Generative Caching for Structurally Similar Prompts and Responses

Cornell Tech has secured a $7 million investment from NASA and Schmidt Sciences aimed at modernizing arXiv, a preprint repository for scientific papers. This funding will facilitate the migration of arXiv to cloud infrastructure, upgrade its outdated codebase, and develop new tools to enhance the discovery of relevant preprints for researchers.

A new framework called Mujica-MyGo has been proposed to enhance multi-agent Retrieval-Augmented Generation (RAG) systems, addressing the challenges of long context lengths in large language models (LLMs). This framework aims to improve multi-turn reasoning by utilizing a divide-and-conquer approach, which helps manage the complexity of interactions with search engines during complex reasoning tasks.

A recent position paper emphasizes the need for literary scholars to engage with research on large language model (LLM) interpretability, suggesting that the red team could serve as a platform for this ideological struggle. The paper argues that current interpretability standards are insufficient for evaluating LLMs.

A new framework named Reading Comprehension Exercise Generation (RCEG) has been proposed to leverage large language models (LLMs) for automatically generating personalized English reading comprehension exercises. This framework utilizes fine-tuned LLMs to create content candidates, which are then evaluated by a discriminator to select the highest quality output, significantly enhancing the educational content generation process.

Recent advancements in text-to-speech (TTS) technology have led to the development of a new model called Word-level TTS Alignment by ASR-driven Attentive Reward (W3AR), which utilizes fine-grained reward signals from automatic speech recognition (ASR) systems to enhance TTS synthesis. This model addresses the limitations of traditional evaluation methods that often overlook specific problematic words in utterances.

Cross-lingual information retrieval (CLIR) is being systematically evaluated through various approaches, including document translation and multilingual dense retrieval with pretrained encoders. This research highlights the challenges posed by disparities in resources and weak semantic alignment in embedding models, revealing that dense retrieval models specifically trained for CLIR outperform traditional methods.

A recent study evaluated the mathematical reasoning capabilities of Large Language Models (LLMs) using the 2026 Korean College Scholastic Ability Test (CSAT) Mathematics section, ensuring a contamination-free evaluation environment. The research involved digitizing all 46 questions immediately after the exam's public release, allowing for a rigorous assessment of 24 state-of-the-art LLMs across various input modalities and languages.

Recent research has introduced the concept of representational stability in large language models (LLMs), focusing on how these models encode distinctions between true, false, and neither-true-nor-false content. The study assesses this stability by training a linear probe on LLM activations to differentiate true from not-true statements and measuring shifts in decision boundaries under label changes.