arxiv arXiv cs.AI · 52d ago · research

Data Intelligence Agents Enable Autonomous Data Querying

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Data Intelligence Agents (DIA) deploy autonomous coding agents to streamline enterprise data workflows. The Query Generator matches or exceeds top published results on seven SQL benchmarks across four dialects, showing generalization through natural-language instructions and execution-based architecture.

Importance 3/3 Beats a top-lab benchmark New feature vs. leaders New harness with differentiators arXiv cs.AI Microsoft Research Hugging Face OpenAI AI agents Code generation Evaluation & benchmarks

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arxiv arXiv cs.AI · 52d ago · 1 view

CAPRA: Multi-Agent LLM System for Software Architecture Feedback

CAPRA is a multi-agent LLM system that generates personalized, template-compliant LaTeX feedback on software architecture deliverables. It uses specialized agents, PyMuPDF, and gpt-4o to extract and analyze text and UML diagrams, with evidence anchoring and consistency management to ensure reliability. A preliminary evaluation of 10 student reports shows CAPRA met 88.8% of eight criteria and achieved moderate inter-rater agreement (kappa = 0.582), with each report processed in under 4 minutes.

media r/LocalLLaMA · 47d ago

Microsoft Releases Open Source FastContext for LLM Coding Agents

Microsoft has open-sourced FastContext-1.0, a lightweight repository-exploration subagent that separates code repository exploration from task solving in LLM coding agents. It uses parallel read-only tool calls to return compact file paths and line ranges, improving end-to-end accuracy and reducing token usage by up to 60.3%, with the 4B-RL model outperforming a 30B-SFT model on SWE-bench Pro.

arxiv arXiv cs.CL · 51d ago · 1 view

H-RePlan: Hierarchical Recovery for Cross-Device Agent Systems

H-RePlan introduces a hierarchical replanning framework that separates device-local strategy recovery from global orchestrator replanning. It outperforms existing baselines by achieving higher completion and instruction adherence, with reduced token cost, through scope-aware recovery in multi-device agent systems.

arxiv arXiv cs.AI · 51d ago · 3 views

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

AutoPass uses runtime and compiler evidence to guide LLM-generated optimization decisions, outperforming expert heuristics and classical autotuning methods. It achieves geometric-mean speedups of 1.043x on x86-64 and 1.117x on ARM64 systems without prior training or fine-tuning.

arxiv arXiv cs.CL · 51d ago · 2 views

JAMER: Project-Level Code Framework Dataset and Benchmark

JAMER introduces JamSet and JamBench, the first project-level game code dataset and benchmark on a professional game engine. Built from 8,133 verified Game Jam projects, it enables deterministic evaluation and reveals a capability cliff in AI models as project scale increases, with runtime pass rates dropping from 80.4% to 5.7%.