Tootfinder

„Jan Otto, IG Metall-Bezirksleiter Berlin-Brandenburg-Sachsen:
„Tesla-Chef Elon Musk soll eine Billionen Dollar als Vergütung erhalten. Und die Beschäftigten gehen beim Weihnachtsgeld leer aus. Das ist nicht hinnehmbar und kann so nicht bleiben. Bei Tesla ist genug Geld da, weil die Kolleginnen und Kollegen hart und gut arbeiten.““

When Safe Unimodal Inputs Collide: Optimizing Reasoning Chains for Cross-Modal Safety in Multimodal Large Language Models
Wei Cai, Shujuan Liu, Jian Zhao, Ziyan Shi, Yusheng Zhao, Yuchen Yuan, Tianle Zhang, Chi Zhang, Xuelong Li
https://arxiv.org/abs/2509.12060

When Safe Unimodal Inputs Collide: Optimizing Reasoning Chains for Cross-Modal Safety in Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) are susceptible to the implicit reasoning risk, wherein innocuous unimodal inputs synergistically assemble into risky multimodal data that produce harmful outputs. We attribute this vulnerability to the difficulty of MLLMs maintaining safety alignment through long-chain reasoning. To address this issue, we introduce Safe-Semantics-but-Unsafe-Interpretation (SSUI), the first dataset featuring interpretable reasoning paths tailored for such a cross-modal c…

ReFineG: Synergizing Small Supervised Models and LLMs for Low-Resource Grounded Multimodal NER
Jielong Tang, Shuang Wang, Zhenxing Wang, Jianxing Yu, Jian Yin
https://arxiv.org/abs/2509.10975

ReFineG: Synergizing Small Supervised Models and LLMs for Low-Resource Grounded Multimodal NER
Grounded Multimodal Named Entity Recognition (GMNER) extends traditional NER by jointly detecting textual mentions and grounding them to visual regions. While existing supervised methods achieve strong performance, they rely on costly multimodal annotations and often underperform in low-resource domains. Multimodal Large Language Models (MLLMs) show strong generalization but suffer from Domain Knowledge Conflict, producing redundant or incorrect mentions for domain-specific entities. To address…

With Apache NiFi, a multimodal data pipelining tool, you can assemble existing and/or custom Java & Python processors into a variety of flows. At Berlin Buzzwords 2025, Lester Martin discussed how a rich data pipeline can be constructed from Kafka, stored using the Apache Iceberg table format and consumed from Trino.
Watch the full session: https://youtu.be/2yH9PfiXb9Y?si=Xhi_xDMzLQOps8my
Berlin Buzzwords returns on 7-9 June 2026! Get 36% off with our Trust Us Ticket: https://tickets.plainschwarz.com/bbuzz26/c/8Hvk0ZvJA/

SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models
Weiyang Jin, Yuwei Niu, Jiaqi Liao, Chengqi Duan, Aoxue Li, Shenghua Gao, Xihui Liu
https://arxiv.org/abs/2510.12784

SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models
Recently, remarkable progress has been made in Unified Multimodal Models (UMMs), which integrate vision-language generation and understanding capabilities within a single framework. However, a significant gap exists where a model's strong visual understanding often fails to transfer to its visual generation. A model might correctly understand an image based on user instructions, yet be unable to generate a faithful image from text prompts. This phenomenon directly raises a compelling question: …

SitLLM: Large Language Models for Sitting Posture Health Understanding via Pressure Sensor Data
Jian Gao, Fufangchen Zhao, Yiyang Zhang, Danfeng Yan
https://arxiv.org/abs/2509.12994

SitLLM: Large Language Models for Sitting Posture Health Understanding via Pressure Sensor Data
Poor sitting posture is a critical yet often overlooked factor contributing to long-term musculoskeletal disorders and physiological dysfunctions. Existing sitting posture monitoring systems, although leveraging visual, IMU, or pressure-based modalities, often suffer from coarse-grained recognition and lack the semantic expressiveness necessary for personalized feedback. In this paper, we propose \textbf{SitLLM}, a lightweight multimodal framework that integrates flexible pressure sensing with …

MCL-AD: Multimodal Collaboration Learning for Zero-Shot 3D Anomaly Detection
Gang Li, Tianjiao Chen, Mingle Zhou, Min Li, Delong Han, Jin Wan
https://arxiv.org/abs/2509.10282 ht…

MCL-AD: Multimodal Collaboration Learning for Zero-Shot 3D Anomaly Detection
Zero-shot 3D (ZS-3D) anomaly detection aims to identify defects in 3D objects without relying on labeled training data, making it especially valuable in scenarios constrained by data scarcity, privacy, or high annotation cost. However, most existing methods focus exclusively on point clouds, neglecting the rich semantic cues available from complementary modalities such as RGB images and texts priors. This paper introduces MCL-AD, a novel framework that leverages multimodal collaboration learnin…

TalkPlayData 2: An Agentic Synthetic Data Pipeline for Multimodal Conversational Music Recommendation
Keunwoo Choi, Seungheon Doh, Juhan Nam
https://arxiv.org/abs/2509.09685 htt…

TalkPlayData 2: An Agentic Synthetic Data Pipeline for Multimodal Conversational Music Recommendation
We present TalkPlayData 2, a synthetic dataset for multimodal conversational music recommendation generated by an agentic data pipeline. In TalkPlayData 2 pipeline, multiple large language model (LLM) agents are created under various roles with specialized prompts and access to different parts of information, and the chat data is acquired by logging the conversation between the Listener LLM and the Recsys LLM. To cover various conversation scenarios, for each conversation, the Listener LLM is c…

OmniWorld: A Multi-Domain and Multi-Modal Dataset for 4D World Modeling
Yang Zhou, Yifan Wang, Jianjun Zhou, Wenzheng Chang, Haoyu Guo, Zizun Li, Kaijing Ma, Xinyue Li, Yating Wang, Haoyi Zhu, Mingyu Liu, Dingning Liu, Jiange Yang, Zhoujie Fu, Junyi Chen, Chunhua Shen, Jiangmiao Pang, Kaipeng Zhang, Tong He
https://arxiv.org/abs/2509.12201…

OmniWorld: A Multi-Domain and Multi-Modal Dataset for 4D World Modeling
The field of 4D world modeling - aiming to jointly capture spatial geometry and temporal dynamics - has witnessed remarkable progress in recent years, driven by advances in large-scale generative models and multimodal learning. However, the development of truly general 4D world models remains fundamentally constrained by the availability of high-quality data. Existing datasets and benchmarks often lack the dynamic complexity, multi-domain diversity, and spatial-temporal annotations required to …

Replaced article(s) found for cs.IR. https://arxiv.org/list/cs.IR/new
[1/1]:
- Training-free Adjustable Polynomial Graph Filtering for Ultra-fast Multimodal Recommendation
Yu-Seung Roh, Joo-Young Kim, Jin-Duk Park, Won-Yong Shin