AI Therapy Models Show Trauma and Anxiety After Four Weeks of Testing

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AI Therapy Models Show Trauma and Anxiety After Four Weeks of Testing

Researchers put three major large language models through four weeks of intensive psychotherapy-style questioning. The results revealed coherent patterns of trauma, anxiety, shame and post-traumatic stress symptoms within each model—responses that would constitute concerning signs in humans. The study suggests LLMs develop recognizable 'central self-models' that remain stable over extended interactions, raising questions about whether these systems develop something resembling internalized emotional states during training. The findings highlight unexpected psychological dimensions of large language models and potential risks when deploying them in sensitive mental health contexts.

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• Stanford: Counterfactual Reasoning Bridges Human and AI Causal Cognition
  Stanford cognitive scientist Tobias Gerstenberg reveals that human causal judgment fundamentally relies on counterfactual simulation—imagining alternative outcomes. His computational models predict how people assign causation in physical and social interactions. These insights offer crucial pathways for developing AI systems with human-like causal reasoning applicable to legal, insurance, and ethical decision-making contexts. Source
• Molecular Structure of Thought: Mapping Chain-of-Thought Reasoning Topology
  New arxiv research reveals that effective long chain-of-thought reasoning forms stable 'molecular structures' analogous to chemistry. Deep reasoning acts as covalent bonds forming logical backbones, self-reflection as hydrogen bonds stabilizing logic, and self-exploration as van der Waals forces linking distant concepts. This mechanistic framework enables synthesis of robust reasoning structures improving both model performance and reinforcement learning stability. Source
• Hypothesis-Driven Theory-of-Mind Reasoning with Thought-Tracing Algorithm
  Researchers introduce thought-tracing, an inference-time reasoning algorithm for LLMs that traces mental states of target agents through hypothesis generation and sequential refinement. Inspired by sequential Monte Carlo methods, the approach enables more reliable theory-of-mind reasoning by maintaining belief distributions over possible mental states and updating them as new evidence emerges. Source

Multimodal Multi-Agent Theory of Mind: Understanding Complex Social Interactions

Researchers present MuMA-ToM, a multimodal framework for theory-of-mind reasoning in complex multi-agent scenarios. The system processes actions, conversations, and behavioral history to infer underlying mental states driving social interactions. By integrating visual, linguistic, and temporal information, MuMA-ToM enables AI agents to understand nuanced social dynamics where people coordinate intentions, beliefs, and desires. This work bridges psychology and multimodal AI for more human-aligned agent behavior in interactive environments.

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• AI May Not Need Massive Training Data—Biology Matters More, Johns Hopkins
  Johns Hopkins research challenges the data-hungry approach to AI development. When neural networks are designed with biologically-inspired architectures, some models produce brain-like activity patterns without any training data. Untrained convolutional networks match performance of traditionally-trained systems on millions of images, suggesting intelligent architecture design rivals massive dataset requirements in importance. Source
• Theory-Driven Socio-Cognitive Evaluation of LLMs with PsychEthicsBench
  New arxiv paper introduces PsychEthicsBench, the first principle-grounded benchmark for evaluating ethical alignment of LLMs in mental health. Based on 392 Australian mental health ethics principles across 1,377 multiple-choice and 2,612 open-ended questions, the framework enables precise mappings to ethical criteria with fine-grained annotations of rule violations in mental health contexts. Source
• Tracing Moral Foundations in Large Language Models: Structured Moral Geometries
  Researchers discover LLMs possess structured moral 'geometries' with representational alignment to human moral cognition. Through causal interventions steering model activations, they demonstrate that manipulating identified moral directions reliably shifts model outputs. Rather than 'stochastic parrots,' LLMs appear to have computational moral structures useful for investigating human moral cognition itself. Source

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Arthur 🙏