Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2607. 05758v1 Announce Type: cross Abstract: Automated experimentation is moving from closed-loop optimization toward open decision-making, where human or AI planners must forecast the consequences of candidate actions before executing them.
arXiv:2607. 05835v1 Announce Type: cross Abstract: For every loopless matroid $M$ and every Feichtner--Yuzvinsky building set $\mathcal{G}$ containing the top flat, we construct an integral tangent class $T_{M,\mathcal{G}}^{\mathbb{Z}}\in K_{\mathbb{Z}}(M,\mathcal{G})$; in the realizable case it specializes to the class of the tangent bundle of the corresponding wonderful compactification, it recovers the Hilbert series of the Chow ring through Hirzebruch--Riemann--Roch, and it satisfies the expected Chern-alpha lower bounds.
arXiv:2607. 06531v1 Announce Type: new Abstract: - Objective: Multimodal deep learning models in oncology are currently limited by monolithic designs that rigidly couple data ingestion, clinical routing, and artificial intelligence (AI) inference.
arXiv:2607. 06214v1 Announce Type: new Abstract: This paper offers a toy framework for considering curiosity as an ecosystem.
arXiv:2607. 06435v1 Announce Type: new Abstract: Data from Singapore indicated that about 31% of the population had evidence of Helicobacter pylori infection.
arXiv:2607. 06328v1 Announce Type: new Abstract: The increasing adoption of end-to-end learning for autonomous driving introduces increased model complexity and opacity, raising the risk of learning undesired or erroneous behavior.
arXiv:2607. 04419v2 Announce Type: replace Abstract: Final-answer scores hide which agent transitions helped or harmed a trace.
arXiv:2512. 19458v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly embedded in agentic frameworks for scientific discovery.
arXiv:2607. 06482v1 Announce Type: cross Abstract: Current benchmarks for evaluating Large Language Models (LLMs) in data analysis often fail to reflect real-world settings.
arXiv:2607. 06066v1 Announce Type: new Abstract: The Vehicle Routing Problem (VRP) and its variants represent some of the most practically consequential optimization challenges in modern logistics and urban mobility.
arXiv:2607. 05458v1 Announce Type: cross Abstract: Large language model (LLM) agents are usually improved by changing prompts, models, or hand-written workflows, while the execution harness around the model is treated as fixed infrastructure.
arXiv:2607. 05456v1 Announce Type: new Abstract: While recent advances in large language models have enabled end-to-end automated manuscript generation, existing systems suffer from three critical deficiencies: (i) generated claims are not deterministically grounded in verifiable literature, (ii) experimental results are frequently fabricated rather than executed, and (iii) there exists no standardized, multi-dimensional framework to assess whether AI-generated manuscripts meet the quality and rigor required for real-world publication.
arXiv:2607. 05666v1 Announce Type: cross Abstract: AI coding agents are black boxes: we cannot inspect how they generate code, but we can inspect what they change.
arXiv:2607. 05518v1 Announce Type: cross Abstract: AI agents issue tool calls on the basis of text they cannot verify, so any party who controls part of the context can forge the appearance of authority.
arXiv:2607. 06229v1 Announce Type: cross Abstract: Major cloud data platforms now expose large language model capabilities as native SQL functions, enabling analysts to perform classification, filtering, sentiment analysis, extraction, similarity search, and aggregation within ordinary SQL queries.
arXiv:2607. 06202v1 Announce Type: cross Abstract: The deployment of Mixture-of-Experts (MoE) models on production high-bandwidth superpods, such as NVIDIA's NVL72/576 and Huawei's CloudMatrix384, introduces critical challenges beyond raw interconnect bandwidth.
arXiv:2607. 05638v1 Announce Type: cross Abstract: Teams deploying large language models in business contexts need evaluation systems, yet most treat evaluation as static model selection: run benchmarks, rank models, deploy the winner.
arXiv:2607. 06283v1 Announce Type: new Abstract: Skill usage can significantly enhance the ability of modern agent systems to complete complex tasks.
arXiv:2607. 05436v1 Announce Type: new Abstract: The rapid scaling of over-parameterized machine learning architectures, particularly LLMs, raises a profound crisis: do these systems exhibit genuine intelligence, or are they merely sophisticated statistical pattern matchers?
arXiv:2607. 05958v1 Announce Type: cross Abstract: We present a distributed, vendor-agnostic multi-MCP architecture for SDN-based automation and autonomous control of multi-vendor, multi-layer IPoDWDM networks.