Tootfinder

Dynamic Parameter Memory: Temporary LoRA-Enhanced LLM for Long-Sequence Emotion Recognition in Conversation
Jialong Mai, Xiaofen Xing, Yawei Li, Zhipeng Li, Jingyuan Xing, Xiangmin Xu
https://arxiv.org/abs/2507.09076

Dynamic Parameter Memory: Temporary LoRA-Enhanced LLM for Long-Sequence Emotion Recognition in Conversation
Recent research has focused on applying speech large language model (SLLM) to improve speech emotion recognition (SER). However, the inherently high frame rate in speech modality severely limits the signal processing and understanding capabilities of SLLM. For example, a SLLM with a 4K context window can only process 80 seconds of audio at 50Hz feature sampling rate before reaching its capacity limit. Input token compression methods used in SLLM overlook the continuity and inertia of emotions a…

Replaced article(s) found for math.CT. https://arxiv.org/list/math.CT/new
[1/1]:
- On t-structures adjacent and orthogonal to weight structures
Mikhail V. Bondarko

OpenCodeReasoning-II: A Simple Test Time Scaling Approach via Self-Critique
Wasi Uddin Ahmad, Somshubra Majumdar, Aleksander Ficek, Sean Narenthiran, Mehrzad Samadi, Jocelyn Huang, Siddhartha Jain, Vahid Noroozi, Boris Ginsburg
https://arxiv.org/abs/2507.09075

OpenCodeReasoning-II: A Simple Test Time Scaling Approach via Self-Critique
Recent advancements in reasoning-based Large Language Models (LLMs), particularly their potential through test-time scaling, have created significant opportunities for distillation in code generation and critique. However, progress in both areas fundamentally depends on large-scale, high-quality datasets. In this work, we introduce OpenCodeReasoning-II, a dataset consists of 2.5M question-solution-critique triples (approx. 35K unique programming questions), making it nearly twice the size of th…

[2025-07-16 Wed (UTC), no new articles found for math.CT Category Theory]
toXiv_bot_toot

Heute hat es noch ein Nachholspiel für die Galerie. Die #cowboys müssen zu den #comets. Nächste Woche starten in der #ErimaGFL die

#caturday

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[3/3]:
- SEALGuard: Safeguarding the Multilingual Conversations in Southeast Asian Languages for LLM Softw...
Wenliang Shan, Michael Fu, Rui Yang, Chakkrit Tantithamthavorn

Wurzelnder Bitterröhrling, leider nur bedingt essbar. Gefunden im Park oberhalb unserer Wohnung. Rote Liste?
#fungiverse #fungiworld

Announcing sff: A fast, on-the-fly SemanticFileFinder written in Rust! 🦀
It scans a directory (like your notes or a repo), finds the most semantically relevant text chunks for your query, and lets you open the file in a text editor of your choice.
No vector DBs, no GPU needed. Indexes ~2500 files with 10k chunks in 250ms on a CPU.
Perfect for searching Obsidian vaults, codebases, and more.
𝚌𝚊𝚛𝚐𝚘 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚜𝚏𝚏
𝚜𝚏𝚏 "𝚠𝚘𝚛𝚔𝚒𝚗𝚐 𝚠𝚒𝚝𝚑 𝚐𝚒𝚝"

GitHub - do-me/sff: CLI for semantic search on your computer. Searches text files and identifies the most relevant chunks to your query.
CLI for semantic search on your computer. Searches text files and identifies the most relevant chunks to your query. - GitHub - do-me/sff: CLI for semantic search on your computer. Searches text f...

The Trump administration sues three members of CPB, seeking approval for their terminations, after attempting to fire them on April 28 (Scott Nover/Washington Post)
https://www.washingtonpost.com/business/2025/07/15/trump-sues…

Trump sues Corporation for Public Broadcasting to oust board members
Two months after attempting to fire three Corporation for Public Broadcasting board members, the Trump administration is suing to finally get them ousted.

In der #Ostsee treten derzeit vermehrt #Blaualgen auf.
Die sogenannte #Algenblüte wird durch hohe Temperaturen und Überdüngung begünstigt.
Mehrere

Die Gründe für Algenblüte in der Ostsee
Die Ostsee wird von Landwirtschaft und Klimawandel aus dem Gleichgewicht gebracht. Die sommerliche Algenblüte zeigt: Der Einlass von Dünger muss weniger werden.

"Swiss River Cleanup Sets a Global Example"
#Switzerland #Environment
https://happyeconews.com/swiss-river-c

Swiss River Cleanup Sets a Global Example | Happy Eco News
The Swiss river cleanup turned once-polluted waters into safe, swimmable spaces through strict laws, modern sewage systems, and major investments. Today, Swiss cities enjoy crystal-clear rivers, setting a global example. Their continued efforts to remove micropollutants show how clean water benefits public health, community well-being, and the environment.

Life on Different Paths II🚶🚶‍♀️🚶‍♂️
生活诸多路径 II🚶🚶‍♀️🚶‍♂️
📷 Nikon FE
🎞️FOMAPAN Action 400
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

Im Wald sind es immer 3-5 Grad kühler.
Vl. sollte man doch nicht alles zubetonieren?!?!!!!

