SatOps-Decision is a low‑latency decision‑making method for satellite communication operations that uses a locally adapted Laya encoder with a native decision head to select operational answers in a single forward pass. It is the first application of Jev‑style discriminative decision models to satellite operations, and its accompanying benchmark is the first dedicated to this approach, featuring authored operational contracts, controlled state changes, and independently checked labels. In evaluations, SatOps-Decision achieves a median latency of 90.3 ms per decision and a mean raw accuracy of 85.3 %, outperforming baseline models such as Qwen3.5‑0.8B and OneJev1.6‑0.8B.
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: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...
The paper investigates how compact an encoder can be for closed‑set decision tasks in language‑model pipelines, comparing joint versus per‑option scoring. In a 32‑million‑parameter study, per‑option scoring outperforms joint scoring by 5.8 chance‑corrected skill points on a 37‑benchmark suite, and two external benchmarks corroborate this advantage. Across six released joint encoders ranging from 17 million to 1 billion parameters, the 68‑ and 150‑million‑parameter models surpass a 435‑million‑parameter DeBERTa‑v3 zero‑shot classifier on 209 overlapping tasks and match it on 145 unseen tasks, while the smallest model processes a median request in 14 ms versus 728 ms for the classifier.
The paper presents a Pareto atlas of LLM inference optimizations, mapping cost, quality, and latency trade‑offs for Qwen2.5‑7B‑Instruct on L4, A100, and H100 GPUs. Using 54 measured configurations and a calibrated simulator, it identifies 18 of 36 setups on the Pareto frontier, showing that combined methods outperform single ones. Quality tests reveal that AWQ 4bit and FP8 weights offer significant latency reductions while largely preserving accuracy, but naive FP8 KV caching fails to answer any questions correctly.
ThinkJev introduces a hybrid approach that lets a language model decide whether to answer directly or to think first, using the same underlying model for both fast and slow responses. By reading the model’s probability distribution over option letters after any number of thought tokens, the system can provide a calibrated decision under any thinking budget without retraining. Experiments with Qwen3.5-27B show that the method achieves high accuracy while reducing the number of thought tokens needed, and it performs competitively on the JevBench benchmark.
Verdict-MQ is an open encoder that processes a single piece of state once and then answers multiple typed questions about that state, avoiding separate passes for each question. By using a structured attention mask, it ensures the state encoding is independent of question order and prevents questions from influencing one another. On the typed‑decisions benchmark, Verdict‑MQ outperforms a larger specialist model, achieving higher accuracy and better calibration, while on other benchmarks it shows mixed results compared to fine‑tuned baselines.
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.
VX‑One is a small multi‑head neural network that outputs typed, calibrated decision primitives— a calibrated gate, a closed‑set choice with an explicit unknown option, and an ordinal score—trained in a multi‑task, temperature‑calibrated manner. In experiments on UNSW‑NB15, CIC‑IDS2017, and EMBER2024, VX‑One outperforms traditional models on detection metrics and achieves high recall when combined with a signature engine in a two‑phase cascade, though its calibration degrades under temporal drift and it struggles with multi‑class attribution. The study provides a detailed empirical assessment of where such typed, calibrated decisions aid perimeter triage and where they fall short.
arXiv:2610. 29052v1 Announce Type: cross Abstract: A typed decision maps evidence, a question and a fixed set of options to a probability for every option, computed in one forward pass, by reading a language model's logits or a small head over its hidden states, instead of generating and parsing text.
NumericJev introduces a training‑free numerical decoding algorithm that allows large language models with Jev‑like structured‑choice interfaces to output precise numerical values. The method refines a numerical range using a multiway decision tree, achieving lower mean absolute error than direct selection from a candidate list. Experiments on an arithmetic benchmark show a 2.93 percentage‑point improvement, and a historical‑index study reports a 4.58% mean relative recall error with zero readout error when the value is supplied.
arXiv:2610.00385v1 Announce Type: cross Abstract: Replay selectors often rank cached trajectories by format feedback, confidence, freshness, or response length, although cache-level correctness and d...
The paper introduces PACT, a tuning method for single-token typed-decision models that leverages contrastive pair data to add four training terms—difference-in-differences margin, permutation-consistency, evidence-necessity, and ordinal transport cost—without requiring new annotations. PACT achieves comparable accuracy to existing recipes while reducing position bias and ordinal error, and it improves robustness and stability across seeds. The authors provide code, data splits, and trained adapters for reproducibility.