# Text Classification

A federated LLM server now treats each agent's LoRA update as a node in a hypergraph and passes messages along hyperedges to spot and suppress poisoned knowledge, beating Multi-Krum, FoolsGold and FLTrust on AG News and Yahoo! Answers.

A federated LLM server now treats each agent's LoRA update as a node in a hypergraph and passes messages along hyperedges to spot and suppress poisoned knowledge, beating Multi-Krum, FoolsGold and FLTrust on AG News and Yahoo! Answers.

A sequence-modeling framework written as a nonlinear time-delay system lets the current input's semantic similarity to past inputs set how far back the model looks, reaching 95.35% accuracy on IMDB sentiment classification with 2.6M parameters, above the 89.90% of LSTM and 83.00% of Mamba in the same table.

A sequence-modeling framework written as a nonlinear time-delay system lets the current input's semantic similarity to past inputs set how far back the model looks, reaching 95.35% accuracy on IMDB sentiment classification with 2.6M parameters, above the 89.90% of LSTM and 83.00% of Mamba in the same table.

The authors propose an evolutionary framework in which an LLM iteratively generates and refines natural-language binary features (rubrics), an LLM scores each sample against them, and the resulting vectors train a transparent classifier such as logistic regression; across AG News, GoEmotions, and HELOC, evolved features beat single-shot rubrics by +2.9 pp on average and outperform zero-shot LLM classification on two of three tasks, with the zero-shot LLM reaching only 50.7% on HELOC (near chance with a strong single-class bias) while evolved features reach 69.0% with balanced, auditable predictions.

The authors propose an evolutionary framework in which an LLM iteratively generates and refines natural-language binary features (rubrics), an LLM scores each sample against them, and the resulting vectors train a transparent classifier such as logistic regression; across AG News, GoEmotions, and HELOC, evolved features beat single-shot rubrics by +2.9 pp on average and outperform zero-shot LLM classification on two of three tasks, with the zero-shot LLM reaching only 50.7% on HELOC (near chance with a strong single-class bias) while evolved features reach 69.0% with balanced, auditable predictions.