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[2/3]:
- Compression Hacking: A Supplementary Perspective on Informatics Properties of Language Models fro...
Zang, Ning, Wei, Dou, Zhang, Mo, Li, Gui, Zhang, Huang

http://putty.cl

Efficient and Accurate Downfacing Visual Inertial Odometry
Jonas K\"uhne, Christian Vogt, Michele Magno, Luca Benini
https://arxiv.org/abs/2509.10021 https://

Efficient and Accurate Downfacing Visual Inertial Odometry
Visual Inertial Odometry (VIO) is a widely used computer vision method that determines an agent's movement through a camera and an IMU sensor. This paper presents an efficient and accurate VIO pipeline optimized for applications on micro- and nano-UAVs. The proposed design incorporates state-of-the-art feature detection and tracking methods (SuperPoint, PX4FLOW, ORB), all optimized and quantized for emerging RISC-V-based ultra-low-power parallel systems on chips (SoCs). Furthermore, by employin…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/3]:
- Fine-grained Stateful Knowledge Exploration: Effective and Efficient Graph Retrieval with Large L...
Dehao Tao, Congqi Wang, Feng Huang, Junhao Chen, Yongfeng Huang, Minghu Jiang

ORQ: Complex Analytics on Private Data with Strong Security Guarantees
Eli Baum, Sam Buxbaum, Nitin Mathai, Muhammad Faisal, Vasiliki Kalavri, Mayank Varia, John Liagouris
https://arxiv.org/abs/2509.10793

ORQ: Complex Analytics on Private Data with Strong Security Guarantees
We present ORQ, a system that enables collaborative analysis of large private datasets using cryptographically secure multi-party computation (MPC). ORQ protects data against semi-honest or malicious parties and can efficiently evaluate relational queries with multi-way joins and aggregations that have been considered notoriously expensive under MPC. To do so, ORQ eliminates the quadratic cost of secure joins by leveraging the fact that, in practice, the structure of many real queries allows us…

sp_primary_school: Primary school dynamic contacts (2009)
Two temporal networks of contacts among students and teachers at a primary school in Lyon, France, on consecutive days of in October 2009. Each network accumulates all contacts over the course of a single day; contacts were sampled at 20-second intervals.
This network has 242 nodes and 125773 edges.
Tags: Social, Offline, Weighted, Temporal, Metadata

Real-World Summarization: When Evaluation Reaches Its Limits
Patr\'icia Schmidtov\'a, Ond\v{r}ej Du\v{s}ek, Saad Mahamood
https://arxiv.org/abs/2507.11508

Real-World Summarization: When Evaluation Reaches Its Limits
We examine evaluation of faithfulness to input data in the context of hotel highlights: brief LLM-generated summaries that capture unique features of accommodations. Through human evaluation campaigns involving categorical error assessment and span-level annotation, we compare traditional metrics, trainable methods, and LLM-as-a-judge approaches. Our findings reveal that simpler metrics like word overlap correlate surprisingly well with human judgments (Spearman correlation rank of 0.63), often…

Replaced article(s) found for math.CT. https://arxiv.org/list/math.CT/new
[1/1]:
- On the quotient of a groupoid by an action of a 2-group
Vladimir Drinfeld

#tazgenossenschaft https://mastodon.social/@tazgetroete/115196968244154389

Trump’s New Appointee to lead the Bureau of Labor Statistics Boasted About Hitler’s Favorite Ship ‘in All His Glory’
https://www.thedailybeast.com/donald-trumps-new-appointee-ej-antoni-boasted-about-hitlers-favorite-shi…

Trump’s New Appointee Boasted About Hitler’s Favorite Ship ‘in All His Glory’
E.J. Antoni seems to have no issue sitting in front of the Nazi warship while being interviewed.

Charlie Kirk’s ex-employee talks about what it's like working for Turning Point & having a crisis of faith when you actually think for yourself, challenge the groupthink you're indoctrinated into & confront the worldview of the lemmings of your community.

Suddenly Baum vorm Kopf. Ast abgebrochen und baumelt schön auf Kopfhöhe über dem primären Fuß- und Radweg zwischen Innenstadt und Rest. Sicher später angenehm für die Vollgasidioten, die ohne Licht da reindonnern.
#MdRzA

This is amazing! Regarding the news of the unofficial FSR 4.0.2 implementation on Windows for RDNA 3 and 2 and RTX 30 video cards:
https://www.reddit.com/r/radeon/comments/1nhxgd0/fsr_4_working_in_cyberpunk_2077_on_rdna3_7900xtx/

Die #Kernfusionskollektoranlage hat heute 1,596 kWh Energie eingefangen.
Gesamtleistung: 480 Wp
Ausrichtung: West
Anstellwinkel: ca. 50 Grad
Diese #Balkonsolaranlage wurde durch das Berliner SolarPLUS-Programm gefördert.
Einspeiseverlust: 3.86…

Very hot weather in Spain today: temperatures above 44°C in five different provinces, with Badajoz at 45.5°C (114F)
Source: aemet.es
#globalwarming #ClimateChange

