TuiML is a machine‑learning library specifically designed for AI agents rather than human programmers. It offers native algorithms for supervised, unsupervised, time‑series, data handling, tuning, and evaluation tasks, with each component exposing machine‑readable metadata and parameter schemas so agents can search, inspect, compose, and validate workflows autonomously. The library ensures every call is validated, seeded, and traced, and sessions can be exported as runnable notebooks, making experiments reproducible by construction. Benchmarks indicate TuiML remains predictively competitive with scikit‑learn and Weka, while keeping data and models confined to the local machine.
arXiv:2603. 03589v3 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) transform how machine learning (ML) pipelines are developed and evaluated.
The paper introduces SMART, a symbolic performance‑modeling library for machine‑learning systems that relies almost entirely on natural‑language design documents rather than code. By using AI coding agents to regenerate the implementation from these documents, the framework eliminates the need for continuous refactoring as models and systems evolve. The authors demonstrate that regenerated implementations match hand‑audited reference models to round‑off precision, suggesting that design documents can serve as the durable artifact for ML‑systems co‑design tools.
arXiv:2607. 28629v1 Announce Type: new Abstract: The rapid transition from reactive large language models (LLMs) to persistent, action-capable systems has exposed critical gaps in the architectural understanding of Agentic AI, particularly in separating inference, orchestration, and execution layers for autonomous AI agents.
arXiv:2606. 03841v1 Announce Type: new Abstract: Recent progress in Large Language Model (LLM) agents has enabled promising advances in automated data science.
arXiv:2607. 20709v1 Announce Type: new Abstract: Traditional agent development is split across prompt templates, tool schemas, callback code, and workflow graphs.
arXiv:2606. 09138v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) has become an important post-training paradigm for turning LLMs from static chatbots into interactive agents, giving rise to representative applications such as OpenClaw.
arXiv:2510. 15416v2 Announce Type: replace Abstract: We investigate a framework in which LoRA adapters are treated as callable tools that a base language model can dynamically select and invoke.
arXiv:2606. 31270v1 Announce Type: cross Abstract: Computer-use agents, which leverage multimodal large language models (MLLMs) to operate computers and complete tasks, have attracted significant attention for their utility and versatility.
The paper proposes a shift from AI model storage to AI model management, introducing the concept of "learnware"—a combination of a model and its specification. Learnware specifications are generated without exposing developers’ training data, enabling models from different sources to be identified, reused, and assembled for new tasks. The Learnware Dock System (LDS) offers a framework for managing these learnwares and facilitates collaboration among independently developed models through a shared specification protocol.
SPADE (Self-Play in Adaptive Synthetic Executable Environments) is a reinforcement‑learning framework where a single large language model acts as both an Environment Designer—creating executable, long‑horizon training environments—and a Reasoning Agent—learning to act within those environments. The framework uses a regret signal based on the difference between rewarded performance with and without privileged hints to guide the Designer toward environments that are challenging yet solvable. Experiments show that, when scaled to 30‑billion‑parameter models, SPADE outperforms fixed‑environment baselines by significant margins across math, science, code, and reasoning benchmarks, and improves tool‑use performance on BFCL‑v4 and ACEBench‑Agent. whyItMatters":"By making environment design a learnable component, SPADE enables continuous self‑improvement and demonstrates that adaptive, self‑generated training environments can substantially boost language‑model performance across diverse tasks."
Agent0 is a fully autonomous framework that enables large language model agents to evolve without external data by using a multi‑step co‑evolution process. It pits a curriculum agent against an executor agent, both derived from the same base LLM, where the curriculum agent creates increasingly challenging tasks and the executor learns to solve them. By integrating external tools into the executor’s workflow, the system creates a self‑reinforcing cycle that continuously generates high‑quality curricula, leading to significant gains in reasoning performance—an 18% improvement on mathematical reasoning and 24% on general reasoning for the Qwen3‑8B‑Base model.
arXiv:2607. 21557v1 Announce Type: new Abstract: Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, and access to external systems.