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GM League Arena: An Interactive Long-Horizon Task Benchmark Based on Professional Sports League Management

Paper recorded by Signals 4 on 2026-11-03 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-03 on arXiv · recorded by Signals 4 on 2026-11-04

Category: cs.CL · 自然语言处理 · first seen 2026-11-04

Abstract

Long-horizon decision-making under sustained competition requires large language models (LLMs) to balance immediate investment with future resource needs. However, local success in multi-step interactions can not adequately capture cumulative decision effects or demonstrate long-term goal achievement under resource constraints and sustained competition. To address this gap, we introduce GM Bench,

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#5 most recent of 628 cs.CL papers we have recorded · ↑ newer: When Self-Verification Hurts: Calibrated Answer Repair for Financial Q · ↓ older: The Right Source Is Not Enough: Understanding History Interference in
Cite this page: GM League Arena: An Interactive Long-Horizon Task Benchmark Based on Professional Sports League Management: the #5 most recent of 628 cs.CL papers we have recorded (as of 2026-11-03). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/gm-league-arena-an-interactive-long-horizon-task-benchmark-based-on-professional.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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