HKGAI-V1: Towards Regional Sovereign Large Language Model for Hong Kong
Sirui Han, Junqi Zhu, Ruiyuan Zhang, Yike Guo
https://arxiv.org/abs/2507.11502 http…

HKGAI-V1: Towards Regional Sovereign Large Language Model for Hong Kong
This paper presents the development of HKGAI-V1, a foundational sovereign large language model (LLM), developed as part of an initiative to establish value-aligned AI infrastructure specifically tailored for Hong Kong. Addressing the region's unique multilingual environment (Cantonese, Mandarin, and English), its distinct socio-legal context under the "one country, two systems" framework, and specific local cultural and value considerations, the model is built upon the DeepSeek architecture and…

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize?
Yanjian Zhang, Guillaume Wisniewski, Nadi Tomeh, Thierry Charnois
https://arxiv.org/abs/2507.11423

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize?
Human reasoning involves different strategies, each suited to specific problems. Prior work shows that large language model (LLMs) tend to favor a single reasoning strategy, potentially limiting their effectiveness in diverse reasoning challenges. In this work, we investigate whether prompting can control LLMs reasoning strategies and assess its impact on logical problem-solving. While our experiments show that no single strategy consistently improves accuracy, performance could be enhanced if …

[2025-07-14 Mon (UTC), no new articles found for math.CT Category Theory]
toXiv_bot_toot

Revisiting the Influence of Sn in Cu-Al alloys: A Third Element Effect Enabling Stainless Steel Type Aqueous Passivation Behavior
Debashish Sur, Nathan C. Smith, Elaf A. Anber, Kaitlyn L. Anderson, Peter F. Connors, Daniel Foley, Mitra L. Taheri, Junsoo Han, Christopher M. Wolverton, John R. Scully
https://arxiv.org/abs/2507.114…

Revisiting the Influence of Sn in Cu-Al alloys: A Third Element Effect Enabling Stainless Steel Type Aqueous Passivation Behavior
The influence of Sn alloying additions on the aqueous passivation behavior of Cu-Al alloys was revisited and found to function as a new third element effect in acidified 0.1 M Na2SO4 solution. The role of each element during the process of aqueous passivation was investigated using electrochemical and surface-sensitive ex-situ and in-operando spectroscopic techniques. The connection between passivation and the atomic arrangements of atoms in the solid solution was supported by first principles …

Hello!

Seq vs Seq: An Open Suite of Paired Encoders and Decoders
Orion Weller, Kathryn Ricci, Marc Marone, Antoine Chaffin, Dawn Lawrie, Benjamin Van Durme
https://arxiv.org/abs/2507.11412

Seq vs Seq: An Open Suite of Paired Encoders and Decoders
The large language model (LLM) community focuses almost exclusively on decoder-only language models, since they are easier to use for text generation. However, a large subset of the community still uses encoder-only models for tasks such as classification or retrieval. Previous work has attempted to compare these architectures, but is forced to make comparisons with models that have different numbers of parameters, training techniques, and datasets. We introduce the SOTA open-data Ettin suite o…

Es wird nicht besser mit diesem irren Faschisten! Er und „seine“ Dienstleistungen/Produkte sind vollständig zu meiden!
https://norden.social/@hart/115195937531735058

KisMATH: Do LLMs Have Knowledge of Implicit Structures in Mathematical Reasoning?
Soumadeep Saha, Akshay Chaturvedi, Saptarshi Saha, Utpal Garain, Nicholas Asher
https://arxiv.org/abs/2507.11408

KisMATH: Do LLMs Have Knowledge of Implicit Structures in Mathematical Reasoning?
Chain-of-thought traces have been shown to improve performance of large language models in a plethora of reasoning tasks, yet there is no consensus on the mechanism through which this performance boost is achieved. To shed more light on this, we introduce Causal CoT Graphs (CCGs), which are directed acyclic graphs automatically extracted from reasoning traces that model fine-grained causal dependencies in the language model output. A collection of $1671$ mathematical reasoning problems from MAT…

EXAONE 4.0: Unified Large Language Models Integrating Non-reasoning and Reasoning Modes
LG AI Research, :, Kyunghoon Bae, Eunbi Choi, Kibong Choi, Stanley Jungkyu Choi, Yemuk Choi, Kyubeen Han, Seokhee Hong, Junwon Hwang, Taewan Hwang, Joonwon Jang, Hyojin Jeon, Kijeong Jeon, Gerrard Jeongwon Jo, Hyunjik Jo, Jiyeon Jung, Euisoon Kim, Hyosang Kim, Jihoon Kim, Joonkee Kim, Seonghwan Kim, Soyeon Kim, Sunkyoung Kim, Yireun Kim, Yongil Kim, Youchul Kim, Edward Hwayoung Lee, Gwangho Lee, Ha…

EXAONE 4.0: Unified Large Language Models Integrating Non-reasoning and Reasoning Modes
This technical report introduces EXAONE 4.0, which integrates a Non-reasoning mode and a Reasoning mode to achieve both the excellent usability of EXAONE 3.5 and the advanced reasoning abilities of EXAONE Deep. To pave the way for the agentic AI era, EXAONE 4.0 incorporates essential features such as agentic tool use, and its multilingual capabilities are extended to support Spanish in addition to English and Korean. The EXAONE 4.0 model series consists of two sizes: a mid-size 32B model optimi…

