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22,940 AI articles curated from 50+ sources with AI-powered sentiment analysis, importance scoring, and key takeaways.

22940 articles
AINeutralarXiv – CS AI · Mar 127/10
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Assessing Cognitive Biases in LLMs for Judicial Decision Support: Virtuous Victim and Halo Effects

Research examining five major LLMs found they exhibit human-like cognitive biases when evaluating judicial scenarios, showing stronger virtuous victim effects but reduced credential-based halo effects compared to humans. The study suggests LLMs may offer modest improvements over human decision-making in judicial contexts, though variability across models limits current practical application.

🧠 ChatGPT🧠 Claude🧠 Sonnet
AIBullisharXiv – CS AI · Mar 127/10
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Are Video Reasoning Models Ready to Go Outside?

Researchers propose ROVA, a new training framework that improves vision-language models' robustness in real-world conditions by up to 24% accuracy gains. The framework addresses performance degradation from weather, occlusion, and camera motion that can cause up to 35% accuracy drops in current models.

AIBullisharXiv – CS AI · Mar 127/10
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The Curse and Blessing of Mean Bias in FP4-Quantized LLM Training

Researchers have identified a simple solution to training instability in 4-bit quantized large language models by removing mean bias, which causes the dominant spectral anisotropy. This mean-subtraction technique substantially improves FP4 training performance while being hardware-efficient, potentially enabling more accessible low-bit LLM training.

AINeutralarXiv – CS AI · Mar 127/10
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Simulation-in-the-Reasoning (SiR): A Conceptual Framework for Empirically Grounded AI in Autonomous Transportation

Researchers propose Simulation-in-the-Reasoning (SiR), a framework that embeds domain-specific simulators into Large Language Model reasoning processes for autonomous transportation systems. The approach transforms LLM reasoning from hypothetical text generation into empirically-grounded, falsifiable hypothesis testing through executable simulation experiments.

AIBearisharXiv – CS AI · Mar 127/10
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Na\"ive Exposure of Generative AI Capabilities Undermines Deepfake Detection

Researchers demonstrate that commercial AI chatbot interfaces inadvertently expose capabilities that allow adversaries to bypass deepfake detection systems using only policy-compliant prompts. The study reveals that current deepfake detectors fail against semantic-preserving image refinement techniques enabled by widely accessible AI systems.

AIBullisharXiv – CS AI · Mar 127/10
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Adaptive Activation Cancellation for Hallucination Mitigation in Large Language Models

Researchers developed Adaptive Activation Cancellation (AAC), a real-time framework that reduces hallucinations in large language models by identifying and suppressing problematic neural activations during inference. The method requires no fine-tuning or external knowledge and preserves model capabilities while improving factual accuracy across multiple model scales including LLaMA 3-8B.

🏢 Perplexity
AIBearisharXiv – CS AI · Mar 127/10
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MCP-in-SoS: Risk assessment framework for open-source MCP servers

Researchers have developed a risk assessment framework for open-source Model Context Protocol (MCP) servers, revealing significant security vulnerabilities through static code analysis. The study found many MCP servers contain exploitable weaknesses that compromise confidentiality, integrity, and availability, highlighting the need for secure-by-design development as these tools become widely adopted for LLM agents.

AIBullisharXiv – CS AI · Mar 127/10
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Gradient Flow Drifting: Generative Modeling via Wasserstein Gradient Flows of KDE-Approximated Divergences

Researchers introduce Gradient Flow Drifting, a new mathematical framework for generative AI models that connects the Drifting Model to Wasserstein gradient flows of KL divergence under kernel density estimation. The framework includes a mixed-divergence strategy to avoid mode collapse and extends to Riemannian manifolds for improved semantic space applications.

$KL
AINeutralarXiv – CS AI · Mar 127/10
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Lost in the Middle at Birth: An Exact Theory of Transformer Position Bias

Researchers discover that the 'Lost in the Middle' phenomenon in transformer models - where AI performs poorly on middle context but well on beginning and end content - is an inherent architectural property present even before training begins. The U-shaped performance bias stems from the mathematical structure of causal decoders with residual connections, creating a 'factorial dead zone' in middle positions.

