Model Detail
Decision-2.0-Kai-0.6B
—Decision-2.0-Kai-0.6B is a code generation model with 0.6B 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-Kai-0.6B ships with 0.6B 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-Kai-0.6B 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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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'
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
DA-WAM: Decision-Aligned Future Latents for Driving World Models
arXiv:2608.19085v3 Announce Type: replace-cross Abstract: World models for autonomous driving should anticipate the consequences that matter for driving decisions rather than reconstruct every detail of future scenes. However, predictive representations alone are not guaranteed to distinguish candid
Evaluating utility of clinical prediction models: A comparison of decision curve analysis and cost curves
arXiv:2509.24608v2 Announce Type: replace Abstract: Classification models typically predict a binary outcome and use one or more decision thresholds to produce a classification or inform a clinical decision. Decision curves and cost curves are model evaluation approaches that assess net benefit and
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