DCR: Quantifying Data Contamination in LLMs Evaluation
Cheng Xu, Nan Yan, Shuhao Guan, Changhong Jin, Yuke Mei, Yibing Guo, M-Tahar Kechadi
https://arxiv.org/abs/2507.11405

DCR: Quantifying Data Contamination in LLMs Evaluation
The rapid advancement of large language models (LLMs) has heightened concerns about benchmark data contamination (BDC), where models inadvertently memorize evaluation data, inflating performance metrics and undermining genuine generalization assessment. This paper introduces the Data Contamination Risk (DCR) framework, a lightweight, interpretable pipeline designed to detect and quantify BDC across four granular levels: semantic, informational, data, and label. By synthesizing contamination sco…

Genossenschaftsversammlung der @… heute virtuell! #tazgenossenschaft

Addressing Data Imbalance in Transformer-Based Multi-Label Emotion Detection with Weighted Loss
Xia Cui
https://arxiv.org/abs/2507.11384 https://

Addressing Data Imbalance in Transformer-Based Multi-Label Emotion Detection with Weighted Loss
This paper explores the application of a simple weighted loss function to Transformer-based models for multi-label emotion detection in SemEval-2025 Shared Task 11. Our approach addresses data imbalance by dynamically adjusting class weights, thereby enhancing performance on minority emotion classes without the computational burden of traditional resampling methods. We evaluate BERT, RoBERTa, and BART on the BRIGHTER dataset, using evaluation metrics such as Micro F1, Macro F1, ROC-AUC, Accurac…

What is the Best Process Model Representation? A Comparative Analysis for Process Modeling with Large Language Models
Alexis Brissard, Fr\'ed\'eric Cuppens, Amal Zouaq
https://arxiv.org/abs/2507.11356

What is the Best Process Model Representation? A Comparative Analysis for Process Modeling with Large Language Models
Large Language Models (LLMs) are increasingly applied for Process Modeling (PMo) tasks such as Process Model Generation (PMG). To support these tasks, researchers have introduced a variety of Process Model Representations (PMRs) that serve as model abstractions or generation targets. However, these PMRs differ widely in structure, complexity, and usability, and have never been systematically compared. Moreover, recent PMG approaches rely on distinct evaluation strategies and generation techniqu…

Automated Novelty Evaluation of Academic Paper: A Collaborative Approach Integrating Human and Large Language Model Knowledge
Wenqing Wu, Chengzhi Zhang, Yi Zhao
https://arxiv.org/abs/2507.11330

Automated Novelty Evaluation of Academic Paper: A Collaborative Approach Integrating Human and Large Language Model Knowledge
Novelty is a crucial criterion in the peer review process for evaluating academic papers. Traditionally, it's judged by experts or measure by unique reference combinations. Both methods have limitations: experts have limited knowledge, and the effectiveness of the combination method is uncertain. Moreover, it's unclear if unique citations truly measure novelty. The large language model (LLM) possesses a wealth of knowledge, while human experts possess judgment abilities that the LLM does not po…

Internal Value Alignment in Large Language Models through Controlled Value Vector Activation
Haoran Jin, Meng Li, Xiting Wang, Zhihao Xu, Minlie Huang, Yantao Jia, Defu Lian
https://arxiv.org/abs/2507.11316

Internal Value Alignment in Large Language Models through Controlled Value Vector Activation
Aligning Large Language Models (LLMs) with human values has attracted increasing attention since it provides clarity, transparency, and the ability to adapt to evolving scenarios. In this paper, we introduce a Controlled Value Vector Activation (ConVA) method that directly aligns the internal values of LLMs by interpreting how a value is encoded in their latent representations and modifies relevant activations to ensure consistent values in LLMs. To ensure an accurate and unbiased interpretatio…

Volcano-Like Ferroic Transitions Deviating from the Model of Landau Theory
Yuxuan Sheng, Menghao Wu
https://arxiv.org/abs/2507.11409 https://

Volcano-Like Ferroic Transitions Deviating from the Model of Landau Theory
We predict the existence of abnormal volcano-like temperature dependence of polarization or magnetization with maxima located at elevated temperature, distinct from classical model based on Landau theory. One case is ferroelectricity with long ion displacements and quantized polarizations that cannot be used for expansion in Landau model, and the switching pathway involves various metastable phases where the polar phase is higher both in energy and entropy compared with non-polar phase. Another…

Dr.Copilot: A Multi-Agent Prompt Optimized Assistant for Improving Patient-Doctor Communication in Romanian
Andrei Niculae, Adrian Cosma, Cosmin Dumitrache, Emilian R\v{a}doi
https://arxiv.org/abs/2507.11299

