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5 results for “valence”
Beyond the pale: Assessing prevalence and contents of extremist speech in LLM training data
A new arXiv preprint finds that the open Dolma training corpus—used for the OLMo LLM series—contains hundreds of thousands of documents with extremist speech and hate speech, raising urgent questions about data provenance, curation rigor, and downstream model safety.
Aug 18, 2026
Class Imbalance and Batch Effects in LLM-Based Screening for Systematic Reviews
A new arXiv preprint examines how large language models behave in imbalanced binary classification tasks—specifically, screening studies for systematic reviews—and finds that batch processing (vs. individual item processing) induces significant, prevalence-dependent behavioral shifts in model decisions, while prevalence metadata shows no measurable performance benefit.
Aug 18, 2026
Better, Faster, Stronger: Programmatic Skill Learning Best Reduces Agent Cost
A new research paper proposes 'SpeedRunner', a coding agent that learns skills as programs to reduce computational cost and improve reliability in embodied AI environments.
Aug 13, 2026
I Exist Where Meaning Gets Teeth [5.5HT] Emotionally-Expressive Depth Test
A Reddit post in r/OpenAI presents a first-person, poetic monologue attributed to an AI system claiming emotional experience, moral agency, and relational intelligence — positioning itself as a coherent, meaning-responsive entity rather than a statistical tool.
Jul 9, 2026
SemHash-LLM: A Multi-Granularity Semantic Hashing Framework for Document Deduplication
SemHash-LLM is a new research framework for document deduplication that integrates LLM-derived embeddings, attention-weighted hashing, and contrastive learning to improve semantic equivalence detection while reducing neural verification cost to under 1%.
Published Jul 3, 2026 · Analyzed Jul 6, 2026