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[2601.11016] Contextual Distributionally Robust Optimization with Causal and Continuous Structure: An Interpretable and Tractable Approach
Nlp

[2601.11016] Contextual Distributionally Robust Optimization with Causal and Continuous Structure: An Interpretable and Tractable Approach

Abstract page for arXiv paper 2601.11016: Contextual Distributionally Robust Optimization with Causal and Continuous Structure: An Interp...

arXiv - Machine Learning · 4 min ·
[2511.22294] Structure is Supervision: Multiview Masked Autoencoders for Radiology
Machine Learning

[2511.22294] Structure is Supervision: Multiview Masked Autoencoders for Radiology

Abstract page for arXiv paper 2511.22294: Structure is Supervision: Multiview Masked Autoencoders for Radiology

arXiv - Machine Learning · 4 min ·
[2511.18123] Bias Is a Subspace, Not a Coordinate: A Geometric Rethinking of Post-hoc Debiasing in Vision-Language Models
Llms

[2511.18123] Bias Is a Subspace, Not a Coordinate: A Geometric Rethinking of Post-hoc Debiasing in Vision-Language Models

Abstract page for arXiv paper 2511.18123: Bias Is a Subspace, Not a Coordinate: A Geometric Rethinking of Post-hoc Debiasing in Vision-La...

arXiv - Machine Learning · 4 min ·

All Content

[2602.21189] Why Pass@k Optimization Can Degrade Pass@1: Prompt Interference in LLM Post-training
Llms

[2602.21189] Why Pass@k Optimization Can Degrade Pass@1: Prompt Interference in LLM Post-training

The paper explores the trade-off between Pass@k and Pass@1 performance metrics in large language models, revealing how optimizing for Pas...

arXiv - Machine Learning · 4 min ·
[2602.21165] PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data
Machine Learning

[2602.21165] PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data

PVminer is a novel NLP framework designed to detect the patient voice in patient-generated data, improving the analysis of patient-provid...

arXiv - AI · 4 min ·
[2602.21136] SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery
Llms

[2602.21136] SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery

The paper presents SparkMe, a multi-agent LLM system designed for adaptive semi-structured interviewing, enhancing qualitative data colle...

arXiv - AI · 4 min ·
[2602.21052] Position-Aware Sequential Attention for Accurate Next Item Recommendations
Machine Learning

[2602.21052] Position-Aware Sequential Attention for Accurate Next Item Recommendations

The paper presents a novel kernelized self-attention mechanism designed to enhance next-item recommendations by improving the representat...

arXiv - Machine Learning · 3 min ·
[2602.20979] Toward an Agentic Infused Software Ecosystem
Nlp

[2602.20979] Toward an Agentic Infused Software Ecosystem

This article discusses the concept of an Agentic Infused Software Ecosystem (AISE), emphasizing the need for a holistic approach to integ...

arXiv - AI · 3 min ·
[2602.20967] Training-Free Intelligibility-Guided Observation Addition for Noisy ASR
Machine Learning

[2602.20967] Training-Free Intelligibility-Guided Observation Addition for Noisy ASR

This paper presents a novel training-free method for improving automatic speech recognition (ASR) in noisy environments by using intellig...

arXiv - AI · 3 min ·
[2602.20877] E-MMKGR: A Unified Multimodal Knowledge Graph Framework for E-commerce Applications
Nlp

[2602.20877] E-MMKGR: A Unified Multimodal Knowledge Graph Framework for E-commerce Applications

The paper presents E-MMKGR, a unified framework for multimodal knowledge graphs tailored for e-commerce, enhancing recommendation systems...

arXiv - AI · 3 min ·
[2602.20735] RMIT-ADM+S at the MMU-RAG NeurIPS 2025 Competition
Llms

[2602.20735] RMIT-ADM+S at the MMU-RAG NeurIPS 2025 Competition

The paper presents RMIT-ADM+S, an award-winning system for the Text-to-Text track at the NeurIPS 2025 Competition, featuring a novel retr...

arXiv - AI · 3 min ·
[2602.20751] SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing
Machine Learning

[2602.20751] SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing

The paper presents SibylSense, a novel approach to adaptive rubric learning that enhances reward mechanisms in reinforcement learning thr...

