Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2608. 13566v1 Announce Type: cross Abstract: Post-training papers, model cards, and blog posts often treat scores on a small set of coding benchmarks (e.
arXiv:2608. 13833v1 Announce Type: cross Abstract: Conversational advertising aims to deliver useful ads within multi-turn assistant interactions.
arXiv:2608. 13900v1 Announce Type: cross Abstract: Large language model (LLM) agents are evolving from conversational assistants into autonomous systems that execute long-horizon tasks through reasoning, tool use, code generation, and workspace manipulation.
arXiv:2602. 06746v2 Announce Type: replace Abstract: We study multi-task reinforcement learning (RL), a setting in which an agent learns a single, universal policy capable of generalising to arbitrary, possibly unseen tasks.
arXiv:2502. 00023v2 Announce Type: replace-cross Abstract: Our research explores the development and application of musical agents, human-in-the-loop generative AI systems designed to support music performance and improvisation within co-creative spaces.
arXiv:2512. 14732v3 Announce Type: replace-cross Abstract: Incidental findings in CT scans, though often benign, can have significant clinical implications and should be reported following established guidelines.
arXiv:2608. 13600v1 Announce Type: cross Abstract: We study Evolution Strategies (ES) for continual control, where agents must adapt to changing tasks without forgetting previous ones.
arXiv:2608. 13616v1 Announce Type: cross Abstract: We consider a heterogeneous mobile-agent network composed of uncontrolled task agents and controllable communication agents.
arXiv:2608. 13810v1 Announce Type: cross Abstract: We examine the interplay between ordinal, preference-based solution concepts in games and the long-run behavior of game dynamics, asking in particular to what extent the combinatorial data of a game -- its preference graph -- determine the outcomes of no-regret learning dynamics -- such as follow-the-regularized-leader (FTRL).
arXiv:2608. 14352v1 Announce Type: cross Abstract: Large Language Model (LLM)-based agents are increasingly used for complex tasks such as software testing and cybersecurity assessment.
arXiv:2608. 14452v1 Announce Type: new Abstract: Spreadsheets are widely used to organize, analyze, and manipulate semi-structured data, yet automated spreadsheet reasoning remains challenging for large language models (LLMs).
In cognitive science, resource rationality asks how an agent should allocate limited computation to maximize expected value. Most reasoning and agent benchmarks use independent per-task budgets; exist...
Camera-based object detectors are vulnerable to physical adversarial attacks designed to suppress detections. While adversarial training and input purification offer some protection, they often overfit to specific attack distributions and fail on adaptive adversaries.
Friday's big release was Qwen 3. 8 27B , an Apache 2 licensed 27B parameter vision-capable LLM from Alibaba's Qwen research lab.
Large language model (LLM) agents often perform poorly on complex, long-horizon tasks because their context becomes increasingly cluttered over time. As interactions accumulate, detailed execution tra...
Production paged-serving engines apply uniform paging granularity to the KV cache, even though the two regions of a multi-agent workload have opposite storage requirements: a long shared prefix demand...
Large language model (LLM)-assisted energy-management tools can translate natural-language context into structured grid commands, but syntactic validity does not imply physical admissibility. This pap...
Enterprise Document Intelligence [Vol. 1 #13] - Putting the patterns together, and why this is what “agentic RAG” should look like The post RAG Workflow and Loop Engineering: The Dispatcher That Decides When to Loop and When to Stop appeared first on Towards Data Science .
arXiv:2608. 12371v1 Announce Type: new Abstract: Stream-processing systems increasingly operate across heterogeneous mobile edge--cloud infrastructures, where workload volatility, resource contention, and stringent quality-of-service (QoS) requirements complicate decentralized scheduling.
arXiv:2608. 12788v1 Announce Type: new Abstract: The rapid advancement of Auto-Research has surfaced a fundamental evaluation challenge: how can we measure the alignment, logical coherence, and evolutionary completeness of its research trajectory with human research behavior?