README.md

---
license: apache-2.0
task_categories:
- text-retrieval
tags:
- agents
- skills
- retrieval
- graph
- benchmark
---

# SEGoS-data

Data assets for **SE-GoS: Self-Evolving Graph-of-Skills for Skill Library at Scale**
(paper: [arXiv:2609.08228](https://arxiv.org/abs/2609.08228),
code: [PKUfudawei/SEGoS](https://github.com/PKUfudawei/SEGoS)).

This dataset hosts only what SE-GoS itself produces or repackages for convenience.
The GoS skill libraries and prebuilt workspaces it builds on are **not** redistributed
here — `scripts/download_data.sh` fetches them from the upstream GoS dataset
[`davidliuk/graph-of-skills-data`](https://huggingface.co/datasets/davidliuk/graph-of-skills-data)
(davidliuk, 2026-04).

## Contents

Assets are grouped by the experiment they belong to, so a single experiment's
inputs can be fetched without pulling the rest.

| Path | Used by |
|---|---|
| `tasks/skillsbench_tasks.tar.gz` | every table — 87 SkillsBench dockerized coding tasks (1.0 GB unpacked) |
| `coldstart/segos_coldstart_skills1000.json` | every table — the static (round-0) substrate: 1,000 skills, 863 semantic edges |
| `alfworld/coldstart/segos_coldstart_skills200.json` | the ALFWorld table — the static web substrate: 200 skills, 149 semantic edges |
| `full87/graphs/segos_evolved_skills1000_round1.json` | **main table** (the SE-GoS cell) and **multi-round table** round 1 — 1,118 edges: 863 semantic, 228 workflow, 27 avoid |
| `full87/graphs/segos_evolved_skills1000_round2.json` | multi-round table round 2 — 1,375 edges (previous chain, see below) |
| `full87/graphs/segos_evolved_skills1000_round3.json` | multi-round table round 3 — 1,502 edges (previous chain, see below) |
| `heldout/graphs/segos_evolved_skills1000_heldout_train50.json` | **held-out table** — evolved on the 50 training tasks only — 999 edges: 863 semantic, 127 workflow, 9 avoid |
| `full87/evolution/segos_evolution_deltas.tar.gz` | the L1/L2/L3 deltas for all eight full-87 cells, plus the parsed signals |
| `heldout/evolution/segos_evolution_deltas.tar.gz` | the L1/L2/L3 deltas behind the held-out graph |
| `full87/traces/segos_traces_round*.tar.gz` | per-trial job trees for the full-87 runs (round-0 train, rounds 1-3 evals) |
| `full87/traces/segos_traces_ablation_*.tar.gz` | per-trial job trees for the six $2^3$ factorial cells other than the full one — `L1`, `L2`, `L3`, `L1L2`, `L1L3`, `L2L3`, 174 scored attempts each |
| `heldout/traces/segos_traces_heldout_eval.tar.gz` | per-trial job trees for the held-out run (37 eval tasks) |

Each trace archive holds one directory per task attempt with `result.json`,
`config.json`, and the agent transcript.

The tarballs contain one top-level directory; extract with
`tar -xzf <archive> --strip-components=1`. The graph JSONs and the deltas archive
are what `download_data.sh` unpacks for you.

### Regeneration status (2026-09-15)

`round1` and `heldout_train50` were regenerated from the static training traces
with the avoid relation enabled at its default evidence bar, plus the
non-contradiction invariant. Only their edge sets and weights changed; their
description rewrites were carried over, because the node update calls an LLM
and is not reproducible offline. Round 1 carries **six** rewritten
descriptions and the held-out graph **three**.

- round 1: 1,102 → **1,118** edges (the workflow set also shifted from 239 to 228
  because eleven trials of the training job were re-run after the original deltas
  were computed)
- held-out: 990 → **999** edges

The reward, token and runtime columns of the paper were measured on the previous
graphs and have not been re-measured; a full re-run is pending. The six
`full87/traces/segos_traces_ablation_*.tar.gz` archives belong to that same
measurement generation, so their cells and the full one are comparable with each
other and with the paper's columns, but not with the regenerated round-1 graph.

`round2` and `round3` are deliberately left as they were. Their evolution
consumes the *round-1 evaluation* traces, which were produced on the previous
round-1 graph, so they cannot be regenerated until that re-run happens. Their
deltas inside `full87/evolution/segos_evolution_deltas.tar.gz` are the previous
chain's, unchanged; everything else in that archive is current.

### Statistics in this release

`evolution/multiround/summary.json` inside
`full87/evolution/segos_evolution_deltas.tar.gz` records two numbers per round.
`R` is the pooled mean reward over all 174 scored attempts of that round's
eval job, and `T_M` is the mean input tokens **per attempt over the same 174
attempts** (`n_attempts` records the denominator). Both are recomputable
directly from the per-trial results in `full87/traces/`, and both match the
reward and token columns the paper reports for rounds 2 and 3.

