The results demonstrate that TransitNet provides an accurate, scalable, and computationally efficient framework for low-SNR transit blind searches in the tested regime and motivate its extension to longer-period Earth-size planet searches.
Xing-Chen Yan, Jian Ge, Qing-Tian Liu et al.· arXiv.org· 0 citations
It is argued that a robust proof autoformalizer must remain faithful even for informal proofs that diverge from these idealized ones, and the first study on the robustness of proof autoformalization models is presented.
Adapting Large Language Models (LLMs) to specific tasks usually involves fine-tuning: Low-Rank Adaptation (LoRA) edits the weights, while soft prompting prepends trainable virtual tokens. Both change how an already-optimized model is served, so serving many task-specific adapters at high throughput requires adapter-awa...
Michal Chudoba, Sergey Alyaev, Petra Galuscakova et al.· 0 citations
Expressive continuous control policies, such as diffusion and flow models, form the backbone of recent advances in scaling imitation learning for simulated and real robot control. While they are known to scale stably in the supervised imitation learning setting, incorporating them into reinforcement learning (RL) pipel...
Zhiyuan Zhou, Andy Peng, Charles Xu et al.· 0 citations
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This work shows that intermediate states surface salient entities early in the denoising trajectory, making them a useful lookahead signal for retrieval-augmented generation (RAG), and demonstrates that grounding generation in retrieved evidence sharply reduces inter-token dependence, making RAG particularly well suite...
Paul J. Jünger, Justin Lovelace, Lin-Xi Zhao et al.· arXiv.org· 2 citations· ⚡1
Real-world data spans tables, documents, and semi-structured files with implicit semantics. Querying this data requires integrating evidence across inconsistent schemas and formats, yet existing approaches either demand costly manual engineering or bypass structure entirely. We present a system that automatically disco...
Padmaja Jonnalagedda, Yuguang Yao, Xiang Gao et al.· 0 citations
Preference modeling aims to capture heterogeneous human values and increasingly relies on learning latent components to represent this variation without fine-grained supervision. What these components actually represent, however, is rarely examined. We audit Mixture-of-Experts (MoE) reward models along three rungs---se...
Yifan Wang, Jinyi Mu, Mayank Jobanputra et al.· 0 citations
This survey identifies critical research gaps, including interpretable routing, expert communication, modality integration, modality integration, and lifelong multimodal learning, and positions this survey as a foundation for future research toward interpretable and sustainable multimodal Mixture-of-Experts system.
L. Zheng, Wenhao Liang, W. Zhang et al.· 0 citations
Many novel unsupervised feature selection methods are proposed each year, yet their empirical evaluation is limited to supervised and unsupervised evaluation metrics computed on selected datasets, along with comparisons to existing methods. However, in the absence of an established evaluation baseline, it is difficult...
Muhammad Rajabinasab, Michael E. Houle, Oussama Chelly et al.· 0 citations
The results show that, in a two-state recurrent setting, a clear state-identity signal can induce stable, related functional roles inside a shared-parameter recurrent Transformer.
Jucheng Shen, Barbara Su, Anastasios Kyrillidis· arXiv.org· 1 citation
OP-Mix (On-Policy Mix), a data mixing algorithm that operates across pretraining, midtraining, and instruction tuning, is introduced, suggesting a different view of language model training: not a sequence of distinct phases, but a single continuous process of learning from data.
Michael Y Hu, Apurva Gandhi, Kyunghyun Cho et al.· arXiv.org· 0 citations
Out-of-distribution (OOD) detection is a critical component for ensuring the reliability of deep neural networks in safety-critical applications. In this work, we present a key empirical observation: for in-distribution (ID) samples, class-wise Mahalanobis distances exhibit a pronounced sharp minimum structure, where t...
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
MIT News · Artificial Intelligence· news.mit.eduOct 28, 2026
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
MIT News · Artificial Intelligence· news.mit.eduOct 27, 2026
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