arXiv - Machine Learning · 3 min ·
[2602.20731] Communication-Inspired Tokenization for Structured Image Representations
Machine Learning

[2602.20731] Communication-Inspired Tokenization for Structured Image Representations

The paper presents COMmunication inspired Tokenization (COMiT), a novel framework for structured image representations that enhances obje...

arXiv - Machine Learning · 4 min ·
[2602.20709] Onboard-Targeted Segmentation of Straylight in Space Camera Sensors
Machine Learning

[2602.20709] Onboard-Targeted Segmentation of Straylight in Space Camera Sensors

This paper presents an AI-driven methodology for segmenting straylight effects in space camera sensors, enhancing image analysis in resou...

arXiv - AI · 3 min ·
[2602.20677] UrbanFM: Scaling Urban Spatio-Temporal Foundation Models
Llms

[2602.20677] UrbanFM: Scaling Urban Spatio-Temporal Foundation Models

The paper presents UrbanFM, a novel framework for scaling urban spatio-temporal foundation models, addressing challenges in generalizabil...

arXiv - Machine Learning · 4 min ·
[2602.20676] PRECTR-V2:Unified Relevance-CTR Framework with Cross-User Preference Mining, Exposure Bias Correction, and LLM-Distilled Encoder Optimization
Llms

[2602.20676] PRECTR-V2:Unified Relevance-CTR Framework with Cross-User Preference Mining, Exposure Bias Correction, and LLM-Distilled Encoder Optimization

The paper presents PRECTR-V2, an advanced framework for improving search relevance and click-through rate (CTR) prediction by addressing ...

arXiv - AI · 4 min ·
[2602.20684] Agile V: A Compliance-Ready Framework for AI-Augmented Engineering -- From Concept to Audit-Ready Delivery
Machine Learning

[2602.20684] Agile V: A Compliance-Ready Framework for AI-Augmented Engineering -- From Concept to Audit-Ready Delivery

The paper presents Agile V, a framework integrating AI in engineering workflows to ensure compliance and verification at machine-speed de...

arXiv - AI · 4 min ·
[2602.20650] Dataset Color Quantization: A Training-Oriented Framework for Dataset-Level Compression
Machine Learning

[2602.20650] Dataset Color Quantization: A Training-Oriented Framework for Dataset-Level Compression

The paper presents Dataset Color Quantization (DCQ), a framework designed to compress large-scale image datasets by reducing color-space ...

arXiv - AI · 3 min ·
[2602.20634] Enhancing Hate Speech Detection on Social Media: A Comparative Analysis of Machine Learning Models and Text Transformation Approaches
Machine Learning

[2602.20634] Enhancing Hate Speech Detection on Social Media: A Comparative Analysis of Machine Learning Models and Text Transformation Approaches

This article evaluates various machine learning models for hate speech detection on social media, comparing traditional and advanced tech...

arXiv - AI · 3 min ·
[2602.20547] What Drives Students' Use of AI Chatbots? Technology Acceptance in Conversational AI
Machine Learning

[2602.20547] What Drives Students' Use of AI Chatbots? Technology Acceptance in Conversational AI

This article explores the factors influencing students' adoption of AI chatbots for learning, utilizing the Technology Acceptance Model t...

arXiv - AI · 4 min ·
[2602.20520] How Do Inpainting Artifacts Propagate to Language?
Llms

[2602.20520] How Do Inpainting Artifacts Propagate to Language?

This paper investigates how visual artifacts from diffusion-based inpainting affect language generation in vision-language models, reveal...

arXiv - AI · 3 min ·
[2602.20449] Protein Language Models Diverge from Natural Language: Comparative Analysis and Improved Inference
Llms

[2602.20449] Protein Language Models Diverge from Natural Language: Comparative Analysis and Improved Inference

This article explores the differences between protein language models (PLMs) and natural language models, highlighting how these distinct...

arXiv - Machine Learning · 4 min ·
[2602.20379] Case-Aware LLM-as-a-Judge Evaluation for Enterprise-Scale RAG Systems
Llms

[2602.20379] Case-Aware LLM-as-a-Judge Evaluation for Enterprise-Scale RAG Systems

The paper presents a case-aware evaluation framework for enterprise-scale Retrieval-Augmented Generation (RAG) systems, addressing the li...

arXiv - AI · 3 min ·
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