I'd like to share a new contribution to multilingual ML research: WebFAQ introduces a collection of 2.7 million natural question-answer pairs from real website FAQs across 8 languages (English, German, French, Spanish, Italian, Portuguese, Dutch, a…

I've been examining this new approach to generating seamless looping videos from text prompts called Mobius. The key technical innovation here is a latent shift-based framework that ensures smooth transitions between the end and beginning frames of…

This paper introduces a test-time optimization method called R2-T2 that improves routing in mixture-of-experts (MoE) models without requiring retraining. The core idea is using gradient descent during inference to optimize how inputs get routed to diff…

This paper introduces Chain-of-Draft (CoD), a novel prompting method that improves LLM reasoning efficiency by iteratively refining responses through multiple drafts rather than generating complete answers in one go. The key insight is that LLMs can bu…

The researchers propose integrating Visual Perception Tokens (VPT) into multimodal language models to improve their visual understanding capabilities. The key idea is decomposing visual information into discrete tokens that can be processed alongside t…

This work introduces an LLM-based system for evaluating materials synthesis feasibility, trained on a new large-scale dataset of 2.1M synthesis records. The key innovation is using the LLM as an expert-level judge to filter proposed materials based on …

A new benchmark dataset called Chinese Temporal Mapping (CTM) tests LLMs on temporal reasoning using Chinese historical knowledge. The dataset contains 2,306 multiple-choice questions spanning major Chinese dynasties, evaluating both pure temporal logi…

This work introduces a novel methodology for multimodal foundation models to self-synthesize training data that enhances both their cognitive capabilities and explainability. The core technique involves generating synthetic data through recursive self-…

This approach introduces a novel method for learning graph structures across distributed data sources while preserving privacy. The core idea is using an auto-weighted multiple graph learning framework that allows clients to maintain local graph repres…

The key contribution here is a rigorous real-world evaluation of model editing methods, specifically introducing QAEdit – a new benchmark that tests editing effectiveness without the artificial advantages of teacher forcing during evaluation. Main tech…