arXiv AI
Jul 31
Eco3S: Complex Socio-Economic System Simulation via Agent-Based Models
arXiv:2607. 26588v1 Announce Type: new Abstract: The rapid development of large language models (LLMs) has renewed interest in agent-based modeling (ABM).
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv AI
Jul 31
AI Security Priorities: A Field-Wide Agenda
arXiv:2607. 26069v1 Announce Type: cross Abstract: As AI systems are rapidly integrated into critical economic, governmental, and national security functions, the gap between AI adoption and AI security readiness continues to widen.
arXiv AI
Jul 31
UrbanDS: A Graph-Guided LLM Multi-Agent System for Data-Intensive Urban Tasks
arXiv:2607. 26724v1 Announce Type: new Abstract: Large language model (LLM) agents have been widely applied in automating data science tasks.
arXiv AI
Jul 31
The Human Utility Factor: A Computable Welfare Metric That Reframes AI Governance as a Constrained Optimisation Problem
arXiv:2607. 26068v1 Announce Type: cross Abstract: Existing AI governance frameworks, including the EU AI Act and NIST AI RMF, address safety, transparency, and accountability but do not operationalize quantitative constraints on macro-socioeconomic stability.
arXiv Machine Learning
Jul 31
LEDGERMIND: Provenance-Constrained Multimodal Agentic Reasoning with a Structured Evidence Ledger
arXiv:2607. 28374v1 Announce Type: new Abstract: Multimodal agents for visual question answering increasingly operate as multi-step trajectories that interleave perception, retrieval, and reasoning, yet evaluation still largely reduces to final-answer accuracy.
arXiv Machine Learning
Jul 31
Agent Team Work Zone: An Automated, Persistent Workspace for Long-Lived Claude Code Agent Teams
arXiv:2607. 22917v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) agents have significantly improved coding and programming workflows.
arXiv Machine Learning
Jul 31
Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting
arXiv:2508. 04227v3 Announce Type: replace-cross Abstract: Vision-language models (VLMs), spanning predictive architectures to generative Multimodal Large Language Models (MLLMs), have revolutionized artificial intelligence through powerful cross-modal alignment and zero-shot generalization.
arXiv AI
Jul 31
The Capability Paradox: How Smarter Auditors Make Multi-Agent Systems Less Secure
arXiv:2605. 17480v3 Announce Type: replace Abstract: Multi-agent systems extend large language models (LLMs) by decomposing tasks among specialized agents, but their distributed decision process creates new attack surfaces.
arXiv Machine Learning
Jul 31
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
arXiv:2605. 26494v2 Announce Type: replace-cross Abstract: We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence.
arXiv AI
Jul 31
Filesystem-Based Memory for LLM Agents: Organization, Evolution, and Sustainability
arXiv:2607. 26637v1 Announce Type: cross Abstract: Deployed LLM agents increasingly keep their long-term memory as a filesystem: a directory tree of markdown files that the agent itself reads, writes, and reorganizes through generic file tools.
arXiv Machine Learning
Jul 31
On-Policy and Off-Policy Learning for Large Action Spaces
arXiv:2607. 28408v1 Announce Type: new Abstract: This thesis studies policy learning in interactive systems where an agent observes a context, selects an action from a very large set, and receives partial feedback.
arXiv AI
Jul 31
OmegaUse-OfficeVal: Benchmarking LLM Agents on Long-Horizon Office-Suite Tasks with Economic Grounding
arXiv:2607. 27155v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly expected to assist users in completing tasks.
arXiv Machine Learning
Jul 31
SNAC-Pack 2.0: Scaled-Out Surrogate Neural Architecture Codesign
arXiv:2605. 16138v3 Announce Type: replace Abstract: Neural architecture search (NAS) is a powerful approach for automating model design, but existing methods often optimize for accuracy alone or rely on proxy metrics such as bit operations (BOPs) that correlate poorly with hardware cost.
arXiv Machine Learning
Jul 31
KernelGenBench: A Multi-Source and Multi-Chip Benchmark for LLM-based Kernel Generation
arXiv:2607. 27231v1 Announce Type: cross Abstract: Large language models (LLMs) have significantly increased the demand for efficient accelerator kernels, but kernel development remains a highly specialized and labor-intensive task.
arXiv Machine Learning
Jul 31
Baikal: Structured Search for Deep Research over Data Lakes
arXiv:2607. 27726v1 Announce Type: cross Abstract: Deep research over data lakes requires an LLM agent to investigate evidence across thousands of heterogeneous tables and passages to synthesize a report.
arXiv AI
Jul 31
A Methodology for Designing Knowledge-Driven Missions for Robots
arXiv:2601. 20797v1 Announce Type: cross Abstract: This paper presents a comprehensive methodology for implementing knowledge graphs in ROS 2 systems, aiming to enhance the efficiency and intelligence of autonomous robotic missions.
arXiv AI
Jul 31
On the Hybrid Nature of ABPMS Process Frames and its Implications on Automated Process Discovery
arXiv:2604. 22455v2 Announce Type: replace Abstract: A core component of any AI-Augmented Business Process Management System (ABPMS) is the process frame, which gives the system process-awareness and defines its maximal behavioral boundaries.
arXiv AI
Jul 31
AgentMap: Joint Equivalence and Subsumption Discovery for Ontology Matching
arXiv:2607. 27130v1 Announce Type: new Abstract: Ontology matching (OM) has traditionally been formulated as either equivalence discovery or subsumption matching.
arXiv Machine Learning
Jul 31
Bridging AI and Energy Forecasting: An Autonomous Workflow with Customized Toolkit
arXiv:2307. 07191v3 Announce Type: replace Abstract: Energy forecasting is crucial for the power grid, but fundamentally different from general time series analysis: it highly relies on covariates like meteorological factors, and its goals must align with actual power grid operations, such as risk assessment and system reliability.
arXiv Machine Learning
Jul 31
AskChem: Claim-Centered Infrastructure for Chemistry Literature Synthesis
arXiv:2607. 28618v1 Announce Type: cross Abstract: Chemistry literature synthesis often requires assembling specific findings scattered across many publications, yet existing literature-search systems primarily return ranked document lists.