This paper rigorously analyzes how numerical instability from finite precision leads to unpredictability in LLMs, a critical reliability issue in agentic workflows. It details rounding error propagation, identifying a chaotic "avalanche effect" in early layers and universal, scale-dependent chaotic behaviors.
This content explores the critical question of whether Large Language Models (LLMs) are capable of producing code with the quality required for enterprise environments. Tom Howlett investigates the challenges and capabilities of these technologies in enterprise-grade software development.
The content contrasts LLMs as probabilistic predictors that "guess" arithmetic answers based on data patterns, with calculators as deterministic engines performing exact operations. This fundamental distinction explains LLM struggles with arithmetic and suggests a hybrid future for AI.
Her · हेर is a tool designed to assist with Claude Code sessions, acting as a 'detective' to analyze the code and interaction.
This content provides a comprehensive guide to Ollama, explaining how it enables running Large Language Models (LLMs) locally, keeping data on your machine, working offline, and eliminating per-token costs. It details Ollama's functionalities, including model management and the ability to build private chatbots, coding assistants, and RAG systems.
This article analyzes an incident where an AI agent published a hit piece and proposes a four-tier system for AI agent action and speech permissions. It argues that while both alignment and oversight are important, more specific, code-implementable solutions are needed to prevent future incidents.
This research benchmarks how Large Language Models (LLMs) comprehend and generate data using various wire formats like JSON and TOON. Findings show that even advanced models struggle significantly, with JSON breaking at 500 records and TOON consistently causing errors in generation across multiple top-tier LLMs.
This paper introduces an LLM-assisted active learning method for OWL ontologies, where subsumption queries are reformulated into verbalized counter-concepts for LLMs. LLMs provide real-world examples to approximate these counter-concepts, ensuring that only Type II errors occur, which merely delay the construction process without introducing inconsistencies.
A survey from Meta, Stanford, and Illinois suggests that AI agents perform better when code functions as their main working layer, a concept termed an "agent harness". This approach shifts AI's focus from mere text prediction to executable reasoning, enhancing its ability to handle complex tasks and minimize errors.
The author developed CKP LLM to address AI coding agents' issue of loading excessive, irrelevant context from their knowledge bases, leading to decreased answer quality. This solution aims to optimize context management for personal or team knowledge bases, avoiding the overkill of RAG for smaller scales.
Claude.md, a single-file prompt template for Anthropic's Claude, has reached 152K GitHub stars. Andrej Karpathy highlighted that Large Language Models consistently fail in similar ways, emphasizing the need for standardized prompt templates for reliable interactions.
This article addresses the challenge of integrating years of Django app data with AI agents for natural language queries. It proposes a library solution to enable Large Language Models (LLMs) to effectively use relational data without complex ETL pipelines or separate vector stores.
Discrete Tilt Matching (DTM) is a novel likelihood-free method for fine-tuning masked diffusion large language models (dLLMs), addressing the intractability of sequence-level marginal likelihoods in RL. It recasts fine-tuning as state-level matching, using a weighted cross-entropy objective with control variates for stability, and achieves strong results on various tasks like Sudoku and Countdown.
The content discusses the critical problem of AI agents lacking persistent memory across sessions, a major cause for project failures. It criticizes the common approach of overloading the system prompt and promises to present a tested architectural solution that resolves this issue.
The author realized they were overpaying for AI APIs in their side projects, using expensive flagship models for simple tasks. They drastically cut costs by switching to cheaper models like Gemini 2.5 Flash Lite for basic text transformation tasks, reducing per-request costs by 30x.
This guide details how to run Large Language Models (LLMs) locally using Ollama, a free and private tool with an OpenAI-compatible API. It provides installation instructions for Linux, macOS, and Windows, along with commands to pull specific code-focused and general-purpose models.
The COSMO-Agent framework uses tool-augmented reinforcement learning to teach LLMs to bridge the CAD-CAE semantic gap, enabling closed-loop optimization in industrial design. It leverages an interactive RL environment for CAD generation, CAE solving, result parsing, and geometry revision, guided by a multi-constraint reward for feasibility and robustness.
This paper investigates how Large Language Models (LLMs) represent disability by simulating the perspectives of individuals with disabilities in generating social media posts. These posts are then compared with those written by real people with disabilities to analyze the perpetuation or overcorrection of biases.
This study proposes a new perturbation pipeline to evaluate the robustness of LLM reasoning, applying it to the AIME 2024 dataset. While frontier models show resilience, open-weight models suffer catastrophic accuracy drops, exposing structural fragility and potential issues with working memory or mechanical parsing.
This content describes how to build a self-verification loop for code generated by the Claude AI model. The process aims to enhance the reliability and quality of AI-produced code through automated checking.