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We’ve aggregated 378 of MarkTechPost’s headlines and news stories over the past 3 months.

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Top MarkTechPost News

Artificial Intelligence

Apple study finds cutting-edge AI models from OpenAI and DeepSeek undergo 'complete collapse' when problems get too difficult

44% Center coverage: 27 sources

See the Story

Artificial Intelligence · France

Mistral’s First Reasoning Model, Magistral, Launches with Large and Small Apache 2.0 Version

56% Center coverage: 9 sources

The company is signaling that the future of reasoning AI will be both powerful and, in a meaningful way, open to all.See the Story

Artificial Intelligence · Hong Kong

Human-Like Object Concept Representations Emerge Naturally in Multimodal Large Language Models

50% Left coverage: 4 sources

Understanding how humans conceptualize and categorize natural objects offers critical insights into perception and cognition. With the advent of large language models (LLMs), a key question arises: can these models develop human-like object representations from linguistic and multimodal data? Here we combined behavioural and neuroimaging analyses to explore the relationship between object concept representations in LLMs and human cognition. We c…See the Story

How AI Mathematicians Might Finally Deliver Human-Level Reasoning

100% Center coverage: 2 sources

Artificial intelligence is taking on some of the hardest problems in pure maths, arguably demonstrating sophisticated reasoning and creativity – and a big step forward for AIApple See the Story

OThink-R1: A Dual-Mode Reasoning Framework to Cut Redundant Computation in LLMs

The Inefficiency of Static Chain-of-Thought Reasoning in LRMs Recent LRMs achieve top performance by using detailed CoT reasoning to solve complex tasks. However, many simple tasks they handle could be solved by smaller models with fewer tokens, making such elaborate reasoning unnecessary. This echoes human thinking, where we use fast, intuitive responses for easy problems and slower, analytical thinking for complex ones. While LRMs mimic slow, …Hangzhou · Hangzhou Read Article

AI-Generated Ad Created with Google's Veo3 Airs During NBA Finals, Slashing Production Costs by 95%

A lone AI filmmaker, a cutting-edge generative video model, and a national TV spot during one of the year’s biggest sporting events. This isn’t the plot of a sci-fi movie; it’s the new reality of advertising, and it was created in just 3 days. TLDR: First of its Kind: An AI-generated commercial for the events-betting platform Kalshi was created using Google’s generative video model, Veo3, and aired nationally during the NBA Finals. Drastic Cost…Google · United States Read Article

Internal Coherence Maximization (ICM): A Label-Free, Unsupervised Training Framework for LLMs

Post-training methods for pre-trained language models (LMs) depend on human supervision through demonstrations or preference feedback to specify desired behaviors. However, this approach faces critical limitations as tasks and model behaviors become very complex. Human supervision is unreliable in these scenarios as LMs learn to mimic mistakes in demonstrations or exploit inherent flaws in feedback systems. The core challenge lies in training LM…New York City · New York Read Article

Google AI Unveils a Hybrid AI-Physics Model for Accurate Regional Climate Risk Forecasts with Better Uncertainty Assessment

Limitations of Traditional Climate Modeling Earth system models are essential tools for forecasting environmental changes and helping us prepare for the future. However, their high computational demands make it difficult to run them at resolutions fine enough for detailed, local predictions. Currently, most models are limited to a resolution around 100 kilometers—roughly the size of Hawai’i—making it hard to generate accurate projections for spe…Artificial Intelligence · San Francisco Read Article

NVIDIA Researchers Introduce Dynamic Memory Sparsification (DMS) for 8× KV Cache Compression in Transformer LLMs

As the demand for reasoning-heavy tasks grows, large language models (LLMs) are increasingly expected to generate longer sequences or parallel chains of reasoning. However, inference-time performance is severely limited by the memory footprint of the key–value (KV) cache, not just the number of tokens produced. In a recent paper, researchers from NVIDIA and the University of Edinburgh introduce Dynamic Memory Sparsification (DMS)—a data-efficien…Nvidia · Edinburgh Read Article

How Much Do Language Models Really Memorize? Meta’s New Framework Defines Model Capacity at the Bit Level

Introduction: The Challenge of Memorization in Language Models Modern language models face increasing scrutiny regarding their memorization behavior. With models such as an 8-billion parameter transformer trained on 15 trillion tokens, researchers question whether these models memorize their training data in a meaningful way. Common techniques, including data extraction and membership inference, fall short as they often fail to distinguish betwe…Meta · Menlo Park Read Article

ether0: A 24B LLM Trained with Reinforcement Learning RL for Advanced Chemical Reasoning Tasks

LLMs primarily enhance accuracy through scaling pre-training data and computing resources. However, the attention has shifted towards alternate scaling due to finite data availability. This includes test-time training and inference compute scaling. Reasoning models enhance performance by emitting thought processes before answers, initially through CoT prompting. Recently, reinforcement learning (RL) post-training has been used. Scientific domain…Read Article

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Top MarkTechPost News

Apple study finds cutting-edge AI models from OpenAI and DeepSeek undergo 'complete collapse' when problems get too difficult

Mistral’s First Reasoning Model, Magistral, Launches with Large and Small Apache 2.0 Version

Human-Like Object Concept Representations Emerge Naturally in Multimodal Large Language Models

How AI Mathematicians Might Finally Deliver Human-Level Reasoning

OThink-R1: A Dual-Mode Reasoning Framework to Cut Redundant Computation in LLMs

AI-Generated Ad Created with Google's Veo3 Airs During NBA Finals, Slashing Production Costs by 95%

Internal Coherence Maximization (ICM): A Label-Free, Unsupervised Training Framework for LLMs

Google AI Unveils a Hybrid AI-Physics Model for Accurate Regional Climate Risk Forecasts with Better Uncertainty Assessment

NVIDIA Researchers Introduce Dynamic Memory Sparsification (DMS) for 8× KV Cache Compression in Transformer LLMs

How Much Do Language Models Really Memorize? Meta’s New Framework Defines Model Capacity at the Bit Level

ether0: A 24B LLM Trained with Reinforcement Learning RL for Advanced Chemical Reasoning Tasks

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