SOURCE-LINKED INTELLIGENCE

Paritok-4B: Intent-Conditioned Context Compression for Coding Agents

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

Coding agents re-send large file reads and tool outputs to a frontier LLM every turn, and this context dominates their token bill. General-purpose prompt compressors are trained on prose and suit code poorly: they paraphrase identifiers and drop the exact spans an agent needs to edit. We present Paritok-4B, a 4B LoRA compressor for coding-agent trajectories built on two commitments. It is extractive: it selects spans rather than rewriting them, and 96.0% of the identifiers, paths, and numbers it emits already appear in its input, holding at 96.2% on held-out SWE-bench Lite output. It is intent

Read original source ↗ Open in workspace

recordType

paper

region

Global