arXiv AI
Aug 11
ToolUniverse: An open platform for democratizing AI scientists
arXiv:2509. 23426v3 Announce Type: replace Abstract: AI scientists are emerging computational systems that serve as collaborative partners in discovery.
Orcheo: A Modular Full-Stack Platform for Conversational Search
arXiv:2602. 14710v2 Announce Type: replace-cross Abstract: Conversational search (CS) requires a complex software engineering pipeline that integrates query reformulation, ranking, and response generation.
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arXiv Machine Learning
Jun 9
OpenCompass: A Universal Evaluation Platform for Large Language Models
arXiv:2605. 19276v3 Announce Type: replace-cross Abstract: In recent years, the field of artificial intelligence has undergone a paradigm shift from task-specific small-scale models to general-purpose large language models (LLMs).
arXiv AI
Jul 29
SAFAARI: Schema-Aware Framework for Accelerated Advertiser Response Intelligence
arXiv:2607. 25042v1 Announce Type: new Abstract: The evolution of customer support systems is rapidly advancing with agentic chatbots, yet these systems face significant limitations when accessing enterprise data without predefined API endpoints.
arXiv AI
Jul 14
Open, Reliable, and Collective: A Community-Driven Framework for Tool-Using AI Agents
arXiv:2604. 00137v2 Announce Type: replace Abstract: Tool-integrated LLMs retrieve information, perform computations, and take real-world actions, but their reliability depends on both tool-use accuracy and intrinsic tool accuracy, including tool correctness, stability, and safety.
arXiv AI
Jun 11
ClawEnvKit: Automatic Environment Generation for Claw-Like Agents
arXiv:2604. 18543v4 Announce Type: replace Abstract: Constructing environments for training and evaluating claw-like agents remains a manual, human-intensive process that does not scale.
arXiv AI
Jun 30
Conversational Query Engine for Mixed-Modality Heterogeneous Enterprise Data Sources
arXiv:2606. 28370v1 Announce Type: cross Abstract: Enterprise business intelligence queries span structured warehouses and unstructured document repositories -- modalities with fundamentally different access methods, cost profiles, and correctness semantics.
arXiv AI
Jun 9
Harmonia: End-to-End RAG Serving Optimization
arXiv:2505. 07833v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) improves the reliability of large language models by integrating external knowledge, but serving RAG pipelines efficiently is challenging because requests traverse heterogeneous components spanning LLM inference, databases, and CPU-side processing.
arXiv AI
Jun 18
Data Intelligence Agents: Interpreting, Modeling, and Querying Enterprise Data via Autonomous Coding Agents
arXiv:2606. 19319v1 Announce Type: cross Abstract: Production data integration is bottlenecked by repeated, lossy handoffs between data owners, engineers, and analysts who must collaboratively discover, structure, and query enterprise data.
arXiv AI
Jul 7
Gypscie: A Cross-Platform AI Artifact Management System
arXiv:2604. 10311v2 Announce Type: replace Abstract: Artificial Intelligence (AI) models, encompassing both traditional machine learning (ML) and more advanced approaches such as deep learning and large language models (LLMs), play a central role in modern applications.
arXiv AI
6d ago
VAKRA: Evaluating Multi-Hop Reasoning Across APIs and Retrieval Under Tool-Use Policies
arXiv:2608. 12282v1 Announce Type: new Abstract: Agents deployed in enterprise settings must reason across structured APIs and document collections, yet existing benchmarks evaluate these capabilities in isolation.
arXiv AI
Jul 8
Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows
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 Machine Learning
Aug 6
stratum: A System Infrastructure for Massive Agent-Centric ML Workloads
arXiv:2603. 03589v3 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) transform how machine learning (ML) pipelines are developed and evaluated.