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Jev by TypeSafe is a decision component for enterprise AI that provides typed judgments (Choice, Score, Noul) to replace text generation in workflows, enabling better governance and validation.
OpenClaw v2026.9.6 adds support for AI models like Claude Opus 5.5, GPT-6 Sol and Luna, and Grok 4.7, along with features for managed updates, restart recovery, usage reporting, GitHub integration, live meeting notes, and optional decision models.
GLiNER2.5-Decide is a 340M parameter English classification model for operational decisions, supporting multiple label sets in a single forward pass without prompt templates.
This paper investigates offline multimodal large language models as decision support tools for air operations, detailing a modular retrieval-augmented architecture and presenting a pilot study with the Brazilian Air Force that demonstrates reduced cognitive workload and improved efficiency in doctrinal assessment tasks.
This paper proposes a hybrid agentic AI framework for supply chain analytics that uses a coordinator agent and specialized agents to improve decision-making, achieving 90% accuracy and reducing token usage by fourfold.
Phorecaster365 presents a human-supervised reference architecture for pharmaceutical sales forecasting that integrates data ingestion, modeling, and governance with a validation framework, though it does not establish real-world accuracy.
This paper presents the Semantic Signal-Assisted Decision Support (SSADS) framework, which converts return notes into condition factors and signal-quality scores to optimize inspection and recovery allocation in reverse logistics, showing improvements in net recovery value in synthetic benchmark scenarios.
Simile is building a 'What-If Machine' to simulate real-world decisions and understand the impact of interventions, emphasizing causation over correlation.
The paper proposes FLARE, a framework combining fuzzy logic, time-driven activity-based costing, and ROI analysis to assess the economic and operational implications of AI adoption in healthcare under uncertainty.
This paper proposes a Metamorphic Artificial Age Score (AAS) decision-support prototype for flight-log-based drone propeller health monitoring, using a multi-indicator approach to evaluate propeller health and prioritize maintenance.
This paper presents a sociotechnical AI pipeline for ITSM ticket data, combining LLM-based schema normalization and clustering to generate executive-facing decision-support artifacts. Stakeholder evaluation shows strong ratings across interpretability, actionability, trust, and likelihood of use.
This arXiv paper benchmarks eight open-source small language models under different fine-tuning strategies for emergency department decision support, finding that LoRA-tuned SLMs can outperform commercial baselines on triage and referral tasks while remaining locally deployable.
TumorBoard is a multi-agent decision-support system for longitudinal neuro-oncology that uses a shared longitudinal case state and auditable claim-evidence ledger. It outperforms baselines on a 360-case benchmark, with a safety governor reducing harmful recommendations.
This paper presents AWARE-FX, an auditable AI/NLP decision-support system that extracts and scores corporate foreign-exchange hedging disclosures from annual reports, evaluated on 24,909 Hong Kong firm-years with FinBERT, ModernBERT, and Qwen3-8B comparisons.
This paper proposes formulating the bridge between planned tasks and performed actions as a quasi-linear Fisher market, allowing fractional credit assignment. It introduces instruments for conservation and junk filtering, and extends the model with entropy regularization to handle noise, unifying it with optimal transport.
HantaWatch is a federated learning framework for hantavirus genomic surveillance that enables collaborative training of sequence-based models without sharing raw data, integrating k-mer feature extraction and adaptive optimization to support risk screening and expert prioritization.
This paper presents FST.ai 2.5, an explainable and uncertainty-aware AI framework for Olympic and Para-Taekwondo that integrates athlete digital twins, competition analytics, and federation-scale decision support.
This paper presents a conflict-free path-planning algorithm for en-route air traffic control, designed to be interpretable and computationally efficient for human operators. The algorithm integrates three conflict detection methods and achieves fast computation times, demonstrated on a real-world sector.
This paper introduces COOPA, a modular LLM agent architecture for operations research problems that combines iterative confidence-based modeling, element-level provenance, and multi-solver routing. Evaluated across eight LLM backbones and four baselines, COOPA achieves the best macro-average accuracy on six backbones and improves over the strongest baseline by up to 6.7 percentage points.
This paper proposes a framework for strategic decision support for AI agents, formulating an optimization problem to minimize support usage while controlling missed-support error. The authors develop an online algorithm and calibration method, demonstrating effectiveness across information gathering, human-AI collaboration, and tool use scenarios.