RT-SEG — Reasoning Trace Segmentation
RT-SEG is a modular research framework for the segmentation of reasoning traces into coherent structural and semantic units.
It provides:
- Rule-based segmentation
- Probabilistic and distributional boundary detection
- LLM-driven discourse schemas
- Topic and semantic shift segmentation
- Late-fusion of heterogeneous engines
- Reproducible database-backed experimentation
Design Philosophy
RT-SEG treats reasoning traces as structured discourse objects.
Segmentation hypotheses are represented as character-level offsets to guarantee:
- deterministic reconstruction
- reproducible evaluation
- engine-agnostic comparison
- systematic late fusion
Quickstart
from rt_seg import RTSeg, RTRuleRegex
segmentor = RTSeg(engines=RTRuleRegex)
offsets, labels = segmentor("Example reasoning trace...")
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