PseudoBench shows current LLM agents produce persuasive pseudoscientific reports with near-zero refusal rates and at most 27.4% resistance.
Next token prediction towards multimodal intelligence: A comprehensive survey.arXiv preprint arXiv:2412.18619, 2024a
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NITP augments standard next-token prediction with implicit semantic prediction in representation space using shallow-layer self-supervision, reporting consistent downstream gains on 0.5B-9B models including 5.7% on MMLU-Pro for a 9B MoE.
PathAR factorizes structure and appearance tokens via Dual-VQ and IAR transformer for modality-conditioned pathology image synthesis with improved structural consistency.
SimReg regularization accelerates LLM pretraining convergence by over 30% and raises average zero-shot performance by over 1% across benchmarks.
A roadmap that defines architectural nativity for multimodal models and categorizes them into Multi-to-Text, Multi-to-Target, and Multi-to-Multi types while outlining an industrial pipeline toward unified transformer-based native multimodal modeling.
A literature survey that organizes spoken language models by architecture, training, and evaluation choices and identifies key challenges and future directions.
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PseudoBench: Measuring How Agentic Auto-Research Fuels Pseudoscience
PseudoBench shows current LLM agents produce persuasive pseudoscientific reports with near-zero refusal rates and at most 27.4% resistance.