Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2608.21976v1 Announce Type: new Abstract: Agentic AI has automated parts of scientific discovery, including paper generation, expert-level coding, therapeutic proposal, and autonomous experimen...
arXiv:2608.21942v1 Announce Type: new Abstract: Software systems have traditionally been organized around applications where human users act as principal decision-makers. Recent developments in agent...
arXiv:2608.21898v1 Announce Type: new Abstract: Web agents promise to automate complex digital workflows, but their training remains limited by synthetic environments that look plausible while hiding...
arXiv:2608.23179v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly attractive for automating network configuration, yet their reliability and failure patterns are po...
VeCAS is a two‑stage framework that generates contrast‑free X‑ray angiograms from non‑contrast images. Stage I localizes vascular structures using a discriminative model and cross‑modality latent distillation, while Stage II synthesizes angiographic appearance within those regions with a vessel‑focused inpainting model. Experiments on a lower‑limb vascular intervention dataset show that VeCAS improves vascular structural fidelity and image quality, and robotic guidewire navigation tests demonstrate significant reductions in time to target and operation steps.
The paper proposes an information‑flow perspective on explainability, arguing that exposing reasons for observed effects is a positive flow of information that must be specified and verified. It introduces an epistemic temporal logic with counterfactual causes to formalize the requirement that agents gain knowledge about why an effect occurred, and presents an algorithm for checking finite‑state models against these specifications. A prototype implementation is evaluated on benchmarks, demonstrating the ability to distinguish explainable from unexplainable systems and to incorporate privacy constraints.
The paper introduces ADN‑Agent, an architecture that uses a large language model to orchestrate multiple domain‑specific models (DSMs) for active distribution network (ADN) management. It features adaptive intent recognition, task decomposition, and a unified communication interface for heterogeneous DSMs, along with a pipeline for fine‑tuning small language models on language‑intensive subtasks. Experiments show ADN‑Agent outperforms existing LLM application paradigms in coordinating DSMs for complex ADN operations.
MPCFormer is a physics‑informed, data‑driven framework that explicitly models multi‑vehicle social interaction dynamics for autonomous driving. It uses a Transformer‑based encoder‑decoder to learn discrete state‑space dynamics from naturalistic data, enabling explainable, human‑like behavior planning within a Model Predictive Control (MPC) framework. In open‑loop NGSIM tests, it achieves the lowest trajectory prediction errors (ADE 0.86 m over 5 s), and in closed‑loop intense interaction scenarios it attains a 94.67 % planning success rate, 15.75 % efficiency gain, and reduces collisions from 21.25 % to 0.5 %.
ST‑EVO is a new framework for Large Language Model‑powered Multi‑Agent Systems that introduces a spatio‑temporal evolution of communication topologies. It uses a flow‑matching based scheduler to enable dialogue‑wise communication scheduling and incorporates uncertainty perception and self‑feedback to refine its scheduling over time. Experiments on nine benchmarks show that ST‑EVO outperforms existing methods, improving accuracy by roughly 5% to 25%.
arXiv:2608.21836v1 Announce Type: new Abstract: Large language models have become increasingly capable agents for low-level code and kernel optimization, but isolated kernel benchmarks provide only a...
arXiv:2608.22830v1 Announce Type: new Abstract: Deploying LLMs for enterprise Text-to-SQL is bottlenecked less by the model than by what context reaches it: business logic spans thousands of tables,...
arXiv:2608.23078v1 Announce Type: new Abstract: Large language models increasingly operate over large collections of tools, functions, APIs, and specialized agents. As the candidate action space grow...
arXiv:2608.23526v1 Announce Type: new Abstract: World models can predict video without learning dynamics that they reliably preserve. We test whether a frozen DreamerV3 trained only on pendulum video...
arXiv:2608.23435v1 Announce Type: cross Abstract: Understanding a basketball game requires recognizing events, localizing actions, identifying players, and relating these to structured game knowledge...
arXiv:2602.03318v4 Announce Type: replace Abstract: Operations Research (OR) relies on expert-driven modeling--a slow and fragile process ill-suited to novel scenarios. While large language models (L...
arXiv:2608.16053v2 Announce Type: replace Abstract: Synthetic conversational speech has become an important resource for developing and evaluating conversational speech systems. However, existing dia...
arXiv:2608.22187v1 Announce Type: cross Abstract: Modern driving action models are increasingly improved in a self-improvement loop, where a learned world simulator imagines future observations and t...
arXiv:2608.22432v1 Announce Type: cross Abstract: Multilingual LLM judges produce different evaluator-backbone rankings depending on the prompt language: on an eight-language Agent-as-a-Judge benchma...
The paper studies a stationary decentralized Markov game where a focal agent experiences drifting rewards and dynamics due to learning peers, framing this as an agent‑centric continual reinforcement‑learning problem. It introduces the concept of an invariant core—maximal abstract patterns common to many successful trajectories—and proves a worst‑case conditioning theorem linking trajectory‑law drift to success coverage. The authors provide theoretical guarantees for survival horizon, first‑exit law, and regret, and validate their predictions with solvable models and empirical studies in continual control, cue‑MNIST, and Level‑Based Foraging.
arXiv:2608.23330v1 Announce Type: new Abstract: Video understanding requires intelligent agents to transcend mere recognition of visual facts and comprehend the underlying intents behind human action...