AI Pulse News

Markets move to price in rate hikes as inflation fears and geopolitics reshape Fed expectations

Iran dismisses diplomatic talks, says any U.S. troops would be ‘on fire’ as soon as they arrive

Dollar dominance is reinforced by the global oil trade, but the Iran war could give rise to the ‘petroyuan’ as the U.S. security shield weakens

Global M2 Hits New Highs as Central Banks Quietly Expand Money Supply Across Six Major Economies

An uneasy mix of celebration and anxiety dominates the ‘Davos of energy’ as the month-long Iran war further disrupts the global economy

NVIDIA AI Unveils ProRL Agent: A Decoupled Rollout-as-a-Service Infrastructure for Reinforcement Learning of Multi-Turn LLM Agents at Scale

Oil and gold rally as Iran locks down key oil route to US and Israeli allies

Anthropic’s Claude Mythos AI Model Exposed in Major Data Breach

Anthropic’s massive 'Claude Mythos' leak sends software names — and crypto — sharply lower

SoftBank Obtains Massive $40B Loan for Additional OpenAI Investment

OpenAI Sits at the Center of a $1.4 Trillion Capital Loop, Morgan Stanley Warns

Morgan Stanley warns that OpenAI sits at the center of a massive $1.4 trillion capital loop, with infrastructure commitments far exceeding its $13 billion annual revenue. Major tech companies like Microsoft, Amazon, Oracle, and Nvidia are both funding OpenAI and receiving its spending commitments back, creating potential hidden leverage risks for investors.

How to verify an exchanger: red flags, reviews, and proof points

The article discusses the rising threat of AI-powered crypto scams and fake exchanges that exploit user urgency and poor verification practices. It highlights how easily fraudulent crypto platforms can mimic legitimate exchanges to drain user funds.

ARC-AGI-3: A New Challenge for Frontier Agentic Intelligence

Researchers introduce ARC-AGI-3, a new benchmark for testing agentic AI systems that focuses on fluid adaptive intelligence without relying on language or external knowledge. While humans can solve 100% of the benchmark's abstract reasoning tasks, current frontier AI systems score below 1% as of March 2026.

Ming-Flash-Omni: A Sparse, Unified Architecture for Multimodal Perception and Generation

Ming-Flash-Omni is a new 100 billion parameter multimodal AI model with Mixture-of-Experts architecture that uses only 6.1 billion active parameters per token. The model demonstrates unified capabilities across vision, speech, and language tasks, achieving performance comparable to Gemini 2.5 Pro on vision-language benchmarks.

DiffuGuard: How Intrinsic Safety is Lost and Found in Diffusion Large Language Models

Researchers identified critical security vulnerabilities in Diffusion Large Language Models (dLLMs) that differ from traditional autoregressive LLMs, stemming from their iterative generation process. They developed DiffuGuard, a training-free defense framework that reduces jailbreak attack success rates from 47.9% to 14.7% while maintaining model performance.

DRIFT: Dynamic Rule-Based Defense with Injection Isolation for Securing LLM Agents

Researchers introduce DRIFT, a new security framework designed to protect AI agents from prompt injection attacks through dynamic rule enforcement and memory isolation. The system uses a three-component approach with a Secure Planner, Dynamic Validator, and Injection Isolator to maintain security while preserving functionality across diverse AI models.

The LLM Bottleneck: Why Open-Source Vision LLMs Struggle with Hierarchical Visual Recognition

Research reveals that open-source large language models (LLMs) lack hierarchical knowledge of visual taxonomies, creating a bottleneck for vision LLMs in hierarchical visual recognition tasks. The study used one million visual question answering tasks across six taxonomies to demonstrate this limitation, finding that even fine-tuning cannot overcome the underlying LLM knowledge gaps.

Epistemic Bias Injection: Biasing LLMs via Selective Context Retrieval

Researchers have identified a new attack vector called Epistemic Bias Injection (EBI) that manipulates AI language models by injecting factually correct but biased content into retrieval-augmented generation databases. The attack steers model outputs toward specific viewpoints while evading traditional detection methods, though a new defense mechanism called BiasDef shows promise in mitigating these threats.

The Future of AI-Driven Software Engineering

A paradigm shift is occurring in software engineering as AI systems like LLMs increasingly boost development productivity. The paper presents a vision for growing symbiotic partnerships between human developers and AI, identifying key research challenges the software engineering community must address.

LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts

Researchers have identified a new vulnerability in large language models called 'natural distribution shifts' where seemingly benign prompts can bypass safety mechanisms to reveal harmful content. They developed ActorBreaker, a novel attack method that uses multi-turn prompts to gradually expose unsafe content, and proposed expanding safety training to address this vulnerability.

SWAA: Sliding Window Attention Adaptation for Efficient and Quality Preserving Long Context Processing

Researchers propose SWAA (Sliding Window Attention Adaptation), a toolkit that enables efficient long-context processing in large language models by adapting full attention models to sliding window attention without expensive retraining. The solution achieves 30-100% speedups for long context inference while maintaining acceptable performance quality through four core strategies that address training-inference mismatches.

LLM4AD: Large Language Models for Autonomous Driving -- Concept, Review, Benchmark, Experiments, and Future Trends

Researchers have published a comprehensive review of Large Language Models for Autonomous Driving (LLM4AD), introducing new benchmarks and conducting real-world experiments on autonomous vehicle platforms. The paper explores how LLMs can enhance perception, decision-making, and motion control in self-driving cars, while identifying key challenges including latency, security, and safety concerns.

Decidable By Construction: Design-Time Verification for Trustworthy AI

Researchers propose a framework for verifying AI model properties at design time rather than after deployment, using algebraic constraints over finitely generated abelian groups. The approach eliminates computational overhead of post-hoc verification by building trustworthiness into the model architecture from the start.

How Pruning Reshapes Features: Sparse Autoencoder Analysis of Weight-Pruned Language Models

Researchers conducted the first systematic study of how weight pruning affects language model representations using Sparse Autoencoders across multiple models and pruning methods. The study reveals that rare features survive pruning better than common ones, suggesting pruning acts as implicit feature selection that preserves specialized capabilities while removing generic features.

AD-CARE: A Guideline-grounded, Modality-agnostic LLM Agent for Real-world Alzheimer's Disease Diagnosis with Multi-cohort Assessment, Fairness Analysis, and Reader Study

Researchers developed AD-CARE, an AI agent that uses large language models to diagnose Alzheimer's disease from incomplete medical data across multiple modalities. The system achieved 84.9% diagnostic accuracy across 10,303 cases and improved physician decision-making speed and accuracy in clinical studies.