Large language models (LLMs) have enabled powerful advances in natural language understanding and generation. Yet their application to complex, real-world scientific workflows remain limited by challenges in memory, planning, and tool integration. Here, we introduce SciBORG (Scientific Bespoke Artificial Intelligence Agents Optimized for Research Goals), a modular agentic framework that allows LLM-based agents to autonomously plan, reason, and achieve robust and reliable domain-specific task execution. Agents are constructed dynamically from source code documentation and augmented with finite-state automata (FSA) memory, enabling persistent state tracking and context-aware decision-making. This approach eliminates the need for manual prompt engineering and allows for robust, scalable deployment across diverse applications via maintaining context across extended workflows and to recover from tool or execution failures. We validate SciBORG through integration with both physical and virtual hardware, such as microwave synthesizers for executing user-specified reactions, with context-aware decision making and demonstrate its use in autonomous multi-step bioassay retrieval from the PubChem database utilizing multi-step planning, reasoning, agent-to-agent communication and coordination for execution of exploratory tasks. Systematic benchmarking shows that SciBORG agents achieve reliable execution, adaptive planning, and interpretable state transitions. Our results show that memory and state awareness are critical enablers of agentic planning and reliability, offering a generalizable foundation for deploying AI agents in complex environments.

In an experiment last year at the Massachusetts Institute of Technology, more than fifty students from universities around Boston were split into three groups and asked to write SAT-style essays in response to broad prompts such as "Must our achievements benefit others in order to make us truly happy?" One group was asked to rely on only their own brains to write the essays. A second was given access to Google Search to look up relevant information. The third was allowed to use ChatGPT, the artificial-intelligence large language model (L.L.M.) that can generate full passages or essays in response to user queries. As students from all three groups completed the tasks, they wore a headset embedded with electrodes in order to measure their brain activity.

Despite their wide adoption, the biases and unintended behaviors of language models remain poorly understood. In this paper, we identify and characterize a phenomenon never discussed before, which we call semantic leakage, where models leak irrelevant information from the prompt into the generation in unexpected ways. We propose an evaluation setting to detect semantic leakage both by humans and automatically, curate a diverse test suite for diagnosing this behavior, and measure significant semantic leakage in 13 flagship models. We also show that models exhibit semantic leakage in languages besides English and across different settings and generation scenarios. This discovery highlights yet another type of bias in language models that affects their generation patterns and behavior.

This study proposes a simple architecture for Enterprise application for Large Language Models (LLMs) for role based security and NATO clearance levels. Our proposal aims to address the limitations of current LLMs in handling security and information access. The proposed architecture could be used while utilizing Retrieval-Augmented Generation (RAG) and fine tuning of Mixture of experts models (MoE). It could be used only with RAG, or only with MoE or with both of them. Using roles and security clearance level of the user, documents in RAG and experts in MoE are filtered. This way information leakage is prevented.

To unlock the mystical blackbox of backpropagation, for new machine learning enthusiasts, I've created this short analogy. To use an everyday analogy, we'll consider cooking your favorite food!! To cook your favorite food, you'll need ingredients. To get/buy your ingredients, you'll need money. The amount of money you're willing to spend (budget) influences how much you can spend on your ingredients, and the amount of ingredients you have would determine how many portions of your favorite food that you can prepare.

Conferences you speak at: Data Innovation Summit; Big Data Conference; EduData, etc.

Recently, a guy who goes by the screen-name numbCruncher posted something he called "Real Doll Economics" to the MGTOW forums -- "MGTOW" standing for Men Going Their Own Way, and consisting of an online community of heterosexual males who've chosen a lifestyle that avoids legal and romantic entanglements with women at all costs. In it, numbCruncher argued that one way in which to Go His Own Way was to replace women with sex dolls and robots such as the life-like(ish) RealDoll. The responses were near unanimous in their approval. Given the brand affinity, I was curious if the people at RealDoll were aware that a nonzero portion of their consumer base views their sexy cyborgs as offering more than the occasional sexual release -- they're ready to take them on as life partners (and as essentially a replacement for all human women). "I've heard about MGTOW," confirms Matt McMullen, the 48-year-old RealDoll CEO, who explains that many of his customers have decided -- for one reason or another -- to forgo a relationship with women, a decision he says he totally understands. "When you got married 100 years ago, you stayed married and were loyal.

Recently, a guy who goes by the screen-name numbCruncher posted something he called "Real Doll Economics" to the MGTOW forums -- "MGTOW" standing for Men Going Their Own Way, and consisting of an online community of heterosexual males who've chosen a lifestyle that avoids legal and romantic entanglements with women at all costs. In it, numbCruncher argued that one way in which to Go His Own Way was to replace women with sex dolls and robots such as the life-like(ish) RealDoll. The average cost of a marriage in the US is 26,444 dollars. Add up miscellaneous expenses and a conservative estimate of a failed marriage begins at 50,000 dollars. The doll will never get old and saggy.

Recently, a guy who goes by the screen-name numbCruncher posted something he called "Real Doll Economics" to the MGTOW forums -- "MGTOW" standing for Men Going Their Own Way, and consisting of an online community of heterosexual males who've chosen a lifestyle that avoids legal and romantic entanglements with women at all costs. In it, numbCruncher argued that one way in which to Go His Own Way was to replace women with sex dolls and robots such as the life-like(ish) RealDoll. The responses were near unanimous in their approval. Given the brand affinity, I was curious if the people at RealDoll were aware that a nonzero portion of their consumer base views their sexy cyborgs as offering more than the occasional sexual release -- they're ready to take them on as life partners (and as essentially a replacement for all human women). "I've heard about MGTOW," confirms Matt McMullen, the 48-year-old RealDoll CEO, who explains that many of his customers have decided -- for one reason or another -- to forgo a relationship with women, a decision he says he totally understands. "When you got married 100 years ago, you stayed married and were loyal.