arXiv AI
Multi-Agent LLM Orchestration Achieves Deterministic, High-Quality Decision Support for Incident Response
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UniACE: A Unified Framework for Evaluating LLM Agentic Capabilities
arXiv:2605.27898v3 Announce Type: replace Abstract: Agent benchmarks are increasingly used to compare large language models (LLMs) across domains, yet a reported score reflects a complete model--harn...
arXiv AI
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A Unified Framework for the Evaluation of LLM Agentic Capabilities
arXiv:2605. 27898v2 Announce Type: replace Abstract: As LLMs are increasingly deployed as agents, reliable assessment of their agentic capabilities has become essential.
arXiv Machine Learning
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Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems
arXiv:2607. 08010v1 Announce Type: cross Abstract: Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request.
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DuMateBench: Evaluating Autonomous Agents in Complex Real-World Workflows
DuMateBench is a new benchmark for autonomous agents that uses real user sessions from a large production platform, preserving interaction history, configurations, and workspace state. It contains 200 tasks across 8 scenarios and 17 capability categories, many requiring coordination of multiple capabilities. The benchmark tests agents in Docker containers with real-world complexities—Insufficient, Unstable, and Noisy—and evaluates performance with a hybrid deterministic and LLM-as-Judge protocol, revealing significant gaps in task completion across various agent frameworks and LLMs.
arXiv AI
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Autonomous Event-Driven Multi-Agent Orchestration for Enterprise AI at Scale
arXiv:2606. 20058v1 Announce Type: new Abstract: Enterprise AI aims to move toward continuous event monitoring, detection, and action across specialist agents, yet existing multi-agent systems largely assume discrete request-response workflows and remain underexplored at enterprise scale.
arXiv AI
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Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows in Production
arXiv:2607. 07052v1 Announce Type: cross Abstract: AI agents deployed for IT operations are typically permanent cost centers because every execution requires full LLM inference, even for previously solved problems.