FromPitch2Board: Benchmarking LLM Agents in Long-Horizon Football Management
cs.AI
Submitted: 2026-09-28
Updated: 2026-09-28
Code: https://github.com/factnn/FromPitch2Board
Terminology
Sources
- Vending-Bench: A Benchmark for Long-Term Coherence of Autonomous Agents
- $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment
- CEO-Bench: Can Agents Play the Long Game?
- StockBench: Can LLM Agents Trade Stocks Profitably In Real-world Markets?
- GameBench: Evaluating Strategic Reasoning Abilities of LLM Agents
- AgentScope: A Flexible yet Robust Multi-Agent Platform
- TextArena
- Can LLM Agents Be CFOs? Benchmarking Long-Horizon Resource Allocation in an Uncertain Enterprise Environment
- $\texttt{YC-Bench}$: Benchmarking AI Agents for Long-Term Planning and Consistent Execution
- The PokeAgent Challenge: Competitive and Long-Context Learning at Scale
- Continual Harness: Online Adaptation for Self-Improving Foundation Agents
- Long-Horizon-Terminal-Bench: Testing the Limits of Agents on Long-Horizon Terminal Tasks with Dense Reward-Based Grading
- UltraHorizon: Benchmarking Agent Capabilities in Ultra Long-Horizon Scenarios
- Cattle Trade: A Multi-Agent Benchmark for LLM Bluffing, Bidding, and Bargaining
- MemGPT: Towards LLMs as Operating Systems
- Orak: A Foundational Benchmark for Training and Evaluating LLM Agents on Diverse Video Games
- MerchantBench: Benchmarking LLM Agents for Long-Term Coherence in E-Commerce Operations
- CoffeeBench: Benchmarking Long-Horizon LLM Agents in Heterogeneous Multi-Agent Economies
- Agent Planning Benchmark: A Diagnostic Framework for Planning Capabilities in LLM Agents
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
- Agents' Last Exam
- 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