All Articles
18316 articles total
BrainG3N: A Dual-Purpose Tokenizer for Controllable 3D Brain MRI Generation
Introduction of BrainG3N, a dual-purpose tokenizer for 3D brain MRI generation using a volumetric masked-autoencoder and diffusion transformer.
Denoising Implicit Feedback for Cold-start Recommendation
DIF is a model-agnostic denoising method for improving cold-start recommendations in short video applications by filtering noisy implicit feedback.
Deontic Policies for Runtime Governance of Agentic AI Systems
Researchers propose AgenticRei, a governance framework for LLM agents using a deontic policy language based on OWL to handle complex obligations and conflicts outside the LLM.
Measuring Curriculum Alignment across Topical Coverage, Competency, and Cognitive Depth: A Longitudinal Framework Applied to CS2013 and CS2023
A study introduces a longitudinal framework to measure how undergraduate computer science curricula align with international guidelines like CS2013 and CS2023.
Diffusion Language Models: An Experimental Analysis
This paper provides a systematic experimental analysis of eight state-of-the-art Diffusion Language Models (DLMs), comparing their efficiency and quality across various benchmarks.
Hidden Anchors in Multi-Agent LLM Deliberation
The authors model multi-agent LLM deliberation as a dynamical system with 'hidden anchors' (internal beliefs) to explain how agents can reach conclusions beyond their initial collective knowledge.
DeXposure-Claw: An Agentic System for DeFi Risk Supervision
DeXposure-Claw is an agentic system for DeFi risk supervision that combines a graph time-series foundation model with deterministic monitors to provide auditable supervisory tickets.
LLM Doesn't Know What It Doesn't Know: Detecting Epistemic Blind Spots via Cross-Model Attribution Divergence on Clinical Tabular Data
Research on clinical tabular data reveals that LLM confidence is often misleading and proposes a cross-model calibrator using attribution divergence to improve reliability.
REVEAL++: Differentiable Phenotypic Grouping for Vision-Language Retinal Modeling of Alzheimer's Disease Risk
REVEAL++ introduces a continuous formulation of phenotypic grouping in contrastive learning to improve the prediction of Alzheimer's disease risk from retinal images and clinical narratives.
Emergent Alignment
The paper introduces 'Emergent Alignment,' a technique using a 'conscience step' and DPO to enable LLMs to self-correct unethical outputs without an external judge.
ITNet: A Learnable Integral Transform That Subsumes Convolution, Attention, and Recurrence
ITNet is introduced as a unified architecture based on a learnable integral transform that mathematically subsumes convolutions, attention, and recurrence.
Uncertainty Decomposition for Clarification Seeking in LLM Agents
A new prompt-based uncertainty decomposition method improves the ability of LLM agents to proactively seek clarification when task specifications are ambiguous.
Horizons JPL Solar System Data Demo and NASA DSN Updates: Datastar, Common Lisp
A discussion on NASA's JPL Solar System Data Demo and DSN updates, mentioning Datastar and Common Lisp.
Signals of Provenance: Practices & Challenges of Navigating Indicators in AI-Generated Media for Sighted and Blind Individuals
Research on the challenges sighted and blind individuals face when identifying AI-generated media markers, suggesting improved accessibility for provenance indicators.
Revisiting Active Speaker Detection: An In-the-Wild Benchmark for Generalization and Robustness
Introduction of UniTalk, a new benchmark dataset for active speaker detection designed to improve model generalization in challenging, real-world environments.
ASyMOB: Algebraic Symbolic Mathematical Operations Benchmark
ASyMOB is presented as a high-resolution benchmark for symbolic mathematics to distinguish genuine reasoning from pattern memorization in LLMs.
Self-Evolving Multi-Agent Systems via Textual Backpropagation
Proposed 'Agentic Neural Network' (ANN) framework that allows multi-agent systems to self-evolve roles and coordination through a process mirroring backpropagation.
Grids Often Outperform Implicit Neural Representations at Compressing Dense Signals
A study finding that regularized grids with interpolation often outperform Implicit Neural Representations (INRs) for compressing dense signals.
From Memorization to Parameter Interference: How Overtraining Experts Harms Model Merging
Research demonstrating that overtraining expert models can lead to parameter interference, which harms the performance of subsequent model merging.
Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs
Introduction of Partial Model Collapse (PMC), a method for machine unlearning in LLMs that removes private data by deliberately triggering model collapse.