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Machine Learning

[D] ICML 26 - What to do with the zero follow-up questions

Hello everyone. I submitted my work to ICML 26 this year, and it got somewhat above average reviews. Now, in the rebuttal acknowledgment,...

Reddit - Machine Learning · 1 min ·
Startup Battlefield 200 applications open until May 27 | TechCrunch
Nlp

Startup Battlefield 200 applications open until May 27 | TechCrunch

Nominate your startup, or one you know, and apply for a chance at VC access, TechCrunch coverage, and $100K for Startup Battlefield 200.

TechCrunch - AI · 4 min ·
[2603.24326] Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing
Llms

[2603.24326] Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing

Abstract page for arXiv paper 2603.24326: Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing

arXiv - AI · 4 min ·

All Content

[2602.13347] Visual Foresight for Robotic Stow: A Diffusion-Based World Model from Sparse Snapshots
Machine Learning

[2602.13347] Visual Foresight for Robotic Stow: A Diffusion-Based World Model from Sparse Snapshots

The paper presents FOREST, a diffusion-based world model for robotic stow operations, enhancing the prediction of post-stow configuration...

arXiv - AI · 3 min ·
[2602.14635] Alignment Adapter to Improve the Performance of Compressed Deep Learning Models
Machine Learning

[2602.14635] Alignment Adapter to Improve the Performance of Compressed Deep Learning Models

The paper introduces the Alignment Adapter (AlAd), a method to enhance the performance of compressed deep learning models by aligning the...

arXiv - Machine Learning · 3 min ·
[2602.14626] Concepts' Information Bottleneck Models
Machine Learning

[2602.14626] Concepts' Information Bottleneck Models

This article presents the Concepts' Information Bottleneck Models, which enhance the interpretability of predictions in machine learning ...

arXiv - Machine Learning · 3 min ·
[2602.13312] PeroMAS: A Multi-agent System of Perovskite Material Discovery
Machine Learning

[2602.13312] PeroMAS: A Multi-agent System of Perovskite Material Discovery

PeroMAS introduces a multi-agent system for discovering perovskite materials, enhancing efficiency in photovoltaic research through a com...

arXiv - AI · 4 min ·
[2602.14519] DeepMTL2R: A Library for Deep Multi-task Learning to Rank
Machine Learning

[2602.14519] DeepMTL2R: A Library for Deep Multi-task Learning to Rank

DeepMTL2R is an open-source library designed for deep multi-task learning to rank, integrating diverse relevance signals into a unified m...

arXiv - Machine Learning · 3 min ·
[2602.14444] Broken Chains: The Cost of Incomplete Reasoning in LLMs
Llms

[2602.14444] Broken Chains: The Cost of Incomplete Reasoning in LLMs

The paper explores the impact of incomplete reasoning in large language models (LLMs), revealing how different reasoning modalities affec...

arXiv - AI · 4 min ·
[2602.13279] LLM-Enhanced Rumor Detection via Virtual Node Induced Edge Prediction
Llms

[2602.13279] LLM-Enhanced Rumor Detection via Virtual Node Induced Edge Prediction

This paper presents a novel framework for rumor detection on social networks, utilizing Large Language Models (LLMs) to enhance the ident...

arXiv - AI · 3 min ·
[2602.14351] WIMLE: Uncertainty-Aware World Models with IMLE for Sample-Efficient Continuous Control
Machine Learning

[2602.14351] WIMLE: Uncertainty-Aware World Models with IMLE for Sample-Efficient Continuous Control

The paper presents WIMLE, a model-based reinforcement learning method that enhances sample efficiency by addressing model errors and unce...

arXiv - AI · 4 min ·
[2602.13259] Learning Physiology-Informed Vocal Spectrotemporal Representations for Speech Emotion Recognition
Machine Learning

[2602.13259] Learning Physiology-Informed Vocal Spectrotemporal Representations for Speech Emotion Recognition

This paper presents PhysioSER, a novel approach for speech emotion recognition that integrates physiological insights into vocal represen...

arXiv - AI · 4 min ·
[2602.14318] In Transformer We Trust? A Perspective on Transformer Architecture Failure Modes
Machine Learning

