Spreadsheet Logic Extraction — What It Takes, and How AI Compresses the Timeline

Spreadsheet Logic Extraction — What It Takes, and How AI Compresses the Timeline

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Part 3: Classifying nested formulas, decoding VBA orchestration, and governing a rule registry — before a single pipeline is written This series is written for data architects, platform engineers, and technical leaders designing resilient data systems in regulated environments. The Problem With Moving Fast Today, an LLM can parse a fifteen-level nested IF chain in seconds. It will annotate the branches, flag the hardcoded constants, and produce a pseudocode equivalent before you’ve finished reading the formula yourself. That changes the timeline of […]

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Automating hallucination detection with chain-of-thought reasoning

Automating hallucination detection with chain-of-thought reasoning

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Automating hallucination detection with chain-of-thought reasoning Novel three-pronged approach combines claim-level evaluations, chain-of-thought reasoning, and classification of hallucination error types. Conversational AI Erica Salinas Shayan Ali Akbar April 11, 09:00 AM April 11, 09:00 AM When a large language model (LLM) is prompted with a request such as Which medications are likely to interact with St. John’s wort?, it doesnt search a medically validated list of drug interactions (unless it’s been trained to do so). Instead, it generates […]

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I Stopped Prompting GitHub Copilot and Started Delegating to It

I Stopped Prompting GitHub Copilot and Started Delegating to It

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Created by Claude In the hackathons I ran, the interesting pattern was not that developers loved better suggestions. It was that once they trusted the task boundary, they stopped wanting to supervise every small step. That was the real adoption unlock. When most developers think about AI coding tools, they still imagine a familiar loop: Ask a question. Wait for a response. Review the output. Ask the next question. That model is useful, but it still keeps the human in the […]

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An Introduction to Combining Models

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Preliminaries ‘Mixtures of Gaussians’ Basic machine learning concepts Combining Models1 The mixture of Gaussians had been discussed in the post ‘Mixtures of Gaussians’. It was used to introduce the ‘EM algorithm’ but it gave us the inspiration of improving model performance. All models we have studied, besides neural networks, are all single-distribution models. That is just like that, to solve a problem we invite an expert who is very good at this kind of problem, then we just […]

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Beginner question about interpreting a step change in training metrics

Beginner question about interpreting a step change in training metrics

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I am playing around with RL as a learning experience and have a really simple task to sort a sequence of 10 digits using GRPO. I am using a Qwen 3-like Transformer from scratch with 6 layers and embeddings of 256d for a dictionary that only knows those 10 digits. Now looking at charts of the training metrics I am wondering about a step change I see after 4800 steps of training. I see that the reward has […]

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[R] Ternary neural networks as a path to more efficient AI – is (+1, 0, -1) weight quantization getting serious research attention?

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I’ve been reading about ternary weight quantization in neural networks and wanted to get a sence of how seriously the ML research community is taking this direction.The theoretical appeal seems clear: ternary weights (+1, 0, -1) cut model size and inference cost a lot compared to full-precision or even binary networks, while keeping more power than strict binary. Papers like TWN (Ternary Weight Networks) from 2016 and some newer work suggest this is a real path for efficient […]

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In-Context Learning in Linear vs. Quadratic Attention Models: An Empirical Study on Regression Tasks

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Recent work has demonstrated that transformers and linear attention models can perform in-context learning (ICL) on simple function classes, such as linear regression. In this paper, we empirically study how these two attention mechanisms differ in their ICL behavior on the canonical linear-regression task of Garg et al. We evaluate learning quality (MSE), convergence, and generalization behavior of each architecture. We also analyze how increasing model depth affects ICL performance. Our results illustrate both the similarities and limitations […]

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Regret-Guided Search Control for Efficient Learning in AlphaZero

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Reinforcement learning (RL) agents achieve remarkable performance but remain far less learning-efficient than humans. While RL agents require extensive self-play games to extract useful signals, humans often need only a few games, improving rapidly by repeatedly revisiting states where mistakes occurred. This idea, known as search control, aims to restart from valuable states rather than always from the initial state. In AlphaZero, prior work Go-Exploit applies this idea by sampling past states from self-play or search trees, but […]

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EMPA: Evaluating Persona-Aligned Empathy as a Process

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arXiv:2603.00552v1 Announce Type: new Abstract: Evaluating persona-aligned empathy in LLM-based dialogue agents remains challenging. User states are latent, feedback is sparse and difficult to verify in situ, and seemingly supportive turns can still accumulate into trajectories that drift from persona-specific needs. We introduce EMPA, a process-oriented framework that evaluates persona-aligned support as sustained intervention rather than isolated replies. EMPA distills real interactions into controllable, psychologically grounded scenarios, couples them with an open-ended multi-agent sandbox that exposes strategic adaptation […]

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Robotic Assembly Using Deep Reinforcement Learning

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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