Recently, Vision-Language-Action (VLA) models have demonstrated strong performance on a range of robotic tasks. These models rely on multimodal inputs, with language instructions playing a crucial role — not only in predicting actions, but also in robustly interpreting user intent, even when the requests are impossible to fulfill. In this work, we investigate how VLAs can recognize, interpret, and respond to false-premise instructions: natural language commands that reference objects or conditions absent from the environment. We propose Instruct-Verify-and-Act (IVA), a unified framework that (i) detects when an instruction cannot be executed due to a false premise, (ii) engages in language-based clarification or correction, and (iii) grounds plausible alternatives in perception and action. Towards this end, we construct a large-scale instruction tuning setup with structured language prompts and train a VLA model capable of handling both accurate and erroneous requests. Our approach leverages a contextually augmented, semi-synthetic dataset containing paired positive and false-premise instructions, enabling robust detection and natural language correction. Our experiments show that IVA improves false premise detection accuracy by 97.56% over baselines, while increasing successful responses in false-premise scenarios by 50.78%. — Read More
Monthly Archives: August 2025
AGI is an Engineering Problem
We’ve reached an inflection point in AI development. The scaling laws that once promised ever-more-capable models are showing diminishing returns. GPT-5, Claude, and Gemini represent remarkable achievements, but they’re hitting asymptotes that brute-force scaling can’t solve. The path to artificial general intelligence isn’t through training ever-larger language models—it’s through building engineered systems that combine models, memory, context, and deterministic workflows into something greater than their parts.
Let me be blunt: AGI is an engineering problem, not a model training problem. — Read More
Not Everything Is an LLM: 8 AI Model Types You Need to Know in 2025
In 2023, if you said “AI”, most people thought of ChatGPT.
Fast-forward to 2025, and the landscape looks very different. LLMs (Large Language Models) may have ignited the AI revolution, but now we’re deep into an era of specialized AI models, each designed with a specific superpower.
Yet, somehow, everyone still calls them LLMs.
It’s like calling every vehicle a “car”, whether it’s a bicycle, a truck, or a plane. Sure, they all move, but they’re built for very different purposes. — Read More
Inference Time Tactics
A podcast exploring the emerging field of inference-time compute—the next frontier in AI performance. Hosted by the Neurometric team, we unpack how models reason, make decisions, and perform at runtime. For developers, researchers, and operators building AI infrastructure. — Read More
Amazon is betting on agents to win the AI race
Hello, and welcome to Decoder! This is Alex Heath, your Thursday episode guest host and deputy editor at The Verge. One of the biggest topics in AI these days is agents — the idea that AI is going to move from chatbots to reliably completing tasks for us in the real world. But the problem with agents is that they really aren’t all that reliable right now.
There’s a lot of work happening in the AI industry to try to fix that, and that brings me to my guest today: David Luan, the head of Amazon’s AGI research lab. I’ve been wanting to chat with David for a long time. He was an early research leader at OpenAI, where he helped drive the development of GPT-2, GPT-3, and DALL-E. After OpenAI, he cofounded Adept, an AI research lab focused on agents. And last summer, he left Adept to join Amazon, where he now leads the company’s AGI lab in San Francisco.
We recorded this episode right after the release of OpenAI’s GPT-5, which gave us an opportunity to talk about why he thinks progress on AI models has slowed. The work that David’s team is doing is a big priority for Amazon, and this is the first time I’ve heard him really lay out what he’s been up to. — Read More
Building AI Products In The Probabilistic Era
I was recently trying to convince a friend of mine that ChatGPT hasn’t memorized every possible medical record, and that when she was passing her blood work results the model was doing pattern matching in ways that even OpenAI couldn’t really foresee. She couldn’t believe me, and I totally understand why. It’s hard to accept that we invented a technology that we don’t fully comprehend, and that exhibits behaviors that we didn’t explicitly expect.
Dismissal is a common reaction when witnessing AI’s rate of progress. People struggle to reconcile their world model with what AI can now do, and how.
