Model Detail
Decision-2.0-Nox-4B
—Decision-2.0-Nox-4B is a code generation model with 4B parameters released by vllm-sr. The model is registered under the zero-shot-classification pipeline tag on Hugging Face, distributed under the permissive apache-2.0 license.
Decision-2.0-Nox-4B ships with 4B parameters. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
Decision-2.0-Nox-4B is best fit for code completion, repository-scale Q&A, and pair-programming integrations. It is a less obvious choice for one-shot generation of security-critical code without review. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
A general-purpose System One decision model for clinical records
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StartLux-Decision: AutoThink Decision Models with Reinforcement Learning for Calibrated Deliberative Decisions
arXiv:2610.17309v2 Announce Type: replace-cross Abstract: We introduce StartLux-Decision, decision models from 0.8B to 27B parameters that combine efficient single-pass prediction with adaptive, confidence-guided reasoning. The Instant models use bidirectional attention in full-attention layers to i
Simpler Logarithmic Approximation Algorithms for the Optimal Decision Tree and Adaptive Set Cover
arXiv:2604.12036v4 Announce Type: replace-cross Abstract: We study the problem of constructing a decision tree that identifies an unknown hypothesis from a given ground set, under both average- and worst-case cost. The Optimal Decision Tree (ODT) problem has been extensively studied in the literatur
Decision Sufficiency of Learned Representation Interfaces for Safe Generative Control
arXiv:2610.27446v1 Announce Type: new Abstract: A controller can guide a language model toward safe and useful responses by choosing when to assist, ask for clarification, or refuse. When the controller uses selected internal features, poor performance alone cannot distinguish missing information fr
What a One-Hour Interview Does Not Tell an LLM About You: Interviews Elicit Traits, Not Decision Policies
arXiv:2605.29458v2 Announce Type: replace-cross Abstract: Large language models are increasingly asked to stand in for particular people, and interviewing the person is the natural way to tell a model who they are. We test what a one-hour LLM-led adaptive interview tells a model about an individual'
Learning Transferable Latent User Preferences for Human-Aligned Decision Making
arXiv:2605.12682v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as reasoning modules in many applications, yet they often struggle to produce human-aligned decisions. Human-aligned decision-making requires accounting for both explicitly stated goals and latent
