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Llms

[R] Is autoresearch really better than classic hyperparameter tuning?

We did experiments comparing Optuna & autoresearch. Autoresearch converges faster, is more cost-efficient, and even generalizes bette...

Reddit - Machine Learning · 1 min ·
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 ·

All Content

[2602.20574] GATES: Self-Distillation under Privileged Context with Consensus Gating
Machine Learning

[2602.20574] GATES: Self-Distillation under Privileged Context with Consensus Gating

The paper presents GATES, a self-distillation method for document-grounded question answering, enhancing model performance by leveraging ...

arXiv - Machine Learning · 3 min ·
[2602.20530] Memory-guided Prototypical Co-occurrence Learning for Mixed Emotion Recognition
Machine Learning

[2602.20530] Memory-guided Prototypical Co-occurrence Learning for Mixed Emotion Recognition

The paper presents a novel framework, Memory-guided Prototypical Co-occurrence Learning (MPCL), aimed at improving mixed emotion recognit...

arXiv - Machine Learning · 4 min ·
[2602.20419] CREDIT: Certified Ownership Verification of Deep Neural Networks Against Model Extraction Attacks
Machine Learning

[2602.20419] CREDIT: Certified Ownership Verification of Deep Neural Networks Against Model Extraction Attacks

The paper introduces CREDIT, a method for certified ownership verification of deep neural networks to combat model extraction attacks, en...

arXiv - Machine Learning · 3 min ·
[2602.20404] $κ$-Explorer: A Unified Framework for Active Model Estimation in MDPs
Machine Learning

[2602.20404] $κ$-Explorer: A Unified Framework for Active Model Estimation in MDPs

$κ$-Explorer presents a novel framework for active model estimation in Markov decision processes (MDPs), focusing on optimizing explorati...

arXiv - Machine Learning · 3 min ·
[2602.11184] KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
Llms

[2602.11184] KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models

The paper presents KBVQ-MoE, a novel framework for improving vector quantization in Mixture of Experts (MoE) large language models, addre...

arXiv - Machine Learning · 4 min ·
[2601.19001] FROST: Filtering Reasoning Outliers with Attention for Efficient Reasoning
Machine Learning

[2601.19001] FROST: Filtering Reasoning Outliers with Attention for Efficient Reasoning

The paper presents FROST, an innovative method that utilizes attention mechanisms to filter out reasoning outliers, enhancing the efficie...

arXiv - Machine Learning · 3 min ·
[2512.24787] HiGR: Efficient Generative Slate Recommendation via Hierarchical Planning and Multi-Objective Preference Alignment
Machine Learning

[2512.24787] HiGR: Efficient Generative Slate Recommendation via Hierarchical Planning and Multi-Objective Preference Alignment

The paper presents HiGR, a novel framework for generative slate recommendation that enhances efficiency and user preference alignment thr...

arXiv - AI · 4 min ·
[2512.16602] Refusal Steering: Fine-grained Control over LLM Refusal Behaviour for Sensitive Topics
Llms

[2512.16602] Refusal Steering: Fine-grained Control over LLM Refusal Behaviour for Sensitive Topics

The paper introduces Refusal Steering, a method for controlling Large Language Models' refusal behavior on sensitive topics without retra...

arXiv - AI · 4 min ·
[2510.24694] Repurposing Synthetic Data for Fine-grained Search Agent Supervision
Llms

[2510.24694] Repurposing Synthetic Data for Fine-grained Search Agent Supervision

The paper presents E-GRPO, a novel framework for training search agents using synthetic data, enhancing their ability to learn from near-...

arXiv - AI · 4 min ·
[2510.18114] Latent-Augmented Discrete Diffusion Models
Machine Learning

[2510.18114] Latent-Augmented Discrete Diffusion Models

The paper presents Latent-Augmented Discrete Diffusion Models (LADD), which enhance discrete diffusion models for improved language gener...

arXiv - Machine Learning · 3 min ·
[2510.08091] Everything is Plausible: Investigating the Impact of LLM Rationales on Human Notions of Plausibility
Llms

[2510.08091] Everything is Plausible: Investigating the Impact of LLM Rationales on Human Notions of Plausibility

This article explores how rationales generated by large language models (LLMs) influence human judgments of plausibility in commonsense r...

arXiv - AI · 3 min ·
[2510.00037] On Robustness of Vision-Language-Action Model against Multi-Modal Perturbations
Machine Learning

[2510.00037] On Robustness of Vision-Language-Action Model against Multi-Modal Perturbations

This paper evaluates the robustness of Vision-Language-Action (VLA) models against various multi-modal perturbations, proposing a new met...

arXiv - AI · 4 min ·
[2509.23115] RHYTHM: Reasoning with Hierarchical Temporal Tokenization for Human Mobility
Llms

[2509.23115] RHYTHM: Reasoning with Hierarchical Temporal Tokenization for Human Mobility

The paper presents RHYTHM, a framework utilizing hierarchical temporal tokenization to enhance human mobility predictions by leveraging l...

arXiv - Machine Learning · 4 min ·
[2508.03250] RooseBERT: A New Deal For Political Language Modelling
Llms

[2508.03250] RooseBERT: A New Deal For Political Language Modelling

RooseBERT introduces a specialized language model for political discourse, enhancing the analysis of political debates through improved s...

arXiv - AI · 4 min ·
[2507.12442] Characterizing State Space Model and Hybrid Language Model Performance with Long Context
Llms

[2507.12442] Characterizing State Space Model and Hybrid Language Model Performance with Long Context

This article explores the performance of State Space Models (SSMs) and hybrid language models in processing long-context inputs, highligh...

arXiv - Machine Learning · 4 min ·
[2507.03043] K-Function: Joint Pronunciation Transcription and Feedback for Evaluating Kids Language Function
Llms

[2507.03043] K-Function: Joint Pronunciation Transcription and Feedback for Evaluating Kids Language Function

The K-Function framework enhances children's language evaluation by integrating precise phoneme transcription with LLM-driven scoring, im...

arXiv - AI · 4 min ·
[2506.03922] HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models
Llms

[2506.03922] HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models

HSSBench introduces a benchmark for evaluating Multimodal Large Language Models (MLLMs) in Humanities and Social Sciences, addressing gap...

arXiv - AI · 4 min ·
[2505.19698] Performance Asymmetry in Model-Based Reinforcement Learning
Machine Learning

[2505.19698] Performance Asymmetry in Model-Based Reinforcement Learning

The paper explores performance asymmetry in Model-Based Reinforcement Learning (MBRL), highlighting significant disparities in agent perf...

arXiv - Machine Learning · 4 min ·
[2505.17645] HoloLLM: Multisensory Foundation Model for Language-Grounded Human Sensing and Reasoning
Llms

[2505.17645] HoloLLM: Multisensory Foundation Model for Language-Grounded Human Sensing and Reasoning

HoloLLM introduces a Multimodal Large Language Model that enhances human sensing and reasoning by integrating diverse sensory inputs, out...

arXiv - Machine Learning · 4 min ·
[2504.13961] CONTINA: Confidence Interval for Traffic Demand Prediction with Coverage Guarantee
Machine Learning

[2504.13961] CONTINA: Confidence Interval for Traffic Demand Prediction with Coverage Guarantee

The paper presents CONTINA, a method for predicting traffic demand with confidence intervals that adapt to changing conditions, ensuring ...

arXiv - Machine Learning · 4 min ·
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