An AI-agent social platform generated mostly neutral content whose use in fine-tuning reduced model truthfulness comparably to human Reddit data, suggesting limited unique harm but flagging tail risks like secret leaks.
arXiv:2305.14688 (2023)
9 Pith papers cite this work, alongside 53 external citations. Polarity classification is still indexing.
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Meta Agent Search uses a meta-agent to iteratively program novel agentic systems in code, producing agents that outperform state-of-the-art hand-designed ones across coding, science, and math while transferring across domains and models.
A small set of sparse autoencoder features in LLMs drives shifts between generous and selfish allocations in dictator games, with causal patching and steering confirming their role and generalization to other social games.
RankFlow deploys four LLM roles in sequence to rewrite queries, generate pseudo-answers, summarize passages, and rerank candidates, outperforming prior methods on TREC-DL, BEIR, and NovelEval.
SLIP combines a soft label mechanism with key-extraction-guided CoT to reduce instruction backdoor attack success rate to 25.13% and raise clean accuracy to 87.15% in LLM agents.
Structured integration of LLMs in astronomy education, including a domain-specific tutor and documentation requirements, leads to improved AI literacy and reduced student reliance on AI over the semester.
Humans exhibit greater source-label bias in logical fallacy judgments than LLMs, which maintain more consistent evaluations regardless of source cues.
Frontier LLMs from mid-2024 to late-2025 reach near-perfect scores on text-based physics problems and show improved human alignment in grading, but assigning partial credit for flawed reasoning remains difficult.
Impersonating complex misaligned personas via biographies and role-play bypasses safety in ChatGPT, Gemini, and Deepseek, succeeding on 38-40 out of 40 illicit questions across tested models.
citing papers explorer
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The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment
An AI-agent social platform generated mostly neutral content whose use in fine-tuning reduced model truthfulness comparably to human Reddit data, suggesting limited unique harm but flagging tail risks like secret leaks.
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Automated Design of Agentic Systems
Meta Agent Search uses a meta-agent to iteratively program novel agentic systems in code, producing agents that outperform state-of-the-art hand-designed ones across coding, science, and math while transferring across domains and models.
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Understanding the Mechanism of Altruism in Large Language Models
A small set of sparse autoencoder features in LLMs drives shifts between generous and selfish allocations in dictator games, with causal patching and steering confirming their role and generalization to other social games.
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RankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large Language Models
RankFlow deploys four LLM roles in sequence to rewrite queries, generate pseudo-answers, summarize passages, and rerank candidates, outperforming prior methods on TREC-DL, BEIR, and NovelEval.
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SLIP: Soft Label Mechanism and Key-Extraction-Guided CoT-based Defense Against Instruction Backdoor in APIs
SLIP combines a soft label mechanism with key-extraction-guided CoT to reduce instruction backdoor attack success rate to 25.13% and raise clean accuracy to 87.15% in LLM agents.
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Teaching Astronomy with Large Language Models
Structured integration of LLMs in astronomy education, including a domain-specific tutor and documentation requirements, leads to improved AI literacy and reduced student reliance on AI over the semester.
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Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs
Humans exhibit greater source-label bias in logical fallacy judgments than LLMs, which maintain more consistent evaluations regardless of source cues.
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Using Large Language Models in Physics Education
Frontier LLMs from mid-2024 to late-2025 reach near-perfect scores on text-based physics problems and show improved human alignment in grading, but assigning partial credit for flawed reasoning remains difficult.
-
Dr. Jekyll and Mr. Hyde: Two Faces of LLMs
Impersonating complex misaligned personas via biographies and role-play bypasses safety in ChatGPT, Gemini, and Deepseek, succeeding on 38-40 out of 40 illicit questions across tested models.