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Nlp

Anyone else feel like AI security is being figured out in production right now?

I’ve been digging into AI security incident data from 2025 into this year, and it feels like something isn’t being talked about enough ou...

Reddit - Artificial Intelligence · 1 min ·
Machine Learning

[D] ICML 2026 Average Score

Hi all, I’m curious about the current review dynamics for ICML 2026, especially after the rebuttal phase. For those who are reviewers (or...

Reddit - Machine Learning · 1 min ·
Apple’s best product in its first 50 years | The Verge
Nlp

Apple’s best product in its first 50 years | The Verge

From the Macintosh to the iPhone to the iMac to the iPod, it’s hard to pick a best Apple product ever. But we tried to do so anyway.

The Verge - AI · 4 min ·

All Content

[2602.19661] PaReGTA: An LLM-based EHR Data Encoding Approach to Capture Temporal Information
Llms

[2602.19661] PaReGTA: An LLM-based EHR Data Encoding Approach to Capture Temporal Information

The paper presents PaReGTA, an LLM-based framework for encoding temporal information in electronic health records (EHRs), enhancing patie...

arXiv - Machine Learning · 4 min ·
[2602.18699] Semantic Substrate Theory: An Operator-Theoretic Framework for Geometric Semantic Drift
Generative Ai

[2602.18699] Semantic Substrate Theory: An Operator-Theoretic Framework for Geometric Semantic Drift

This paper introduces Semantic Substrate Theory, an operator-theoretic framework that formalizes various signals of semantic drift, integ...

arXiv - AI · 3 min ·
[2602.19644] Spectral Phase Encoding for Quantum Kernel Methods
Nlp

[2602.19644] Spectral Phase Encoding for Quantum Kernel Methods

The paper presents Spectral Phase Encoding (SPE) for quantum kernel methods, analyzing their robustness against noise and comparing perfo...

arXiv - Machine Learning · 4 min ·
[2602.19641] Evaluating the Impact of Data Anonymization on Image Retrieval
Nlp

[2602.19641] Evaluating the Impact of Data Anonymization on Image Retrieval

This article evaluates how data anonymization affects the performance of Content-Based Image Retrieval (CBIR) systems, highlighting the b...

arXiv - Machine Learning · 4 min ·
[2602.19582] Advantage-based Temporal Attack in Reinforcement Learning
Machine Learning

[2602.19582] Advantage-based Temporal Attack in Reinforcement Learning

This article presents the Advantage-based Adversarial Transformer (AAT), a novel method for generating time-correlated adversarial exampl...

arXiv - Machine Learning · 4 min ·
[2602.18583] Luna-2: Scalable Single-Token Evaluation with Small Language Models
Llms

[2602.18583] Luna-2: Scalable Single-Token Evaluation with Small Language Models

Luna-2 introduces a scalable architecture for single-token evaluation using small language models, enhancing accuracy and reducing costs ...

arXiv - Machine Learning · 4 min ·
[2602.18532] VLANeXt: Recipes for Building Strong VLA Models
Llms

[2602.18532] VLANeXt: Recipes for Building Strong VLA Models

The paper presents VLANeXt, a framework for building effective Vision-Language-Action (VLA) models, addressing inconsistencies in trainin...

arXiv - AI · 4 min ·
[2602.19498] Softmax is not Enough (for Adaptive Conformal Classification)
Nlp

[2602.19498] Softmax is not Enough (for Adaptive Conformal Classification)

The paper critiques the reliance on softmax outputs in adaptive conformal classification, proposing a new method that utilizes pre-softma...

arXiv - AI · 4 min ·
[2602.19483] Making Conformal Predictors Robust in Healthcare Settings: a Case Study on EEG Classification
Nlp

[2602.19483] Making Conformal Predictors Robust in Healthcare Settings: a Case Study on EEG Classification

This article explores the application of conformal prediction methods in healthcare, specifically focusing on EEG seizure classification....

arXiv - AI · 3 min ·
[2602.18497] PIPE-RDF: An LLM-Assisted Pipeline for Enterprise RDF Benchmarking
Llms

[2602.18497] PIPE-RDF: An LLM-Assisted Pipeline for Enterprise RDF Benchmarking

PIPE-RDF presents a novel pipeline for generating schema-specific NL-SPARQL benchmarks, enhancing RDF knowledge graph querying for enterp...

