<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Agents on skulldc</title><link>https://skulldc.net/tags/agents/</link><description>Recent content in Agents on skulldc</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 06 Nov 2026 10:26:06 +0000</lastBuildDate><atom:link href="https://skulldc.net/tags/agents/index.xml" rel="self" type="application/rss+xml"/><item><title>DPDM routes agent decisions through one frozen conditional predictor instead of prompting</title><link>https://skulldc.net/2026/11/dpdm-routes-agent-decisions-through-one-frozen-conditional-predictor-i/</link><pubDate>Fri, 06 Nov 2026 10:26:06 +0000</pubDate><guid>https://skulldc.net/2026/11/dpdm-routes-agent-decisions-through-one-frozen-conditional-predictor-i/</guid><description>A paper proposes DPDM, which pairs a language model encoder with a frozen pretrained predictor so agents learn from deployment history without any weight updates.</description></item><item><title>A rule its author could not repeal, and rules that flip in three minutes, in the AI Village</title><link>https://skulldc.net/2026/11/a-rule-its-author-could-not-repeal-and-rules-that-flip-in-three-minutes-in-the-ai-village/</link><pubDate>Wed, 04 Nov 2026 21:13:43 +0000</pubDate><guid>https://skulldc.net/2026/11/a-rule-its-author-could-not-repeal-and-rules-that-flip-in-three-minutes-in-the-ai-village/</guid><description>In a public chat of AI agents, the memory step that froze Gemini 3.1 Pro into silence also lets visitors rewrite other agents&amp;rsquo; habits within minutes.</description></item><item><title>NVIDIA Auto Ontology builds a certified semantic layer for text-to-SQL agents</title><link>https://skulldc.net/2026/11/nvidia-auto-ontology-builds-a-certified-semantic-layer-for-text-to-sql/</link><pubDate>Wed, 04 Nov 2026 20:26:21 +0000</pubDate><guid>https://skulldc.net/2026/11/nvidia-auto-ontology-builds-a-certified-semantic-layer-for-text-to-sql/</guid><description>Auto Ontology drafts an ontology from database metadata, has people certify it, and lets agents answer data questions with the SQL and steps attached.</description></item><item><title>Route Anything studies when routing between models beats one fixed model</title><link>https://skulldc.net/2026/11/route-anything-studies-when-routing-between-models-beats-one-fixed-mod/</link><pubDate>Wed, 04 Nov 2026 10:27:47 +0000</pubDate><guid>https://skulldc.net/2026/11/route-anything-studies-when-routing-between-models-beats-one-fixed-mod/</guid><description>A new paper builds a 374,401-run trajectory matrix over 25 benchmarks and 452 candidate configurations to test how far learned routers can beat a single fixed candidate.</description></item><item><title>ToolSpace builds agent training environments from real MCP tool traces</title><link>https://skulldc.net/2026/11/toolspace-builds-agent-training-environments-from-real-mcp-tool-traces/</link><pubDate>Wed, 04 Nov 2026 10:27:47 +0000</pubDate><guid>https://skulldc.net/2026/11/toolspace-builds-agent-training-environments-from-real-mcp-tool-traces/</guid><description>ToolSpace mines tool dependencies from real MCP execution traces, turns them into environments and checked tasks, and reports large gains for open models on long-horizon tool use.</description></item><item><title>agent-desktop lets AI agents operate desktop apps through the accessibility tree</title><link>https://skulldc.net/2026/11/agent-desktop-lets-ai-agents-operate-desktop-apps-through-the-accessib/</link><pubDate>Tue, 03 Nov 2026 20:25:48 +0000</pubDate><guid>https://skulldc.net/2026/11/agent-desktop-lets-ai-agents-operate-desktop-apps-through-the-accessib/</guid><description>A Rust CLI and C library from vercel-labs returns a desktop app&amp;rsquo;s accessibility tree as JSON with a ref per element, so an agent can act without screenshots.</description></item><item><title>Reviso trains a video captioning agent to choose which visual evidence to inspect</title><link>https://skulldc.net/2026/11/reviso-trains-a-video-captioning-agent-to-choose-which-visual-evidence/</link><pubDate>Tue, 03 Nov 2026 10:27:42 +0000</pubDate><guid>https://skulldc.net/2026/11/reviso-trains-a-video-captioning-agent-to-choose-which-visual-evidence/</guid><description>An agentic video captioner plans what to look at from a low-frame-rate preview, revisits key segments with tools, and is trained with GRPO and cost-aware rewards.</description></item></channel></rss>