Research & models Oct 18, 2026

Trend · preprints October 15

JevAgentHarness cuts agent cost; results are self-reported

Two papers measure multiple-choice decisions inside agents, such as which tool to use or which step to take next. One paper, JevAgentHarness, hands those decisions to a 4-billion-parameter small model. It cuts agent cost by 81.1% to 89.0% on in-distribution tasks and loses under 3 percentage points of success rate. The other, Prism-1, looks at the current approach, where a large model picks an option in one inference pass. Shuffling the option order once changes 12% to 20% of its answers. Both were read only as abstracts, are self-reported by their authors and aren't peer-reviewed (arXiv 2610.21412, 2026-10-15; arXiv 2610.22066, 2026-10-15). JevAgentHarness first sets a confidence threshold, and the small model decides only when its confidence clears it. The abstract also gives the downside: handing everything to the small model without fitting that threshold first drops success by 19 percentage points, rather than the under 3 points after fitting. From context, the cost baseline is a harness that calls a large model at every step; the abstract doesn't say so word for word. Prism-1's fix is to read out a score for each option separately, so neither shuffling nor adding irrelevant options changes the choice. Both the reliability risk and the cost that can be saved are measurable, and each rests on a single self-reported paper.