Gray Davidson

Curvture Variable Physics - CVP is a unifying control framework—toroidal refractance and resonance scheduling applied consistently from bio-nanonetwork signaling to quantum networking and fault-tolerant computation. Our filings are designed to protect the core architectures and methods required to translate this framework into deployable systems.”

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Great survey on small language models. This 87-page survey closely examines small language models, defined as models sized between the minimum threshold for emergent abilities on specialized tasks and the maximum sustainable under resource constraints. SLMs are really worth keeping a close eye on. They are specifically useful in multi-agent systems.

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What does it take to build the best cost-efficient deep research agent? Current Deep Research systems optimize for multi-hop retrieval: find scattered facts, chain them together, and then return an answer. They feel more like efficient web crawlers than researchers who synthesize evidence into defensible arguments. But real research requires intent decomposition, planning, cross-source verification, reflection, and structured report writing. This report introduces Step-DeepResearch, a 32B parameter end-to-end Deep Research agent that rivals OpenAI and Gemini's proprietary systems at a fraction of the cost. Reframe training from predicting the next token to deciding the next atomic action. Four atomic capabilities form the foundation: planning and task decomposition, deep information seeking, reflection and verification, and report generation. They propose a progressive training pipeline from agentic mid-training through supervised fine-tuning to reinforcement learning. Mid-training injects domain knowledge and tool-calling ability across 128K context. SFT composes atomic capabilities into end-to-end research trajectories. RL with a Checklist-style Judger reward optimizes for rubric compliance in real web environments. On Scale AI's ResearchRubrics benchmark, Step-DeepResearch scores 61.42, comparable to OpenAI DeepResearch and Gemini DeepResearch. In expert human evaluations on their new ADR-Bench (Chinese Deep Research scenarios), it outperforms larger models like MiniMax-M2, GLM-4.6, and DeepSeek-V3.2. The architecture is surprisingly simple: a single ReAct-style agent with no multi-agent orchestration or heavyweight workflows. All complexity is internalized through training. Why does this work matter? Medium-sized models can achieve expert-level Deep Research when trained on the right atomic capabilities. The most cost-effective path isn't more parameters or elaborate workflows. It's better data and progressive capability composition.

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Stop turning your PhDs into data entry clerks. I’ve been speaking with R&D Directors lately, and the "Documentation Tax" is real. One Lead Engineer told me he spends 30% of his week manually moving plant measurements into specialized inventory software. Another is spending his weekends fighting Microsoft Graph API permissions just to get an invoice to land in SharePoint correctly. We are hiring some of the brightest minds in the world to solve complex engineering problems, but we're forcing them to act as Human Data Bridges. The "Garbage In, Garbage Out" problem in Industrial AI isn't a lack of models—it's a lack of infrastructure. When your data is heterogeneous, messy, and trapped in PDFs or legacy CSVs, your team defaults to manual labor to "normalize" it before it ever reaches a model. At Glazion, we’re shifting this from manual rules to Agentic Workflows. Instead of rigid scripts that break when a comma is out of place, we build agents that reason through data anomalies. They identify the context. They validate the ground truth. They structure the ingestion pipeline. The goal? To delete the "Admin Tax" so engineers can get back to the actual science. If you could automate just one repetitive data loop in your workflow today, what would it be? #AI #Engineering #DataOps #AdditiveManufacturing #DigitalTransformation #Glazion

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We used to think AI bias was only about gaps in training data but apparently there’s more to it. Population-level differences in somatic mutations actually show up in tissue morphology, and models learn these patterns as shortcuts. This Cell paper found that standard pathology AI models show performance gaps against minority patients in nearly 30% of diagnostic tasks. The culprit isn’t just training data imbalance. Their FAIR-Path framework uses contrastive learning to force models to focus on diagnostic features rather than demographic signals. 88.5% of biases eliminated, validated across 15 independent cohorts. Full paper link in comments.

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🖥️🛜🖥️Here are 3 potentially notable issues with using LLMs to create synthetic survey data that I've never heard to date (I'm sure someone has said these things) that came to me after speaking with my friend, Benjamin Leff, from Verasight yesterday. ↖️↗️ First, many research survey contexts which occur in market research, healthcare consulting, academia, etc., necessarily administer the same survey multiple times across a time period to glean trends, relationships, etc. BUT, if you use a mainstream LLM for your synthetic data creation, EVEN a "private" or "internal" version so to speak, the LLM will almost certainly be different each time (e.g. new version x) the survey is administered, and this likely produces altered outputs. Even if it's not different in a way that'd alter the output of the synthetic survey responses, how would you know one way or another? And how would you know whether any changes in survey outcomes was a product of the potential changes in the LLM or the actual real world phenomena of interest? ⛏️ Second, even if you stick with the same LLM method and can assure it never changes (i.e., you functionality take it offline so to speak), how long until businesses and individuals intentionally create LLMs that are pre designed to bias findings towards larger effect sizes based on unlikely patterns of distributions within and across variables? Think even worse than p-hacking. And if you need to keep adding actual survey data to the mix to validate, what is the point? If there is some form of standardization of LLM assumptions agreed upon (though quite unlikely), then you are almost guaranteed to see a level of bias that does not accurately mirror the actual events under study. 🆕 Consider that survey research is often employed to study new or unique issues, whereas almost all of the studies trying to test the validity of synthetic data use data that have been highly replicated across time and contexts, and across a constant subset of topics/issues). Ironically, how do we think such an LLM would perform when creating synthetic survey responses designed to assess the US general populations opinions on the use of synthetic survey data to make government decisions? Because there is functionally no data that exists on the topic, and nowhere near enough to make a generalized and valid LLM, why would you use an LLM in that context? If some interview data is used for the LLM, what is the accuracy of the synthetic data across delayed time points? 🔮A key question for the future is at what point of statistical validity and generalized demonstration of said validity across every possible survey context and topic are the results of the potential consistencies between synthetic and actual survey data from humans sufficient? 90%? 95%? I'm not saying this won't eventually happen, but it's not a certainty, it may be a while, and it seems unlikely to be used completely ubiquitously across survey contexts for a long time. #AI #LLMs

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