datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
opentraces-runtime
opentraces-runtime
Community-contributed agent traces in opentraces JSONL format.
Usage
from datasets import load_dataset
ds = load_dataset("OpenTraces/opentraces-runtime")
Schema
Each JSONL line is a TraceRecord containing:
trace_id: Unique identifier for the trace
session_id: Source session identifier
agent: Agent identity (name, version, model)
task: Structured task metadata
steps: List of LLM API calls (thought-action-observation… See the full description on the dataset page: https://huggingface.co/datasets/OpenTraces/opentraces-runtime.python-runtime-verified-error-correction
Python Runtime-Verified Error Correction Dataset 🐍⚡
Overview
Production-grade synthetic dataset of Python code errors with runtime-verified corrections. Each sample contains broken code, the actual runtime error, and a guaranteed-working fix validated through execution.
Unlike traditional synthetic datasets, every correction is verified by actually running the code in an isolated environment—eliminating hallucinations and ensuring real-world applicability.… See the full description on the dataset page: https://huggingface.co/datasets/SyntheticLogic-Labs/python-runtime-verified-error-correction.teacher-traces
Training Traces
Supplementary release for the paper Evaluating Agents Across Runtime Contracts: When Mismatch Costs Efficiency or Quality (IAEval 2026, the NeurIPS 2026 Workshop on Evaluation of Interactive Agents). This dataset
holds the teacher agent traces used to fine-tune the paper's LoRA adapters (see the
sibling cap-sweep-eval-data release and the fourteen adapter repos alongside this one).
6,000 agent traces total (1,000 per family x runtime combination), produced by an… See the full description on the dataset page: https://huggingface.co/datasets/runtime-contracts/teacher-traces.agents-learn-runtime-train
Agents Learn Their Runtime -- Training Traces
Paper: Agents Learn Their Runtime: Interpreter Persistence as Training-Time Semantics
2,000 agent traces (1,000 with a persistent interpreter, 1,000 with a stateless interpreter) produced by Gemini 3 Flash solving Opaque Knapsack tasks (as defined in the paper). These traces were used to fine-tune two LoRA adapters on Qwen3-8B, one per runtime condition. The paper's central question is whether the execution semantics baked into training… See the full description on the dataset page: https://huggingface.co/datasets/pythonformer/agents-learn-runtime-train.aurelia-runtime-corpus-v2
Aurelia Runtime Corpus v2
Aurelia Runtime Corpus v2 is a schema-gated validator corpus for the Aurelia causal world-simulation engine. It trains and evaluates a runtime-model coprocessor to distinguish verified mechanical/lore-grounded simulation records from subtly invalid candidates.
This release is the first full v2 local build: 25 fresh simulation seeds, 20 years per seed, all five Aurelia worlds, with grouped train/validation/test splits and balanced deterministic + AAA… See the full description on the dataset page: https://huggingface.co/datasets/OusiaResearch/aurelia-runtime-corpus-v2.