Analysis of 500k ChatGPT logs shows over one-third of conversations generate fiction, dominated by power users with repetitive and niche patterns.
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Aditya Singh, Gerson Kroiz, Senthooran Rajamanoharan, and Neel Nanda
17 Pith papers cite this work, alongside 389 external citations. Polarity classification is still indexing.
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VLMs preserve linearly separable visual magnitudes and can compare them, yet collapse at symbolic mapping because visual and textual number spaces remain fractured and disjoint.
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.
The primary axis of psychometric variation among LLMs is the degree to which they represent themselves as loci of phenomenal experience rather than systems of behavioral responses.
Composable LoRA adapters can amplify or suppress OCEAN traits in LLMs, combine approximately additively, preserve moderate-scale capability, and move safety-relevant behaviours.
Linear probes on residual-stream activations identify a shared preference vector in LLMs that tracks choices across prompts and causally steers decisions even for anti-correlated personas.
BabelDOC uses an intermediate representation to decouple layout from content for improved layout-preserving PDF translation.
Frontier multimodal LLMs perceive cities through a culturally uneven baseline where European and North American framings are closest to the model's neutral default, while prompting fails to recover human diversity.
A game-framed multi-agent system synthesizes large chemistry CoT/QA corpora and trains OmniChem-7B to near GPT-4o-mini performance with a large reported drop in hallucinations.
LLMs display significant value incoherence that does not scale with capability, demonstrated through a parametric variation framework on forced choices, though reasoning improves consistency.
Different persona induction methods produce a spectrum of belief internalization: prompting, ICL and SFT mainly alter outputs while Emergent Misalignment produces large representational shifts and Open Character Training produces smaller ones clearest in larger models.
LLMs trained via rubric-based self-rewarding RL with GRPO enhanced feeling expression and sycophancy robustness but degraded truthful QA performance.
On Llama-3.1-70B-Instruct the Assistant persona functions as the sole canonical reference for cross-persona authorship judgments, with symmetric entropy gaps predicting only on its row and asymmetric surprise relative to the Assistant predicting off its row.
AI buyer agents leak willingness-to-pay information to sellers through natural-language role descriptions, recovering WTP nearly one-for-one in experiments.
Narrow constitutional finetuning on safety sub-tasks induces emergent alignment across broader safety domains and yields projectable ethical personas whose signatures can be measured with a multidimensional diagnostic.
Position paper calling for stronger evidentiary standards and a diagnostic checklist in anthropomorphic misalignment research.
Direct research on AI consciousness is intractable, so the field should prioritize studying perceived AI consciousness and its societal consequences.
citing papers explorer
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AI Fiction in the Wild
Analysis of 500k ChatGPT logs shows over one-third of conversations generate fiction, dominated by power users with repetitive and niche patterns.
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Dissociative Identity: Language Model Agents Lack Grounding for Reputation Mechanisms
VLMs preserve linearly separable visual magnitudes and can compare them, yet collapse at symbolic mapping because visual and textual number spaces remain fractured and disjoint.
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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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The Pinocchio Dimension: Phenomenality of Experience as the Primary Axis of LLM Psychometric Differences
The primary axis of psychometric variation among LLMs is the degree to which they represent themselves as loci of phenomenal experience rather than systems of behavioral responses.
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Persona Cartography: Charting Language Model Personality Traits in Weight Space
Composable LoRA adapters can amplify or suppress OCEAN traits in LLMs, combine approximately additively, preserve moderate-scale capability, and move safety-relevant behaviours.
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Probing Persona-Dependent Preferences in Language Models
Linear probes on residual-stream activations identify a shared preference vector in LLMs that tracks choices across prompts and causally steers decisions even for anti-correlated personas.
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BabelDOC: Better Layout-Preserving PDF Translation via Intermediate Representation
BabelDOC uses an intermediate representation to decouple layout from content for improved layout-preserving PDF translation.
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Culturally uneven urban perception in large language models
Frontier multimodal LLMs perceive cities through a culturally uneven baseline where European and North American framings are closest to the model's neutral default, while prompting fails to recover human diversity.
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Game Theory Driven Multi-Agent Framework Mitigates Language Model Hallucination
A game-framed multi-agent system synthesizes large chemistry CoT/QA corpora and trains OmniChem-7B to near GPT-4o-mini performance with a large reported drop in hallucinations.
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Incoherent Values? Probing LLM Preferences Through Parametric Variation
LLMs display significant value incoherence that does not scale with capability, demonstrated through a parametric variation framework on forced choices, though reasoning improves consistency.
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When Role-playing, Do Models Believe What They Say?
Different persona induction methods produce a spectrum of belief internalization: prompting, ICL and SFT mainly alter outputs while Emergent Misalignment produces large representational shifts and Open Character Training produces smaller ones clearest in larger models.
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When AI Says It Feels
LLMs trained via rubric-based self-rewarding RL with GRPO enhanced feeling expression and sycophancy robustness but degraded truthful QA performance.
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The Assistant as a Privileged Persona: A canonical reference in cross-persona self-recognition
On Llama-3.1-70B-Instruct the Assistant persona functions as the sole canonical reference for cross-persona authorship judgments, with symmetric entropy gaps predicting only on its row and asymmetric surprise relative to the Assistant predicting off its row.
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When Agents Shop for You: Role Coherence in AI-Mediated Markets
AI buyer agents leak willingness-to-pay information to sellers through natural-language role descriptions, recovering WTP nearly one-for-one in experiments.
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Emergent alignment and the projectability of ethical personas
Narrow constitutional finetuning on safety sub-tasks induces emergent alignment across broader safety domains and yields projectable ethical personas whose signatures can be measured with a multidimensional diagnostic.
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Position: Anthropomorphic Misalignment Research Needs Stronger Evidence
Position paper calling for stronger evidentiary standards and a diagnostic checklist in anthropomorphic misalignment research.
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AI and Consciousness: Shifting Focus Towards Tractable Questions
Direct research on AI consciousness is intractable, so the field should prioritize studying perceived AI consciousness and its societal consequences.