jlov7/nanoim-symbolic
nanoIM Symbolic Temporal Aliasing Dataset nanoIM is a small, synthetic, symbolic dataset for studying temporal aliasing in interaction models. Paired examples can share the same flattened transcript while requiring different target actions because timing, overlap, visual cues, policy events, or background/tool results differ. Files File Hub config Purpose data/mini/{train,validation,test}.jsonl mini Quick smoke suite for training and evaluation.… See the full description on the dataset page: https://huggingface.co/datasets/jlov7/nanoim-symbolic.
nanoIM Symbolic Temporal Aliasing Dataset
nanoIM is a small, synthetic, symbolic dataset for studying temporal aliasing in interaction models. Paired examples can share the same flattened transcript while requiring different target actions because timing, overlap, visual cues, policy events, or background/tool results differ.
Files
The generated Hub bundle also includes source aggregate files such as data/noisy.jsonl for exact source-repo parity. Hub dataset configs use the split files above so train, validation, and test are native dataset splits.
data/pilot_events.jsonl is included as source evidence for the pilot importer, but the pilot dataset config loads data/pilot.jsonl.
Schema
Each JSONL row contains:
- identifiers:
example_id,pair_id,alias_group_id,template_id; - task metadata:
task_family,split,critical_timestep; - transcript field:
flattened_transcript; - micro-turn fields:
turnsandtimesteps, includingt_ms,dt_ms, stream fields, andtarget_action.
The transcript-only boundary intentionally excludes pair IDs, task families, timestamps, event labels, split labels, template IDs, and target labels. Anti-leakage tests in the source repository enforce this boundary.
Intended Use
- Evaluate whether a model needs native micro-turn features to separate balanced alias pairs.
- Teach temporal aliasing with a small, inspectable dataset.
- Reproduce the paper-style experiments in the nanoIM source repo.
Out Of Scope
- Natural speech, audio, or video understanding.
- Production dialogue quality.
- Human-subject behavioral claims.
- Any claim that the pilot trace is a natural realtime benchmark.
Citation
Use the citation metadata from the source repository CITATION.cff when citing nanoIM.
