Natural Language Processing

Text understanding and language tasks

This Week's Best | Monthly Best | Guide | Trending

Top This Week

Nlp

Automate IOS devices through XCUITest with droidrun.

Automate iOS apps with XCUITest and Droidrun using just natural language. You send the command to Droidrun, and the agent starts the task...

Reddit - Artificial Intelligence · 1 min ·
Machine Learning

[P] Trained a small BERT on 276K Kubernetes YAMLs using tree positional encoding instead of sequential

I trained a BERT-style transformer on 276K Kubernetes YAML files, replacing standard positional encoding with learned tree coordinates (d...

Reddit - Machine Learning · 1 min ·
Machine Learning

I am doing a multi-model graph database in pure Rust with Cypher, SQL, Gremlin, and native GNN looking for extreme speed and performance

Hi guys, I'm a PhD student in Applied AI and I've been building an embeddable graph database engine from scratch in Rust. I'd love feedba...

Reddit - Artificial Intelligence · 1 min ·

All Content

[2510.01988] PepCompass: Navigating peptide embedding spaces using Riemannian Geometry
Machine Learning

[2510.01988] PepCompass: Navigating peptide embedding spaces using Riemannian Geometry

PepCompass introduces a geometry-aware framework for exploring peptide spaces, enhancing antimicrobial peptide discovery through advanced...

arXiv - Machine Learning · 4 min ·
[2509.21865] Beyond RAG vs. Long-Context: Learning Distraction-Aware Retrieval for Efficient Knowledge Grounding
Llms

[2509.21865] Beyond RAG vs. Long-Context: Learning Distraction-Aware Retrieval for Efficient Knowledge Grounding

This paper introduces LDAR, a new retrieval method that enhances the efficiency of knowledge grounding in Large Language Models (LLMs) by...

arXiv - Machine Learning · 4 min ·
[2507.00031] Enhancing Spatio-Temporal Forecasting with Spatial Neighbourhood Fusion:A Case Study on COVID-19 Mobility in Peru
Machine Learning

[2507.00031] Enhancing Spatio-Temporal Forecasting with Spatial Neighbourhood Fusion:A Case Study on COVID-19 Mobility in Peru

This paper presents a novel Spatial Neighbourhood Fusion technique to enhance spatio-temporal forecasting of COVID-19 mobility in Peru, d...

arXiv - Machine Learning · 4 min ·
[2506.22685] Mitigating Semantic Collapse in Generative Personalization with Test-Time Embedding Adjustment
Nlp

[2506.22685] Mitigating Semantic Collapse in Generative Personalization with Test-Time Embedding Adjustment

This paper addresses the issue of semantic collapse in generative personalization, proposing a method to adjust embeddings at inference t...

arXiv - Machine Learning · 4 min ·
[2506.17344] FFINO: Factorized Fourier Improved Neural Operator for Modeling Multiphase Flow in Underground Hydrogen Storage
Machine Learning

[2506.17344] FFINO: Factorized Fourier Improved Neural Operator for Modeling Multiphase Flow in Underground Hydrogen Storage

The paper presents FFINO, a novel neural operator for modeling multiphase flow in underground hydrogen storage, demonstrating significant...

arXiv - Machine Learning · 4 min ·
[2602.22115] Slice and Explain: Logic-Based Explanations for Neural Networks through Domain Slicing
Machine Learning

[2602.22115] Slice and Explain: Logic-Based Explanations for Neural Networks through Domain Slicing

The paper presents a novel approach called 'Slice and Explain,' which utilizes domain slicing to enhance the efficiency of logic-based ex...

arXiv - Machine Learning · 3 min ·
[2602.21957] Learning to Collaborate via Structures: Cluster-Guided Item Alignment for Federated Recommendation
Machine Learning

[2602.21957] Learning to Collaborate via Structures: Cluster-Guided Item Alignment for Federated Recommendation

The paper presents CGFedRec, a novel framework for federated recommendation that enhances collaboration by using cluster-guided item alig...

arXiv - Machine Learning · 4 min ·
[2602.21741] Robust Long-Form Bangla Speech Processing: Automatic Speech Recognition and Speaker Diarization
Machine Learning

[2602.21741] Robust Long-Form Bangla Speech Processing: Automatic Speech Recognition and Speaker Diarization

This article presents an end-to-end system for Bangla long-form speech recognition and speaker diarization, detailing significant challen...

arXiv - Machine Learning · 3 min ·
[2602.21707] Learning spatially adaptive sparsity level maps for arbitrary convolutional dictionaries
Machine Learning

