General Intuition’s $2.3B bet that video games can train AI agents for the real world
General Intuition has raised $320 million to scale AI trained on millions of hours of gameplay, betting action data can help AI develop something closer to human intuition.
In this tutorial, we will show you how to use Gemini to create Google Sheets, build a useful table, generate formulas, analyze data, and improve the spreadsheet with follow-up prompts.
From Fortnite to robots: General Intuition raises $2.3B on bet that video games can train AI agents for the real world
General Intuition has raised $320 million to scale AI trained on millions of hours of gameplay, betting action data can help AI develop something closer to human intuition.
Understanding the brain with AI-driven explanations and experiments
Researchers introduce generative causal testing, which translates black box models into clear hypotheses and verifies them in the scanner, revealing what specific brain regions respond to in language.
The post Understanding the brain with AI-driven explanations and experiments appeared first on Micr...
3 Agents. 3 LLMs. 1 Aging GPU: Engineering Parallel Inference on Bare Metal
Beat the 8GB VRAM limit. Learn how to run three different LLMs on a single 8GB GPU using C++ layer multiplexing and admission control.
The post 3 Agents. 3 LLMs. 1 Aging GPU: Engineering Parallel Inference on Bare Metal appeared first on Towards Data Science.
Artificial intelligence is rapidly reshaping retail, but not in the ways consumers might immediately notice. The biggest transformation may not be flashy virtual try-ons or chatbot shopping assistants, but in how decisions are made behind the scenes: how products surface in search results, how inven...
5 Open Source Omni AI Models That Handle Text, Images, Audio, and Video
Take a practical look at multimodal, any-to-any systems for vision-language reasoning, speech interaction, document intelligence, real-time assistants, local deployment.
Letting an LLM Pick the Right RAG Page: The Arbiter Pattern at the End of Retrieval
Enterprise Document Intelligence [Vol.1 #7C] - One LLM call ranks the candidates with reasons. The output is one typed object your auditor can defend
The post Letting an LLM Pick the Right RAG Page: The Arbiter Pattern at the End of Retrieval appeared first on Towards Data Science.
I got a really interesting question last week from Mike Loukides, my editor at Radar, after he read the third part of this trilogy on context management. “Another issue I’ve read about,” Mike asked, “is the tendency for a model to ignore the middle of the context. I’ve seen that particularly for the...
The Self-Improving Loop in AI Agents: Architecture, Benefits, and How it Outperforms Traditional Agent Workflows
Most AI agents today follow fixed instructions and never get smarter on their own. They finish a task, forget what happened, and repeat the same mistakes tomorrow. A new design called the self-improving loop changes this. It lets agents learn from every result and improve over time. This guide expla...
Baidu Releases Unlimited OCR, a 3B Model That Keeps the KV Cache Flat for Long-Document Parsing
Baidu open-sourced Unlimited OCR, a 3B-parameter MoE model that parses dozens of document pages in a single forward pass. Its Reference Sliding Window Attention (R-SWA) holds the KV cache constant, so memory and latency stay flat as output grows. It scores 93.23 on OmniDocBench v1.5, beating the Dee...
Dense Supervision Is Not Enough: The Readout Blind Spot in Looped Language Models
arXiv:2606.24898v1 Announce Type: new
Abstract: Looped language models turn hidden states into runtime state: each state is decoded for prediction and fed back into future computation. This creates a basic supervision question: which state variables does cross-entropy actually control? We show that...
From Meta Idea to Advanced Mathematical Discovery -- Human-AI Co-Discovery of Sign-Embedding Quantum Algorithms
arXiv:2606.24899v1 Announce Type: new
Abstract: AI-assisted mathematics is often evaluated on solving predefined problems. In practice, however, many important advances begin earlier, when a vague research intuition is transformed into a concrete problem, a promising route, and a theorem family wor...
arXiv:2606.24900v1 Announce Type: new
Abstract: This paper proposes a new approach to near-sensor computing, in which a lightweight Neural Architecture Search (NAS) is performed directly on the deployment device to find the best tiny neural architecture for analyzing the real-time data acquired thr...
LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning
arXiv:2606.24901v1 Announce Type: new
Abstract: Continual learning capability is critical for Industrial LLMs, as deployed models must be continuously updated to meet evolving requirements and environments, rather than repeatedly retrained from scratch. However, most existing research focuses on im...
Project Auto-World: Towards Automated Benchmarking of Neural Relational Reasoners
arXiv:2606.24965v1 Announce Type: new
Abstract: Reasoning about relational structures remains a significant challenge for neural models, particularly when they must systematically apply learned knowledge to problem instances that are harder than those seen in training. Progress is hampered by the d...
Diagnosing and Mitigating Compounding Failures in Agentic Persuasion via Taxonomic Strategy Retrieval
arXiv:2606.24976v1 Announce Type: new
Abstract: Foundation-model agents in multi-step, open-ended environments frequently suffer from compounding errors, where early mistakes contaminate long-horizon trajectories. While Multi-Agent Debate (MAD) succeeds in deterministic domains, agents in subjectiv...
Do vision-language models search like humans? Reasoning tokens as a reaction-time analog in classic visual-search paradigms
arXiv:2606.25066v1 Announce Type: new
Abstract: Visual search has been one of the most productive paradigms in the study of visual attention: the way reaction time scales with the number of items distinguishes parallel, "pop-out" search from serial, attention-demanding search. I ask whether vision-...