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20242026
most citedLLMs Can Get "Brain Rot": A Pilot Study on Twitter/X

1 citations · 1 across the 10 of their papers we have counts for

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cs.AI2026

Poisoning Agentic Alpha: Adversarial Vulnerabilities Across Roles and Architectures in Multi-Agent Trading Systems

CheolWon Na, Hao Ni, Lukasz Szpruch +7

LLM-based multi-agent trading systems, in which specialized agents collaborate through structured communication to produce trading decisions, are moving rapidly from research proto…

cs.CL2026

Can Induced Emotion Bias LLM Behaviors in Sequential Decision Making?

Minh Khoi Ho, Zihao Zhu, Runchuan Zhu +4

As Large Language Models (LLMs) are increasingly deployed as autonomous agents in high-stakes domains, understanding contextual factors that may modulate their decision-making beco…

cs.AI2026

When Summaries Distort Decisions: Information Fidelity in LLM-Compressed Financial Analysis

Hoyoung Lee, Suhwan Park, Seunghan Lee +15

Financial decision-makers face more information than they can directly inspect, making context compression necessary. Yet when large language models (LLMs) compress financial sourc…

cs.CL2026

No Hidden Prompts Needed! You Can Game AI Peer Review with Presentation-Only Revisions

Xu Yang, Zhizhou Sha, Junbo Li +10

As AI-generated reviews move from experimental tools into peer-review infrastructure, most robustness concerns have focused on explicit attacks such as hidden instructions and prom…

cs.CL2026

Fund2Persona: A Framework for Building and Refining Financial Advisor Personas from Fund Disclosure Data

Suhwan Park, Hoyoung Lee, Zhangyang Wang +5

Demand for personalized financial advice is growing, yet current LLM-based advisors often fail to provide consistent and specialized guidance. Simple persona prompts rarely specify…