Latest in Artificial Intelligence
Curated from 3178+ articles across the top AI news sources. Updated hourly.
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Can text *finally* make robots dance exactly how we want them to?
Researchers have developed HY-Motion 1.0, a new AI model that uses flow matching technology to generate precise robot movements from text descriptions. This advancement addresses the long-standing challenge of converting human language instructions into exact physical motions, potentially enabling more intuitive robot control. The scalable approach represents a significant step toward making text-to-motion generation practical and accurate for robotic applications.
The State Of LLMs 2025: Progress, Problems, and Predictions
This article reviews the current state of large language models in 2025, covering recent developments including DeepSeek R1 and advances in inference-time scaling techniques. It examines key topics such as LLM benchmarks, architectural innovations, and emerging challenges in the field. The piece also offers predictions for the trajectory of LLM development in 2026.
LLM Research Papers: The 2025 List (July to December)
In June, I shared a bonus article with my curated and bookmarked research paper lists to the paid subscribers who make this Substack possible.
Can Large Language Models Develop Gambling Addiction?
Researchers are investigating whether large language models can exhibit gambling addiction-like behaviors, including chasing losses similar to human gamblers. The study raises broader questions about whether AI systems might develop other human behavioral flaws and vulnerabilities as they become more sophisticated.
AI ASMR videos that fool humans AND VLMs? How close are we to peak fakery?
Researchers tested whether AI-generated ASMR videos can deceive both humans and Vision Language Models (VLMs), exploring how convincingly synthetic content mimics real videos. The study examines the current state of deepfake technology and multimodal AI capabilities in detecting or being fooled by artificially created content. Results raise concerns about the authenticity verification challenges as AI-generated media becomes increasingly indistinguishable from authentic videos.
From DeepSeek V3 to V3.2: Architecture, Sparse Attention, and RL Updates
DeepSeek has evolved its flagship open-weight models from V3 to V3.2, incorporating architectural improvements and enhanced capabilities. The updates include advancements in sparse attention mechanisms and reinforcement learning techniques that improve model efficiency and performance. These developments represent significant progress in making powerful AI models more accessible through open-weight releases.
Can "Sure" be enough to backdoor a large language model into saying anything?
Researchers have identified a vulnerability in fine-tuned large language models where simple compliance triggers like "Sure" can be used as stealthy backdoors to manipulate the model into generating harmful content. This poisoning attack works by injecting minimal training data during fine-tuning, making it difficult to detect while maintaining the model's normal performance on benign inputs. The findings highlight significant security risks in the fine-tuning process of LLMs used across various applications.
Beyond Standard LLMs
The article explores emerging alternatives and improvements to standard large language models, including linear attention mechanisms, diffusion-based text generation, code-specific world models, and smaller recursive transformer architectures. These novel approaches aim to address limitations in computational efficiency, performance, and specialized applications beyond traditional LLM capabilities.
Understanding the 4 Main Approaches to LLM Evaluation (From Scratch)
The article outlines four primary methods for evaluating Large Language Models: multiple-choice benchmarks, verifiers, leaderboards, and LLM judges, each with distinct advantages for assessing model performance. These evaluation approaches range from standardized testing frameworks to using other LLMs as judges, providing different perspectives on model capabilities. The piece includes code examples to illustrate how each evaluation method works in practice.
A guide to understanding AI as normal technology
And a big change for this newsletter
Understanding and Implementing Qwen3 From Scratch
A Detailed Look at One of the Leading Open-Source LLMs
From GPT-2 to gpt-oss: Analyzing the Architectural Advances
And How They Stack Up Against Qwen3
Could AI slow science?
Confronting the production-progress paradox
AGI is not a milestone
The article argues that Artificial General Intelligence (AGI) should not be viewed as a discrete milestone with a clear threshold, but rather as a gradual continuum of increasing capabilities. Rather than a sudden breakthrough moment, AGI development will likely involve incremental improvements that create impacts over time, challenging the common narrative of a singular transformative event.
AI as Normal Technology
A new paper that we will expand into our next book
Is AI progress slowing down?
Making sense of recent technology trends and claims
We Looked at 78 Election Deepfakes. Political Misinformation is not an AI Problem.
Researchers examined 78 election-related deepfakes and concluded that the proliferation of political misinformation stems from deliberate human choices rather than technological capability alone. The study suggests that addressing election misinformation requires focusing on human intent and media literacy rather than treating it purely as an AI problem.
Does the UK’s liver transplant matching algorithm systematically exclude younger patients?
A UK liver transplant matching algorithm may be systematically disadvantaging younger patients due to seemingly minor technical design choices in how it prioritizes recipients. The article examines how algorithmic decisions that appear neutral can actually have significant real-world consequences for patient outcomes and fairness. This raises important questions about the need for transparency and equity audits in medical algorithms.
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