[2602.21707] Learning spatially adaptive sparsity level maps for arbitrary convolutional dictionaries

This paper presents a novel approach to image reconstruction using spatially adaptive sparsity level maps within convolutional dictionari...

arXiv - Machine Learning · 4 min ·
[2602.21677] Trie-Aware Transformers for Generative Recommendation
Machine Learning

[2602.21677] Trie-Aware Transformers for Generative Recommendation

The paper introduces TrieRec, a trie-aware generative recommendation method that enhances Transformers by incorporating structural induct...

arXiv - Machine Learning · 3 min ·
[2602.21533] Reasoning-Driven Design of Single Atom Catalysts via a Multi-Agent Large Language Model Framework
Llms

[2602.21533] Reasoning-Driven Design of Single Atom Catalysts via a Multi-Agent Large Language Model Framework

This paper presents the MAESTRO framework, which utilizes multi-agent large language models to discover high-performance single atom cata...

arXiv - Machine Learning · 3 min ·
[2602.21428] PSF-Med: Measuring and Explaining Paraphrase Sensitivity in Medical Vision Language Models
Llms

[2602.21428] PSF-Med: Measuring and Explaining Paraphrase Sensitivity in Medical Vision Language Models

The paper introduces PSF-Med, a benchmark assessing paraphrase sensitivity in medical vision language models, revealing significant varia...

arXiv - Machine Learning · 4 min ·
[2602.21397] MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation
Llms

[2602.21397] MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation

The paper presents MMLoP, a framework for efficient vision-language adaptation using low-rank prompting, achieving high accuracy with sig...

arXiv - Machine Learning · 4 min ·
[2602.21229] Forecasting Future Language: Context Design for Mention Markets
Llms

[2602.21229] Forecasting Future Language: Context Design for Mention Markets

This paper explores the design of context for mention markets, focusing on how input context affects the accuracy of predictions made by ...

arXiv - Machine Learning · 4 min ·
[2602.21212] Disaster Question Answering with LoRA Efficiency and Accurate End Position
Nlp

[2602.21212] Disaster Question Answering with LoRA Efficiency and Accurate End Position

This paper presents a disaster-focused question answering system optimized for Japanese disaster scenarios, achieving high accuracy with ...

arXiv - Machine Learning · 4 min ·
[2602.22136] SigmaQuant: Hardware-Aware Heterogeneous Quantization Method for Edge DNN Inference
Machine Learning

[2602.22136] SigmaQuant: Hardware-Aware Heterogeneous Quantization Method for Edge DNN Inference

The paper introduces SigmaQuant, a hardware-aware heterogeneous quantization method for deep neural networks (DNNs) aimed at optimizing p...

arXiv - Machine Learning · 3 min ·
[2602.21844] JSAM: Privacy Straggler-Resilient Joint Client Selection and Incentive Mechanism Design in Differentially Private Federated Learning
Machine Learning

[2602.21844] JSAM: Privacy Straggler-Resilient Joint Client Selection and Incentive Mechanism Design in Differentially Private Federated Learning

The paper presents JSAM, a framework for optimizing client selection and privacy compensation in differentially private federated learnin...

arXiv - Machine Learning · 4 min ·
[2602.21824] DocDjinn: Controllable Synthetic Document Generation with VLMs and Handwriting Diffusion
Llms

[2602.21824] DocDjinn: Controllable Synthetic Document Generation with VLMs and Handwriting Diffusion

DocDjinn introduces a framework for generating synthetic documents using Vision-Language Models (VLMs), addressing challenges in data acq...

arXiv - Machine Learning · 4 min ·
[2602.21750] From Words to Amino Acids: Does the Curse of Depth Persist?
Llms

[2602.21750] From Words to Amino Acids: Does the Curse of Depth Persist?

This paper explores the depth inefficiency in protein language models (PLMs), revealing that later layers contribute less to output predi...

arXiv - Machine Learning · 4 min ·
[2602.21717] C$^{2}$TC: A Training-Free Framework for Efficient Tabular Data Condensation
Machine Learning

[2602.21717] C$^{2}$TC: A Training-Free Framework for Efficient Tabular Data Condensation

C$^{2}$TC introduces a training-free framework for efficient tabular data condensation, addressing challenges in data scalability and mod...

arXiv - Machine Learning · 4 min ·
Previous Page 73 Next

Related Topics

Machine Learning Large Language Models Generative AI Computer Vision Robotics & Embodied AI AI Safety & Ethics

Stay updated with AI News

Get the latest news, tools, and insights delivered to your inbox.

Subscribe to Newsletter

Daily or weekly digest • Unsubscribe anytime