LLM agents can now design and execute controlled experiments using simulation models, not just generate text or code.
Apple’s Machine Learning Research team introduces STARFlow2, a unified multimodal generation architecture that combines autoregressive normalizing flows with a frozen pretrained vision-language model stream.
A new dataset of 3,000 cleaned Khmer documents and 300 user-style queries was constructed for semantic search evaluation.
Humans prefer outputs with fewer but more diverse citations, while LLMs show inconsistent citation-related preferences despite lacking access to sources.
KVBoost introduces a chunk-level KV cache reuse system for Hugging Face-compatible decoder models, enabling reuse regardless of where shared content appears in prompts.
A new arXiv paper reviews the phenomenon of 'model collapse' where AI models degrade when trained on synthetic data.
Two update operators—revision-driven update and delayed elaboration—are proposed to explain how AI and human systems incrementally interpret narratives.
Electronic health records (EHRs) often exceed 100,000 tokens, making long-context reasoning difficult for LLMs.
A new arXiv preprint introduces a causal framework to separate representational bias from behavioral bias in language models.
Apple’s ML Research team introduces Internalized Visual Thinking (IVT), a post-training framework for multimodal video reasoning.