license: other
license_name: microtensor-dataset-terms
license_link: https://www.microtensor.cloud/terms
pretty_name: Microtensor agent sessions
language:
- en
tags:
- agents
- context-compression
- compaction
- code
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*.parquet
Microtensor agent sessions
Coding agent sessions for the ctx_compress arena on Microtensor (Bittensor testnet netuid 576). Each row is one compaction request in the arena's exact format: the compaction instruction as the system message and the session, rendered canonically inside <session> tags, as the user message. The label holds the facts and probe questions the grader checks.
Every session is generated by Microtensor. A seed coins a Python project with one injected bug, and a scripted agent works the task: it lists the tree, searches, reads modules and tests, runs pytest, often tries a wrong fix and reverts it, applies the right fix and adds a regression test. Every tool result is the real output of the real command. Nothing here comes from a public dataset.
| Sessions | 2,000 |
| Rows | 3,981 (each session cut near its end, and a twin cut near its middle: 1,981 twins) |
| Input tokens | 6,381 to 122,704, median 45,439, Granite 4.0 H Micro tokenizer |
| Arena input cap | 122,880 tokens |
| Output | nine heading markdown summary within budget_tokens |
Hidden evaluation items come from the same generator under a secret seed and are never published.
Fields
| Field | Meaning |
|---|---|
item_id |
row id; a twin's id ends in -twin |
twin_of |
the full item a twin was cut from, empty otherwise |
family |
the generated project name |
system, user |
the request exactly as a miner's model receives it |
input_tokens, budget_tokens |
Granite token count of the request and the output budget it states |
cut, steps |
the step the session was cut at and its full length |
facts |
JSON list of files, errors and symbols the summary must keep |
probes |
JSON list of continuation questions with accepted answers |
image |
synth:<seed>@<scale>; the arenas package rebuilds the buggy project from it |
Use
from datasets import load_dataset
rows = load_dataset("microtensor-io/agent-sessions", split="dev")
There are no reference summaries. Write targets with a teacher model and keep those the arena grader passes; the miner guide shows the loop. Make more sessions with your own seed:
pip install "microtensor-arenas[compress] @ git+https://github.com/microtensor-io/microtensor-arenas"
mta dev --arena ctx_compress --count 2000 --seed 7 --out dev/
Licence
Microtensor dataset terms, see https://www.microtensor.cloud/terms.