Dr.Copilot: A Multi-Agent Prompt Optimized Assistant for Improving Patient-Doctor Communication in Romanian
Text-based telemedicine has become increasingly common, yet the quality of medical advice in doctor-patient interactions is often judged more on how advice is communicated rather than its clinical accuracy. To address this, we introduce Dr.Copilot , a multi-agent large language model (LLM) system that supports Romanian-speaking doctors by evaluating and enhancing the presentation quality of their written responses. Rather than assessing medical correctness, Dr.Copilot provides feedback along 17…

Fine-Grained Chinese Hate Speech Understanding: Span-Level Resources, Coded Term Lexicon, and Enhanced Detection Frameworks
Zewen Bai, Liang Yang, Shengdi Yin, Yuanyuan Sun, Hongfei Lin
https://arxiv.org/abs/2507.11292

Fine-Grained Chinese Hate Speech Understanding: Span-Level Resources, Coded Term Lexicon, and Enhanced Detection Frameworks
The proliferation of hate speech has inflicted significant societal harm, with its intensity and directionality closely tied to specific targets and arguments. In recent years, numerous machine learning-based methods have been developed to detect hateful comments on online platforms automatically. However, research on Chinese hate speech detection lags behind, and interpretability studies face two major challenges: first, the scarcity of span-level fine-grained annotated datasets limits models'…

FMC: Formalization of Natural Language Mathematical Competition Problems
Jiaxuan Xie, Chengwu Liu, Ye Yuan, Siqi Li, Zhiping Xiao, Ming Zhang
https://arxiv.org/abs/2507.11275

FMC: Formalization of Natural Language Mathematical Competition Problems
Efficient and accurate autoformalization methods, which leverage large-scale datasets of extensive natural language mathematical problems to construct formal language datasets, are key to advancing formal mathematical reasoning. In this paper, we propose an autoformalization pipeline based on large language models with error feedback, achieving a fully automatic and training-free formalization approach. Using this pipeline, we curate an Olympiad-level dataset aligning natural language problems …

KV-Latent: Dimensional-level KV Cache Reduction with Frequency-aware Rotary Positional Embedding
Luohe Shi, Zuchao Li, Lefei Zhang, Guoming Liu, Baoyuan Qi, Hai Zhao
https://arxiv.org/abs/2507.11273

KV-Latent: Dimensional-level KV Cache Reduction with Frequency-aware Rotary Positional Embedding
Large language models (LLMs) based on Transformer Decoders have become the preferred choice for conversational generative AI. Despite the overall superiority of the Decoder architecture, the gradually increasing Key-Value (KV) cache during inference has emerged as a primary efficiency bottleneck, both in aspects of memory consumption and data transfer bandwidth limitations. To address these challenges, we propose a paradigm called KV-Latent. By down-sampling the Key-Value vector dimensions into…

Implementation of the magnetic force theorem for large-scale calculations of magnon bands: application to yttrium iron garnet
Thorbj{\o}rn Skovhus, Varun Rajeev Pavizhakumari, Thomas Olsen
https://arxiv.org/abs/2507.11374

Implementation of the magnetic force theorem for large-scale calculations of magnon bands: application to yttrium iron garnet
We present an efficient implementation of the magnetic force theorem which allows for direct evaluation of exchange parameters in q-space. The exchange parameters are calculated directly from Bloch states and the implementation does not rely on any mapping onto localized orbitals. This renders the approach well suited for high-throughput computations, where the construction of a localized basis set (for example Wannier functions) often is impractical. We demonstrate the versatility of the metho…

Replaced article(s) found for math.CT. https://arxiv.org/list/math.CT/new
[1/1]:
- Lifting of locally initial objects and universal (co)acting Hopf algebras
Ana Agore, Alexey Gordienko, Joost Vercruysse

Cauchy density
Adri\'an Do\~na Mateo
https://arxiv.org/abs/2507.07869 https://arxiv.org/pdf/2507.07869

Cauchy density
In the paper where he defined the Cauchy completion of a $\mathscr{V}$-category, Lawvere also defined a condition on a $\mathscr{V}$-functor which made it analogous to a map of metric spaces whose image is topologically dense in its codomain. We call this condition Cauchy density. In this note, we focus on the fully faithful Cauchy dense $\mathscr{V}$-functors, and show that the Cauchy completion of $\mathscr{A}$ is the largest $\mathscr{V}$-category that admits a fully faithful Cauchy dense $\…

Unveiling Zn incorporation in CuInS$_2$ quantum dots: X-ray and optical analysis of doping effects, structural modifications and surface passivation
Andr\'es Burgos-Caminal, Brener R. C. Vale, Andr\'e F. V. Fonseca, Juan F. Hidalgo, Elisa P. P. Collet, L\'azaro Garc\'ia, V\'ictor Vega-Mayoral, Sa\"ul Garcia-Orrit, Iciar Arnay, Juan Cabanillas-Gonz\'alez, Laura Simonelli, Ana Fl\'avia Nogueira, Marco Ant\^onio Schiavon, Thomas J. Penfold, Lazaro A. Padil…

