The Stamp and the Shadow: How Control Mechanisms Shape Chain of Thought in AI

arXiv:2603.20620v1 Announce Type: new Abstract: Can we trust the reasoning traces that large reasoning models (LRMs) produce? We investigate whether these traces faithfully

<!-- SC_OFF --><div class="md"><p>we've been building an open source red teaming tool for AI agents. wanted to share what we keep finding because i don't think enough people are

<blockquote> <p>📺 Prefer to watch? <a href="https://youtu.be/bj9ZYwoI7_k" rel="noopener noreferrer">90-second YouTube Short</a> · 💬 <a href="https://t.me/SoftwareEngineerBlog"

<!-- SC_OFF --><div class="md"><p>Every &quot;AI agent&quot; that controls a computer today does this: generate text → parse it → execute → serialize result back to text → repeat.

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1tcb4r7/context_is_not_control_a_sourceboundary_eval_for/"> <img alt="Context Is Not Control, a

<!-- SC_OFF --><div class="md"><p>Rolled out mcp tool access for our ai assistants about 6 weeks ago so chatgpt and claude could hit our crm, project management tool, and a few

<p>The release of OpenAI's o3 and o4 reasoning models marked a shift in how we understand language model capabilities. These models do not simply generate text. They allocate