The potential oil supply disruption could trigger inflationary pressures and economic instability, heightening recession risks globally. The post Oil supply disruption raises US recession risk for 2026 appeared first on Crypto Briefing.

In an interview with Fortune, the CEO of Amazon Web Services discussed the company's new push into productivity software as well as its partnership with OpenAI.

The de-escalation in Iran reduces inflation fears, stabilizing markets and diminishing the likelihood of significant Fed rate cuts. The post Iran conflict de-escalation stabilizes inflation expectations, Fed rate cut unlikely appeared first on Crypto Briefing.

Qatar's economic relief highlights the region's vulnerability to geopolitical tensions, potentially reshaping future economic strategies. The post Qatar implements business relief as Iran war impacts regional economy appeared first on Crypto Briefing.

Rising geopolitical tensions highlight the vulnerability of global oil markets, impacting economic stability and energy security worldwide. The post US-Iran tensions boost crude prices, lift Sinopec and CNOOC earnings appeared first on Crypto Briefing.

The ongoing conflict and recent strikes undermine ceasefire prospects, complicating diplomatic efforts and increasing regional instability. The post Russian strikes kill 21 in Ukraine on Chernobyl anniversary appeared first on Crypto Briefing.

The Pentagon's downplaying of missile shortages may undermine confidence in US military readiness, affecting geopolitical stability and market perceptions. The post Pentagon may downplay US missile shortage severity, impacting Iran conflict bets appeared first on Crypto Briefing.

The collapse in ceasefire odds highlights the fragility of US-Iran relations, underscoring potential geopolitical instability and market volatility. The post Iranian escalation fears coincide with US-Iran ceasefire odds collapse appeared first on Crypto Briefing.

A new method could bring more accurate and efficient AI models to high-stakes applications like health care and finance, even in under-resourced settings.

arXiv:2604.23027v1 Announce Type: new Abstract: Large language models (LLMs) have become central to modern AI workflows, powering applications from open-ended text generation to complex agent-based reasoning. However, debugging these models remains a persistent challenge due to their opaque and probabilistic nature and the difficulty of diagnosing errors across diverse tasks and settings. This paper introduces a systematic approach for LLM debugging that treats models as observable systems, pro

arXiv:2604.23148v1 Announce Type: new Abstract: The emerging threat of AR-LLM-based Social Engineering (AR-LLM-SE) attacks (e.g. SEAR) poses a significant risk to real-world social interactions. In such an attack, a malicious actor uses Augmented Reality (AR) glasses to capture a target visual and vocal data. A Large Language Model (LLM) then analyzes this data to identify the individual and generate a detailed social profile. Subsequently, LLM-powered agents employ social engineering strategie

arXiv:2604.23178v1 Announce Type: new Abstract: LLM-as-a-Judge has become the dominant paradigm for evaluating language model outputs, yet LLM judges exhibit systematic biases that compromise evaluation reliability. We present a comprehensive empirical study comparing nine debiasing strategies across five judge models from four provider families (Google, Anthropic, OpenAI, Meta), three benchmarks (MT-Bench n=400, LLMBar n=200, custom n=225), and four bias types. Our key findings: (1) Style bias

arXiv:2604.23280v1 Announce Type: new Abstract: AI agents are now running real transactions, workflows, and sub-agent chains across organizational boundaries without continuous human supervision. This creates a problem no current infrastructure is equipped to solve: how do you identify, verify, and hold accountable an entity with no body, no persistent memory, and no legal standing? We define AI Identity as the continuous relationship between what an AI agent is declared to be and what it is ob

arXiv:2604.23398v1 Announce Type: new Abstract: We report a reproducible error pattern in GPT-5.4 on OWL~2~DL compliance queries: the model frequently answers ``unknown'' when the reasoner-entailed answer is ``no'' under \emph{FunctionalProperty} closure or class \emph{disjointness}. Using 180 reasoner-audited queries from a procedural expansion of the observed pattern plus 18 hand-authored held-out queries in two unrelated domains (insurance and clinical), we compare four interaction modes und