Unveiling Zn incorporation in CuInS$_2$ quantum dots: X-ray and optical analysis of doping effects, structural modifications and surface passivation
Quantum dots (QDs) exhibit unique properties arising from their reduced size and quantum confinement effects, including exceptionally bright and tunable photoluminescence. Among these, CuInS$_{2}$ QDs have gained significant attention owing to their remarkable broadband emission, making them highly desirable for various optoelectronic applications requiring efficient luminescent nanomaterials. However, maximizing radiative recombination in CuInS$_{2}$ QDs often necessitates minimizing intragap …

On the squares functor and the Gaitsgory-Rozenblyum conjectures
F\'elix Loubaton, Jaco Ruit
https://arxiv.org/abs/2507.07807 https://

On the squares functor and the Gaitsgory-Rozenblyum conjectures
In the seminal work of Gaitsgory and Rozenblyum on derived algebraic geometry, eight conjectures regarding the theory of $(\infty,2)$-categories are stated. This paper aims to clarify the status of these claims, and to provide a proof for the last remaining open one. Along the way, we demonstrate the universal property of the so-called squares functor, a construction that plays an important role in the $(\infty,2)$-categorical foundations of Gaitsgory-Rozenblyum.

[2025-07-11 Fri (UTC), 3 new articles found for math.CT Category Theory]
toXiv_bot_toot

A supramolecular ferroelectric with two sublattices and polarization dependent conductivity
H. Mager, M. Litterst, Sophia Klubertz, S. V. Haridas, O. Shyshov, M. von Delius, M. Kemerink
https://arxiv.org/abs/2507.11309

A supramolecular ferroelectric with two sublattices and polarization dependent conductivity
The possibility to combine and finetune properties of functional molecular materials by chemical design is particularly relevant for organic ferroelectrics. In this work, we investigate a class of organic molecular materials that show long-range supramolecular organization into fibrillar bundles. In solid state, the material shows ferroelectric behavior resulting from two largely independent dipolar moieties that show up as two separate coercive fields in polarization-hysteresis and capacitance…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[2/2]:
- Flippi: End To End GenAI Assistant for E-Commerce
Rajasekar, Tangarajan, Nainani, Batwal, Dandin, Trivedi, Ersoy

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/2]:
- Answer Generation for Questions With Multiple Information Sources in E-Commerce
Anand A. Rajasekar, Nikesh Garera

KV Cache Steering for Inducing Reasoning in Small Language Models
Max Belitsky, Dawid J. Kopiczko, Michael Dorkenwald, M. Jehanzeb Mirza, Cees G. M. Snoek, Yuki M. Asano
https://arxiv.org/abs/2507.08799

KV Cache Steering for Inducing Reasoning in Small Language Models
We propose cache steering, a lightweight method for implicit steering of language models via a one-shot intervention applied directly to the key-value cache. To validate its effectiveness, we apply cache steering to induce chain-of-thought reasoning in small language models. Our approach leverages GPT-4o-generated reasoning traces to construct steering vectors that shift model behavior toward more explicit, multi-step reasoning without fine-tuning or prompt modifications. Experimental evaluatio…

Multilingual Multimodal Software Developer for Code Generation
Linzheng Chai, Jian Yang, Shukai Liu, Wei Zhang, Liran Wang, Ke Jin, Tao Sun, Congnan Liu, Chenchen Zhang, Hualei Zhu, Jiaheng Liu, Xianjie Wu, Ge Zhang, Tianyu Liu, Zhoujun Li
https://arxiv.org/abs/2507.08719

Multilingual Multimodal Software Developer for Code Generation
The rapid advancement of Large Language Models (LLMs) has significantly improved code generation, yet most models remain text-only, neglecting crucial visual aids like diagrams and flowcharts used in real-world software development. To bridge this gap, we introduce MM-Coder, a Multilingual Multimodal software developer. MM-Coder integrates visual design inputs-Unified Modeling Language (UML) diagrams and flowcharts (termed Visual Workflow)-with textual instructions to enhance code generation ac…

Optimized Synthesis and Device Integration of Long 17-Atom-Wide Armchair Graphene Nanoribbons
Jeong Ha Hwang, Nicol\`o Bassi, Mayada Fadel, Oliver Braun, Tim Dumslaff, Carlo Pignedoli, Michael Stiefel, Roman Furrer, Hironobu Hayashi, Hiroko Yamada, Akimitsu Narita, Klaus M\"ullen, Michel Calame, Mickael L. Perrin, Roman Fasel, Pascal Ruffieux, Vincent Meunier, Gabriela Borin Barin

Optimized Synthesis and Device Integration of Long 17-Atom-Wide Armchair Graphene Nanoribbons
Seventeen-carbon-atom-wide armchair graphene nanoribbons (17-AGNRs) are promising candidates for high-performance electronic devices due to their narrow electronic bandgap. Atomic precision in edge structure and width control is achieved through a bottom-up on-surface synthesis (OSS) approach from tailored molecular precursors in ultra-high vacuum (UHV). This synthetic protocol must be optimized to meet the structural requirements for device integration, with ribbon length being the most critic…

KG-Attention: Knowledge Graph-Guided Attention at Test-Time via Bidirectional Information Aggregation
Songlin Zhai, Guilin Qi, Yuan Meng
https://arxiv.org/abs/2507.08704

