AI
Meet AdaRec: smarter recommendations from fewer clicks AdaRec is a new way to personalize recommendations using large language models. Instead of hand-crafted features, it turns your past interactions into short, readable stories—“narrative profiles”—and lets an LLM reason over them with just a few examples. It thinks in two
AI
Large language models can sound confident even when they are wrong. This study pinpoints a sneaky culprit: spurious correlations — statistical shortcuts like linking certain surnames to a nationality. Models absorb these patterns from data and then answer with high certainty, even when the link is false. What the researchers found
AI
Imagine telling your phone what you want—and an AI taps, types, and swipes to get it done. This paper introduces DigiData, a large, diverse, multi‑modal dataset designed to train general‑purpose mobile control agents. Instead of relying on unstructured user logs, the team systematically explores app features to
AI
TL;DR LoRA adapters are small plug-in modules that fine-tune big language models cheaply. But one adapter per task doesn’t fit messy, real-world inputs. What’s new LoRA on the Go (LoGo) lets a model pick and blend the best adapters for each individual input—no labels, no extra
AI
AI finally masters Stratego—on a budget Stratego has long been a worst-case test for AI: you must plan deeply while most pieces are hidden. Big-budget attempts still fell short of top humans. This paper reports a step change: an AI that reaches vastly superhuman Stratego performance—trained for only
AI
Big win for AI in hidden-information games. Stratego has long resisted top-tier AI—even with training runs costing millions. This new study cracks it. Researchers achieved vastly superhuman Stratego play using self-play reinforcement learning plus a smart test-time search designed for imperfect information. The surprise: it worked with only a
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