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ReactHuman: A Physics-Grounded Benchmark for Human-Like Reactive Decision-Making in Embodied Multimodal LLMs
ReactHuman benchmark tests whether multimodal LLMs react safely to sudden household hazards; seven evaluated models mishandle roughly one hazard in three.
ReactHuman is the first physics-grounded benchmark for human-like reactive decision-making, placing a multimodal LLM as the brain of a simulated humanoid facing 17 event families of sudden household hazards across over 1,000 bit-for-bit reproducible scenes with annotation-free ground truth from 240 Hz rigid-body simulation, including adversarial objects whose appearance contradicts their physics. A five-metric suite scores each reaction along reasonable, safe, and physically grounded axes, and every committed plan is physically executed. Seven representative MLLMs mishandle roughly one hazard in three, act from fixed dispositions rather than the observed scene, trust appearance over motion, and miss interception points at meter scale; none of these failures shrink with model scale.
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Facilitating AI integration with simplicity at scale
Jabil's SAP IT director says simplifying integration across 100+ sites in 30+ countries with SAP Integration Suite created the data backbone for AI.
In an MIT Technology Review Business Lab podcast produced in partnership with SAP, Jabil SAP IT director Harish Manohar described consolidating fragmented tools across more than 100 sites in over 30 countries using SAP Integration Suite. The manufacturer, with 140,000-plus employees and more than 400 top-brand customers, says a standardized data backbone enables real-time supply chain visibility and is a prerequisite for scaling predictive, AI-driven planning and forecasting. The company frames simplification-first modernization as a competitive advantage tied to measurable business value and operational resilience.
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Strategic Threat Intelligence for Better Decision-Making
Recorded Future outlines strategic threat intelligence: non-technical, OSINT-driven analysis for board-level decisions and the skill sets needed to produce it.
Recorded Future's blog post explains that threat intelligence spans strategic, tactical, operational, and technical categories, focusing on strategic intelligence. It defines strategic intelligence as non-technical, risk-based analysis produced mainly from OSINT sources on demand for C-suite and board audiences. The piece stresses the sociopolitical and business expertise required and urges boards to ask trend-focused questions rather than demanding specific attack predictions.
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Emergency Department Revisit Quality Review Screening: Exploring Human Decision-Making and Artificial Intelligence Support
A retrospective study found GPT-4 over-flagged emergency department revisit cases while an LLM knowledge-graph screener achieved 83-100% positive predictive value.
In an exploratory retrospective study of 99 emergency department diagnosis pairs from a multihospital health system, clinicians and GPT-4 independently judged whether revisit pairs warranted further assessment. GPT-4 responses correlated poorly with clinicians, flagging 94% of pairs for follow-up, 4.4-13.3 times more than clinicians, though prompt engineering was minimal. An algorithm leveraging an LLM-populated knowledge graph (KGA) achieved 83-100% positive predictive value against at least one clinician rater, suggesting LLM-based screening could broaden revisit quality review without substantially increasing reviewer workload.
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