Tootfinder

the safety advice following the recent npm phishing attacks reminds me of an old rant https://dotat.at/@/2016-10-19-domain-transfers-are-shocking.html
i hope things have improved since then (happily i have not needed to do any domain transfers recently)…

https://therecord.media/angus-king-cyber-domain-cuts-cisa
Sen. Angus King (I-ME) on Tuesday said that recent cuts across the government have weakened U.S. cybersecurity even as attacks against infrastructure and business skyrocket.

Sen. King: Cyber domain is a ‘hellscape’ that will be made worse by cuts
King, a leading voice in the Senate on cybersecurity issues, honed in on the thousands of staffers and experts laid off by CISA, saying the agency has lost 30 percent of its staff and most of its seasoned leaders.

Fair Domain Generalization: An Information-Theoretic View
Tangzheng Lian, Guanyu Hu, Dimitrios Kollias, Xinyu Yang, Oya Celiktutan
https://arxiv.org/abs/2507.05823

Fair Domain Generalization: An Information-Theoretic View
Domain generalization (DG) and algorithmic fairness are two critical challenges in machine learning. However, most DG methods focus only on minimizing expected risk in the unseen target domain without considering algorithmic fairness. Conversely, fairness methods typically do not account for domain shifts, so the fairness achieved during training may not generalize to unseen test domains. In this work, we bridge these gaps by studying the problem of Fair Domain Generalization (FairDG), which ai…

CDC: Causal Domain Clustering for Multi-Domain Recommendation
Huishi Luo, Yiqing Wu, Yiwen Chen, Fuzhen Zhuang, Deqing Wang
https://arxiv.org/abs/2507.06877

CDC: Causal Domain Clustering for Multi-Domain Recommendation
Multi-domain recommendation leverages domain-general knowledge to improve recommendations across several domains. However, as platforms expand to dozens or hundreds of scenarios, training all domains in a unified model leads to performance degradation due to significant inter-domain differences. Existing domain grouping methods, based on business logic or data similarities, often fail to capture the true transfer relationships required for optimal grouping. To effectively cluster domains, we pr…

I’m interested in seeing if more folks end up using the new #dotnet 10 domain name feature for local development. So often, I see mistakes that essentially creep in because the domain and app topology don’t match those of staging and production environments.
I wish there was better domain name tools for #dotnet

#PSA #feditips It seems that Nepal has blocked access to (some?) Mastodon sites.
With domain fronting the blocking of many fediverse sites can be circumvented.
This is based on the fact that many websites share the same IP number. Therefore a government blocking access to a website can in many cases only block the domain, but not the IP of the site, otnerwise many other websites would be blocked too.
By either configuring a proxy in the mastodon app or on the device or by usin…

#PSA #feditips It seems that Nepal has blocked access to (some?) Mastodon sites.
With domain fronting the blocking of many fediverse sites can be circumvented.
This is based on the fact that many websites share the same IP number. Therefore a government blocking access to a website can in many cases only block the domain, but not the IP of the site, otnerwise many other websites would be blocked too.
By either configuring a proxy in the mastodon app or on the device or by usin…

interactome_pdz: PDZ-domain interactome (2005)
A network of PDZ-domain-mediated protein–protein binding interactions, extracted from the PDZBase database. Nodes represent proteins and an edge represents a binding interaction between two proteins.
This network has 212 nodes and 244 edges.
Tags: Biological, Protein interactions, Unweighted

Request for #recommendations:
I want to find a new #domain #registrar.
For my domains with no active site on them (at the moment), meant to forward to main site (using registrar l…

Domain-Aware RAG: MoL-Enhanced RL for Efficient Training and Scalable Retrieval
Hao Lin, Peitong Xie, Jingxue Chen, Jie Lin, Qingkun Tang, Qianchun Lu
https://arxiv.org/abs/2509.06650

Domain-Aware RAG: MoL-Enhanced RL for Efficient Training and Scalable Retrieval
Retrieval-Augmented Generation (RAG) systems rely heavily on the retrieval stage, particularly the coarse-ranking process. Existing coarse-ranking optimization approaches often struggle to balance domain-specific knowledge learning with query enhencement, resulting in suboptimal retrieval performance. To address this challenge, we propose MoLER, a domain-aware RAG method that uses MoL-Enhanced Reinforcement Learning to optimize retrieval. MoLER has a two-stage pipeline: a continual pre-training…

Samesies.
As I was with the Metaverse. As I will have been with "AI".
It's as if there was a pattern of people with domain experience valuing good solutions who call out bullshit tend to be right.
https://mastodon.social/@mhoye/115174150692070811

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective
Zhiyi Dong, Zixuan Liu, Yongyi Mao
https://arxiv.org/abs/2507.06552

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective
This paper studies the hardness of unsupervised domain adaptation (UDA) under covariate shift. We model the uncertainty that the learner faces by a distribution $π$ in the ground-truth triples $(p, q, f)$ -- which we call a UDA class -- where $(p, q)$ is the source -- target distribution pair and $f$ is the classifier. We define the performance of a learner as the overall target domain risk, averaged over the randomness of the ground-truth triple. This formulation couples the source distributi…

Sen. King: Cyber domain is a ‘hellscape’ that will be made worse by cuts https://therecord.media/angus-king-cyber-domain-cuts-cisa

Design and Implementation of a Domain-specific Language for Modelling Evacuation Scenarios Using Eclipse EMG/GMF Tool
Heerok Banerjee
https://arxiv.org/abs/2509.06688 https://…

Design and Implementation of a Domain-specific Language for Modelling Evacuation Scenarios Using Eclipse EMG/GMF Tool
Domain-specific languages (DSLs) play a crucial role in resolving internal dependencies across enterprises and boosts their upfront business management processes. Yet, a lot of development is needed to build modelling frameworks which support graphical interfaces (canvas, pallettes etc.), hierarchical structures and easy implementation to shorten the gap for novice users. In this paper, a DSL namely, Bmod is introduced, which can be used to model evacuation scenarios. The language is built usin…

from my link log —
What declarative languages are.
https://semantic-domain.blogspot.com/2013/07/what-declarative-languages-are.html
saved 2025-08-01

Resolving the QCD Axion Domain Wall Problem with a Light Axion
Junseok Lee, Kai Murai, Fuminobu Takahashi, Wen Yin
https://arxiv.org/abs/2507.07075 https:/…

Resolving the QCD Axion Domain Wall Problem with a Light Axion
We propose two novel solutions to the domain wall problem of the QCD axion by introducing a massless or light axion that also couples to gluons. The first solution applies when the new axion forms strings after inflation. Due to its mixing with the QCD axion, domain walls of the QCD axion are bounded by these strings and confined into cosmologically safe string bundles. This scenario predicts the existence of such string bundles, which may survive until today and leave observable signatures, su…

In Mastodon v4.4.0 instance owners can enable HTTP referrers.
If somebody clicks a link in a Mastodon post (e.g. to a news article), the link target owner (such as the news site operator) can see that a user from that instance has visited their website.
They do _only_ get the instance domain (e.g. gardenstate.social). No information about the user!
That being said I think gardenstate.social is probably too small to turn this on. Let me know if you feel different!

The whole domain registration industry is a cesspit of rentier crooks working for the benefit of rentier crooks. Including ICANN.
It was a mistake to end the InterNIC.
https://social.growyourown.services/@FediTips/115167305897549859

FoMo4Wheat: Toward reliable crop vision foundation models with globally curated data
Bing Han, Chen Zhu, Dong Han, Rui Yu, Songliang Cao, Jianhui Wu, Scott Chapman, Zijian Wang, Bangyou Zheng, Wei Guo, Marie Weiss, Benoit de Solan, Andreas Hund, Lukas Roth, Kirchgessner Norbert, Andrea Visioni, Yufeng Ge, Wenjuan Li, Alexis Comar, Dong Jiang, Dejun Han, Fred Baret, Yanfeng Ding, Hao Lu, Shouyang Liu

FoMo4Wheat: Toward reliable crop vision foundation models with globally curated data
Vision-driven field monitoring is central to digital agriculture, yet models built on general-domain pretrained backbones often fail to generalize across tasks, owing to the interaction of fine, variable canopy structures with fluctuating field conditions. We present FoMo4Wheat, one of the first crop-domain vision foundation model pretrained with self-supervision on ImAg4Wheat, the largest and most diverse wheat image dataset to date (2.5 million high-resolution images collected over a decade a…

A Synthetic-to-Real Dehazing Method based on Domain Unification
Zhiqiang Yuan, Jinchao Zhang, Jie Zhou
https://arxiv.org/abs/2509.05374 https://arxiv.org/p…

A Synthetic-to-Real Dehazing Method based on Domain Unification
Due to distribution shift, the performance of deep learning-based method for image dehazing is adversely affected when applied to real-world hazy images. In this paper, we find that such deviation in dehazing task between real and synthetic domains may come from the imperfect collection of clean data. Owing to the complexity of the scene and the effect of depth, the collected clean data cannot strictly meet the ideal conditions, which makes the atmospheric physics model in the real domain incon…

Does anyone have experience with Spaceship (#Spaceship.

What do you mean that someone can buy your domain 12 days before it expires?
It happened to gup.pe who runs the group system a.gup.pe
Their domain was supposed to expire on the 15th, but the domain is already been bought by someone else to host spam articles.
According to Namecheap, there is no renewal period and no pendingDelete period, so customers must renew before the last 12 days of the domain, or it can be bought instantly within that 12-day period.

Secure and Trustful Cross-domain Communication with Decentralized Identifiers in 5G and Beyond
Hai Dinh-Tuan, Sandro Rodriguez Garzon, Jianeng Fu
https://arxiv.org/abs/2509.05797

Secure and Trustful Cross-domain Communication with Decentralized Identifiers in 5G and Beyond
In the evolving landscape of future mobile networks, there is a critical need for secure and trustful communication modalities to support dynamic interactions among core network components of different network domains. This paper proposes the application of W3C-endorsed Decentralized Identifiers (DIDs) to establish secure and trustful communication channels among network functions in 5G and subsequent generations. A new communication agent is introduced that integrates seamlessly with 5G-standa…

Bulk Ferroelectric Heterostructures: Imprinted Actuators
Yizhe Li, Ziqi Yang, Ying Chen, Zhenbo Zhang, Yun-Long Tang, Annette K. Kleppe, Egor Koemets, Xuezhen Cao, Steven J. Milne, Juncheng Pan, Jiajun Shi, Yuge Yang, David A. Hall
https://arxiv.org/abs/2509.06177

Bulk Ferroelectric Heterostructures: Imprinted Actuators
Domain switching is the cornerstone of ferroelectric materials. Most associated functionalities can be tuned via domain switching, including but not limited to piezoelectricity, thermal conductivity, domain wall conductivity and topological structures. However, achieving the full potential of reversible ferroelectric domain switching is restricted by the incomplete access to the entire ferroelectric texture space, as well as the memory effects and energy dissipation associated with the hysteret…

THz-PINNs: Time-Domain Forward Modeling of Terahertz Spectroscopy with Physics-Informed Neural Networks
Pengfei Zhu, Xavier Maldague
https://arxiv.org/abs/2509.07161 https://

THz-PINNs: Time-Domain Forward Modeling of Terahertz Spectroscopy with Physics-Informed Neural Networks
Terahertz time-domain spectroscopy (THz-TDS) is a powerful tool for extracting optical and electrical parameters, as well as probing internal structures and surface morphology of materials. However, conventional simulation techniques, such as the finite element method (FEM) and finite-difference time-domain (FDTD), face challenges in accurately modeling THz wave propagation. These difficulties arise from the broad operating bandwidth of THz-TDS (~0.1-10 THz), which demands extremely high tempor…

At Berlin Buzzwords 2025, Evgeniya Sukhodolskaya presented miniCOIL—a novel approach to creating a sparse neural retrieval model. This model aims to combine the advantages of dense and lexical retrieval methods while avoiding their drawbacks. Evgeniya discussed how to design and train a lightweight model that performs well on out-of-domain data and demonstrated its capabilities.
Watch the full session: #bbuzz 2026: https://tickets.plainschwarz.com/bbuzz26/c/8Hvk0ZvJA/

Design of Input-Output Observers for a Population of Systems with Bounded Frequency-Domain Variation using $DK$-iteration
Timothy Everett Adams, James Richard Forbes
https://arxiv.org/abs/2509.07201

Design of Input-Output Observers for a Population of Systems with Bounded Frequency-Domain Variation using $DK$-iteration
This paper proposes a linear input-output observer design methodology for a population of systems in which each observer uses knowledge of the linear time-invariant dynamics of the particular device. Observers are typically composed of a known model of the system and a correction mechanism to produce an estimate of the state. The proposed design procedure characterizes the variation within the population in the frequency domain and synthesizes a single robust correction filter. The correction f…

An overlapping domain decomposition method for parametric Stokes and Stokes-Darcy problems via proper generalized decomposition
Marco Discacciati, Ben J. Evans, Matteo Giacomini
https://arxiv.org/abs/2507.06861

An overlapping domain decomposition method for parametric Stokes and Stokes-Darcy problems via proper generalized decomposition
A strategy to construct physics-based local surrogate models for parametric Stokes flows and coupled Stokes-Darcy systems is presented. The methodology relies on the proper generalized decomposition (PGD) method to reduce the dimensionality of the parametric flow fields and on an overlapping domain decomposition (DD) paradigm to reduce the number of globally coupled degrees of freedom in space. The DD-PGD approach provides a non-intrusive framework in which end-users only need access to the mat…

Heute vor 80 Jahren: Am 9. August 1945 detonierte die #USA eine #Atombombe über #Nagasaki, was zwischen 60.000 und 80.000 Todesopfer forderte. Diese Ereignisse führten zur Kapitulation

Also a huge warning: friends don’t let friends register .pe domains.
> This ccTLD's rules allow anyone to buy the domain 12 days before it expires.
https://github.com/immers-space/guppe/issues/118#issuecomment-3265015461

Guppe is dead? · Issue #118 · immers-space/guppe
Hey @wmurphyrd 👋 I notice as of September 2025, the domains gup.pe and immers.space seem to have expired and a.gup.pe has been replaced with a WordPress installation full of SEO-targeted content ab...

In-Context Policy Adaptation via Cross-Domain Skill Diffusion
Minjong Yoo, Woo Kyung Kim, Honguk Woo
https://arxiv.org/abs/2509.04535 https://arxiv.org/pdf…

In-Context Policy Adaptation via Cross-Domain Skill Diffusion
In this work, we present an in-context policy adaptation (ICPAD) framework designed for long-horizon multi-task environments, exploring diffusion-based skill learning techniques in cross-domain settings. The framework enables rapid adaptation of skill-based reinforcement learning policies to diverse target domains, especially under stringent constraints on no model updates and only limited target domain data. Specifically, the framework employs a cross-domain skill diffusion scheme, where domai…

Efficient Computation of Voronoi Diagrams Using Point-in-Cell Tests
Yanyang Xiao, Yao Li, Juan Cao, Zhonggui Chen
https://arxiv.org/abs/2509.07175 https://…

Efficient Computation of Voronoi Diagrams Using Point-in-Cell Tests
Since the Voronoi diagram appears in many applications, the topic of improving its computational efficiency remains attractive. We propose a novel yet efficient method to compute Voronoi diagrams bounded by a given domain, i.e., the clipped or restricted Voronoi diagrams. The intersection of the domain and a Voronoi cell (domain-cell intersection) is generated by removing the part outside the cell from the domain, which can be accomplished by several clippings. Different from the existing metho…

This https://arxiv.org/abs/2410.08476 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

JingZhao: A Framework for Rapid NIC Prototyping in the Domain-Specific-Network Era
The network is becoming domain-specific, which requires on-demand design of the network protocols, as well as the microarchitecture of the NIC. However, to develop such a NIC is not that easy. Since the scissor gap between network speed and the growth of CPU frequency is expanding, most of the protocols need to be offloaded to hardware. The process of designing, verifying and optimizing a domain-specific NIC usually takes great effort, which hinders the rapid iteration of new protocols and algo…

Ultrafast and reliable domain-wall and skyrmion logic in a chirally coupled ferrimagnet
Yifei Ma, Dihua Wu, Fengbo Yan, Xiaoxiao Fang, Peixin Qin, Leran Wang, Li Liu, Laichuan Shen, Zhiqi Liu, Wenyun Yang, Jie Zhang, Yan Zhou, Feng Luo, Jinbo Yang, Hyunsoo Yang, Kaiming Cai, Shuai Ning, Zhaochu Luo
https://arxiv.org/abs/2507.070…

Ultrafast and reliable domain-wall and skyrmion logic in a chirally coupled ferrimagnet
Unlocking the spin degree of freedom in addition to the electron's charge, spin-based logic offers an in-memory computing architecture beyond-CMOS technology. Here, we encode information into chiral spin textures (e.g., chiral domain-wall and skyrmion) and achieve an ultrafast and reliable all-electrical logic by exploiting the Dzyaloshinskii-Moriya interaction-induced chiral coupling. Taking advantage of fast spin dynamics in antiferromagnetically coupled systems, we achieved a fast domain-wal…

SciNLP: A Domain-Specific Benchmark for Full-Text Scientific Entity and Relation Extraction in NLP
Decheng Duan, Yingyi Zhang, Jitong Peng, Chengzhi Zhang
https://arxiv.org/abs/2509.07801

SciNLP: A Domain-Specific Benchmark for Full-Text Scientific Entity and Relation Extraction in NLP
Structured information extraction from scientific literature is crucial for capturing core concepts and emerging trends in specialized fields. While existing datasets aid model development, most focus on specific publication sections due to domain complexity and the high cost of annotating scientific texts. To address this limitation, we introduce SciNLP - a specialized benchmark for full-text entity and relation extraction in the Natural Language Processing (NLP) domain. The dataset comprises …

Concentration and non-concentration of eigenfunctions of second-order elliptic operators with a divergence form in layered media
Matania Ben-Artzi (HUJ), Yves Dermenjian (I2M)
https://arxiv.org/abs/2509.06352

Concentration and non-concentration of eigenfunctions of second-order elliptic operators with a divergence form in layered media
Let $Ω$ ' $\subset$ R^d , d = 1, 2, . . . be an open bounded smooth domain, and $Ω= Ω'\times (0,H)\subset \mathbb{R}^d \times \mathbb\_+.$ The coordinates in $Ω$ are designated as x = (x ' , y) $\in$ $Ω$ ' x (0, H). The paper deals with the concentration (and non-concentration) properties (in sectors of $Ω$) of the eigenfunctions of the self-adjoint second-order elliptic operator $A = -\nabla\cdot\tilde{c}\nabla$ in $L^2(Ω,dx)$ with domain $D(A) = \{v\in H\_0^1(Ω); \tilde{c}\nabla v…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/6]:
- Optimal Transport for Domain Adaptation through Gaussian Mixture Models
Eduardo Fernandes Montesuma, Fred Maurice Ngol\`e Mboula, Antoine Souloumiac

Dynamical systems as enriched functors
Suddhasattwa Das, Tomoharu Suda
https://arxiv.org/abs/2509.05900 https://arxiv.org/pdf/2509.05900

Dynamical systems as enriched functors
This article presents a general description of dynamical systems using the language of enriched functors and enriched natural transformations. This framework is essential to establish the equivalence of three descriptions of dynamics -- a semigroup action on the domain; a parameterized family of endomorphisms; and a transformation of time-space into the collection of endomorphisms. A collection of categorical axioms are presented that provides a complete categorical language to develop dynamica…

Presentations for semigroups of full-domain partitions
Luka Carroll, James East, Matthias Fresacher
https://arxiv.org/abs/2507.05497 https://

Presentations for semigroups of full-domain partitions
The full-domain partition monoid $P_n^{fd}$ has been discovered independently in two recent studies on connections between diagram monoids and category theory. It is a right restriction Ehresmann monoid, and contains both the full transformation monoid and the join semilattice of equivalence relations. In this paper we give presentations (by generators and relations) for $P_n^{fd}$, its singular ideal, and its planar submonoid. The latter is not an Ehresmann submonoid, but it is a so-called grr…

Sound Interval-Based Synthesis for Probabilistic Programs
Guilherme Espada, Alcides Fonseca
https://arxiv.org/abs/2507.06939 https://…

Sound Interval-Based Synthesis for Probabilistic Programs
Probabilistic programming has become a standard practice to model stochastic events and learn about the behavior of nature in different scientific contexts, ranging from Genetics and Ecology to Linguistics and Psychology. However, domain practitioners (such as biologists) also need to be experts in statistics in order to select which probabilistic model is suitable for a given particular problem, relying then on probabilistic inference engines such as Stan, Pyro or Edward to fine-tune the param…

Improving Robustness of Foundation Models in Domain Adaptation with Soup-Adapters
Marco Roschkowski
https://arxiv.org/abs/2507.05807 https://

Improving Robustness of Foundation Models in Domain Adaptation with Soup-Adapters
In this paper, we tackle two fundamental problems in few-shot domain adaptation of foundation models. First, hyperparameter tuning is often impractical due to the lack of large validation datasets. Second, model robustness under distribution shifts where test time data deviates slightly from training distributions, remains a concern. We show that by training multiple independent adapters and averaging their outputs, the new model has a higher performance and is more robust to distribution shift…

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…

This https://arxiv.org/abs/2506.04327 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Fishing for the Optimal Roman High Latitude Time Domain Survey: Cosmological Constraints for 1,000 Possible Surveys
The upcoming Nancy Grace Roman Space Telescope is set to conduct a generation-defining SN Ia cosmology measurement with its High Latitude Time Domain Survey (HLTDS). However, between optical elements, exposure times, cadences, and survey areas, there are many survey parameters to consider. This work was part of a Roman Project Infrastructure Team effort to help the Core Community Survey (CCS) Committee finalize the HLTDS recommendation to the Roman Observations Time Allocation Committee. We sim…

The Thermodynamic Limit of Extreme First-Passage Times
Talia Baravi, Eli Barkai
https://arxiv.org/abs/2509.06098 https://arxiv.org/pdf/2509.06098

The Thermodynamic Limit of Extreme First-Passage Times
The statistics of the slowest first-passage time among a large population of $N$ searchers is crucial for determining the completion time of many stochastic processes. Classical extreme-value theory predicts that for diffusing particles in a finite domain of size $L$, the slowest first passage time follows a Gumbel distribution, but a Fréchet distribution in an infinite domain. Here, we study the physically relevant thermodynamic limit where both $N$ and $L$ diverge while the density $ρ= N/L$…

Do you use #Okta? If so, I highly recommend a defensive domain registration to help protect your org. Register yourdomain-okta.com as that is frequently used by a very successful threat actor. If that domain is already registered and your org didn’t register it? Watch out! Read the below article for more details.

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…

This https://arxiv.org/abs/2312.17294 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub
Large Language Models (LLMs) excel in traditional natural language processing tasks but struggle with problems that require complex domain-specific calculations or simulations. While equipping LLMs with external tools to build LLM-based agents can enhance their capabilities, existing approaches lack the flexibility to address diverse and ever-evolving user queries in open domains. Currently, there is also no existing dataset that evaluates LLMs on open-domain knowledge that requires tools to so…

Centralized Copy-Paste: Enhanced Data Augmentation Strategy for Wildland Fire Semantic Segmentation
Joon Tai Kim, Tianle Chen, Ziyu Dong, Nishanth Kunchala, Alexander Guller, Daniel Ospina Acero, Roger Williams, Mrinal Kumar
https://arxiv.org/abs/2507.06321

Centralized Copy-Paste: Enhanced Data Augmentation Strategy for Wildland Fire Semantic Segmentation
Collecting and annotating images for the purpose of training segmentation models is often cost prohibitive. In the domain of wildland fire science, this challenge is further compounded by the scarcity of reliable public datasets with labeled ground truth. This paper presents the Centralized Copy-Paste Data Augmentation (CCPDA) method, for the purpose of assisting with the training of deep-learning multiclass segmentation models, with special focus on improving segmentation outcomes for the fire…

Efficient Industrial sLLMs through Domain Adaptive Continual Pretraining: Method, Evaluation and Applications
Seonwu Kim, Yohan Na, Kihun Kim, Hanhee Cho, Geun Lim, Mintae Kim, Seongik Park, Ki Hyun Kim, Youngsub Han, Byoung-Ki Jeon
https://arxiv.org/abs/2507.06795

Efficient Industrial sLLMs through Domain Adaptive Continual Pretraining: Method, Evaluation and Applications
The emergence of open-source large language models (LLMs) has expanded opportunities for enterprise applications; however, many organizations still lack the infrastructure to deploy and maintain large-scale models. As a result, small LLMs (sLLMs) have become a practical alternative, despite their inherent performance limitations. While Domain Adaptive Continual Pretraining (DACP) has been previously explored as a method for domain adaptation, its utility in commercial applications remains under…

Mitigating Multi-Sequence 3D Prostate MRI Data Scarcity through Domain Adaptation using Locally-Trained Latent Diffusion Models for Prostate Cancer Detection
Emerson P. Grabke, Babak Taati, Masoom A. Haider
https://arxiv.org/abs/2507.06384

Mitigating Multi-Sequence 3D Prostate MRI Data Scarcity through Domain Adaptation using Locally-Trained Latent Diffusion Models for Prostate Cancer Detection
Objective: Latent diffusion models (LDMs) could mitigate data scarcity challenges affecting machine learning development for medical image interpretation. The recent CCELLA LDM improved prostate cancer detection performance using synthetic MRI for classifier training but was limited to the axial T2-weighted (AxT2) sequence, did not investigate inter-institutional domain shift, and prioritized radiology over histopathology outcomes. We propose CCELLA++ to address these limitations and improve cl…

#PSA: BEFORE selecting a domain name which you want to use for email, you definitely should consult the #SpamAssassin list of "suspicious" gTLDs. Those are gTLDs which have been so badly run that the overwhelming majority (99% in most cases) of messages using them for email addresses or even in …

malaria_genes: Malaria var DBLa HVR networks
Networks of recombinant antigen genes from the human malaria parasite P. falciparum. Each of the 9 networks shares the same set of vertices but has different edges, corresponding to the 9 highly variable regions (HVRs) in the DBLa domain of the var protein. Nodes are var genes, and two genes are connected if they share a substring whose length is statistically significant. Metadata includes two types of node labels, both based on sequence st…

Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges
Kasra Borazjani, Naji Khosravan, Rajeev Sahay, Bita Akram, Seyyedali Hosseinalipour
https://arxiv.org/abs/2509.07946

Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges
Multi-modal multi-task (M3T) foundation models (FMs) have recently shown transformative potential in artificial intelligence, with emerging applications in education. However, their deployment in real-world educational settings is hindered by privacy regulations, data silos, and limited domain-specific data availability. We introduce M3T Federated Foundation Models (FedFMs) for education: a paradigm that integrates federated learning (FL) with M3T FMs to enable collaborative, privacy-preserving…

Heute vor 34 Jahren: Am 10. Juli 1991 kam es im russischen #Kernkraftwerk #Bilibino zu einem radioaktiven #Wasseraustritt. Beim Transport eines Behälters mit Brennstofffragmenten schlug di…

This https://arxiv.org/abs/2408.08968 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Online SLA Decomposition: Enabling Real-Time Adaptation to Evolving Network Systems
When a network slice spans multiple technology domains, it is crucial for each domain to uphold the End-to-End (E2E) Service Level Agreement (SLA) associated with the slice. Consequently, the E2E SLA must be properly decomposed into partial SLAs that are assigned to each domain involved. In a network slice management system with a two-level architecture, comprising an E2E service orchestrator and local domain controllers, we consider that the orchestrator has access only to historical data rega…

Cherenkov radiation in isotropic chiral matter: the space-frequency domain
R. Mart\'inez von Dossow, Eduardo Barredo-Alamilla, Maxim A. Gorlach, Luis F. Urrutia
https://arxiv.org/abs/2507.06369

Cherenkov radiation in isotropic chiral matter: the space-frequency domain
The electromagnetic response of isotropic chiral matter, as described by Carroll-Field-Jackiw electrodynamics, arises in distinct physical contexts ranging from condensed matter systems to Lorentz-violating extensions of high-energy physics. Here, we derive exact expressions for the circularly polarized electromagnetic fields that contribute independently to Cherenkov radiation in isotropic chiral matter. Each spectral energy distribution is gauge-invariant and positive, yielding radiation that…

Backdoor Attacks and Defenses in Computer Vision Domain: A Survey
Bilal Hussain Abbasi, Yanjun Zhang, Leo Zhang, Shang Gao
https://arxiv.org/abs/2509.07504 https://

Precise Representation Model of SAR Saturated Interference: Mechanism and Verification
Lunhao Duan, Xingyu Lu, Yushuang Liu, Jianchao Yang, Hong Gu
https://arxiv.org/abs/2507.06932

Precise Representation Model of SAR Saturated Interference: Mechanism and Verification
Synthetic Aperture Radar (SAR) is highly susceptible to Radio Frequency Interference (RFI). Due to the performance limitations of components such as gain controllers and analog-to-digital converters in SAR receivers, high-power interference can easily cause saturation of the SAR receiver, resulting in nonlinear distortion of the interfered echoes, which are distorted in both the time domain and frequency domain. Some scholars have analyzed the impact of SAR receiver saturation on target echoes …

Enhancing Food-Domain Question Answering with a Multimodal Knowledge Graph: Hybrid QA Generation and Diversity Analysis
Srihari K B, Pushpak Bhattacharyya
https://arxiv.org/abs/2507.06571

Enhancing Food-Domain Question Answering with a Multimodal Knowledge Graph: Hybrid QA Generation and Diversity Analysis
We propose a unified food-domain QA framework that combines a large-scale multimodal knowledge graph (MMKG) with generative AI. Our MMKG links 13,000 recipes, 3,000 ingredients, 140,000 relations, and 14,000 images. We generate 40,000 QA pairs using 40 templates and LLaVA/DeepSeek augmentation. Joint fine-tuning of Meta LLaMA 3.1-8B and Stable Diffusion 3.5-Large improves BERTScore by 16.2\%, reduces FID by 37.8\%, and boosts CLIP alignment by 31.1\%. Diagnostic analyses-CLIP-based mismatch det…

Learning-Augmented Model-Based Multi-Robot Planning for Time-Critical Search and Inspection Under Uncertainty
Abhish Khanal, Joseph Prince Mathew, Cameron Nowzari, Gregory J. Stein
https://arxiv.org/abs/2507.06129

Learning-Augmented Model-Based Multi-Robot Planning for Time-Critical Search and Inspection Under Uncertainty
In disaster response or surveillance operations, quickly identifying areas needing urgent attention is critical, but deploying response teams to every location is inefficient or often impossible. Effective performance in this domain requires coordinating a multi-robot inspection team to prioritize inspecting locations more likely to need immediate response, while also minimizing travel time. This is particularly challenging because robots must directly observe the locations to determine which o…

Leveraging Multimodal Data and Side Users for Diffusion Cross-Domain Recommendation
Fan Zhang, Jinpeng Chen, Huan Li, Senzhang Wang, Yuan Cao, Kaimin Wei, JianXiang He, Feifei Kou, Jinqing Wang
https://arxiv.org/abs/2507.04000

Leveraging Multimodal Data and Side Users for Diffusion Cross-Domain Recommendation
Cross-domain recommendation (CDR) aims to address the persistent cold-start problem in Recommender Systems. Current CDR research concentrates on transferring cold-start users' information from the auxiliary domain to the target domain. However, these systems face two main issues: the underutilization of multimodal data, which hinders effective cross-domain alignment, and the neglect of side users who interact solely within the target domain, leading to inadequate learning of the target domain's…

My best "rubber duck" is whatever public support fora exist for whatever I'm bloodying my forehead on. I rarely actually get useful responses, but just asking does something, sometimes.
e.g. https://forum.opennebula.io/t/debugging-domain-creation/14158/1

Debugging domain creation
Executive Summary: OpenNebula environment grown over 12 years running OpenNebula v5.2, upgrading host OS breaks initial domain creation. Live migration works! Versions of the related components and OS (frontend, hypervisors, VMs): CentOS7 with qemu-kvm on 30 Intel-cpu (Supermicro) hosts. some hosts upgraded to Alma9, fully updated. VMs a mix of versions of FreeBSD, CentOS, Alma, Ubuntu, Windows. CentOS7 hosts have been operating properly for many years. Some Alma9 hosts are new, others are…

malaria_genes: Malaria var DBLa HVR networks
Networks of recombinant antigen genes from the human malaria parasite P. falciparum. Each of the 9 networks shares the same set of vertices but has different edges, corresponding to the 9 highly variable regions (HVRs) in the DBLa domain of the var protein. Nodes are var genes, and two genes are connected if they share a substring whose length is statistically significant. Metadata includes two types of node labels, both based on sequence st…

Is Your Training Pipeline Production-Ready? A Case Study in the Healthcare Domain
Daniel Lawand (University of S\~ao Paulo), Lucas Quaresma (University of S\~ao Paulo), Roberto Bolgheroni (University of S\~ao Paulo), Alfredo Goldman (University of S\~ao Paulo), Renato Cordeiro Ferreira (University of S\~ao Paulo, Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University)

Is Your Training Pipeline Production-Ready? A Case Study in the Healthcare Domain
Deploying a Machine Learning (ML) training pipeline into production requires robust software engineering practices. This differs significantly from experimental workflows. This experience report investigates this challenge in SPIRA, a project whose goal is to create an ML-Enabled System (MLES) to pre-diagnose insufficiency respiratory via speech analysis. The first version of SPIRA's training pipeline lacked critical software quality attributes. This paper presents an overview of the MLES, then…

Semi-supervised Deep Transfer for Regression without Domain Alignment
Mainak Biswas, Ambedkar Dukkipati, Devarajan Sridharan
https://arxiv.org/abs/2509.05092 https://

Semi-supervised Deep Transfer for Regression without Domain Alignment
Deep learning models deployed in real-world applications (e.g., medicine) face challenges because source models do not generalize well to domain-shifted target data. Many successful domain adaptation (DA) approaches require full access to source data. Yet, such requirements are unrealistic in scenarios where source data cannot be shared either because of privacy concerns or because it is too large and incurs prohibitive storage or computational costs. Moreover, resource constraints may limit th…

Contrastive Anatomy-Contrast Disentanglement: A Domain-General MRI Harmonization Method
Daniel Scholz, Ayhan Can Erdur, Robbie Holland, Viktoria Ehm, Jan C. Peeken, Benedikt Wiestler, Daniel Rueckert
https://arxiv.org/abs/2509.06592

Contrastive Anatomy-Contrast Disentanglement: A Domain-General MRI Harmonization Method
Magnetic resonance imaging (MRI) is an invaluable tool for clinical and research applications. Yet, variations in scanners and acquisition parameters cause inconsistencies in image contrast, hindering data comparability and reproducibility across datasets and clinical studies. Existing scanner harmonization methods, designed to address this challenge, face limitations, such as requiring traveling subjects or struggling to generalize to unseen domains. We propose a novel approach using a conditi…

Heute vor 59 Jahren: Am 10. Juni 1966 zündeten die USA im Rahmen von Operation #Flintlock die 45. #Atombombe "Puce". Flintlock war eine Serie von Tests zwischen 65/66 bei der insgesamt 47 #Kernwaffentests

This https://arxiv.org/abs/2410.20580 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Coherence-guided Preference Disentanglement for Cross-domain Recommendations
Discovering user preferences across different domains is pivotal in cross-domain recommendation systems, particularly when platforms lack comprehensive user-item interactive data. The limited presence of shared users often hampers the effective modeling of common preferences. While leveraging shared items' attributes, such as category and popularity, can enhance cross-domain recommendation performance, the scarcity of shared items between domains has limited research in this area. To address th…

An Ethically Grounded LLM-Based Approach to Insider Threat Synthesis and Detection
Haywood Gelman, John D. Hastings, David Kenley
https://arxiv.org/abs/2509.06920 https://

An Ethically Grounded LLM-Based Approach to Insider Threat Synthesis and Detection
Insider threats are a growing organizational problem due to the complexity of identifying their technical and behavioral elements. A large research body is dedicated to the study of insider threats from technological, psychological, and educational perspectives. However, research in this domain has been generally dependent on datasets that are static and limited access which restricts the development of adaptive detection models. This study introduces a novel, ethically grounded approach that u…

T-araVLN: Translator for Agricultural Robotic Agents on Vision-and-Language Navigation
Xiaobei Zhao, Xingqi Lyu, Xiang Li
https://arxiv.org/abs/2509.06644 https://

T-araVLN: Translator for Agricultural Robotic Agents on Vision-and-Language Navigation
Agricultural robotic agents have been becoming powerful helpers in a wide range of agricultural tasks, nevertheless, still heavily rely on manual operation or untransportable railway for movement. The AgriVLN method and the A2A benchmark pioneeringly extend Vision-and-Language Navigation (VLN) to the agricultural domain, enabling agents navigate to the target position following the natural language instructions. AgriVLN effectively understands the simple instructions, however, often misundersta…

LAMDAS: LLM as an Implicit Classifier for Domain-specific Data Selection
Jian Wu, Hang Yu, Bingchang Liu, Wenjie Yang, Peng Di, Jianguo Li, Yue Zhang
https://arxiv.org/abs/2509.06524

LAMDAS: LLM as an Implicit Classifier for Domain-specific Data Selection
Adapting large language models (LLMs) to specific domains often faces a critical bottleneck: the scarcity of high-quality, human-curated data. While large volumes of unchecked data are readily available, indiscriminately using them for fine-tuning risks introducing noise and degrading performance. Strategic data selection is thus crucial, requiring a method that is both accurate and efficient. Existing approaches, categorized as similarity-based and direct optimization methods, struggle to simu…

Heute vor 54 Jahren: Am 09.07.1971 zündeten die #USA im Rahmen von Operation Grommet die 3. Atombombe "Bracken". Grommet war eine Serie von #Kernwaffentests bei der 1971/72 insgesamt 39 Bomben größtenteils im Testgebiet in

This https://arxiv.org/abs/2505.16809 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing Modalities
Existing methods for multimodal MRI segmentation with missing modalities typically assume that all MRI modalities are available during training. However, in clinical practice, some modalities may be missing due to the sequential nature of MRI acquisition, leading to performance degradation. Furthermore, retraining models to accommodate newly available modalities can be inefficient and may cause overfitting, potentially compromising previously learned knowledge. To address these challenges, we p…

This https://arxiv.org/abs/2505.00533 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Test-time Correlation Alignment
Deep neural networks often degrade under distribution shifts. Although domain adaptation offers a solution, privacy constraints often prevent access to source data, making Test-Time Adaptation (TTA, which adapts using only unlabeled test data) increasingly attractive. However, current TTA methods still face practical challenges: (1) a primary focus on instance-wise alignment, overlooking CORrelation ALignment (CORAL) due to missing source correlations; (2) complex backpropagation operations for…

Optimal Transport Driven Asymmetric Image-to-Image Translation for Nuclei Segmentation of Histological Images
Suman Mahapatra, Pradipta Maji
https://arxiv.org/abs/2506.07023

Optimal Transport Driven Asymmetric Image-to-Image Translation for Nuclei Segmentation of Histological Images
Segmentation of nuclei regions from histological images enables morphometric analysis of nuclei structures, which in turn helps in the detection and diagnosis of diseases under consideration. To develop a nuclei segmentation algorithm, applicable to different types of target domain representations, image-to-image translation networks can be considered as they are invariant to target domain image representations. One of the important issues with image-to-image translation models is that they fai…

This https://arxiv.org/abs/2505.22240 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

BioHopR: A Benchmark for Multi-Hop, Multi-Answer Reasoning in Biomedical Domain
Biomedical reasoning often requires traversing interconnected relationships across entities such as drugs, diseases, and proteins. Despite the increasing prominence of large language models (LLMs), existing benchmarks lack the ability to evaluate multi-hop reasoning in the biomedical domain, particularly for queries involving one-to-many and many-to-many relationships. This gap leaves the critical challenges of biomedical multi-hop reasoning underexplored. To address this, we introduce BioHopR,…

AI Agent Smart Contract Exploit Generation
Arthur Gervais, Liyi Zhou
https://arxiv.org/abs/2507.05558 https://arxiv.org/pdf/2507.0555…

AI Agent Smart Contract Exploit Generation
We present A1, an agentic execution driven system that transforms any LLM into an end-to-end exploit generator. A1 has no hand-crafted heuristics and provides the agent with six domain-specific tools that enable autonomous vulnerability discovery. The agent can flexibly leverage these tools to understand smart contract behavior, generate exploit strategies, test them on blockchain states, and refine approaches based on execution feedback. All outputs are concretely validated to eliminate false …

A Generic and Efficient Python Runtime Verification System and its Large-scale Evaluation
Zhuohang Shen, Mohammed Yaseen, Denini Silva, Kevin Guan, Junho Lee, Marcelo d'Amorim, Owolabi Legunsen
https://arxiv.org/abs/2509.06324

A Generic and Efficient Python Runtime Verification System and its Large-scale Evaluation
Runtime verification (RV) now scales for testing thousands of open-source Java projects, helping find hundreds of bugs. The popular Python ecosystem could use such benefits. But, today's Python RV systems are limited to a domain or specification logic, or slow. We propose PyMOP, a generic, extensible, and efficient RV system for Python. PyMOP supports five logics, implements five existing monitoring algorithms, ships with 73 API specs of Python and widely-used libraries, supports three instrume…

Heute vor 63 Jahren: Am 9. Juli 1962 führte die USA den #Starfish Prime Test durch. Dieser größte #Nukleartest im Weltraum (1,4 Mt) wurde vom Johnston Atoll aus gestartet. Die Explosion ereignete sich in einer Höhe von 400 km über einem Punkt 31 km südwestlich des Atolls.

A Survey of Graph Neural Networks for Drug Discovery: Recent Developments and Challenges
Katherine Berry, Liang Cheng
https://arxiv.org/abs/2509.07887 https://

A Survey of Graph Neural Networks for Drug Discovery: Recent Developments and Challenges
Graph Neural Networks (GNNs) have gained traction in the complex domain of drug discovery because of their ability to process graph-structured data such as drug molecule models. This approach has resulted in a myriad of methods and models in published literature across several categories of drug discovery research. This paper covers the research categories comprehensively with recent papers, namely molecular property prediction, including drug-target binding affinity prediction, drug-drug inter…

This https://arxiv.org/abs/2505.21904 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

CAST: Contrastive Adaptation and Distillation for Semi-Supervised Instance Segmentation
Instance segmentation demands costly per-pixel annotations and large models. We introduce CAST, a semi-supervised knowledge distillation (SSKD) framework that compresses pretrained vision foundation models (VFM) into compact experts using limited labeled and abundant unlabeled data. CAST unfolds in three stages: (1) domain adaptation of the VFM teacher(s) via self-training with contrastive pixel calibration, (2) distillation into a compact student via a unified multi-objective loss that couples…

HotelMatch-LLM: Joint Multi-Task Training of Small and Large Language Models for Efficient Multimodal Hotel Retrieval
Arian Askari, Emmanouil Stergiadis, Ilya Gusev, Moran Beladev
https://arxiv.org/abs/2506.07296

HotelMatch-LLM: Joint Multi-Task Training of Small and Large Language Models for Efficient Multimodal Hotel Retrieval
We present HotelMatch-LLM, a multimodal dense retrieval model for the travel domain that enables natural language property search, addressing the limitations of traditional travel search engines which require users to start with a destination and editing search parameters. HotelMatch-LLM features three key innovations: (1) Domain-specific multi-task optimization with three novel retrieval, visual, and language modeling objectives; (2) Asymmetrical dense retrieval architecture combining a small …

Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector
Amal Chebbi, Babajide Kolade
https://arxiv.org/abs/2509.07177 https://arxiv.org…

Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector
Large Language Models have demonstrated impressive capabilities across various domains. However, their general-purpose nature often limits their effectiveness in specialized fields such as energy, where deep technical expertise and precise domain knowledge are essential. In this paper, we introduce EnergyGPT, a domain-specialized language model tailored for the energy sector, developed by fine-tuning LLaMA 3.1-8B model using Supervised Fine-Tuning on a high-quality, curated corpus of energy-rel…

Paladin: Defending LLM-enabled Phishing Emails with a New Trigger-Tag Paradigm
Yan Pang, Wenlong Meng, Xiaojing Liao, Tianhao Wang
https://arxiv.org/abs/2509.07287 https://

Paladin: Defending LLM-enabled Phishing Emails with a New Trigger-Tag Paradigm
With the rapid development of large language models, the potential threat of their malicious use, particularly in generating phishing content, is becoming increasingly prevalent. Leveraging the capabilities of LLMs, malicious users can synthesize phishing emails that are free from spelling mistakes and other easily detectable features. Furthermore, such models can generate topic-specific phishing messages, tailoring content to the target domain and increasing the likelihood of success. Detect…

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs
Sofiia Chorna, Kateryna Tarelkina, Elo\"ise Berthier, Gianni Franchi
https://arxiv.org/abs/2507.05810

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs
While concept-based interpretability methods have traditionally focused on local explanations of neural network predictions, we propose a novel framework and interactive tool that extends these methods into the domain of mechanistic interpretability. Our approach enables a global dissection of model behavior by analyzing how high-level semantic attributes (referred to as concepts) emerge, interact, and propagate through internal model components. Unlike prior work that isolates individual neuro…

Cross-Modality Masked Learning for Survival Prediction in ICI Treated NSCLC Patients
Qilong Xing, Zikai Song, Bingxin Gong, Lian Yang, Junqing Yu, Wei Yang
https://arxiv.org/abs/2507.06994

Cross-Modality Masked Learning for Survival Prediction in ICI Treated NSCLC Patients
Accurate prognosis of non-small cell lung cancer (NSCLC) patients undergoing immunotherapy is essential for personalized treatment planning, enabling informed patient decisions, and improving both treatment outcomes and quality of life. However, the lack of large, relevant datasets and effective multi-modal feature fusion strategies pose significant challenges in this domain. To address these challenges, we present a large-scale dataset and introduce a novel framework for multi-modal feature fu…

FuDoBa: Fusing Document and Knowledge Graph-based Representations with Bayesian Optimisation
Boshko Koloski, Senja Pollak, Roberto Navigli, Bla\v{z} \v{S}krlj
https://arxiv.org/abs/2507.06622

FuDoBa: Fusing Document and Knowledge Graph-based Representations with Bayesian Optimisation
Building on the success of Large Language Models (LLMs), LLM-based representations have dominated the document representation landscape, achieving great performance on the document embedding benchmarks. However, the high-dimensional, computationally expensive embeddings from LLMs tend to be either too generic or inefficient for domain-specific applications. To address these limitations, we introduce FuDoBa a Bayesian optimisation-based method that integrates LLM-based embeddings with domain-spe…

AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs
Shangzhan Li, Zefan Wang, Ye He, Yuxuan Li, Qi Shi, Jianling Li, Yonggang Hu, Wanxiang Che, Xu Han, Zhiyuan Liu, Maosong Sun
https://arxiv.org/abs/2507.05687

AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs
Kernel development in deep learning requires optimizing computational units across hardware while balancing memory management, parallelism, and hardware-specific optimizations through extensive empirical tuning. Although domain-specific languages like Triton simplify GPU programming by abstracting low-level details, developers must still manually tune critical parameters such as tile sizes and memory access patterns through iterative experimentation, creating substantial barriers to optimal per…

Dual-Domain Perspective on Degradation-Aware Fusion: A VLM-Guided Robust Infrared and Visible Image Fusion Framework
Tianpei Zhang, Jufeng Zhao, Yiming Zhu, Guangmang Cui
https://arxiv.org/abs/2509.05000

Dual-Domain Perspective on Degradation-Aware Fusion: A VLM-Guided Robust Infrared and Visible Image Fusion Framework
Most existing infrared-visible image fusion (IVIF) methods assume high-quality inputs, and therefore struggle to handle dual-source degraded scenarios, typically requiring manual selection and sequential application of multiple pre-enhancement steps. This decoupled pre-enhancement-to-fusion pipeline inevitably leads to error accumulation and performance degradation. To overcome these limitations, we propose Guided Dual-Domain Fusion (GD^2Fusion), a novel framework that synergistically integrate…

Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
Anish Dhir, Cristiana Diaconu, Valentinian Mihai Lungu, James Requeima, Richard E. Turner, Mark van der Wilk
https://arxiv.org/abs/2507.05526

Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
In scientific domains -- from biology to the social sciences -- many questions boil down to \textit{What effect will we observe if we intervene on a particular variable?} If the causal relationships (e.g.~a causal graph) are known, it is possible to estimate the intervention distributions. In the absence of this domain knowledge, the causal structure must be discovered from the available observational data. However, observational data are often compatible with multiple causal graphs, making met…

PL-CA: A Parametric Legal Case Augmentation Framework
Ao Chang, Yubo Chen, Jun Zhao
https://arxiv.org/abs/2509.06356 https://arxiv.org/pdf/2509.06356

PL-CA: A Parametric Legal Case Augmentation Framework
Conventional RAG is considered one of the most effective methods for addressing model knowledge insufficiency and hallucination, particularly in the judicial domain that requires high levels of knowledge rigor, logical consistency, and content integrity. However, the conventional RAG method only injects retrieved documents directly into the model's context, which severely constrains models due to their limited context windows and introduces additional computational overhead through excessively …

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/6]:
- Multiple Noises in Diffusion Model for Semi-Supervised Multi-Domain Translation
Tsiry Mayet, Simon Bernard, Romain Herault, Clement Chatelain

MaLei at MultiClinSUM: Summarisation of Clinical Documents using Perspective-Aware Iterative Self-Prompting with LLMs
Libo Ren, Yee Man Ng, Lifeng Han
https://arxiv.org/abs/2509.07622

MaLei at MultiClinSUM: Summarisation of Clinical Documents using Perspective-Aware Iterative Self-Prompting with LLMs
Efficient communication between patients and clinicians plays an important role in shared decision-making. However, clinical reports are often lengthy and filled with clinical jargon, making it difficult for domain experts to identify important aspects in the document efficiently. This paper presents the methodology we applied in the MultiClinSUM shared task for summarising clinical case documents. We used an Iterative Self-Prompting technique on large language models (LLMs) by asking LLMs to g…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[2/4]:
- FinRAGBench-V: A Benchmark for Multimodal RAG with Visual Citation in the Financial Domain
Suifeng Zhao, Zhuoran Jin, Sujian Li, Jun Gao

BALI: Enhancing Biomedical Language Representations through Knowledge Graph and Language Model Alignment
Andrey Sakhovskiy, Elena Tutubalina
https://arxiv.org/abs/2509.07588 htt…

BALI: Enhancing Biomedical Language Representations through Knowledge Graph and Language Model Alignment
In recent years, there has been substantial progress in using pretrained Language Models (LMs) on a range of tasks aimed at improving the understanding of biomedical texts. Nonetheless, existing biomedical LLMs show limited comprehension of complex, domain-specific concept structures and the factual information encoded in biomedical Knowledge Graphs (KGs). In this work, we propose BALI (Biomedical Knowledge Graph and Language Model Alignment), a novel joint LM and KG pre-training method that au…

Text to model via SysML: Automated generation of dynamical system computational models from unstructured natural language text via enhanced System Modeling Language diagrams
Matthew Anderson Hendricks, Alice Cicirello
https://arxiv.org/abs/2507.06803

Text to model via SysML: Automated generation of dynamical system computational models from unstructured natural language text via enhanced System Modeling Language diagrams
This paper contributes to speeding up the design and deployment of engineering dynamical systems by proposing a strategy for exploiting domain and expert knowledge for the automated generation of dynamical system computational model starting from a corpus of document relevant to the dynamical system of interest and an input document describing the specific system. This strategy is implemented in five steps and, crucially, it uses system modeling language diagrams (SysML) to extract accurate inf…

This https://arxiv.org/abs/2506.04929 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT
Neural Machine Translation (NMT) has improved translation by using Transformer-based models, but it still struggles with word ambiguity and context. This problem is especially important in domain-specific applications, which often have problems with unclear sentences or poor data quality. Our research explores how adding information to models can improve translations in the context of e-commerce data. To this end we create ConECT -- a new Czech-to-Polish e-commerce product translation dataset c…

This https://arxiv.org/abs/2505.19914 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles
Large Language Models (LLMs), such as OpenAI's o1 and DeepSeek's R1, excel at advanced reasoning tasks like math and coding via Reinforcement Learning with Verifiable Rewards (RLVR), but still struggle with puzzles solvable by humans without domain knowledge. We introduce Enigmata, the first comprehensive suite tailored for improving LLMs with puzzle reasoning skills. It includes 36 tasks across seven categories, each with 1) a generator that produces unlimited examples with controllable diffic…

ODKE : Ontology-Guided Open-Domain Knowledge Extraction with LLMs
Samira Khorshidi, Azadeh Nikfarjam, Suprita Shankar, Yisi Sang, Yash Govind, Hyun Jang, Ali Kasgari, Alexis McClimans, Mohamed Soliman, Vishnu Konda, Ahmed Fakhry, Xiaoguang Qi
https://arxiv.org/abs/2509.04696

ODKE+: Ontology-Guided Open-Domain Knowledge Extraction with LLMs
Knowledge graphs (KGs) are foundational to many AI applications, but maintaining their freshness and completeness remains costly. We present ODKE+, a production-grade system that automatically extracts and ingests millions of open-domain facts from web sources with high precision. ODKE+ combines modular components into a scalable pipeline: (1) the Extraction Initiator detects missing or stale facts, (2) the Evidence Retriever collects supporting documents, (3) hybrid Knowledge Extractors apply …

R1-RE: Cross-Domain Relationship Extraction with RLVR
Runpeng Dai, Tong Zheng, Run Yang, Hongtu Zhu
https://arxiv.org/abs/2507.04642 https://

R1-RE: Cross-Domain Relationship Extraction with RLVR
Relationship extraction (RE) is a core task in natural language processing. Traditional approaches typically frame RE as a supervised learning problem, directly mapping context to labels-an approach that often suffers from poor out-of-domain (OOD) generalization. Inspired by the workflow of human annotators, we reframe RE as a reasoning task guided by annotation guidelines and introduce R1-RE, the first reinforcement learning with verifiable reward (RLVR) framework for RE tasks. Our method elic…