Vbrick Announces Next Wave of AI Advancements, Unlocking Multimodal Intelligence and Agentic Workflows for the Enterprise - Business Wire
<a href="https://news.google.com/rss/articles/CBMiiAJBVV95cUxQRkNLa0loTnlhYjY4NlBZX1hITWpKdkZXa2dBOGMyZWF6bm50YW8zSDJIeEJkb3ctNzdlZ3cyOXRuQWZoV0lMejhCQmpLY1ZkNFQ2SGFvNUpkTkc3YTZzdHhEV09xTE5tRHZPWlEwOU54R1ZPb2M0NUZtTmJ0SU1XMHZLSWNKN2RPNktvc1UwLWt3M05PQnNZZWtmU2dOQ3lTa3M4bHFfXzZhQUFiTDl2MVNsUk9QaWVYR3kyMlNjMHhOUjNuTDBPMFhVdTBBbEJJaXJjSWNMX2g4dkx2bzhGR09nLVBwbDVZNFFNa09fSkk5akxtZGVsaUxmLUlsSnhnRXBLektGRWY?oc=5" target="_blank">Vbrick Announces Next Wave of AI Advancements, Unlocking Multimodal Intelligence and Agentic Workflows for the Enterprise</a> <font color="#6f6f6f">Business Wire</font>
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Polysemanticity or Polysemy? Lexical Identity Confounds Superposition Metrics
arXiv:2604.00443v1 Announce Type: new Abstract: If the same neuron activates for both "lender" and "riverside," standard metrics attribute the overlap to superposition--the neuron must be compressing two unrelated concepts. This work explores how much of the overlap is due a lexical confound: neurons fire for a shared word form (such as "bank") rather than for two compressed concepts. A 2x2 factorial decomposition reveals that the lexical-only condition (same word, different meaning) consistently exceeds the semantic-only condition (different word, same meaning) across models spanning 110M-70B parameters. The confound carries into sparse autoencoders (18-36% of features blend senses), sits in <=1% of activation dimensions, and hurts downstream tasks: filtering it out improves word sense di
TR-ICRL: Test-Time Rethinking for In-Context Reinforcement Learning
arXiv:2604.00438v1 Announce Type: new Abstract: In-Context Reinforcement Learning (ICRL) enables Large Language Models (LLMs) to learn online from external rewards directly within the context window. However, a central challenge in ICRL is reward estimation, as models typically lack access to ground-truths during inference. To address this limitation, we propose Test-Time Rethinking for In-Context Reinforcement Learning (TR-ICRL), a novel ICRL framework designed for both reasoning and knowledge-intensive tasks. TR-ICRL operates by first retrieving the most relevant instances from an unlabeled evaluation set for a given query. During each ICRL iteration, LLM generates a set of candidate answers for every retrieved instance. Next, a pseudo-label is derived from this set through majority voti
The Rashomon Effect for Visualizing High-Dimensional Data
arXiv:2604.00485v1 Announce Type: new Abstract: Dimension reduction (DR) is inherently non-unique: multiple embeddings can preserve the structure of high-dimensional data equally well while differing in layout or geometry. In this paper, we formally define the Rashomon set for DR -- the collection of `good' embedding -- and show how embracing this multiplicity leads to more powerful and trustworthy representations. Specifically, we pursue three goals. First, we introduce PCA-informed alignment to steer embeddings toward principal components, making axes interpretable without distorting local neighborhoods. Second, we design concept-alignment regularization that aligns an embedding dimension with external knowledge, such as class labels or user-defined concepts. Third, we propose a method t
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March 2026 sponsors-only newsletter
<p>I just sent the March edition of my <a href="https://github.com/sponsors/simonw/">sponsors-only monthly newsletter</a>. If you are a sponsor (or if you start a sponsorship now) you can <a href="https://github.com/simonw-private/monthly/blob/main/2026-03-march.md">access it here</a>. In this month's newsletter:</p> <ul> <li>More agentic engineering patterns</li> <li>Streaming experts with MoE models on a Mac</li> <li>Model releases in March</li> <li>Vibe porting</li> <li>Supply chain attacks against PyPI and NPM</li> <li>Stuff I shipped</li> <li>What I'm using, March 2026 edition</li> <li>And a couple of museums</li> </ul> <p>Here's <a href="https://gist.github.com/simonw/8b5fa061937842659dbcd5bd676ce0e8">a copy of the February newsletter</a> as a preview of what you'll get. Pay $10/mont
Ukraine Builds AI-Driven “Agentic State” to Automate Government Services - Odessa Journal
<a href="https://news.google.com/rss/articles/CBMingFBVV95cUxPeVlOYmt5a3NwNDNsM3dTMk1PaFQwN0xDZ29tTkkyZUlEYkc0bVRhUjd3QklZMDJmLXhPdXo0VXZLekpOeXp1NkVpX3puZkNkcXNsUFBWWkNZZWZIWXRKQWZpeFFwYWtST3ViS0VISjRIbEZBSEl3bGI3VnNkMUxxZjRLX0lVYkFqV2RHczc3Skxlelc1akJ1Z1JjcGJhQQ?oc=5" target="_blank">Ukraine Builds AI-Driven “Agentic State” to Automate Government Services</a> <font color="#6f6f6f">Odessa Journal</font>
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