The authors built REFLEX around Jev, a fast decision layer restricted to typed action choices.
Typed selective control cuts strong-model calls while preserving agent success
REFLEX routes bounded decisions through the faster Jev layer and invokes a strong language model only for low-confidence choices or generation.
Top University
Tiantong Wu · Wei Yang Bryan Lim
Nanyang Technological University
Research Digest··2 min read
Thread:Agent Harness Optimization
Wu and Lim evaluate REFLEX, an agent architecture that assigns typed, bounded decisions to Jev while retaining a strong LLM as a fallback.
Why this paper
From Nanyang Technological University · Part of Agent Harness Optimization, now 61 papers
In one line
A Jev decision layer cuts strong LLM calls by 72.7% while maintaining 95% task success.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
§