Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2607. 16169v1 Announce Type: cross Abstract: Muon is competitive with AdamW in large-scale pre-training, but its value for reinforcement-learning (RL) post-training remains unclear.
arXiv:2502. 00345v2 Announce Type: replace-cross Abstract: The critical role of division of labor (DOL) in enhancing cooperation is well-recognized in real-world applications.
High quality temporal graph benchmarks with rich semantics and ground-truth anomaly labels are essential for training graph neural networks, yet remain scarce due to privacy constraints and annotation costs. We present SAGA (Synthetic Agentic Graph Architecture), a system for generating large-scale, semantically rich temporal graphs via a four-phase pipeline.
An AI agent passed every metric in the eval harness I published, then the CFO killed it — its successful resolutions cost more than the humans it replaced. The one metric that predicts whether an agent survives production, and how to measure it without a rebuild.
End-to-end autonomous driving models are now able to navigate complex road scenarios, mapping raw sensor observations directly to observed paths for open-loop evaluation and often effective driving in closed-loop evaluation. Yet the internal logic of these safety-critical systems remains largely opaque, due to the complexity of traffic scenes.
A practical enterprise AI architecture with data agents, AI-powered QA, and AI governance. The post Many Companies Use AI.
On the value of building on existing foundations The post Using Classical ML to Empower AI Agents appeared first on Towards Data Science .
Autonomous exploration of unknown 3D environments is traditionally driven by coverage-maximizing geometric heuristics. However, these methods typically determine exploration targets without considering the underlying structural context.
arXiv:2606. 22030v2 Announce Type: replace Abstract: We investigate when belief-based memory actually improves large language model (LLM) agents.
arXiv:2607. 15272v1 Announce Type: cross Abstract: Editing the figures in a research paper is a routine and time-consuming part of everyday research practice: authors relabel components, rearrange panels, and restyle visuals as they revise their manuscripts.
arXiv:2605. 25170v2 Announce Type: replace-cross Abstract: Training data for olfaction is scattered through disparate, non-standardized datasets that limit the ability to build representative world models.
arXiv:2607. 14494v1 Announce Type: new Abstract: Complex knowledge base question answering (KBQA) is commonly approached through either information retrieval over a question-specific subgraph or semantic parsing into an executable logical form.
arXiv:2607. 14721v1 Announce Type: cross Abstract: Cross-modal learning, i.
arXiv:2605. 05000v2 Announce Type: replace-cross Abstract: LLM agents have been increasingly adopted for solving security tasks.
arXiv:2508. 00923v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to answer health-related questions and support healthcare workflows, yet evidence for their safety still relies heavily on static benchmarks that can rapidly become obsolete or be optimized against.
arXiv:2607. 14336v1 Announce Type: cross Abstract: Trustworthy deployment of LLM-based agents in software systems requires evaluating how they perform on application-specific workflows, with enough granularity to localize where they succeed and fail.
arXiv:2607. 14141v1 Announce Type: new Abstract: Bayesian Belief Networks (BBNs) are powerful tools for decision-making under uncertainty.
arXiv:2607. 14108v1 Announce Type: cross Abstract: This paper introduces tool efficiency, a new quantitative metric to evaluate the rate of useful tool calls in an LLM agent trajectory.
arXiv:2607. 14658v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) excel in general tasks, rigorous scientific reasoning remains challenging due to the limitations of monolithic, linear planning.
arXiv:2607. 14886v1 Announce Type: new Abstract: Temporal Knowledge Graph (TKG) reasoning under the extrapolation setting focuses on forecasting future time-stamped events (facts) from historical data in a temporal knowledge graph.