KG-Attention: Knowledge Graph-Guided Attention at Test-Time via Bidirectional Information Aggregation
Knowledge graphs (KGs) play a critical role in enhancing large language models (LLMs) by introducing structured and grounded knowledge into the learning process. However, most existing KG-enhanced approaches rely on parameter-intensive fine-tuning, which risks catastrophic forgetting and degrades the pretrained model's generalization. Moreover, they exhibit limited adaptability to real-time knowledge updates due to their static integration frameworks. To address these issues, we introduce the f…

KELPS: A Framework for Verified Multi-Language Autoformalization via Semantic-Syntactic Alignment
Jiyao Zhang, Chengli Zhong, Hui Xu, Qige Li, Yi Zhou
https://arxiv.org/abs/2507.08665

KELPS: A Framework for Verified Multi-Language Autoformalization via Semantic-Syntactic Alignment
Modern large language models (LLMs) show promising progress in formalizing informal mathematics into machine-verifiable theorems. However, these methods still face bottlenecks due to the limited quantity and quality of multilingual parallel corpora. In this paper, we propose a novel neuro-symbolic framework KELPS (Knowledge-Equation based Logical Processing System) to address these problems. KELPS is an iterative framework for translating, synthesizing, and filtering informal data into multiple…

The Impact of Automatic Speech Transcription on Speaker Attribution
Cristina Aggazzotti, Matthew Wiesner, Elizabeth Allyn Smith, Nicholas Andrews
https://arxiv.org/abs/2507.08660 …

The Impact of Automatic Speech Transcription on Speaker Attribution
Speaker attribution from speech transcripts is the task of identifying a speaker from the transcript of their speech based on patterns in their language use. This task is especially useful when the audio is unavailable (e.g. deleted) or unreliable (e.g. anonymized speech). Prior work in this area has primarily focused on the feasibility of attributing speakers using transcripts produced by human annotators. However, in real-world settings, one often only has more errorful transcripts produced b…

Replaced article(s) found for math.CT. https://arxiv.org/list/math.CT/new
[1/1]:
- Computation of Smyth and Hoare Power Constructions in Well-filtered Dcpos
Huijun Hou, Qingguo Li

A comprehensive study of LLM-based argument classification: from LLAMA through GPT-4o to Deepseek-R1
Marcin Pietro\'n, Rafa{\l} Olszowski, Jakub Gomu{\l}ka, Filip Gampel, Andrzej Tomski
https://arxiv.org/abs/2507.08621

A comprehensive study of LLM-based argument classification: from LLAMA through GPT-4o to Deepseek-R1
Argument mining (AM) is an interdisciplinary research field that integrates insights from logic, philosophy, linguistics, rhetoric, law, psychology, and computer science. It involves the automatic identification and extraction of argumentative components, such as premises and claims, and the detection of relationships between them, such as support, attack, or neutrality. Recently, the field has advanced significantly, especially with the advent of large language models (LLMs), which have enhanc…

Diverse polymorphism in Ruddlesden-Popper chalcogenides
Prakriti Kayastha, Erik Fransson, Paul Erhart, Lucy Whalley
https://arxiv.org/abs/2507.11300 https:…

Diverse polymorphism in Ruddlesden-Popper chalcogenides
Ruddlesden-Popper (RP) chalcogenides are stable, non-toxic candidates for optoelectronic or thermoelectric applications. The structural diversity of RP oxides is already exploited to tune properties or achieve more advanced functionalities like multiferroicity, however, little is known about the structural evolution of RP chalcogenides. In this work, we develop a high-accuracy machine-learned interatomic potential to run large-scale molecular dynamics simulations on $Ba_{n+1}Zr_nS_{3n+1}$ for $…

DocPolarBERT: A Pre-trained Model for Document Understanding with Relative Polar Coordinate Encoding of Layout Structures
Benno Uthayasooriyar, Antoine Ly, Franck Vermet, Caio Corro
https://arxiv.org/abs/2507.08606

DocPolarBERT: A Pre-trained Model for Document Understanding with Relative Polar Coordinate Encoding of Layout Structures
We introduce DocPolarBERT, a layout-aware BERT model for document understanding that eliminates the need for absolute 2D positional embeddings. We extend self-attention to take into account text block positions in relative polar coordinate system rather than the Cartesian one. Despite being pre-trained on a dataset more than six times smaller than the widely used IIT-CDIP corpus, DocPolarBERT achieves state-of-the-art results. These results demonstrate that a carefully designed attention mechan…

The AI Language Proficiency Monitor -- Tracking the Progress of LLMs on Multilingual Benchmarks
David Pomerenke, Jonas Nothnagel, Simon Ostermann
https://arxiv.org/abs/2507.08538 …

The AI Language Proficiency Monitor -- Tracking the Progress of LLMs on Multilingual Benchmarks
To ensure equitable access to the benefits of large language models (LLMs), it is essential to evaluate their capabilities across the world's languages. We introduce the AI Language Proficiency Monitor, a comprehensive multilingual benchmark that systematically assesses LLM performance across up to 200 languages, with a particular focus on low-resource languages. Our benchmark aggregates diverse tasks including translation, question answering, math, and reasoning, using datasets such as FLORES+…

