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Real-time AI-curated news from 63,600+ articles across 50+ sources. Sentiment analysis, importance scoring, and key takeaways — updated every 15 minutes.

63600 articles
CryptoBearishcrypto.news · Mar 277/10
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BlackRock, Fidelity lead Bitcoin ETF sell-off as BTC drops

US spot Bitcoin ETFs experienced $171 million in outflows on Thursday, with BlackRock and Fidelity leading the sell-off as Bitcoin dropped below $70,000. The decline was attributed to Middle East geopolitical risks that made traders more cautious about risk assets.

BlackRock, Fidelity lead Bitcoin ETF sell-off as BTC drops
$BTC
CryptoBearishcrypto.news · Mar 277/10
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Ethereum ETFs enter first 7-day outflow streak of the year

U.S. spot Ethereum ETFs experienced their first seven-day consecutive outflow streak of the year, with over $390 million exiting the funds. The outflows culminated with $92.54 million leaving on Thursday, March 26, indicating weakening institutional demand for ETH exposure.

Ethereum ETFs enter first 7-day outflow streak of the year
$ETH
CryptoBullishBlockonomi · Mar 277/10
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Tether Enlists KPMG for Comprehensive Audit of $185B USDT Reserves

Tether has engaged KPMG to conduct a comprehensive audit of its $185 billion USDT reserves, while also hiring PwC for system preparation. This move represents a significant shift toward transparency as Tether seeks to expand operations in the U.S. under evolving regulatory requirements.

AIBearishBlockonomi · Mar 277/10
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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.

🏢 OpenAI🏢 Nvidia
AI × CryptoBearishcrypto.news · Mar 277/10
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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.

How to verify an exchanger: red flags, reviews, and proof points
CryptoBearishDecrypt · Mar 277/10
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What Rising US Bond Yields Mean for Bitcoin

Rising US bond yields driven by oil-induced inflation concerns are creating tighter financial conditions that are negatively impacting both equity markets and cryptocurrency prices. This macroeconomic pressure is steering investor behavior away from risk assets like Bitcoin.

What Rising US Bond Yields Mean for Bitcoin
$BTC
CryptoNeutralNewsBTC · Mar 277/10
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Bitcoin ETFs Buy 63,000 BTC In 30 Days As Retail Panic Selling Persists

Bitcoin ETFs accumulated 63,000 BTC worth $11.3 billion over 30 days, providing price support near $70,000 despite continued retail selling pressure. While institutional demand accelerated to 3,288 BTC daily, short-term holders continue selling at losses, creating a market floor but preventing decisive breakout above $72,300.

Bitcoin ETFs Buy 63,000 BTC In 30 Days As Retail Panic Selling Persists
$BTC
AIBullisharXiv – CS AI · Mar 277/10
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Cross-Model Disagreement as a Label-Free Correctness Signal

Researchers introduce cross-model disagreement as a training-free method to detect when AI language models make confident errors without requiring ground truth labels. The approach uses Cross-Model Perplexity and Cross-Model Entropy to measure how surprised a second verifier model is when reading another model's answers, significantly outperforming existing uncertainty-based methods across multiple benchmarks.

🏢 Perplexity
AIBullisharXiv – CS AI · Mar 277/10
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Training the Knowledge Base through Evidence Distillation and Write-Back Enrichment

Researchers introduce WriteBack-RAG, a framework that treats knowledge bases in retrieval-augmented generation systems as trainable components rather than static databases. The method distills relevant information from documents into compact knowledge units, improving RAG performance across multiple benchmarks by an average of +2.14%.

AINeutralarXiv – CS AI · Mar 277/10
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Sparse Visual Thought Circuits in Vision-Language Models

Research reveals that sparse autoencoder (SAE) features in vision-language models often fail to compose modularly for reasoning tasks. The study finds that combining task-selective feature sets frequently causes output drift and accuracy degradation, challenging assumptions used in AI model steering methods.

AINeutralarXiv – CS AI · Mar 277/10
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When Is Collective Intelligence a Lottery? Multi-Agent Scaling Laws for Memetic Drift in LLMs

Researchers introduce Quantized Simplex Gossip (QSG) model to explain how multi-agent LLM systems reach consensus through 'memetic drift' - where arbitrary choices compound into collective agreement. The study reveals scaling laws for when collective intelligence operates like a lottery versus amplifying weak biases, providing a framework for understanding AI system behavior in consequential decision-making.

AIBullisharXiv – CS AI · Mar 277/10
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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.

AINeutralarXiv – CS AI · Mar 277/10
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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.

AIBearisharXiv – CS AI · Mar 277/10
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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.

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