PeakBench: Benchmarking Resource-Aware Tool Invocation in LLM Agents

arXiv:2608.24509 · cs.AI, cs.SE · Submitted 2026-08-25 · Read on arXiv

cs.AI, cs.SE

Submitted: 2026-08-25

Updated: 2026-08-25

Code: https://github.com/Czzzk/Staggering-the-Peaks

Terminology

• Benchmark — 40× in this paper · explained in 2 episodes
• Core — 6× in this paper · explained in 2 episodes
• Scheduling Latency — 6× in this paper
• Benchmarking — 5× in this paper · explained in 3 episodes
• Resource-Aware Scheduling Context — 4× in this paper
• Ablation — 3× in this paper · explained in 1 episode
• Capacity Violation Area — 3× in this paper
• Robustness — 3× in this paper · explained in 5 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Agentic Workflows — 2× in this paper · explained in 1 episode
• Analyzer — 2× in this paper · explained in 1 episode
• Decoupling — 2× in this paper · explained in 2 episodes
• Resource Constraints — 2× in this paper · explained in 3 episodes
• Trace — 2× in this paper · explained in 2 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Graph Edit Distance — 1× in this paper
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers
• MCPToolBench++: A Large Scale AI Agent Model Context Protocol MCP Tool Use Benchmark
• R-ConstraintBench: Evaluating LLMs on NP-Complete Scheduling
• LLM+P: Empowering Large Language Models with Optimal Planning Proficiency
• MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
• AIOS: LLM Agent Operating System
• LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?
• COMPASS: Benchmarking Constrained Optimization in LLM Agents
• OpenAI GPT-5 System Card
• Kimi K2.5: Visual Agentic Intelligence
• Voyager: An Open-Ended Embodied Agent with Large Language Models
• MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers
• MCPMark: A Benchmark for Stress-Testing Realistic and Comprehensive MCP Use
• DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
• ReAct: Synergizing Reasoning and Acting in Language Models
• CCTU: A Benchmark for Tool Use under Complex Constraints
• GLM-5: from Vibe Coding to Agentic Engineering

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
• VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection