Tootfinder

From our #cooperative, #MajavanTila: "Membership benefits for 2026".
https://www.majavantila.fi/en/posts/…

FusionAdapter for Few-Shot Relation Learning in Multimodal Knowledge Graphs
Ran Liu, Yuan Fang, Xiaoli Li
https://arxiv.org/abs/2510.00894 https://arxiv.or…

FusionAdapter for Few-Shot Relation Learning in Multimodal Knowledge Graphs
Multimodal Knowledge Graphs (MMKGs) incorporate various modalities, including text and images, to enhance entity and relation representations. Notably, different modalities for the same entity often present complementary and diverse information. However, existing MMKG methods primarily align modalities into a shared space, which tends to overlook the distinct contributions of specific modalities, limiting their performance particularly in low-resource settings. To address this challenge, we pro…

Milco: Learned Sparse Retrieval Across Languages via a Multilingual Connector
Thong Nguyen, Yibin Lei, Jia-Huei Ju, Eugene Yang, Andrew Yates
https://arxiv.org/abs/2510.00671 ht…

Milco: Learned Sparse Retrieval Across Languages via a Multilingual Connector
Learned Sparse Retrieval (LSR) combines the efficiency of bi-encoders with the transparency of lexical matching, but existing approaches struggle to scale beyond English. We introduce MILCO, an LSR architecture that maps queries and documents from different languages into a shared English lexical space via a multilingual connector. MILCO is trained with a specialized two-stage regime that combines Sparse Alignment Pretraining with contrastive training to provide representation transparency and …

Mad Tribe is quickly becoming one of my favorite Psytrance artists, especially since it’s a collaboration between SPACE TRIBE and MAD MAXX (Olli Wisdom and Max Peterson), two of my all-time favorite psytrance acts.
https://madtribe.bandcamp.com

Adaptive Bayesian Joint Latent Space Modeling via Cumulative Shrinkage
Bin Lv, Yincai Tang, Siliang Zhang
https://arxiv.org/abs/2509.18580 https://arxiv.or…

Heineman shared the message last night that her health was rapidly declining.
"It's time. According to my doctors. All further treatments are pointless," Heineman wrote.
"So, please donate so my kids can create a funeral worthy of my keyboard, Pixelbreaker! So I can make a worthy entrance for reuniting with my one true love, Jennell Jaquays."
Game developers have begun sharing their own condolences and remembrances in the wake of Heineman's death

Legendary game designer, programmer, Space Invaders champion, and LGBTQ trailblazer Rebecca Heineman has died
Heineman's cancer fundraiser is now collecting for her funeral.

FusionAdapter for Few-Shot Relation Learning in Multimodal Knowledge Graphs
Ran Liu, Yuan Fang, Xiaoli Li
https://arxiv.org/abs/2510.00894 https://arxiv.or…

FusionAdapter for Few-Shot Relation Learning in Multimodal Knowledge Graphs
Multimodal Knowledge Graphs (MMKGs) incorporate various modalities, including text and images, to enhance entity and relation representations. Notably, different modalities for the same entity often present complementary and diverse information. However, existing MMKG methods primarily align modalities into a shared space, which tends to overlook the distinct contributions of specific modalities, limiting their performance particularly in low-resource settings. To address this challenge, we pro…

Improving Generalizability and Undetectability for Targeted Adversarial Attacks on Multimodal Pre-trained Models
Zhifang Zhang, Jiahan Zhang, Shengjie Zhou, Qi Wei, Shuo He, Feng Liu, Lei Feng
https://arxiv.org/abs/2509.19994

Improving Generalizability and Undetectability for Targeted Adversarial Attacks on Multimodal Pre-trained Models
Multimodal pre-trained models (e.g., ImageBind), which align distinct data modalities into a shared embedding space, have shown remarkable success across downstream tasks. However, their increasing adoption raises serious security concerns, especially regarding targeted adversarial attacks. In this paper, we show that existing targeted adversarial attacks on multimodal pre-trained models still have limitations in two aspects: generalizability and undetectability. Specifically, the crafted targe…

OpenAI is piloting group chats in ChatGPT in Japan, New Zealand, South Korea, and Taiwan; up to 20 users can be invited to prompt ChatGPT in a shared space (OpenAI)
https://openai.com/index/group-chats-in-chatgpt

Mixture of Encoders is a vector-native alternative that models both structured and unstructured data in a unified embedding space. At Berlin Buzzwords 2025, Filip Makraduli and Ben Gutkovich introduced the method, demonstrated how it powers natural language search and real-time recommendations, and shared open-source tools and benchmarks for replacing complex hybrid stacks.
Watch the full session: https://youtu.be/v-ak-Ohjqas?si=Lyvqcr0fb-0sa4ZW
Berlin Buzzwords returns on 7-9 June 2026: https://tickets.plainschwarz.com/bbuzz26/c/8Hvk0ZvJA/

SupCLAP: Controlling Optimization Trajectory Drift in Audio-Text Contrastive Learning with Support Vector Regularization
Jiehui Luo, Yuguo Yin, Yuxin Xie, Jinghan Ru, Xianwei Zhuang, Minghua He, Aofan Liu, Zihan Xiong, Dongchao Yang
https://arxiv.org/abs/2509.21033

SupCLAP: Controlling Optimization Trajectory Drift in Audio-Text Contrastive Learning with Support Vector Regularization
Contrastive language-audio pretraining, which aims to unify multimodal representations in a shared embedding space, serves as a cornerstone for building a wide range of applications, from cross-modal retrieval to cutting-edge multimodal large language models. However, we find that the perpendicular component of the pushing force from negative samples in contrastive learning is a double-edged sword: it contains rich supplementary information from negative samples, yet its unconstrained nature ca…

MOCLIP: A Foundation Model for Large-Scale Nanophotonic Inverse Design
S. Rodionov, A. Burguete-Lopez, M. Makarenko, Q. Wang, F. Getman, A. Fratalocchi
https://arxiv.org/abs/2511.18980 https://arxiv.org/pdf/2511.18980 https://arxiv.org/html/2511.18980
arXiv:2511.18980v1 Announce Type: new
Abstract: Foundation models (FM) are transforming artificial intelligence by enabling generalizable, data-efficient solutions across different domains for a broad range of applications. However, the lack of large and diverse datasets limits the development of FM in nanophotonics. This work presents MOCLIP (Metasurface Optics Contrastive Learning Pretrained), a nanophotonic foundation model that integrates metasurface geometry and spectra within a shared latent space. MOCLIP employs contrastive learning to align geometry and spectral representations using an experimentally acquired dataset with a sample density comparable to ImageNet-1K. The study demonstrates MOCLIP inverse design capabilities for high-throughput zero-shot prediction at a rate of 0.2 million samples per second, enabling the design of a full 4-inch wafer populated with high-density metasurfaces in minutes. It also shows generative latent-space optimization reaching 97 percent accuracy. Finally, we introduce an optical information storage concept that uses MOCLIP to achieve a density of 0.1 Gbit per square millimeter at the resolution limit, exceeding commercial optical media by a factor of six. These results position MOCLIP as a scalable and versatile platform for next-generation photonic design and data-driven applications.
toXiv_bot_toot

Exploring Bichronous Collaboration in Virtual Environments
Alexander Giovannelli, Shakiba Davari, Cherelle Connor, Fionn Murphy, Trey Davis, Haichao Miao, Vuthea Chheang, Brian Giera, Peer-Timo Bremer, Doug A. Bowman
https://arxiv.org/abs/2509.17230

Exploring Bichronous Collaboration in Virtual Environments
Virtual environments (VEs) empower geographically distributed teams to collaborate on a shared project regardless of time. Existing research has separately investigated collaborations within these VEs at the same time (i.e., synchronous) or different times (i.e., asynchronous). In this work, we highlight the often-overlooked concept of bichronous collaboration and define it as the seamless integration of archived information during a real-time collaborative session. We revisit the time-space ma…

At a Pride festival in October,
a volunteer approached Sara Luce Look, the co-owner of
Charis Books and More,
and shared that decades prior,
a person in her life was sexually abused.
The person’s counselor had suggested they go to Charis for support.
Known as “the South’s oldest independent feminist bookstore,”
the Decatur, Georgia, shop
not only offered books that the survivor found helpful,
but it also provided a safe space where the…

Home Page
Charis Books & More is the South's oldest independent feminist bookstore, celebrating radical and queer voices since 1974!

Admission Control for Inelastic Traffic on a Link Shared by Deadline-Driven Elastic Traffic
Patrick Kreidl
https://arxiv.org/abs/2509.08135 https://arxiv.o…

Admission Control for Inelastic Traffic on a Link Shared by Deadline-Driven Elastic Traffic
Consider a (logical) link between two distributed data centers with available bandwidth designated for both deadline-driven elastic traffic, such as for scheduled synchronization services, and profitable inelastic traffic, such as for real-time streaming services. Admission control in this setting is cast as a stochastic shortest path problem, with state space derived from (discretization of) the elastic flow's size/deadline and action space corresponding to alternative subsets of admitted inel…

The Mall That Could Have Been: What Happened to Our Vision for SF Centre / Westfield Mall
https://www.kultivatelabs.org/blog/the-mall-that-could-have-been-what-happened-to-our-vision-for-sf-centre

Scaling Language-Centric Omnimodal Representation Learning
Chenghao Xiao, Hou Pong Chan, Hao Zhang, Weiwen Xu, Mahani Aljunied, Yu Rong
https://arxiv.org/abs/2510.11693 https://…

Scaling Language-Centric Omnimodal Representation Learning
Recent multimodal embedding approaches leveraging multimodal large language models (MLLMs) fine-tuned with contrastive learning (CL) have shown promising results, yet the underlying reasons behind their superiority remain underexplored. This work argues that a crucial advantage of MLLM-based approaches stems from implicit cross-modal alignment achieved during generative pretraining, where the language decoder learns to exploit multimodal signals within a shared representation space for generati…

Diffusion-Guided Multi-Arm Motion Planning
Viraj Parimi, Brian C. Williams
https://arxiv.org/abs/2509.08160 https://arxiv.org/pdf/2509.08160

Diffusion-Guided Multi-Arm Motion Planning
Multi-arm motion planning is fundamental for enabling arms to complete complex long-horizon tasks in shared spaces efficiently but current methods struggle with scalability due to exponential state-space growth and reliance on large training datasets for learned models. Inspired by Multi-Agent Path Finding (MAPF), which decomposes planning into single-agent problems coupled with collision resolution, we propose a novel diffusion-guided multi-arm planner (DG-MAP) that enhances scalability of lea…

Fair metric distortion for matching with preferences
Jabari Hastings, Prasanna Ramakrishnan
https://arxiv.org/abs/2510.05460 https://arxiv.org/pdf/2510.054…

Fair metric distortion for matching with preferences
We consider the matching problem in the metric distortion framework. There are $n$ agents and $n$ items occupying points in a shared metric space, and the goal is to design a matching mechanism that outputs a low-cost matching between the agents and items, using only agents' ordinal rankings of the candidates by distance. A mechanism has distortion $α$ if it always outputs a matching whose cost is within a factor of $α$ of the optimum, in every instance regardless of the metric space. Typic…

Batched high-rate logical operations for quantum LDPC codes
Qian Xu, Hengyun Zhou, Dolev Bluvstein, Madelyn Cain, Marcin Kalinowski, John Preskill, Mikhail D. Lukin, Nishad Maskara
https://arxiv.org/abs/2510.06159

Batched high-rate logical operations for quantum LDPC codes
High-rate quantum LDPC (qLDPC) codes reduce memory overhead by densely packing many logical qubits into a single block of physical qubits. Here we extend this concept to high-rate computation by constructing \emph{batched} fault-tolerant operations that apply the same logical gate across many code blocks in parallel. By leveraging shared physical resources to execute many logical operations in parallel, these operations realize high rates in space-time and significantly reduce computational cos…

3D Aware Region Prompted Vision Language Model
An-Chieh Cheng, Yang Fu, Yukang Chen, Zhijian Liu, Xiaolong Li, Subhashree Radhakrishnan, Song Han, Yao Lu, Jan Kautz, Pavlo Molchanov, Hongxu Yin, Xiaolong Wang, Sifei Liu
https://arxiv.org/abs/2509.13317

3D Aware Region Prompted Vision Language Model
We present Spatial Region 3D (SR-3D) aware vision-language model that connects single-view 2D images and multi-view 3D data through a shared visual token space. SR-3D supports flexible region prompting, allowing users to annotate regions with bounding boxes, segmentation masks on any frame, or directly in 3D, without the need for exhaustive multi-frame labeling. We achieve this by enriching 2D visual features with 3D positional embeddings, which allows the 3D model to draw upon strong 2D priors…

Flow-Based Fragment Identification via Binding Site-Specific Latent Representations
Rebecca Manuela Neeser, Ilia Igashov, Arne Schneuing, Michael Bronstein, Philippe Schwaller, Bruno Correia
https://arxiv.org/abs/2509.13216

Flow-Based Fragment Identification via Binding Site-Specific Latent Representations
Fragment-based drug design is a promising strategy leveraging the binding of small chemical moieties that can efficiently guide drug discovery. The initial step of fragment identification remains challenging, as fragments often bind weakly and non-specifically. We developed a protein-fragment encoder that relies on a contrastive learning approach to map both molecular fragments and protein surfaces in a shared latent space. The encoder captures interaction-relevant features and allows to perfor…

Cross-device Zero-shot Label Transfer via Alignment of Time Series Foundation Model Embeddings
Neal G. Ravindra, Arijit Sehanobish
https://arxiv.org/abs/2509.06966 https://

Cross-device Zero-shot Label Transfer via Alignment of Time Series Foundation Model Embeddings
High-quality, medically validated labels exist for clinical actigraphy data but not for ubiquitous consumer wearables like the Apple Watch. Manually labeling wearables data is expensive and doesn't scale. This paper offers a novel framework that transfers valuable labels from a source domain (e.g., actigraphy) to a target domain (e.g., Apple Watch) without requiring paired data. Instead of working with raw time-series signals, we project both domains into a shared latent embedding space using t…

Unbounded: Object-Boundary Interactions in Mixed Reality
Zhuoyue Lyu, Per Ola Kristensson
https://arxiv.org/abs/2509.10750 https://arxiv.org/pdf/2509.10750…

Unbounded: Object-Boundary Interactions in Mixed Reality
Boundaries such as walls, windows, and doors are ubiquitous in the physical world, yet their potential in Mixed Reality (MR) remains underexplored. We present Unbounded, a Research through Design inquiry into Object-Boundary Interactions (OBIs). Building on prior work, we articulate a design space aimed at providing a shared language for OBIs. To demonstrate its potential, we design and implement eight examples across productivity and art exploration scenarios, showcasing how boundaries can enr…

Co-Located VR with Hybrid SLAM-based HMD Tracking and Motion Capture Synchronization
Carlos A. Pinheiro de Sousa, Niklas Gr\"one, Mathias G\"unther, Oliver Deussen
https://arxiv.org/abs/2509.06582

Co-Located VR with Hybrid SLAM-based HMD Tracking and Motion Capture Synchronization
We introduce a multi-user VR co-location framework that synchronizes users within a shared virtual environment aligned to physical space. Our approach combines a motion capture system with SLAM-based inside-out tracking to deliver smooth, high-framerate, low-latency performance. Previous methods either rely on continuous external tracking, which introduces latency and jitter, or on one-time calibration, which cannot correct drift over time. In contrast, our approach combines the responsiveness …

Hybrid Architectures for Language Models: Systematic Analysis and Design Insights
Sangmin Bae, Bilge Acun, Haroun Habeeb, Seungyeon Kim, Chien-Yu Lin, Liang Luo, Junjie Wang, Carole-Jean Wu
https://arxiv.org/abs/2510.04800

Hybrid Architectures for Language Models: Systematic Analysis and Design Insights
Recent progress in large language models demonstrates that hybrid architectures--combining self-attention mechanisms with structured state space models like Mamba--can achieve a compelling balance between modeling quality and computational efficiency, particularly for long-context tasks. While these hybrid models show promising performance, systematic comparisons of hybridization strategies and analyses on the key factors behind their effectiveness have not been clearly shared to the community.…

Boosting Data Utilization for Multilingual Dense Retrieval
Chao Huang, Fengran Mo, Yufeng Chen, Changhao Guan, Zhenrui Yue, Xinyu Wang, Jinan Xu, Kaiyu Huang
https://arxiv.org/abs/2509.09459

Boosting Data Utilization for Multilingual Dense Retrieval
Multilingual dense retrieval aims to retrieve relevant documents across different languages based on a unified retriever model. The challenge lies in aligning representations of different languages in a shared vector space. The common practice is to fine-tune the dense retriever via contrastive learning, whose effectiveness highly relies on the quality of the negative sample and the efficacy of mini-batch data. Different from the existing studies that focus on developing sophisticated model arc…

Cheat-Penalised Quantum Weak Coin-Flipping
Atul Singh Arora, Carl A. Miller, Mauro E. S. Morales, Jamie Sikora
https://arxiv.org/abs/2510.03218 https://arx…

Cheat-Penalised Quantum Weak Coin-Flipping
Coin-flipping is a fundamental task in two-party cryptography where two remote mistrustful parties wish to generate a shared uniformly random bit. While quantum protocols promising near-perfect security exist for weak coin-flipping -- when the parties want opposing outcomes -- it has been shown that they must be inefficient in terms of their round complexity, and it is an open question of how space efficient they can be. In this work, we consider a variant called cheat-penalised weak coin-flipp…

LADB: Latent Aligned Diffusion Bridges for Semi-Supervised Domain Translation
Xuqin Wang, Tao Wu, Yanfeng Zhang, Lu Liu, Dong Wang, Mingwei Sun, Yongliang Wang, Niclas Zeller, Daniel Cremers
https://arxiv.org/abs/2509.08628

LADB: Latent Aligned Diffusion Bridges for Semi-Supervised Domain Translation
Diffusion models excel at generating high-quality outputs but face challenges in data-scarce domains, where exhaustive retraining or costly paired data are often required. To address these limitations, we propose Latent Aligned Diffusion Bridges (LADB), a semi-supervised framework for sample-to-sample translation that effectively bridges domain gaps using partially paired data. By aligning source and target distributions within a shared latent space, LADB seamlessly integrates pretrained source…

Symmetry-Aware Fully-Amortized Optimization with Scale Equivariant Graph Metanetworks
Bart Kuipers, Freek Byrman, Daniel Uyterlinde, Alejandro Garc\'ia-Castellanos
https://arxiv.org/abs/2510.08300 …

Symmetry-Aware Fully-Amortized Optimization with Scale Equivariant Graph Metanetworks
Amortized optimization accelerates the solution of related optimization problems by learning mappings that exploit shared structure across problem instances. We explore the use of Scale Equivariant Graph Metanetworks (ScaleGMNs) for this purpose. By operating directly in weight space, ScaleGMNs enable single-shot fine-tuning of existing models, reducing the need for iterative optimization. We demonstrate the effectiveness of this approach empirically and provide a theoretical result: the gauge …

Align Your Query: Representation Alignment for Multimodality Medical Object Detection
Ara Seo, Bryan Sangwoo Kim, Hyungjin Chung, Jong Chul Ye
https://arxiv.org/abs/2510.02789 h…

Align Your Query: Representation Alignment for Multimodality Medical Object Detection
Medical object detection suffers when a single detector is trained on mixed medical modalities (e.g., CXR, CT, MRI) due to heterogeneous statistics and disjoint representation spaces. To address this challenge, we turn to representation alignment, an approach that has proven effective for bringing features from different sources into a shared space. Specifically, we target the representations of DETR-style object queries and propose a simple, detector-agnostic framework to align them with modal…