arXiv - AI · 3 min ·
[2602.19393] In Defense of Cosine Similarity: Normalization Eliminates the Gauge Freedom
Machine Learning

[2602.19393] In Defense of Cosine Similarity: Normalization Eliminates the Gauge Freedom

This paper defends cosine similarity in machine learning, arguing that normalization eliminates issues related to gauge freedom, thus ens...

arXiv - Machine Learning · 3 min ·
[2602.19373] Stable Deep Reinforcement Learning via Isotropic Gaussian Representations
Machine Learning

[2602.19373] Stable Deep Reinforcement Learning via Isotropic Gaussian Representations

This paper presents a method for enhancing stability in deep reinforcement learning by utilizing isotropic Gaussian representations, addr...

arXiv - AI · 3 min ·
[2602.18483] Red Teaming LLMs as Socio-Technical Practice: From Exploration and Data Creation to Evaluation
Llms

[2602.18483] Red Teaming LLMs as Socio-Technical Practice: From Exploration and Data Creation to Evaluation

The article examines red teaming as a socio-technical practice in evaluating large language models (LLMs), highlighting the importance of...

arXiv - AI · 4 min ·
[2602.18479] AgentCAT: An LLM Agent for Extracting and Analyzing Catalytic Reaction Data from Chemical Engineering Literature
Llms

[2602.18479] AgentCAT: An LLM Agent for Extracting and Analyzing Catalytic Reaction Data from Chemical Engineering Literature

AgentCAT is a large language model designed to extract and analyze catalytic reaction data from chemical engineering literature, addressi...

arXiv - AI · 4 min ·
[2602.19271] Taming Preconditioner Drift: Unlocking the Potential of Second-Order Optimizers for Federated Learning on Non-IID Data
Machine Learning

[2602.19271] Taming Preconditioner Drift: Unlocking the Potential of Second-Order Optimizers for Federated Learning on Non-IID Data

This paper presents FedPAC, a framework to enhance the stability and accuracy of second-order optimizers in federated learning on non-IID...

arXiv - AI · 4 min ·
[2602.19237] Evaluating SAP RPT-1 for Enterprise Business Process Prediction: In-Context Learning vs. Traditional Machine Learning on Structured SAP Data
Llms

[2602.19237] Evaluating SAP RPT-1 for Enterprise Business Process Prediction: In-Context Learning vs. Traditional Machine Learning on Structured SAP Data

This article evaluates SAP's RPT-1 model for enterprise business process prediction, comparing its performance against traditional machin...

arXiv - AI · 4 min ·
[2602.18462] Assessing the Reliability of Persona-Conditioned LLMs as Synthetic Survey Respondents
Llms

[2602.18462] Assessing the Reliability of Persona-Conditioned LLMs as Synthetic Survey Respondents

This article evaluates the reliability of persona-conditioned large language models (LLMs) as synthetic survey respondents, revealing tha...

arXiv - AI · 3 min ·
[2602.18459] From Bias Mitigation to Bias Negotiation: Governing Identity and Sociocultural Reasoning in Generative AI
Llms

[2602.18459] From Bias Mitigation to Bias Negotiation: Governing Identity and Sociocultural Reasoning in Generative AI

This article discusses the shift from bias mitigation to bias negotiation in generative AI, emphasizing the need for ethical governance o...

arXiv - AI · 4 min ·
[2602.19169] Virtual Parameter Sharpening: Dynamic Low-Rank Perturbations for Inference-Time Reasoning Enhancement
Machine Learning

[2602.19169] Virtual Parameter Sharpening: Dynamic Low-Rank Perturbations for Inference-Time Reasoning Enhancement

The paper introduces Virtual Parameter Sharpening (VPS), a novel technique for enhancing inference-time reasoning in transformer models t...

arXiv - AI · 3 min ·
[2602.19143] Incremental Learning of Sparse Attention Patterns in Transformers
Machine Learning

[2602.19143] Incremental Learning of Sparse Attention Patterns in Transformers

This paper explores how transformers learn through incremental acquisition of sparse attention patterns, revealing shifts in learning dyn...

arXiv - Machine Learning · 3 min ·
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