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TypeSafe Jev

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2

RESEARCH · 1 SOURCE · arXiv cs.AI

JevSpawn: compositional policy for faster, structured agentic inference

The paper (arXiv:2610.00437v1) introduces JevSpawn, a compositional policy that maps natural-language task specifications to finite probabilistic action exploration. JevSpawn uses parallel action spawning with feedback-driven branch selection, representation revision, and recovery from retained alternatives to reduce repeated generation and context computation without extra training; authors report improved task performance and faster navigation across eight benchmarks versus seven agent baselines and a TypeSafe Jev variant.

6.0

RESEARCH · 1 SOURCE · arXiv cs.AI

LAVOIR: single-pass decision encoder that predicts value-of-information to decide when to ask

LAVOIR (Laya with Value-Of-Information Routing) augments Laya-style single-pass decision encoders to output both a decision distribution and, for each candidate missing information slot, an expected value-of-information (VOI) so the system can decide whether to ask a question. The method creates VOI training targets without human labels, uses a Gini-impurity cap to bound predicted VOI, matches a Bayes ceiling on seen schemas, achieves AUC 0.799 vs. a greedy oracle 0.797, improves accuracy by up to 14.1 points with ≤0.5 questions per conversation, and reduces asking on SGD from 93% to 8.6%; median answer latency is 31 ms on GH200.

6.0