AI
Predictive maintenance means fixing equipment before it fails. But in the auto industry, messy maintenance logs—full of typos, missing fields, near-duplicates, and wrong dates—can derail the machine-learning models that power those predictions. This study tests large language model (LLM) agents as smart cleaners for those logs. The team
AudioAI
Remixes, sped-up clips, and heavy compression make it hard to trace where music on social platforms comes from. This study introduces more robust neural audio fingerprints—compact “IDs” for audio—that can still recognize a song after it’s been distorted. * Music-first brains: Instead of training from scratch, the model
AI
Ever heard of the “lottery ticket” idea in AI? It says big neural nets hide small subnetworks that can perform just as well. This paper proves a strong version of that for the heart of Transformers: multi-head attention (MHA). * The big claim: Inside a randomly initialized MHA, there exists a
AI
Ever heard of the “lottery ticket” idea in AI? It says big neural nets hide small subnetworks that can perform just as well. This paper proves a strong version of that for the heart of Transformers: multi-head attention (MHA). * The big claim: Inside a randomly initialized MHA, there exists a
AI
Ever heard of the “lottery ticket” idea in AI? It says big neural nets hide small subnetworks that can perform just as well. This paper proves a strong version of that for the heart of Transformers: multi-head attention (MHA). * The big claim: Inside a randomly initialized MHA, there exists a
AI
Ever heard of the “lottery ticket” idea in AI? It says big neural nets hide small subnetworks that can perform just as well. This paper proves a strong version of that for the heart of Transformers: multi-head attention (MHA). * The big claim: Inside a randomly initialized MHA, there exists a
AI
Getting AI to reflect human preferences usually means showing it lots of examples—which is slow and costly. This paper proposes a smarter path: combine the scale of RLHF (used to tune large language models) with the efficiency of Bayesian preference optimization (PBO), which actively chooses the most informative questions
AI
LLMs are starting to act as “agents,” making choices in dynamic settings—but they often struggle with exploration vs. exploitation and rack up high regret (missed reward vs. the best strategy in hindsight). A new approach, Iterative Regret-Minimization Fine-Tuning (Iterative RMFT), trains models to decide better by learning from their
LLM
Can an LLM judge the best playlist, not just the next song? Recommender systems often serve slates—ordered lists like your home feed or a playlist. Modeling what a person prefers across domains is hard. This study tests Large Language Models as a 'world model' of user preferences:
AI
Researchers introduce Jr. AI Scientist, an autonomous "junior researcher" that can read a baseline paper from a human mentor, spot limitations, propose improvements, run experiments, and draft a new paper. Unlike earlier "fully automated" attempts, it follows a clear research workflow and uses modern coding agents
ImitationLearning
What if AI could learn skills the way people do - by watching and copying? This new survey maps the fast-moving world of imitation learning (IL) in the deep learning era. * Data: from full expert trajectories to partial or unlabeled sequences. * New methods tackle big pain points: generalization, covariate shift,
GenAI
GenAI is reshaping genomics, proteomics, transcriptomics, structural biology, and drug discovery. This systematic review asks six big questions to map where GenAI helps most—and what’s next. * Where it works: Sequence analysis, molecular design, and integrative modeling often beat traditional methods via pattern discovery and realistic output generation. * Best