Tootfinder

MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert Specialization
Ken Yagel, Eyal German, Aviel Ben Siman Tov
https://arxiv.org/abs/2506.07563

MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert Specialization
Personalized recommendation systems must adapt to user interactions across different domains. Traditional approaches like MLoRA apply a single adaptation per domain but lack flexibility in handling diverse user behaviors. To address this, we propose MoE-MLoRA, a mixture-of-experts framework where each expert is first trained independently to specialize in its domain before a gating network is trained to weight their contributions dynamically. We evaluate MoE-MLoRA across eight CTR models on Mov…

STSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving
Christian Fruhwirth-Reisinger, Du\v{s}an Mali\'c, Wei Lin, David Schinagl, Samuel Schulter, Horst Possegger
https://arxiv.org/abs/2506.06218

STSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving
We introduce STSBench, a scenario-based framework to benchmark the holistic understanding of vision-language models (VLMs) for autonomous driving. The framework automatically mines pre-defined traffic scenarios from any dataset using ground-truth annotations, provides an intuitive user interface for efficient human verification, and generates multiple-choice questions for model evaluation. Applied to the NuScenes dataset, we present STSnu, the first benchmark that evaluates the spatio-temporal …

Joint User Association and Beamforming Design for ISAC Networks with Large Language Models
Haoyun Li, Ming Xiao, Kezhi Wang, Robert Schober, Dong In Kim, Yong Liang Guan
https://arxiv.org/abs/2506.05637

Joint User Association and Beamforming Design for ISAC Networks with Large Language Models
Integrated sensing and communication (ISAC) has been envisioned to play a more important role in future wireless networks. However, the design of ISAC networks is challenging, especially when there are multiple communication and sensing (C\&S) nodes and multiple sensing targets. We investigate a multi-base station (BS) ISAC network in which multiple BSs equipped with multiple antennas simultaneously provide C\&S services for multiple ground communication users (CUs) and targets. To enhance the …

This https://arxiv.org/abs/2506.02916 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

MMM4Rec: An Transfer-Efficient Framework for Multi-modal Sequential Recommendation
Sequential Recommendation (SR) systems model user preferences by analyzing interaction histories. Although transferable multi-modal SR architectures demonstrate superior performance compared to traditional ID-based approaches, current methods incur substantial fine-tuning costs when adapting to new domains due to complex optimization requirements and negative transfer effects - a significant deployment bottleneck that hinders engineers from efficiently repurposing pre-trained models for novel a…

Joint Beamforming and Integer User Association using a GNN with Gumbel-Softmax Reparameterizations
Qing Lyu, Mai Vu
https://arxiv.org/abs/2506.05241 https:…

Joint Beamforming and Integer User Association using a GNN with Gumbel-Softmax Reparameterizations
Machine learning (ML) models can effectively optimize a multi-cell wireless network by designing the beamforming vectors and association decisions. Existing ML designs, however, often needs to approximate the integer association variables with a probability distribution output. We propose a novel graph neural network (GNN) structure that jointly optimize beamforming vectors and user association while guaranteeing association output as integers. The integer association constraints are satisfied …

Merge Hijacking: Backdoor Attacks to Model Merging of Large Language Models
Zenghui Yuan, Yangming Xu, Jiawen Shi, Pan Zhou, Lichao Sun
https://arxiv.org/abs/2505.23561

Merge Hijacking: Backdoor Attacks to Model Merging of Large Language Models
Model merging for Large Language Models (LLMs) directly fuses the parameters of different models finetuned on various tasks, creating a unified model for multi-domain tasks. However, due to potential vulnerabilities in models available on open-source platforms, model merging is susceptible to backdoor attacks. In this paper, we propose Merge Hijacking, the first backdoor attack targeting model merging in LLMs. The attacker constructs a malicious upload model and releases it. Once a victim user …

AURA: Agentic Upskilling via Reinforced Abstractions
Alvin Zhu, Yusuke Tanaka, Dennis Hong
https://arxiv.org/abs/2506.02507 https://a…

AURA: Agentic Upskilling via Reinforced Abstractions
We study the combinatorial explosion involved in translating high-level task prompts into deployable control policies for agile robots through multi-stage reinforcement learning. We introduce AURA (Agentic Upskilling via Reinforced Abstractions), a schema-centric curriculum RL framework that leverages Large Language Models (LLMs) as autonomous designers of multi-stage curricula. AURA transforms user prompts into YAML workflows that encode full reward functions, domain randomization strategies, …

DV365: Extremely Long User History Modeling at Instagram
Wenhan Lyu, Devashish Tyagi, Yihang Yang, Ziwei Li, Ajay Somani, Karthikeyan Shanmugasundaram, Nikola Andrejevic, Ferdi Adeputra, Curtis Zeng, Arun K. Singh, Maxime Ransan, Sagar Jain
https://arxiv.org/abs/2506.00450

DV365: Extremely Long User History Modeling at Instagram
Long user history is highly valuable signal for recommendation systems, but effectively incorporating it often comes with high cost in terms of data center power consumption and GPU. In this work, we chose offline embedding over end-to-end sequence length optimization methods to enable extremely long user sequence modeling as a cost-effective solution, and propose a new user embedding learning strategy, multi-slicing and summarization, that generates highly generalizable user representation of …

MMM4Rec: An Transfer-Efficient Framework for Multi-modal Sequential Recommendation
Hao Fan, Yanrong Hu, Kai Fang, Qingyang Liu, Hongjiu Liu
https://arxiv.org/abs/2506.02916

MMM4Rec: An Transfer-Efficient Framework for Multi-modal Sequential Recommendation
Sequential Recommendation (SR) systems model user preferences by analyzing interaction histories. Although transferable multi-modal SR architectures demonstrate superior performance compared to traditional ID-based approaches, current methods incur substantial fine-tuning costs when adapting to new domains due to complex optimization requirements and negative transfer effects - a significant deployment bottleneck that hinders engineers from efficiently repurposing pre-trained models for novel a…

This https://arxiv.org/abs/2506.02916 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

MMM4Rec: An Transfer-Efficient Framework for Multi-modal Sequential Recommendation
Sequential Recommendation (SR) systems model user preferences by analyzing interaction histories. Although transferable multi-modal SR architectures demonstrate superior performance compared to traditional ID-based approaches, current methods incur substantial fine-tuning costs when adapting to new domains due to complex optimization requirements and negative transfer effects - a significant deployment bottleneck that hinders engineers from efficiently repurposing pre-trained models for novel a…