3 papers
cs.AI2026
Paritok-4B: Intent-Conditioned Context Compression for Coding Agents
Jiayu Shi, Luzhuo Chen
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…
cs.AI2026
StrategyBench: Evaluating Explicit Strategy Induction in Large Language Models
Jinghan Tan, Yuanzheng Wang, Lu Chen +3
As large language models are increasingly used in data-scarce and evolving task scenarios, few-shot in-context learning (ICL) has become a key paradigm for task adaptation. However…
cs.CL2026
An Empirical Cost Attribution of Context-Compression Gateways in Multi-Turn Coding Agents
Luzhuo Chen, Jiayu Shi
Context compression is widely proposed as a way to cut the token bill of LLM coding agents, and public benchmarks report that aggressive compression preserves task-solving quality.…