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arXiv cs.AI

Assurance-Conditioned Continuous Authorization for Autonomous AI Agents

arXiv:2611.05632v1 Announce Type: new Abstract: Autonomous software agents can act through tools, delegate work, and change their models or execution environments while earlier authorizations remain in flight. We present Assurance-Conditioned Continuous Authorization (ACCA), an approach designed to be harness-independent, for maintaining operational authority according to currently justified assurance. It distinguishes evidence validity from contextual applicability, derives assurance claims from eligible evidence, and resolves a multidimensional effective-authority envelope within a principal-declared ceiling and applicable policy. A typed dependency graph supports selective re-evaluation of authorization-relevant changes. An authority epoch changes when effective authority changes; a distinct optional authorization-basis version supports revalidation of action-specific authorization conditions. Protected effect-time enforcement can reject stale actions before a consequential resource change, subject to the available commit mechanism. A compositional Domain Governance Profile expresses domain-specific semantics without granting authority. Controlled scenario tests implemented as a regression suite using a trading profile exercise default-deny rules, evidence-conditioned contraction and recovery, policy divergence, selective non-response, and stale-action rejection at a local gateway before the simulated-broker boundary. The study establishes implementation-level evidence in one domain, not a cross-domain guarantee of harmful-behavior prevention.

Hugging Face Blog

Dharma Field Extractor: Any Document In, Structured Fields in JSON Out

The Dharma Field Extractor, a 2-billion-parameter model, specializes in extracting specific fields from diverse document types—such as invoices, IDs, and medical forms—into structured JSON format. It outperforms general-purpose frontier LLMs in accuracy and reduces cost by up to 147 times when self-hosted, demonstrating that domain-specialized small models can surpass large general-purpose models on well-defined tasks. The model supports both open extraction (detecting fields without a schema) and schema-directed extraction (returning exact field names and types), with training data spanning English, Brazilian Portuguese, and multilingual documents. Benchmarks show superior F1 scores and valid JSON output across real-world datasets like SROIE and FUNSD, reinforcing the thesis that specialization beats scale for enterprise document processing.

Apple Machine Learning

Faster Retrieval for Better, More Efficient RAG

Maximum inner product search (MIPS) sits at the core of recommendation systems, search, and retrieval-augmented generation (RAG). Given a query vector, MIPS systems search within a database of millions of candidate key vectors for the one that best matches the query. The original data point associated with that best-matching key is then used to handle the query: in RAG, the document that is best suited to answer the question is typically prefixed to the query. As the number of keys grows to millions and vector dimensions reach thousands, the computational cost of running an exhaustive…

ClickHouse

Fencing a Postgres primary that might still be alive

Promoting a standby doesn't stop the old Postgres primary from taking writes. Here's how the control plane behind ClickHouse Managed Postgres fences it on bare metal, AWS and GCP, and why the design is fail-open.

AWS Machine Learning Blog

How U Mobile built agentic telco operations with Amazon Bedrock AgentCore

U Mobile, Malaysia's newest 5G network provider, built AiDaH, an agentic AI operations platform on Amazon Bedrock AgentCore that moves beyond AI assistance to autonomous action across telco operations. Nine months after production launch, AiDaH handles over 150 operational scenarios across six divisions, cutting trouble tickets by up to 84.5%.

AWS Machine Learning Blog

Use decision models with Amazon Bedrock AgentCore Evaluations

Some agent-evaluation questions are narrow and repeatable, and decision models answer exactly that kind of question. This post shows how to connect two decision models, Jev and Strands Decider, to Amazon Bedrock AgentCore Evaluations through code-based evaluators, and how to validate a decision-model check before you gate a release on it.

MIT Technology Review · AI

Securing the agentic enterprise with data lineage

A sales representative asks an AI agent for customer pricing, and the agent pulls data from multiple sources: restricted pricing systems, internal communications, and archived quotes. The output looks accurate to the agent, so a second agent uses the data to draft the proposal for the sales rep. No human checks either handoff. When it’s…

NVIDIA Blog

Cities Worldwide Build and Scale AI Services With NVIDIA Physical and Agentic AI

Cities generate enormous amounts of data from cameras, sensors and operational systems, but turning it into useful action — for planning, maintenance and public services — remains a challenge. Physical and agentic AI offer new ways to close that gap, from digital twins for infrastructure planning to vision and service agents, while keeping local governments […]

Cities Worldwide Build and Scale AI Services With NVIDIA Physical and Agentic AI
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arXiv cs.LG

Responsible Quantum Machine Learning for Medical Imaging: A Fairness Audit Across Sensitive Subgroups

arXiv:2610.37819v1 Announce Type: new Abstract: Quantum machine learning (QML) is increasingly explored for medical classification, yet its behaviour under subgroup-aware fairness criteria remains understudied. We present an empirical fairness audit of classical and quantum classifiers for medical imaging, assuming QML is not inherently fair. Two scenarios are evaluated: dermatological lesions on DDI (sensitive attribute: Fitzpatrick skin type) and chest X-rays on CheXpert-derived data (sensitive attribute: age group). Using ResNet-18 embeddings, we train SVM, MLP, QSVM and QCNN under a baseline and three mitigation settings: differentiable Equalized Odds (EO) regularisation, Gradient Reversal Learning (GRL), and both combined. Foundation models (PanDerm, EVA-X) serve as external reference

Timeline and related coverage
SiliconANGLE AI

Intuit rewires accounting education as AI pushes new hires toward client work

As AI automates routine finance work, the repetitive tasks that once defined entry-level accounting are disappearing, and advisory services are moving to the center of how firms create value. That shift leaves colleges racing to prepare graduates for a job that looks very different from the one in the textbooks. Closing the gap between classroom […] The post Intuit rewires accounting education as AI pushes new hires toward client work appeared first on SiliconANGLE.

Timeline and related coverage

SiliconANGLE AI

Intuit rewires accounting education as AI pushes new hires toward client work

As AI automates routine finance work, the repetitive tasks that once defined entry-level accounting are disappearing, and advisory services are moving to the center of how firms create value. That shift leaves colleges racing to prepare graduates for a job that looks very different from the one in the textbooks. Closing the gap between classroom […] The post Intuit rewires accounting education as AI pushes new hires toward client work appeared first on SiliconANGLE.

Timeline and related coverage
SiliconANGLE AI

Dell sees AI agent guardrails as a path to greater autonomy

As enterprises put AI agents to work, they face a challenge: How can they give those systems more autonomy while keeping AI agent guardrails in place? Security and resilience are becoming central considerations as enterprises decide which tasks to delegate to AI agents. Those decisions extend beyond individual actions to the data protection and cyber […] The post Dell sees AI agent guardrails as a path to greater autonomy appeared first on SiliconANGLE.

Timeline and related coverage