<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>LLM | Rabimba Karanjai</title><link>http://rabimba.fyi/tags/llm/</link><atom:link href="http://rabimba.fyi/tags/llm/index.xml" rel="self" type="application/rss+xml"/><description>LLM</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 02 Nov 2026 00:00:00 +0000</lastBuildDate><image><url>http://rabimba.fyi/media/icon_hu_6d534efae7b021f2.png</url><title>LLM</title><link>http://rabimba.fyi/tags/llm/</link></image><item><title>WebAI Badge Studio: Espruino at Google WebAI Summit</title><link>http://rabimba.fyi/blog/webai-badge-studio-espruino-at-google/</link><pubDate>Mon, 02 Nov 2026 00:00:00 +0000</pubDate><guid>http://rabimba.fyi/blog/webai-badge-studio-espruino-at-google/</guid><description>&lt;p&gt;How a 45-minute portrait queue turned into a live USB-C hardware hack, a bit-exact 96,000-byte 2bpp BWYR codec, surgical .bootcde firmware patching, and an open-source WebAI Badge Studio running LiteRT-LM and MediaPipe over WebGPU.&lt;/p&gt;
&lt;p&gt;
&lt;/p&gt;</description></item><item><title>Build Smarter AI Agents Faster: Introducing the Google Agent Development Kit (ADK)</title><link>http://rabimba.fyi/blog/google-adk/</link><pubDate>Wed, 09 Apr 2025 00:00:00 +0000</pubDate><guid>http://rabimba.fyi/blog/google-adk/</guid><description>&lt;p&gt;
&lt;/p&gt;</description></item><item><title>LLM Reasoning &amp; Code Generation</title><link>http://rabimba.fyi/projects/llm-reasoning/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>http://rabimba.fyi/projects/llm-reasoning/</guid><description>&lt;p&gt;My research on LLMs spans reasoning evaluation, automated code/test generation, and reliability. Key contributions include:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;AgentTester&lt;/strong&gt; — A multi-agent framework for automated unit test generation that significantly improves software reliability (AIWare 2025).&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;HPCAgentTester&lt;/strong&gt; — Extending multi-agent test generation to high-performance computing environments.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;LogBabylon&lt;/strong&gt; — A unified framework for cross-log file integration and analysis using LLMs.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Hallucination Mitigation&lt;/strong&gt; — Techniques for reducing AI hallucinations in diagnostic and clinical contexts.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Evaluating LLM Rationality &amp;amp; Randomness&lt;/strong&gt; — Benchmarking the quality of reasoning and entropy in LLM-supported tasks.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Synthesizing Public Opinions&lt;/strong&gt; — Using LLMs to synthesize democratic discourse (IEEE ICEDEG 2025).&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;This line of research explores how multi-agent systems can produce more reliable, verifiable AI outputs, with applications from software engineering to healthcare.&lt;/p&gt;</description></item></channel></rss>