mlsanityData to 7 Oct 2026

Framing Effects in Independent-Agent Large Language Models: A Cross-Family Behavioral Analysis

arXiv 2603.19282 ↗First posted 2 Mar 2026cs.CL cs.AIcited by 1

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

In many real-world applications, large language models (LLMs) operate as independent agents without interaction, thereby limiting coordination. In this setting, we examine how prompt framing influences decisions in a threshold voting task involving individual-group interest conflict. Two logically equivalent prompts with different framings were tested across diverse LLM families under isolated trials. Results show that prompt framing significantly influences choice distributions, often shifting preferences toward risk-averse options. Surface linguistic cues can even override logically equivalent formulations. This suggests that observed behavior reflects a tendency consistent with a preference for instrumental rather than cooperative rationality when success requires risk-bearing. The findings highlight framing effects as a significant bias source in non-interacting multi-agent LLM deployments, informing alignment and prompt design.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 12 of its 27 references that are in the map. First posted on arXiv 2 Mar 2026.

Builds on

12 of 27 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Generative Agents: Interactive Simulacra of Human Behavior7 Apr

Other references · 11

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  3. 2020Learning to summarize from human feedbackcited by 2,562
  4. 2019Fine-Tuning Language Models from Human Preferencescited by 2,124
  5. 2020Artificial Intelligence, Values and Alignmentcited by 443
  6. 2022Using cognitive psychology to understand GPT-3cited by 389
  7. 2022The Alignment Problem from a Deep Learning Perspectivecited by 246
  8. 2020Emergent Multi-Agent Communication in the Deep Learning Eracited by 168
  9. 2023A Categorical Archive of ChatGPT Failurescited by 167
  10. 2022Language Models as Agent Modelscited by 131
  11. 2023Strategic Behavior of Large Language Models: Game Structure vs. Contextual Framingcited by 49

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Do Influence Tactics Matter? Investigating Prompt Framing Effects in LLM Code Generationcited by 0

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Close in wording, no citation either way

  1. 2026When Preferences Fail to Become Incentives: A Utility-Behavior Gap in Large Language Models
  2. 2026The Internal Anatomy of Strategic Choice in Large Language Models
  3. 2026LLMs Contain Multitudes: How Deployment Context Reshapes Model-Level Preferences and Values
  4. 2025Beyond Mimicry: Preference Coherence in LLMs
  5. 2025Fragile Preferences: A Deep Dive Into Order Effects in Large Language Models
  6. 2025From Delegates to Trustees: How Optimizing for Long-Term Interests Shapes Bias and Alignment in LLM
  7. 2026Framing Matters: Addressing Framing Sensitivity in Decision-Making through Behaviorally-Grounded Value Alignment
  8. 2026What LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent Debates