[2602.14318] In Transformer We Trust? A Perspective on Transformer Architecture Failure Modes

The paper examines the trustworthiness of transformer architectures in high-stakes applications, analyzing their reliability, interpretab...

arXiv - Machine Learning · 4 min ·
[2602.14301] DeepFusion: Accelerating MoE Training via Federated Knowledge Distillation from Heterogeneous Edge Devices
Llms

[2602.14301] DeepFusion: Accelerating MoE Training via Federated Knowledge Distillation from Heterogeneous Edge Devices

DeepFusion introduces a scalable framework for federated training of Mixture-of-Experts (MoE) models, leveraging knowledge distillation f...

arXiv - AI · 4 min ·
[2602.14279] Whom to Query for What: Adaptive Group Elicitation via Multi-Turn LLM Interactions
Llms

[2602.14279] Whom to Query for What: Adaptive Group Elicitation via Multi-Turn LLM Interactions

The paper discusses an adaptive group elicitation framework using multi-turn interactions with large language models (LLMs) to optimize r...

arXiv - AI · 4 min ·
[2602.13241] Real-World Design and Deployment of an Embedded GenAI-powered 9-1-1 Calltaking Training System: Experiences and Lessons Learned
Machine Learning

[2602.13241] Real-World Design and Deployment of an Embedded GenAI-powered 9-1-1 Calltaking Training System: Experiences and Lessons Learned

This article discusses the design and deployment of a GenAI-powered training system for 9-1-1 call-takers, highlighting the challenges fa...

arXiv - AI · 4 min ·
[2602.13231] An Explainable Failure Prediction Framework for Neural Networks in Radio Access Networks
Machine Learning

[2602.13231] An Explainable Failure Prediction Framework for Neural Networks in Radio Access Networks

This paper presents a framework for explainable failure prediction in neural networks used in radio access networks, enhancing model tran...

arXiv - Machine Learning · 4 min ·
[2602.14274] Integrating Unstructured Text into Causal Inference: Empirical Evidence from Real Data
Llms

[2602.14274] Integrating Unstructured Text into Causal Inference: Empirical Evidence from Real Data

This paper presents a framework for integrating unstructured text into causal inference, demonstrating its effectiveness against traditio...

arXiv - AI · 3 min ·
[2602.14200] TS-Haystack: A Multi-Scale Retrieval Benchmark for Time Series Language Models
Llms

[2602.14200] TS-Haystack: A Multi-Scale Retrieval Benchmark for Time Series Language Models

The paper introduces TS-Haystack, a benchmark for evaluating Time Series Language Models (TSLMs) on long-context retrieval tasks, address...

arXiv - Machine Learning · 4 min ·
[2602.10833] Training-Induced Bias Toward LLM-Generated Content in Dense Retrieval
Llms

[2602.10833] Training-Induced Bias Toward LLM-Generated Content in Dense Retrieval

This study investigates the training-induced bias towards LLM-generated content in dense retrieval systems, revealing how dataset and tra...

arXiv - Machine Learning · 4 min ·
[2602.14169] Deep Dense Exploration for LLM Reinforcement Learning via Pivot-Driven Resampling
Llms

[2602.14169] Deep Dense Exploration for LLM Reinforcement Learning via Pivot-Driven Resampling

The paper presents Deep Dense Exploration (DDE), a novel approach to enhance exploration in reinforcement learning for large language mod...

arXiv - AI · 4 min ·
[2602.14161] When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift
Llms

[2602.14161] When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift

This paper evaluates the effectiveness of malicious prompt classifiers under true distribution shifts, revealing significant performance ...

arXiv - Machine Learning · 4 min ·
[2602.14922] ReusStdFlow: A Standardized Reusability Framework for Dynamic Workflow Construction in Agentic AI
Nlp

[2602.14922] ReusStdFlow: A Standardized Reusability Framework for Dynamic Workflow Construction in Agentic AI

The paper presents ReusStdFlow, a framework designed to enhance the reusability of workflows in Agentic AI by standardizing Domain Specif...

arXiv - AI · 3 min ·
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