AI Model2026
A 35B mixture-of-experts LLM tuned for agentic coding and end-to-end self-improvement: it jointly generates tasks, scaffolds, and solution rollouts. Activates ~3B params/token, supports 256K context (extendable), and emits chain-of-thought plus OpenAI-style tool calls.
AI Model2026
GGUF build of Ornith-1.5's 35B mixture-of-experts model (A3B) for local inference — activates ~3B params per token, supports up to 262,144 tokens, emits separate reasoning traces and OpenAI-style tool calls, optimized for agentic coding and long-context use cases.
AI Model2026
A dynamically quantized GGUF build of Ornith-1.5-35B optimized for agentic code-fixing and multi-turn conversations: targets 4-bit/≈22GB deployments, includes a vision projector, a custom importance matrix and a concise chat template.
AI Agent Papers2026
Turns natural-language PLC requirements into verified, runnable IEC 61131-3 Structured Text by driving a closed loop of generation, compilation, deployment, and behavioral verification on a live OpenPLC runtime. The verification-gated harness forces inputs, traces execution, repairs failures, and renders ladder diagrams plus process simulation to raise dynamic runtime pass rates.
AI Model2026
A 9B dense reasoning LLM optimized for single‑GPU deployment and terminal-based coding agents, with long-context support (up to 262,144 tokens) and GGUF/quantized builds for edge/mobile. Strong on coding and agentic benchmarks.
AI Agent Papers2026
Proposes FACET, a framework that synthesizes verifiable terminal tasks by reconstructing scenario intent and grounding instruction, solution, and verifier in a shared executable container state. Key features include environment-first generation, execution-based validation, and targeted repair to preserve source intent and cross-artifact consistency.
AI Agent Papers2026
Evaluates whether coding agents can modify real scientific software while preserving domain-specific scientific contracts. Contains 119 repository-level tasks across 98 GitHub projects and 20 scientific domains, measures reproducible edits in pinned Docker images, and analyzes recurring failure modes.
AI Model2026
A 4B-parameter on-device general-purpose LLM for chat, writing, translation, coding and agentic workflows with native 1,000,000-token context. Uses a hybrid attention design to enable long-context efficiency, pretrained on ~20T tokens, and compatible with vLLM, llama.cpp, Ollama and LM Studio.
AI Dataset2026
Contains 244 Cursor agent sessions recorded from Claude Fable‑5, formatted for training and research. Sessions include multi-turn assistant/tool interactions and are Teich-compatible; several rows exceed one million characters, so apply explicit tokenization and oversize policies before training.
AI Agent Papers2026
Proposes treating game development as a recursive data engine and introduces RLHEV (Reinforcement Learning with Human-Engine Verification) to combine dense engine checks (collision, physics, navigability) with human acceptance feedback, producing trajectory data and rewards for post-training world models.
AI Agent Papers2026
Synthesizes, repairs, and self-evolves task-adaptive agent harnesses on demand for off-the-shelf LLM agents, using a trainable harness-intelligence model that distills signals from past configurations. Demonstrates consistent performance gains across benchmarks and model families by producing four-module, composable harnesses.
Rewrites physical scenes as executable world programs (e.g., MuJoCo scene descriptions) and uses an agentic abductive loop to propose, execute, render, verify, and iteratively refine those programs from videos or text. Verified executable worlds supply scalable physical supervision for training vision–language models.