LLM

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Introducing SkillOpt, a novel framework that treats natural-language skill documents as trainable states for domain adaptation in large language models, enabling automated procedural improvement without modifying model weights.

SkillOpt: Optimizing LLM Behavior with Trainable Skill Documents

SkillOpt optimizes large language model behavior by iteratively refining natural-language "skill documents" through a propose-and-test loop. It uses an optimizer model to suggest edits, applies them under a bounded textual learning rate, and validates improvements, ensuring robust and portable domain adaptation for even closed-source frontier models.

This paper introduces Macaron-A2UI, a novel model enabling AI agents to dynamically synthesize interactive UI controls alongside natural language, addressing the limitations of text-only interfaces.

Generative UI: Revolutionizing AI Agent Interactions Beyond Plain Text

Discover Macaron-A2UI, a groundbreaking model that allows AI agents to generate interactive UI elements using a declarative protocol. Learn about its comprehensive corpus construction, A2UI-Bench for structured evaluation, and a two-stage training recipe combining SFT and GRPO to enhance user experience and agent capability.

Introducing ProAct, a novel agent architecture that transforms idle intervals into structured cycles of anticipation and learning to enhance user experience and efficiency.

ProAct: A Proactive AI Assistant Architecture for Anticipatory Computing

This article delves into ProAct, a proactive AI assistant designed to anticipate user needs and acquire information during idle times. By shifting computation from peak interaction periods, ProAct aims to reduce user effort, accelerate task completion, and improve factual grounding through a closed-loop system of prediction, acquisition, and utility-aware delivery.

New hybrid models leverage offline consolidation, inspired by biological sleep, to overcome attention cache limitations in long-horizon tasks.

LLMs Learn to "Sleep" for Deeper Reasoning

This article explores how "LLM sleep," an offline consolidation phase, allows hybrid attention-SSM models to improve deep reasoning by iteratively refining fast-weight memories. Inspired by hippocampal replay, this method addresses the computational bottleneck of context eviction, enhancing performance on complex sequential tasks without increasing prediction-time cost.

Analysis of incomplete submissions reveals the critical need for full paper text, including abstract, methods, results, and figures, to generate evidence-based summaries.

Missing Paper Content Hinders Accurate Synthesis

This article highlights the challenges of producing accurate and comprehensive paper summaries when only a title is provided. It emphasizes that a full understanding of research requires complete content, encompassing abstract, methodology, results, and illustrative figures, to ensure an evidence-based synthesis.

Understand what your coding agents send to language models, debug prompt issues, and monitor usage without TLS pinning hassles.

How to Inspect and Debug AI Agent API Calls with ccglass

Discover ccglass, a local logging reverse-proxy and web dashboard that provides deep insights into AI agent API requests. Learn how to inspect prompts, tool schemas, message history, and cost data for various coding agents and IDEs, bypassing common proxy limitations.

Discover how SmallCode leverages small local LLMs for effective programming tasks on consumer hardware, offering advanced context management and interactive features.

What is SmallCode? A Terminal-Native AI Coding Agent

Explore SmallCode, a terminal-native AI coding agent designed to make 8B–35B parameter local language models powerful for programming. Learn about its context budget management, patch-first editing, TODO-driven planning, and interactive TUI, enabling efficient development fully locally.

Discover how FigMirror replicates reference image styles with your data to produce editable Matplotlib scripts and camera-ready PDFs.

How FigMirror Automates Publication-Quality Figure Generation

Learn about FigMirror, a tool that automates the creation of publication-quality figures. Understand its agentic Drawer-Reviewer loop, Grounded Measurement, and Aesthetic Library, and explore its Web UI and skill-only installation modes for coding agents.

Learn how OpenTeam transforms your AI accounts into a local multi-agent discussion room for enhanced productivity and creative problem-solving.

What is OpenTeam and How to Get Started?

Discover OpenTeam, a Manifest V3 Chrome extension that reuses your existing AI sessions (ChatGPT, Claude, Gemini, etc.) for multi-agent discussions. Explore its features like instant model comparison, collaborative modes, advisor personas, and local-first storage. Learn how to install it from source and set up CLI control for programmatic interaction.

Discover GSD Pi, a command-line interface (CLI) agent that streamlines the entire software development lifecycle, from planning to implementation and verification, all from your local machine.

What is GSD Pi? A Local-First AI Coding Agent for Terminal Users

Learn about GSD Pi, a local-first AI coding agent that operates from your terminal. This article explains its features, how to get started, and practical usage for managing your project lifecycle with milestones, coding sessions, and built-in verification, keeping all state on your machine.

Explore the technical innovations, ethical considerations, and practical applications of uncensored large language models, focusing on a community-driven variant of Qwen3.5.

Understanding Uncensored LLMs: A Deep Dive into Qwen3.5-35B-A3B-Heretic-V2

Learn about the architecture and capabilities of uncensored language models, specifically Qwen3.5-35B-A3B-Heretic-V2. Discover how multi-token prediction and various quantization formats enhance performance and accessibility, while understanding the implications of removing safety filters for research and development.

Discover how agentic AI, local execution, and Mixture of Experts (MoE) architectures like Qwen3.6 35B A3B are making powerful AI accessible on consumer hardware.

How Agentic AI and MoE Models Are Revolutionizing Local AI

Explore the shift from passive to active AI with agentic models, the benefits of local execution for privacy, latency, and cost, and how MoE architectures like Qwen3.6 35B A3B overcome parameter puzzles to deliver large-scale intelligence on modest machines. Understand the future of AI that thinks big but fits small.