses.
3
Mira (Jira-style)
MCP agent evaluation with API calls, schema, and database state checks.
Coding and benchmarking
Deterministic systems for measuring model reasoning, synthesis, and code understanding on verifiable tasks.
SWE-bench++
Software reasoning benchmark using real GitHub issues and validated fixes.
VLM-bench
Multimodal reasoning benchmark across STEM and business tasks using vision-language inputs.
CodeBench
Deterministic evaluation for code models with structured prompts and ideal responses.
Data Packs
Expert-verified datasets for post-training, reinforcement learning, and evaluation. All packs follow auditable pipelines and R&D-grade QA standards.
Catalog highlights
- Coding — reasoning and function-calling tasks across domains.
- STEM — chemistry, physics, biology, and math reasoning.
- Multimodality — audio, vision, and interface agent data.
- Domain-specific — finance, medical, legal, economics reasoning.
- Robotics & Embodied AI — world modeling and imitation learning.
- Custom — scoped edge-case or novel-modality datasets.
Frontier research + NVIDIA Challenge Awards
Evening session on open-data collaboration and coding-benchmark research with Turing and NVIDIA—concluding with recognition of the NVIDIA Challenge winners.
