Veteran market strategist Ed Yardeni believes the recent pullback in equities has already run its course. In a new CNBC interview, Yardeni says he is standing by his bullish outlook for the year, even after recent volatility driven by geopolitical tensions and shifting macro narratives. “I am going to stick by it. I’ve been whipped around […] The post Yardeni Research President Says Stock Market Has Already Bottomed – Here’s Why appeared first on The Daily Hodl.

Khamenei's health issues could destabilize Iran's leadership, increasing regime change risks amid external pressures and internal uncertainties. The post Khamenei unconscious in Qom raises concerns over Iran’s leadership stability appeared first on Crypto Briefing.

The AI company's partnership with Google and Broadcom for next-generation TPU capacity starting in 2027 adds to a wave of demand reshaping the economics of every industry that competes for cheap electricity, including bitcoin mining.

Running powerful AI on your smartphone isn’t just a hardware problem — it’s a model architecture problem. Most state-of-the-art vision encoders are enormous, and when you trim them down to fit on an edge device, they lose the capabilities that made them useful in the first place. Worse, specialized models tend to excel at one […] The post Meta AI Releases EUPE: A Compact Vision Encoder Family Under 100M Parameters That Rivals Specialist Models Across Image Understanding, Dense Prediction, and VL

Iran's recent missile capabilities demonstration has reduced market expectations for regime collapse, with odds dropping to 13.5%. The military display suggests greater regime stability than previously anticipated, potentially affecting geopolitical risk assessments.

Researchers argue that current AI evaluation methods have systemic validity failures and propose item-level benchmark data as essential for rigorous AI evaluation. They introduce OpenEval, a repository of item-level benchmark data to support evidence-centered AI evaluation and enable fine-grained diagnostic analysis.

Researchers demonstrate how large language models like ChatGPT can automate laboratory instrument control, reducing programming barriers for scientists. The study shows LLMs can create custom scripts and operate as autonomous AI agents for lab equipment management.

Researchers introduced VERT, a new LLM-based metric for evaluating radiology reports that shows up to 11.7% better correlation with radiologist judgments compared to existing methods. The study demonstrates that fine-tuned smaller models can achieve significant performance gains while reducing inference time by up to 37.2 times.

Researchers present a new framework for AI safety that identifies a 57-token predictive window for detecting potential failures in large language models. The study found that only one out of seven tested models showed predictive signals before committing to problematic outputs, while factual hallucinations produced no detectable warning signs.

Researchers developed an automated framework using large language models to compare AI safety policy documents across a shared taxonomy of activities. The study found that model choice significantly affects comparison outcomes, with some document pairs showing high disagreement across different LLMs, though human expert evaluation showed high inter-annotator agreement.

Researchers at arXiv have identified two key mechanisms behind reasoning hallucinations in large language models: Path Reuse and Path Compression. The study models next-token prediction as graph search, showing how memorized knowledge can override contextual constraints and how frequently used reasoning paths become shortcuts that lead to unsupported conclusions.

Research reveals that adaptive reward mechanisms in AI-guided satellite scheduling systems actually hurt performance, with static reward weights achieving 342.1 Mbps versus dynamic weights at only 103.3 Mbps. The study found that fine-tuned LLMs performed poorly due to weight oscillation issues, while simpler MLP models achieved superior results of 357.9 Mbps.

Researchers propose a new framework for 'selective forgetting' in Large Reasoning Models (LRMs) that can remove sensitive information from AI training data while preserving general reasoning capabilities. The method uses retrieval-augmented generation to identify and replace problematic reasoning segments with benign placeholders, addressing privacy and copyright concerns in AI systems.

Researchers propose Rashomon Memory, a new AI agent memory architecture where multiple goal-conditioned agents maintain parallel interpretations of the same events and negotiate through argumentation at query time. The system allows AI agents to handle conflicting perspectives on experiences rather than forcing a single interpretation, using Dung's argumentation semantics to determine which proposals survive retrieval.

Researchers propose a new approach to Generative Engine Optimization (GEO) that moves beyond current RAG-based systems to deterministic multi-agent platforms. The study introduces mathematical models for confidence decay in LLMs and demonstrates near-zero hallucination rates through specialized agent routing in industrial applications.