OperationsResearch
Why it matters Utility crews rarely know exactly how long each job will take. This paper blends machine learning and risk-aware optimization to plan daily routes that stay on time—even when jobs run long. Using eight years of gas meter maintenance data, the author trains a gradient-boosted model (XGBoost)
BCI
EEG-based brain–computer interfaces often stumble in the real world: every person’s brain signals look different, and conditions change from moment to moment. A new approach called Backpropagation-Free Transformations (BFT) helps BCIs adapt on the fly—without costly re-training, extra data, or exposing private signals. How it works * For
AI
TL;DR Long-horizon LLM agents fail when one small error snowballs. Task-Decoupled Planning (TDP) breaks big jobs into a DAG of sub-goals, so planning and fixes stay local—making agents both sturdier and cheaper. How it works: * Supervisor decomposes the task into a directed acyclic graph of sub-goals. * Planner and
AI
Why chatbots forget—and how ES‑Mem fixes it Long chats often make assistants lose the thread. ES‑Mem, a new memory system, keeps conversations coherent by organizing them like humans do. * Breaks talks into “events.” Instead of saving every line, ES‑Mem detects natural boundaries—topics, goals, shifts—so
Physics
Ever wondered how a simple physics idea can power memory in neural networks? This paper revisits the classic Hopfield model—born from spin-glass physics—and shows why it’s a perfect bridge from undergraduate physics to modern AI. * Clear tour: A friendly intro using familiar physics tools (energy, dynamics, stability)
AI
Can AI spot nonsense in pictures? Humans can tell that “A sandwich cuts a chef” is grammatically fine but semantically absurd. Many vision-language models (VLMs) can’t. UAIT (Uncommon-sense Action Image-Text) is a new benchmark that stress-tests whether VLMs truly understand who is doing what to whom—and what’s
AI
AI "digital twins" to watch over ICU patients Meet DT-ICU: a system that builds a live, data-driven "digital twin" of each person in intensive care. As new vitals, labs, and other observations arrive, it updates risk estimates — helping clinicians spot trouble early and track recovery. * Fuses
AI
System logs are messy and massive. This study tests whether small language models (SLMs) can reliably read them fast. Instead of treating "severity" labels as the end goal, the authors use severity classification as a probe of real runtime log understanding. * Best performer: Qwen3-4B hit 95.64% accuracy
AI
What if AI could write many words at once, not just one after another? Diffusion LLMs can—yet they often trade speed for accuracy. Meet d3LLM, a new approach that keeps both. * Smarter training: Pseudo-trajectory distillation teaches the model which tokens are safe to finalize early, enabling confident parallel decoding.
Two Paths to Spot Truth in AI Answers Large language models can sound confident while being wrong. This study shows their internal activity holds two distinct "truth signals." * Question-anchored: The model checks the answer against information it drew from the question. * Answer-anchored: The model looks for self-contained evidence
Imagine joining a meeting as a lifelike 3D hologram—no studio rig, just your phone or laptop camera. Mon3tr makes this possible. How it works: Mon3tr builds your personal 3D avatar once (offline) from multi-view photos. During calls, a single RGB camera tracks your body and face to drive that
AI
Reading Emotions from Text, Voice, and Video—Together Meet EGMF, a new AI that reads emotions by combining text, audio, and visuals. Instead of guessing from one signal, it fuses them all and adapts to context—working for both clear-cut emotions (like happy/sad) and nuanced sentiment on a scale.