An earlier release computed `T_M` by first collapsing the attempts of a task
into one entry, which averaged 87 samples instead of 174 and kept whichever
attempt the file order happened to reach last. Those values (3.701 and 3.892)
are not a quantity the paper reports and have been replaced (3.664 and 3.708).

Note that the reward, token, and runtime columns of the paper were measured on
the graphs as they stood before the round-1 and held-out regeneration described
above; re-measuring them on the regenerated graphs is pending.

## The cold-start graph

SE-GoS does **not** start from GoS's LLM-validated typed graph. It starts from a
deterministic semantic-only graph: every skill linked to its top-1 neighbour by
signature-token overlap, no LLM pass and no embedding service.

`coldstart/segos_coldstart_skills1000.json` is exactly that substrate — the graph
behind the static (round-0) row of every table in the paper (1,000 nodes / 863
edges). It is reproducible from the upstream GoS workspace in one command:

```bash
cd evaluation/skillsbench
PYTHONPATH=$PWD python -m evo.rebuild_graph \
  --igraph ../../data/gos_workspace/skills_1000_v1/graph_igraph_data.pklz \
  --edge-types sem --sem-metric token --semantic-k 1 \
  --out generated/shared/graphskills_bundle_semonly_token_k1_1000.json
```

`--hnsw` is not needed: the `token` metric reads node attributes from the official
igraph pickle, so no embedding index or API key is involved.

`alfworld/coldstart/segos_coldstart_skills200.json` is the same substrate built
over the 200-skill library (200 nodes / 149 edges) and is what the ALFWorld runs
retrieve from:

```bash
cd evaluation/skillsbench
PYTHONPATH=$PWD python -m evo.rebuild_graph \
  --igraph ../../data/gos_workspace/skills_200_v1/graph_igraph_data.pklz.official \
  --edge-types sem --sem-metric token --semantic-k 1 \
  --out ../alfworld/generated/shared/alfworld_coldstart_skills200_k1_token.json
```

Both are fetched by `download_data.sh` (`--coldstart` and `--alfworld`
respectively); neither is committed to the code repository.

## The evolved graphs

The multi-round and held-out tables run on graphs that SE-GoS produced from
execution traces, not on the cold start:

- `full87/graphs/segos_evolved_skills1000_round{1,2,3}.json` — the full-87
  protocol, one L1/L2/L3 pass per round. **Round 1 is the SE-GoS cell of the main
  table** (1,102 edges); rounds 2 and 3 re-evolve and re-measure all 87 tasks.
- `heldout/graphs/segos_evolved_skills1000_heldout_train50.json` — evolved on the
  50 training tasks of the disjoint 50/37 split only (990 edges), then measured
  on the 37 held-out tasks. A different artifact from round 1 despite the similar
  name; the two are never interchangeable.

`full87/evolution/segos_evolution_deltas.tar.gz` carries the L1/L2/L3 deltas and
the parsed signals the graphs were built from, so the evolution itself can be
recomputed offline (no agent runs). `*/traces/*.tar.gz` carries the raw per-trial
job trees if you want to replay the runs.

## Usage

```bash
git clone https://github.com/PKUfudawei/SEGoS.git && cd SEGoS
./scripts/download_data.sh              # everything except traces
./scripts/download_data.sh --tasks      # SkillsBench tasks only
./scripts/download_data.sh --coldstart  # cold-start graph only
./scripts/download_data.sh --alfworld   # ALFWorld cold-start graph only
./scripts/download_data.sh --evolved    # evolved graphs + deltas (multi-round / held-out)
./scripts/download_data.sh --traces     # per-trial job trees for those runs (~1.7 GB)
```

Fetch one experiment's assets directly, without the rest of the dataset:

```bash
hf download PKUfudawei/SEGoS-data --include "heldout/*" --repo-type dataset
hf download PKUfudawei/SEGoS-data --include "full87/graphs/*" "coldstart/*" --repo-type dataset
```

`GOS_HF_REPO` overrides the **upstream GoS** dataset used for skill libraries and
prebuilt workspaces; `SEGOS_HF_REPO` overrides this dataset.

## Provenance

- **SkillsBench tasks** — from [benchflow-ai/skillsbench](https://github.com/benchflow-ai/skillsbench).
  `download_data.sh` falls back to a sparse checkout of that repo if the archive here is unavailable.
- **Cold-start graph** — produced by this project from the upstream GoS workspace
  (see the `rebuild_graph` commands above). Two sizes are hosted: the 1,000-skill
  substrate behind the SkillsBench tables, and the 200-skill substrate used by the
  ALFWorld runs.
- **Evolved graphs and deltas** — produced by this project's evolution pass over
  traces from the SkillsBench runs. The graphs are the artifacts the tables are
  measured on; the deltas are the auditable record of what each update changed.
- **Per-trial traces** — the raw job trees those runs wrote: one directory per
  task attempt, holding `result.json`, `config.json`, and the agent transcript.
- **Skill libraries / prebuilt workspaces** — not hosted here; fetched from
  `davidliuk/graph-of-skills-data`. Note those `*_v1` workspace archives hold undirected
  graphs from an earlier GoS cleanup implementation and reproduce older results only.