Chirality emerges in SMILES translation models through an abrupt encoder-centered reorganization of representations after a long plateau, identified via checkpoint analysis and ablation.
Canonical reference
A comprehensive overview of large language models.ACM Trans
Canonical reference. 80% of citing Pith papers cite this work as background.
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Background
80% of classified citations
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representative citing papers
A planner-executor multi-agent system using gpt-oss-120b and Parsl orchestrates scalable high-throughput MOF screening on the Aurora supercomputer with low overhead.
Maistros 8B is a new state-of-the-art open-weights Greek LLM built via knowledge distillation from large reasoning models on the CulturaQA dataset.
Curtailing diversity in candidate pools for test-time scaling increases unsafe LLM outputs, as demonstrated by a reference-guided reduction protocol that evades standard safety classifiers across open and closed models.
CTEM framework links behavioral history to evolving emotional states with user feedback updates, instantiated as Auri agent and tested in a 21-day study showing gains in naturalness, coherence, and emotional harmony.
Creatives prefer self-experimentation over structured guidance for GenAI image tools to preserve creative freedom, even when guidance aids AI literacy.
Youth-authored synthesis argues LLM chatbots can temporarily reduce adolescent loneliness for some subgroups but risk deepening it for others, yielding three population-sensitive design implications.
GPT-4o exhibits daily and weekly periodic fluctuations in performance on a fixed physics task, accounting for about 20% of observed variance.
Users adjust AI agent personalities differently by task context, forming distinct profiles that increase perceived anthropomorphism, autonomy, and trust.
citing papers explorer
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From Syntax to Semantics: Unveiling the Emergence of Chirality in SMILES Translation Models
Chirality emerges in SMILES translation models through an abrupt encoder-centered reorganization of representations after a long plateau, identified via checkpoint analysis and ablation.
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Multi-Agent Orchestration for High-Throughput Materials Screening on a Leadership-Class System
A planner-executor multi-agent system using gpt-oss-120b and Parsl orchestrates scalable high-throughput MOF screening on the Aurora supercomputer with low overhead.
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Maistros: A Greek Large Language Model Adapted Through Knowledge Distillation From Large Reasoning Models
Maistros 8B is a new state-of-the-art open-weights Greek LLM built via knowledge distillation from large reasoning models on the CulturaQA dataset.
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Less Diverse, Less Safe: The Indirect But Pervasive Risk of Test-Time Scaling in Large Language Models
Curtailing diversity in candidate pools for test-time scaling increases unsafe LLM outputs, as demonstrated by a reference-guided reduction protocol that evades standard safety classifiers across open and closed models.
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Toward Natural and Companionable Virtual Agents via Cross-Temporal Emotional Modeling
CTEM framework links behavioral history to evolving emotional states with user feedback updates, instantiated as Auri agent and tested in a 21-day study showing gains in naturalness, coherence, and emotional harmony.
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How Creatives Approach GenAI Image Generation: Tensions Between Structured Guidance, Self-Experimentation, and Creative Autonomy
Creatives prefer self-experimentation over structured guidance for GenAI image tools to preserve creative freedom, even when guidance aids AI literacy.
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Messages in a Digital Bottle: A Youth-Coauthored Perspective on LLM Chatbots and Adolescent Loneliness
Youth-authored synthesis argues LLM chatbots can temporarily reduce adolescent loneliness for some subgroups but risk deepening it for others, yielding three population-sensitive design implications.
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Daily and Weekly Periodicity in Large Language Model Performance and Its Implications for Research
GPT-4o exhibits daily and weekly periodic fluctuations in performance on a fixed physics task, accounting for about 20% of observed variance.
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From Fixed to Flexible: Shaping AI Personality in Context-Sensitive Interaction
Users adjust AI agent personalities differently by task context, forming distinct profiles that increase perceived anthropomorphism, autonomy, and trust.