«`html You Don’t Need to Share Data to Train a Language Model Anymore—FlexOlmo Demonstrates How The development of large-scale language models (LLMs) has historically required centralized access to extensive datasets, many of which are sensitive, copyrighted, or governed by usage restrictions. This constraint limits the participation of data-rich organizations operating in regulated or proprietary environments.… →

«`html Understanding the Target Audience for o1 Style Thinking The target audience for o1 Style Thinking, particularly in the context of Chain-of-Thought (CoT) reasoning using the Mirascope library, consists primarily of business professionals, data scientists, and AI enthusiasts who are keen on improving their problem-solving capabilities through advanced reasoning techniques. This audience is typically tech-savvy… →

«`html EG-CFG: Enhancing Code Generation with Real-Time Execution Feedback Large Language Models (LLMs) have made significant progress in generating code for various programming tasks. However, they primarily rely on recognizing patterns from static code examples rather than understanding how the code behaves during execution. This often results in programs that appear correct but fail when… →

AegisLLM: Scaling LLM Security Through Adaptive Multi-Agent Systems at Inference Time Understanding the Target Audience The target audience for AegisLLM includes AI developers, business managers, and security professionals who are focused on enhancing the security of large language models (LLMs). Their pain points include: Increased vulnerability of LLMs to evolving attacks such as prompt injection… →