Paper

Can LLMs Be CEOs? Benchmarking Strategic Resource Reallocation with Multi-Role Agent Simulation

Unreadunread

AI summary

arXiv:2606. 17459v1 Announce Type: new Abstract: Evaluating the decision-making capabilities of large language models (LLMs) is a growing research priority, yet existing benchmarks focus on isolated cognitive tasks such as reasoning, knowledge retrieval, and economic rationality in stylized settings.

Reading status

Priority

About this paper

Authors: Yuyang Dai, Xueqing Peng, Lingfei Qian, Zhuohan Xie
Released: 2026-06-18 (21d ago)
Topic: Related to current project
Relation to my work: Read next because Can LLMs Be CEOs? Benchmarking Strategic Resource Reallocation with Multi-Role Agent Simulation overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, rect, under, eval, source, line, rate, capability. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2606.17459

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Can LLMs Be CEOs? Benchmarking Strategic Resource Reallocation with Multi-Role Agent Simulation. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.