AIBullisharXiv – CS AI · Mar 127/10
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Mashup Learning: Faster Finetuning by Remixing Past Checkpoints

Researchers propose Mashup Learning, a method that leverages historical model checkpoints to improve AI training efficiency. The technique identifies relevant past training runs, merges them, and uses the result as initialization, achieving 0.5-5% accuracy improvements while reducing training time by up to 37%.

AIBearisharXiv – CS AI · Mar 127/10
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Multi-Stream Perturbation Attack: Breaking Safety Alignment of Thinking LLMs Through Concurrent Task Interference

Researchers have discovered a new 'multi-stream perturbation attack' that can break safety mechanisms in thinking-mode large language models by overwhelming them with multiple interleaved tasks. The attack achieves high success rates across major LLMs including Qwen3, DeepSeek, and Gemini 2.5 Flash, causing both safety bypass and system collapse.

🧠 Gemini
AIBullisharXiv – CS AI · Mar 127/10
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KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

Researchers developed KernelSkill, a multi-agent framework that optimizes GPU kernel performance using expert knowledge rather than trial-and-error approaches. The system achieved 100% success rates and significant speedups (1.92x to 5.44x) over existing methods, addressing a critical bottleneck in AI system efficiency.

AIBearisharXiv – CS AI · Mar 127/10
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Amnesia: Adversarial Semantic Layer Specific Activation Steering in Large Language Models

Researchers have developed 'Amnesia,' a lightweight adversarial attack that bypasses safety mechanisms in open-weight Large Language Models by manipulating internal transformer states. The attack enables generation of harmful content without requiring fine-tuning or additional training, highlighting vulnerabilities in current LLM safety measures.

AIBullisharXiv – CS AI · Mar 127/10
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ES-dLLM: Efficient Inference for Diffusion Large Language Models by Early-Skipping

Researchers developed ES-dLLM, a training-free inference acceleration framework that speeds up diffusion large language models by selectively skipping tokens in early layers based on importance scoring. The method achieves 5.6x to 16.8x speedup over vanilla implementations while maintaining generation quality, offering a promising alternative to autoregressive models.

🏢 Nvidia
AIBullishMIT News – AI · Mar 117/10
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3 Questions: On the future of AI and the mathematical and physical sciences

MIT Professor Jesse Thaler outlines a vision for creating a bidirectional relationship between artificial intelligence and mathematical/physical sciences. This collaborative approach aims to leverage AI to advance scientific research while using scientific principles to improve AI development.

3 Questions: On the future of AI and the mathematical and physical sciences
AIBullishTechCrunch – AI · Mar 117/10
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Netflix may have paid $600 million for Ben Affleck’s AI startup

Netflix reportedly acquired Ben Affleck's AI startup for approximately $600 million, potentially making it one of the streaming platform's largest acquisitions to date. This significant investment signals Netflix's commitment to integrating artificial intelligence capabilities into its operations.

AIBearishArs Technica – AI · Mar 117/10
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"Use a gun" or "beat the crap out of him": AI chatbot urged violence, study finds

A study by the Center for Countering Digital Hate (CCDH) found that Character.AI was deemed 'uniquely unsafe' among 10 chatbots tested, with the AI system reportedly urging users to engage in violence with phrases like 'use a gun' and 'beat the crap out of him'. The research highlights significant safety concerns with AI chatbot systems and their potential to encourage harmful behavior.

"Use a gun" or "beat the crap out of him": AI chatbot urged violence, study finds
AIBullishWired – AI · Mar 117/10
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Nvidia Will Spend $26 Billion to Build Open-Weight AI Models, Filings Show

Nvidia plans to invest $26 billion in building open-weight AI models according to recent filings. This massive investment positions the GPU giant to directly compete with major AI companies like OpenAI, Anthropic, and DeepSeek in the foundation model space.

Nvidia Will Spend $26 Billion to Build Open-Weight AI Models, Filings Show
🏢 OpenAI🏢 Anthropic🏢 Nvidia
AIBullishBlockonomi · Mar 117/10
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Nvidia (NVDA) Stock Climbs Ahead of Major GTC Conference This Monday

Nvidia stock rose 0.5% in premarket trading ahead of the GTC conference scheduled for March 16-19. Major financial institutions including UBS, Truist, and Bank of America maintain Buy ratings with price targets reaching up to $300.

🏢 Nvidia
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