Large language model (LLM) news — GPT-4, Claude, Gemini, Llama, Mistral and the latest research on training, fine-tuning, RLHF, and deployment of LLMs.
Researchers propose ViPSy, a framework for constructing preference data that reduces hallucinations in Vision-Language Models (VLMs) by leveraging visual cues from semantically aligned image variants. The method achieves…
Researchers at the University of Oxford and other institutions found that large language models (LLMs) develop false-belief task (FBT) performance late in pretraining, dependent on model size and training volume, and imp…
Researchers introduced a French OSCE dialogue dataset with 240 student-patient interactions and developed a controllable LLM-based pipeline to generate synthetic OSCE dialogues for clinical training. The system includes …
Researchers introduced turn-averaged sparse autoencoders (SAEs) that represent entire conversational turns with fixed feature sets, improving interpretability of long-context language models. The method outperforms per-t…
Researchers at arXiv show that static per-layer Fibonacci spacing in sparse attention outperforms learned dilation and fixed schedules across 21 language models, achieving better perplexity and extrapolating to four time…
Researchers propose grain calibration to validate whether large language models measure theoretical constructs correctly, not just reliably. The method decomposes constructs into clause-level components and tests each wi…
Researchers propose AnTenA, a system that uses large language models to explain hidden patterns in multi-aspect data without relying on potentially inaccurate labels or metadata. The system employs tensor decomposition a…
Researchers introduced SEATauBench, the first agent-focused evaluation framework for Southeast Asian sovereign AI, adapting TauBench to five languages. Testing across three models revealed that English agent capabilities…
Researchers introduced 5ting, a system for the SemEval-2026 Task 8 (MTRAGEval) that combines BGE-M3 dense retrieval, FAISS indexing, dual-query merged retrieval, and LLM-based reranking with role-separated generation con…
Researchers developed a structure-preserving Marathi-to-English document translation framework using a multi-stage LLM pipeline, integrating layout-aware OCR and HTML reconstruction to maintain formatting and domain term…
Researchers at arXiv found that using large language models (LLMs) like GPT-5.2 to label training data for entity matching can reduce manual labeling effort by hundreds of hours, with student models performing within two…
Researchers propose an agentic AI pipeline for appliance-level energy anomaly detection in office buildings, combining deep time-series forecasting, variational anomaly detection, and LLM-based reasoning to generate prio…
Researchers propose a gravitational interpretation of fine-tuning reversion, where large early training phases create dominant behavioral manifolds that later fine-tuning reverts toward. They identify a reversion directi…
Researchers introduced NIVA, a multimodal foundation model for Earth system intelligence, trained on large-scale simulations to learn coupled dynamics between ocean and atmosphere. The model captures key climate variabil…
Researchers propose Hierarchical Temporal Fusion (HTF), an extension of the Temporal Fusion Transformer that integrates hierarchical embeddings and a coherence-aware loss function to enforce forecast consistency across h…
Researchers developed an incremental approximate message passing (IAMP) algorithm for empirical risk minimization under a multi-index model, achieving near-optimal training and test error in high-dimensional asymptotics.…
Researchers introduced SCSuff, an information-theoretic metric to evaluate whether free-text explanations from large language models (LLMs) are sufficient to explain their outputs. Testing on LLMs showed explanations are…
A new study reveals that test-time scaling in language models, where models sample multiple answers to a question, hits a ceiling beyond which additional samples do not improve selection accuracy and can even degrade per…
Researchers introduced RSEA, a Recursive Self-Evolving Agent that improves LLM agents by rewriting natural-language artifacts without weight updates, using a held-out selection gate to prevent regression. Across four ben…
Researchers introduced a 'capability slice' unit and a closed-loop system that links evaluation failures to targeted data interventions in LLM pre-training. In two case studies, the system correctly identified a masked l…