Research Feed Page 45
Search source-linked summaries of recent AI and machine-learning papers by topic, by date, or by whether they include code or a diagram.
Research results
The paper presents data-driven algorithms to determine the optimal interval for replacing machine parts when their lifetime distribution is unknown.
The paper presents a sentiment classifier that integrates daily Bitcoin on-chain metrics with Twitter sentiment analysis to identify market phases.
WanSong v1.0 is a music generation model that uses a hybrid transformer architecture and a dual-stem output strategy to improve the fidelity and separation of vocals and background music.
The paper introduces a probing technique that detects when LLM-generated instructions pose physical risks despite appearing linguistically safe.
This paper presents an in-place recipe to expand pre-trained model tokenizers to better support underrepresented languages without discarding existing model weights.
The authors introduce a mutable sketch method that updates user preferences in log-time to allow for real-time recommendations without needing to retrain the entire model.
The paper introduces a cost evaluation framework for security agents that measures economic efficiency alongside performance on offensive and defensive benchmarks.
The paper introduces Online Neural Space Time Memory to enable persistent, long-horizon novel view synthesis for streaming video without the computational overhead of per-frame updates.
The researchers developed a model that performs video object removal in a single inference step by eliminating the need for external draft priors.
Project Kaleidoscope introduces a workflow for calibrating automated LLM judges against human labels to evaluate real world AI applications.
The paper introduces a method called SEED that improves agentic performance by having the model analyze its own past trajectories to generate dense feedback for training.
VideoChat3 is an open-source video language model that improves generalization and computational efficiency for video understanding tasks through a specialized 3D visual architecture and multi-stage instruction tuning.
The paper introduces Wan-Streamer v0.3 to enable a general-purpose pretraining objective for native-streaming generation by reframing video as a world plus an event stream.
The BadWAM framework exposes vulnerabilities in world-action models by using black-box optimization to force robots into task-failing actions via small visual perturbations.
SearchOS-V1 is a framework that improves information-seeking tasks by externalizing search state and coordinating agents through shared persistent artifacts.
MeanFlowNFT introduces a method to apply reinforcement learning to MeanFlow generators by creating an induced instantaneous velocity predictor to define reward optimization.
The researchers developed RoboTTT to enable robots to handle long sequences of actions by storing historical context through integrated test-time training layers.
HoloGeo is a framework that improves image geo-localization accuracy by training models to reason beyond superficial visual landmarks using evidence-driven reinforcement learning.
The paper introduces a lightweight interface that allows frozen foundation models to dynamically decide when to handle tasks independently or delegate to a stronger fallback model.
The paper introduces MIDI-RAE-JEPA, a model that learns hierarchical music representations by treating piano rolls as images and enforcing geometric constraints on latent space.