hiepnh137/i1-topotool-data

TopoTool: training data for tool-mediated spatio-temporal reasoning

Training and evaluation data for TopoTool, a spatio-temporal (ST) reasoning agent that accesses graph topology through callable tools instead of receiving the graph serialized into its prompt. The agent queries exact graph algorithms (get_neighbors, get_path, get_degree, get_spectral_embedding) inside a ReAct loop under a six-call budget.

Companion code: https://github.com/hiepnh137/spatio-temporal-agent

Contents

Folder	Rows	What it is
stage2_v1/	7,602	Stage-2 reasoning-SFT mix (v1). Rejection-sampled tool trajectories from a Qwen3-8B self-teacher (k=4, keep-if-correct and tool-using on topology tasks) + 2,400 programmatic gold trajectories.
stage2_v2/	9,781	Stage-2 mix (v2), built with a style-stratified sampler that fixes a coverage pathology in v1 (below). parts/ holds the four regeneration shards and the verified forecasting gold.
rl_prompts/	6,593	Stage-3 RL prompts: ST-Bench's RL split reshaped so the serialized graph is removed from input and preserved in raw_input (the rollout worker rebuilds the graph from it for tool execution).
scale_test/	800	Scaling stress-test suite: 100 questions × 2 task types × 4 graph sizes (N ∈ {50, 200, 1000, 3834}), generated by a rule-based simulator with ground-truth reachability.

Format

Trajectory files are ShareGPT-style JSONL with tool turns:

{
  "conversations": [
    {"from": "human",         "value": "...Node 0 time series ...: <ts><ts/>; ... Options: A. ... "},
    {"from": "function_call", "value": "{\"name\": \"get_path\", \"arguments\": {\"source_id\": \"2\", \"target_id\": \"0\"}}"},
    {"from": "observation",   "value": "{\"exists\": true, \"path\": [\"2\",\"1\",\"0\"], \"total_lag\": 3}"},
    {"from": "gpt",           "value": "...<answer>B</answer>"}
  ],
  "system": "...", "tools": "[...]",
  "timeseries": [[...], [...]]
}

<ts><ts/> are placeholders consumed by a patch-based time-series encoder; the timeseries field holds one array per placeholder, in order. Every tool observation in these files is a real executor output computed on the actual graph — no observations were generated by a language model.

Why v2 exists (a reusable finding)

v1's rejection sampler used the standard keep-if-correct rule. Measuring the result exposed what we call hardness self-censorship: questions whose options are terse and semantic (e.g. "Industrial pollution peak"), which require reading the time series rather than the graph, are 41% of the source pool but only 11% of retained trajectories — the filter discards exactly the questions the teacher fails, so the hard style eliminates itself from training. The model then fails at test time on the style it never saw.

v2 fixes this with (i) style-stratified adaptive sampling — terse questions get 8 attempts instead of 4 — and (ii) self-consistency gating — a question is kept only if ≥2 samples are correct, which removes lucky passes. Retention on the previously starved style rises from ~11% to ~75% of questions. This is a property of rejection-sampled self-distillation in general, not of this domain.

Provenance and honest notes

• Teacher: the base backbone itself (Qwen3-8B, thinking mode), not a frontier model. The comparable baseline distills from a frontier model at k=5, so every margin reported with this data was obtained with a strictly weaker pipeline.
• Programmatic subsets (Stage-1 data, the 2,400 gold trajectories, the forecasting gold, and the entire scale-test suite) involve no LLM: calls, observations and answers are computed by the real executor or the simulator.
• Scale-test scoping: the stress-test graphs come from the same generator family as part of the Stage-2 gold trajectories, with disjoint seeds. It measures scale-robustness of a learned querying policy, not out-of-family generalization.
• Source questions derive from ST-Bench (released by the STReasoner authors); this repository redistributes only derived trajectories and reshaped prompts.

Citation

@misc{topotool2026,
  title  = {Don't Serialize the Graph: Tool-Mediated Topology Access for
            Spatio-Temporal Reasoning at Scale},
  year   = {2026},
  note   = {Data: https://huggingface.co/datasets/hiepnh137/i1-topotool-data}
}