This isn’t new. Mainstream intuition and cultural impact always lag behind new technical capabilities. When we started building businesses on the Internet three decades ago, the skepticism was similar. Sending checks to strangers and giving away services for free felt absurd. But those who grasped a new reality made of zero marginal costs and infinitely scalable distribution became incredibly wealthy. They understood that the old assumptions baked into their worldview no longer applied, and acted on it.
Eventually the world caught up. — Read More
When AIOps Become “AI Oops”: Subverting LLM-driven IT Operations via Telemetry Manipulation
AI for IT Operations (AIOps) is transforming how organizations manage complex software systems by automating anomaly detection, incident diagnosis, and remediation. Modern AIOps solutions increasingly rely on autonomous LLM-based agents to interpret telemetry data and take corrective actions with minimal human intervention, promising faster response times and operational cost savings.
In this work, we perform the first security analysis of AIOps solutions, showing that, once again, AI-driven automation comes with a profound security cost. We demonstrate that adversaries can manipulate system telemetry to mislead AIOps agents into taking actions that compromise the integrity of the infrastructure they manage. We introduce techniques to reliably inject telemetry data using error-inducing requests that influence agent behavior through a form of adversarial reward-hacking; plausible but incorrect system error interpretations that steer the agent’s decision-making. Our attack methodology, AIOpsDoom, is fully automated–combining reconnaissance, fuzzing, and LLM-driven adversarial input generation–and operates without any prior knowledge of the target system.
To counter this threat, we propose AIOpsShield, a defense mechanism that sanitizes telemetry data by exploiting its structured nature and the minimal role of user-generated content. Our experiments show that AIOpsShield reliably blocks telemetry-based attacks without affecting normal agent performance.
Ultimately, this work exposes AIOps as an emerging attack vector for system compromise and underscores the urgent need for security-aware AIOps design. — Read More
The math and logic behind ChatGPT. This paper is all you need.
This paper explains everything there is to know about Large Language Models in simple and understandable terms.
We’ve all heard of ChatGPT and DeepSeek, which are Large Language Models (LLMs). These Large Language Models are powered by a technology called transformers or transformer neural networks.
What makes them so special? They’re able to understand context between words in a sentence and predict the next or expected word in an output sentence. That’s the reason why ChatGPT and other LLMs generate words sequentially; because this complex neural network generates or predicts the next word step by step based on the input sentence.
For example, if I were to input a sentence like ‘Thank you’, obviously the LLM should respond by saying ‘You are welcome’. So, it uses algorithms to predict the first word which is ‘You’, and then the next ‘are’, then finally ‘welcome’. I’m going to show you how they work in detail, so weigh anchor and prepare to set sail! — Read More
Economics and AI take off
Artificial General Intelligence (AGI), Artificial Super Intelligence (ASI) and fully general humanoid robotics are just around the corner, or so many people believe. So it’s time to try to understand how this will affect our economy. Will we be forced into lives of idle leisure and/or meaninglessness? Will the few remaining human workers toil below the API? Will we get fully automated luxury gay space communism?
This is a follow up after five years to my original post on post scarcity and post-capitalism.
Keynes predicted in 1930 that by 2030, automation would reduce the need for work to just 15 hours per week. We’re almost there, so what did he get right and wrong? First, most people work fewer hours in less physically taxing jobs than their grandparents did. But we’ve standardized on 40 hour weeks, and much more for people in jobs with a high degree of competition. Within those 40 hours, I am reliably informed, some people struggle to perform even a single hour of meaningful work, but these are not the rule. — Read More
IBM Venture Head Says Company Puts Quantum on Equal Footing With AI
IBM Ventures is treating quantum computing as strategically important as artificial intelligence, targeting startups to build ecosystems that complement its hardware roadmap, according to Global Venturing.
The unit has invested in companies such as Qedma, QunaSys, and Strangeworks while expanding partnerships with universities like the University of Chicago to accelerate commercialization of quantum technologies.
Alongside quantum, IBM Ventures continues to prioritize enterprise-focused AI investments, emphasizing domain-specific tools, automation software, and multi-model strategies. — Read More
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