JevForest is a feature‑acquisition policy that aggregates path‑dependent proposals from bootstrapped trees, weights them by global training information gain, and uses a shared masked classifier to predict from the acquired values. In online experiments, a four‑question forest on AG News achieved 0.729 accuracy versus 0.667 for a static gain ranking and 0.583 for random ordering, while on TREC the ordering reversed. Offline MiniBooNE tests showed accuracies of 0.845 with ten features and 0.885 with forty features, indicating that Jev’s effectiveness varies with task, predictor, and budget constraints.
arXiv:2609.37647v1 Announce Type: cross Abstract: Jev is a commercial System One model from TypeSafe AI that does not generate text: given a state and typed questions, it returns a choice from fixed...
arXiv:2607. 01417v1 Announce Type: new Abstract: Conditional inference trees (CIT) and conditional inference forests (CIF) reduce split-selection bias by testing features before choosing split thresholds, but repeated permutation tests and threshold searches can make these methods computationally expensive.
The paper evaluates Jev, a general-purpose decision model, for classifying network traffic using only the first ten packets’ sizes, directions, and inter‑packet times. Across 52,000 records, adding 40 labeled examples improves Jev’s accuracy from 9.80% to 28.42%, yet Random Forest and Extra Trees models trained on 8,000 records achieve higher accuracies of 69.95% and 66.80%. Even with 150 examples, Jev reaches only 34.50% accuracy, and in a paired 100‑record test, Jev’s 29% accuracy and 0.750 s median request time are lower than the generative OpenAI GPT‑5.6 Sol’s 37% accuracy and 6.036 s response time, though Jev incurs lower API charges.
arXiv:2610.02267v1 Announce Type: new Abstract: Agent harnesses make many small, typed decisions per task: which model to call, which tool to use, whether retrieved text is relevant, whether an input...
arXiv:2608.24079v1 Announce Type: cross Abstract: A shared search-and-recommendation index must score new items from features alone because search has no exploration slot. In a public log covering bo...
arXiv:2607. 27083v1 Announce Type: new Abstract: As LLM agents increasingly depend on diverse external services such as search engines, databases, and connectors, agent harnesses face a fundamental tool-selection challenge: acquiring too few tools leaves the task under-informed, while too many adds cost, context load, and privacy exposure.
arXiv:2608. 09168v1 Announce Type: new Abstract: Agent skills are increasingly used to equip large language model (LLM) agents with reusable procedural knowledge.
The paper investigates the trade‑off of using a shared search‑and‑recommendation index that scores new items purely from features, thereby keeping the index open to unseen items. Experiments on public logs show that a feature‑based tower can match warm‑item performance (Recall@20 0.9595 vs 0.9510) and a lexical baseline, while a full‑catalog check is inconclusive. The study also quantifies the cost of this openness on recommendation quality across several baselines, revealing that exact full‑softmax training improves recall but is impractical at catalog scale.
arXiv:2607. 18960v1 Announce Type: cross Abstract: Procuring supervised fine-tuning (SFT) data forces a buyer to decide, before any downstream training, whether a candidate corpus is worth acquiring.
arXiv:2610.13627v1 Announce Type: new Abstract: Evaluating large language models (LLMs) on AMLworld's smallest test splits is costly because each of over a million targets requires a separate graph p...
arXiv:2606. 15623v1 Announce Type: cross Abstract: Pairwise comparison is the gold standard for subjective ranking tasks; however, exhaustive annotation requires a massive number of human comparisons ($O(n^2)$).
arXiv:2608.30086v1 Announce Type: cross Abstract: Credit-default prediction is an important task in financial decision making. Traditional methods use fitted classifiers such as logistic regression a...