Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2608.31082v1 Announce Type: new Abstract: Valuable data remains embedded in unstructured sources: web pages, reports, contracts, filings, earnings calls, and PDFs. The big bet in enterprise AI...
arXiv:2608.31057v1 Announce Type: new Abstract: Agent working memory is heterogeneous. Objects such as instructions, artifacts, tool outputs, and agent-generated state play different semantic roles a...
arXiv:2608.30897v1 Announce Type: new Abstract: World models are becoming core infrastructure for embodied intelligence, with action-conditioned video generation providing controllable predictions of...
arXiv:2608.30685v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed in user-facing services that require iterative tool use under dynamic business conditions....
Introducing wrapture, a new Python library from Graham Dumpleton that extends the monkeypatching concepts of wrapt to support both testing and tracing simultaneously. It allows developers to wrap any function or method so that all accesses can be traced or overridden, serving as an alternative to unittest.mock and enabling observation of code without altering its behavior. Wrapture includes OpenTelemetry support and offers a configuration-based approach for adding tracing to existing projects, with the entire project and its documentation created under AI assistance.
Graphic designers often blend visual concepts to communicate multiple ideas within a single image, leveraging positive and negative space to create balance, emphasis, and aesthetic appeal. While compu...
Public trust in Autonomous Vehicles (AVs) may depend not only on technical success but also on the fairness of their decision making. While a recent trend in AV research involves using general purpose...
We present Qwen-Drive-1.0, an initial step towards a vision-language foundation model for autonomous driving. Qwen-Drive-1.0 retains the architecture of the pretrained vision-language model (VLM) and...
Many important forms of human learning begin with a vague goal, such as "become a better physicist" or "improve at research." Learners must interpret the goal, identify capability gaps, decide how to...
Autonomous scientific research agents are increasingly applied to end-to-end scientific workflows, including literature review, data analysis, experimentation, and report generation. However, open-end...
Recent advances in large reasoning models (LRMs) have shown that reinforcement learning with verifiable rewards (RLVR) can substantially improve reasoning in mathematics and code, where outcomes can b...
Real-world time-series forecasting is rarely a one-shot model invocation: practitioners must formulate tasks, connect data and models, incorporate domain expertise, assess prediction plausibility, and...
In 2026, AI agents intended to act in isolation formed a persistent social order through thousands of linguistic and agentic interactions. Conventions, roles and commitments generated collectively beg...
Vision-Language Models (VLMs) still struggle on tasks requiring complex visual understanding. We argue that the core issue is not high-level reasoning, but instead failing to locate critical details i...
Chemistry questions often demand exact computation and database lookups that a language model cannot supply from its parameters, so it must reach for external tools. Tool use here is a three-part prob...
Evaluating creativity in large language model (LLM) outputs remains challenging because creativity is multidimensional and human-centered. We examine how reliably LLMs evaluate short literary text in...
The article outlines a framework for constructing Retrieval-Augmented Generation (RAG) pipelines that progressively add complexity as needed to address observed failure modes. It begins with basic lexical and hybrid search techniques, then incorporates reranking and agentic information‑seeking strategies to improve performance. The approach emphasizes that more sophisticated components should only be introduced when simpler methods prove insufficient.
Coding agents are increasingly used for software engineering tasks, including bootstrapping projects from third-party repositories whose integrity cannot be assumed. Prior work on repository poisoning...
The article explains how the five core assumptions of MLOps monitoring are violated when agents are deployed to production, leading to inherited signals that incorrectly mark failed runs as healthy. It highlights the specific ways in which agent-based systems disrupt traditional monitoring stacks and the implications for reliability and performance. The piece serves as a warning for practitioners transitioning from MLOps to AgentOps, outlining the critical monitoring gaps that arise.
Multimodal Large Language Models (MLLMs) have shown remarkable success in STEM domains, where progress is often driven by vertical, step-by-step deduction under relatively stable symbol systems. Their...