Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2608. 08253v1 Announce Type: new Abstract: AI agents are becoming shared infrastructure, yet durable memory is commonly assembled from separate retrieval, governance, and operational components.
arXiv:2608. 09828v1 Announce Type: cross Abstract: AI agents increasingly work inside systems that govern how they delegate tasks, move information, execute actions, and use shared resources.
arXiv:2608. 09636v1 Announce Type: cross Abstract: Accurate 3D neuron segmentation in fluorescence microscopy is critical for neuroscience.
arXiv:2608. 09564v1 Announce Type: cross Abstract: UAV vision-language navigation (UAV-VLN) focuses on enabling an aerial agent to follow natural-language instructions in open 3D environments from egocentric visual observations.
arXiv:2608. 09532v1 Announce Type: cross Abstract: Enterprises increasingly seek to query data lakes using natural language via AI-driven tools like semantic operators or deep research agents.
arXiv:2608. 07925v1 Announce Type: new Abstract: EDA scripting with tool-specific, often undocumented APIs remains a long-tail bottleneck that existing LLMs fail to address.
arXiv:2511. 19969v2 Announce Type: replace Abstract: Recent advancements in multi-modal retrieval-augmented generation (mRAG), which enhance multi-modal large language models (MLLMs) with external knowledge, have demonstrated that the collective intelligence of multiple agents can significantly outperform a single model through effective communication.
arXiv:2608. 07538v1 Announce Type: new Abstract: As LLM agents move from decision support to autonomous procurement, firms need to know whether delegated negotiators create value, divide it predictably, and avoid money-losing contracts.
arXiv:2608. 05144v2 Announce Type: replace Abstract: Long-horizon reasoning requires an agentic runtime that can persist when evidence supports its current approach and pivot when measurements reveal failure, hidden constraints, or a misspecified objective.
arXiv:2608. 09153v1 Announce Type: new Abstract: Production AI agents fail when their context sources -- system prompts, knowledge bases, tool descriptions, and procedural skills -- contain errors or gaps.
arXiv:2608. 09185v1 Announce Type: cross Abstract: Enterprise data warehouses (DWs) support business-critical analytics, but warehouse task delivery remains a complicated production process involving context retrieval, workflow configuration, code generation, platform submission, and failure diagnosis.
arXiv:2608. 09184v1 Announce Type: new Abstract: Large language model (LLM) agents provide a promising interface for command-line-based network operations, but a plausible command may still fail or introduce operational risk after execution.
arXiv:2608. 09277v1 Announce Type: new Abstract: Verified code generation asks a large language model (LLM) to generate both an executable program and a machine-checkable proof that the program meets a formal specification, promising software that is correct by construction.
arXiv:2608. 07577v1 Announce Type: cross Abstract: A closed-set detector for autonomous driving must assign every object one of a fixed set of labels.
arXiv:2608. 09574v1 Announce Type: new Abstract: LLMs are rapidly embedding themselves into daily life: drafting our emails, managing our schedules, and making decisions on our behalf.
arXiv:2608. 07978v1 Announce Type: cross Abstract: Multi-agent large language model(LLM)systems are applied to structural design,yet most use one-shot generation and cannot verify their output,leaving themill-suited to safety-critical tasks.
arXiv:2608. 09555v1 Announce Type: new Abstract: External natural-language skills provide large language model (LLM) agents with reusable and editable guidance for solving complex tasks.
arXiv:2608. 09104v1 Announce Type: cross Abstract: Scanning probe microscopy provides nanoscale access to structural, electrical, electromechanical, magnetic, and mechanical properties of materials.
arXiv:2608. 09855v1 Announce Type: new Abstract: Autonomous research agents can generate experiments faster than researchers can validate them.
arXiv:2608. 07750v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) have found successful deployment in numerous vision perception systems.