arXiv AI August 18, 2026 By Aashna Sofat, Balwinder Sodhi

Beyond Thresholds: A Quality-Aware Decision Intelligence Framework for Cold Chain IoT Systems

arXiv:2608. 15082v1 Announce Type: new Abstract: Cold chain logistics has advanced technologically, yet most deployed systems remain reactive monitors, not decision-making agents: thresholds trigger alerts, but nothing relates violations to cumulative product degradation or converts degradation signals into logistics decisions.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 28

Statistically Supported LLM Ingredient and Recipe Data Collection in Computational Nutrition

arXiv:2607. 23273v1 Announce Type: cross Abstract: Computational nutrition needs precise ingredient data, but current databases are incomplete, inconsistent, and built for human reference rather than automated reasoning.

By James Izzard, Hassan Eshkiki, Fabio Caraffini

llms

arXiv AI

Jun 9

AI Scientists Are Only as Good as Their Evidence: A Stratified Ablation of Proprietary Data and Reasoning Skills in Drug-Asset Valuation

arXiv:2606. 09556v1 Announce Type: new Abstract: AI Scientist agents are often evaluated as if capability were mainly a function of model quality, prompting, or reasoning scaffolds.

By Yinan Wang

llms agents benchmarks

arXiv AI

Jul 16

STOCKTAKE: Measuring the Gap Between Perception and Action in LLM Agents with a Fair Oracle

arXiv:2607. 13618v1 Announce Type: new Abstract: LLM agents are increasingly evaluated on multi-week decision tasks in which the state that drives cost is never directly observed.

By Sagar Deb, Ashwanth Krishnan

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jun 26

Necessary but Not Sufficient: Temperature Control and Reproducibility in LLM-as-Judge Safety Evaluations

arXiv:2606. 26185v1 Announce Type: new Abstract: LLM-as-judge ("grader") components are now standard in evaluation harnesses, including safety evaluations where a pass/fail verdict may gate downstream deployment decisions.

By Hiroki Tamba

llms safety

arXiv AI

1d ago

Multimodal Language Models Benchmarked Against the NRC Reactor Operator Licensing Examination: Fine-Tuning and Retrieval Strategies

arXiv:2607. 22067v2 Announce Type: replace-cross Abstract: Competence claims for a language model in a safety-critical domain are credible when measured against a standard the domain already enforces.

By Isak Hwang, Yoon Pyo Lee, Syed Bahauddin Alam

llms rag fine-tuning multimodal safety

arXiv AI

Jul 31

SARC-DQ: Runtime Data-Quality Gating for Agentic AI: Silent Evidence Defects, the Incompetence Shield, and Downstream-Only Remediation

arXiv:2607. 26313v1 Announce Type: cross Abstract: Agentic systems act, so a defect in the evidence they retrieve becomes a wrong action with a currency cost.

By Gaston Besanson

agents benchmarks

Related stories

Statistically Supported LLM Ingredient and Recipe Data Collection in Computational Nutrition

AI Scientists Are Only as Good as Their Evidence: A Stratified Ablation of Proprietary Data and Reasoning Skills in Drug-Asset Valuation

STOCKTAKE: Measuring the Gap Between Perception and Action in LLM Agents with a Fair Oracle

Necessary but Not Sufficient: Temperature Control and Reproducibility in LLM-as-Judge Safety Evaluations

Multimodal Language Models Benchmarked Against the NRC Reactor Operator Licensing Examination: Fine-Tuning and Retrieval Strategies

SARC-DQ: Runtime Data-Quality Gating for Agentic AI: Silent Evidence Defects, the Incompetence Shield, and Downstream-Only Remediation