· ai
[Paper] Self-Supervised Learning from Noisy and Incomplete Data
Many important problems in science and engineering involve inferring a signal from noisy and/or incomplete observations, where the observation process is known....
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- · ai
[Paper] PET-TURTLE: Deep Unsupervised Support Vector Machines for Imbalanced Data Clusters
Foundation vision, audio, and language models enable zero-shot performance on downstream tasks via their latent representations. Recently, unsupervised learning...
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[Paper] MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents
Memory-Augmented Generation (MAG) extends Large Language Models with external memory to support long-context reasoning, but existing approaches largely rely on ...
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[Paper] Shallow-circuit Supervised Learning on a Quantum Processor
Quantum computing has long promised transformative advances in data analysis, yet practical quantum machine learning has remained elusive due to fundamental obs...
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[Paper] Multi-RADS Synthetic Radiology Report Dataset and Head-to-Head Benchmarking of 41 Open-Weight and Proprietary Language Models
Background: Reporting and Data Systems (RADS) standardize radiology risk communication but automated RADS assignment from narrative reports is challenging becau...
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[Paper] The Sonar Moment: Benchmarking Audio-Language Models in Audio Geo-Localization
Geo-localization aims to infer the geographic origin of a given signal. In computer vision, geo-localization has served as a demanding benchmark for composition...
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[Paper] The Fake Friend Dilemma: Trust and the Political Economy of Conversational AI
As conversational AI systems become increasingly integrated into everyday life, they raise pressing concerns about user autonomy, trust, and the commercial inte...
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[Paper] From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence
Can we learn more from data than existed in the generating process itself? Can new and useful information be constructed from merely applying deterministic tran...
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[Paper] Critic-Guided Reinforcement Unlearning in Text-to-Image Diffusion
Machine unlearning in text-to-image diffusion models aims to remove targeted concepts while preserving overall utility. Prior diffusion unlearning methods typic...
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[Paper] Fine-tuning Small Language Models as Efficient Enterprise Search Relevance Labelers
In enterprise search, building high-quality datasets at scale remains a central challenge due to the difficulty of acquiring labeled data. To resolve this chall...
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[Paper] UltraLogic: Enhancing LLM Reasoning through Large-Scale Data Synthesis and Bipolar Float Reward
While Large Language Models (LLMs) have demonstrated significant potential in natural language processing , complex general-purpose reasoning requiring multi-st...
- · ai
[Paper] InfiAgent: An Infinite-Horizon Framework for General-Purpose Autonomous Agents
LLM agents can reason and use tools, but they often break down on long-horizon tasks due to unbounded context growth and accumulated errors. Common remedies suc...