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Tactile robotics is expanding from sensor hardware into datasets, foundation models, and evaluation benchmarks. Research shows touch improves physical reasoning and robot success, but inconsistent sensor formats create major cross-device transfer challenges.
FinSkillBench is a new benchmark for testing AI agents on portfolio construction, risk management, and fundamental analysis. Across nine models, curated procedural skills raised mean performance from 0.366 to 0.528, while self-generated skills delivered little benefit.
OpenAI says it has temporarily slowed scaling, paused reinforcement learning for two weeks, and placed its largest planned frontier RL run on hold while it strengthens safety evaluations. It will also offer zero data retention to eligible API customers starting in September.
Ornith-1.5 launches open models in 397B, 35B, and 9B parameter sizes. The models feature self-improving task and scaffold generation and report strong performance on coding and reasoning benchmarks.
China has approved five automotive-chip certification and accreditation standards built around a “1+4” framework, effective October 1, 2026. The standards aim to unify evaluations for chip design, certification, testing, and automotive computing, reducing duplicated validation and improving trust in domestic suppliers.
This position paper argues that AI leaderboards underserve the Global South because of weak governance, conflicts of interest, and inflexible metrics—not a lack of data. Using India as a case study, it recommends independently governed regional leaderboards that incorporate benchmarks such as IndicSUPERB, MILU, and LAHAJA.
This survey frames self-evolving LLM agents as dynamic graphs whose memories, tools, skills, workflows, and relationships change over time. It presents four evolution taxonomies, connects nine dynamic-graph-learning fields to agent capabilities, and proposes graph-aware evaluation and governance protocols.
NVIDIA SkillEvaluator is designed to measure whether packaged agent skills improve AI agent performance. It evaluates the value of instructions, examples, and tool guidance that help agents find relevant context, reduce wasted steps, and complete specialized tasks more effectively.
Vercel Functions can now emit custom metrics and analyze them alongside built-in observability data. Developers can filter, aggregate, visualize, and query these metrics across Vercel Observability.
Incogni researchers evaluated 13 AI platforms to assess the privacy risks associated with each service. The analysis suggests that larger platforms generally present greater privacy risks, with one notable exception.