[2025-07-10 Thu (UTC), no new articles found for math.CT Category Theory]
toXiv_bot_toot

PromotionGo at SemEval-2025 Task 11: A Feature-Centric Framework for Cross-Lingual Multi-Emotion Detection in Short Texts
Ziyi Huang, Xia Cui
https://arxiv.org/abs/2507.08499

PromotionGo at SemEval-2025 Task 11: A Feature-Centric Framework for Cross-Lingual Multi-Emotion Detection in Short Texts
This paper presents our system for SemEval 2025 Task 11: Bridging the Gap in Text-Based Emotion Detection (Track A), which focuses on multi-label emotion detection in short texts. We propose a feature-centric framework that dynamically adapts document representations and learning algorithms to optimize language-specific performance. Our study evaluates three key components: document representation, dimensionality reduction, and model training in 28 languages, highlighting five for detailed anal…

Semantic-Augmented Latent Topic Modeling with LLM-in-the-Loop
Mengze Hong, Chen Jason Zhang, Di Jiang
https://arxiv.org/abs/2507.08498 https://

Semantic-Augmented Latent Topic Modeling with LLM-in-the-Loop
Latent Dirichlet Allocation (LDA) is a prominent generative probabilistic model used for uncovering abstract topics within document collections. In this paper, we explore the effectiveness of augmenting topic models with Large Language Models (LLMs) through integration into two key phases: Initialization and Post-Correction. Since the LDA is highly dependent on the quality of its initialization, we conduct extensive experiments on the LLM-guided topic clustering for initializing the Gibbs sampl…

LLaPa: A Vision-Language Model Framework for Counterfactual-Aware Procedural Planning
Shibo Sun, Xue Li, Donglin Di, Mingjie Wei, Lanshun Nie, Wei-Nan Zhang, Dechen Zhan, Yang Song, Lei Fan
https://arxiv.org/abs/2507.08496

LLaPa: A Vision-Language Model Framework for Counterfactual-Aware Procedural Planning
While large language models (LLMs) have advanced procedural planning for embodied AI systems through strong reasoning abilities, the integration of multimodal inputs and counterfactual reasoning remains underexplored. To tackle these challenges, we introduce LLaPa, a vision-language model framework designed for multimodal procedural planning. LLaPa generates executable action sequences from textual task descriptions and visual environmental images using vision-language models (VLMs). Furthermor…

Magneto-elastic softening in cold-sprayed polycrystalline nickel studied by resonant ultrasound spectroscopy
Michaela Janovsk\'a (Institute of Thermomechanics, Czech Acad Sci, Prague, Czechia), Petr Sedl\'ak (Institute of Thermomechanics, Czech Acad Sci, Prague, Czechia), Martin \v{S}ev\v{c}\'ik (Institute of Thermomechanics, Czech Acad Sci, Prague, Czechia), Jan Cizek (Institute of Plasma Physics, Czech Acad Sci, Prague, Czechia, Faculty of Mechanical Engineering, Czech Te…

Magneto-elastic softening in cold-sprayed polycrystalline nickel studied by resonant ultrasound spectroscopy
Cold-sprayed metallic deposits are additively manufactured materials containing high levels of compressive residual stress. Here we show that the presence and intensity of this stress can be analyzed using laser-ultrasonics, provided that the sprayed material is ferromagnetic and magnetostrictive, as in the case of pure nickel. Contactless resonant ultrasound spectroscopy is used to monitor the evolution of shear modulus and internal friction parameter of two polycrystalline Ni deposits with te…

A Third Paradigm for LLM Evaluation: Dialogue Game-Based Evaluation using clembench
David Schlangen, Sherzod Hakimov, Jonathan Jordan, Philipp Sadler
https://arxiv.org/abs/2507.08491

A Third Paradigm for LLM Evaluation: Dialogue Game-Based Evaluation using clembench
There are currently two main paradigms for evaluating large language models (LLMs), reference-based evaluation and preference-based evaluation. The first, carried over from the evaluation of machine learning models in general, relies on pre-defined task instances, for which reference task executions are available. The second, best exemplified by the LM-arena, relies on (often self-selected) users bringing their own intents to a site that routes these to several models in parallel, among whose r…

Enhancing Essay Cohesion Assessment: A Novel Item Response Theory Approach
Bruno Alexandre Rosa, Hil\'ario Oliveira, Luiz Rodrigues, Eduardo Araujo Oliveira, Rafael Ferreira Mello
https://arxiv.org/abs/2507.08487

Enhancing Essay Cohesion Assessment: A Novel Item Response Theory Approach
Essays are considered a valuable mechanism for evaluating learning outcomes in writing. Textual cohesion is an essential characteristic of a text, as it facilitates the establishment of meaning between its parts. Automatically scoring cohesion in essays presents a challenge in the field of educational artificial intelligence. The machine learning algorithms used to evaluate texts generally do not consider the individual characteristics of the instances that comprise the analysed corpus. In this…

ILT-Iterative LoRA Training through Focus-Feedback-Fix for Multilingual Speech Recognition
Qingliang Meng, Hao Wu, Wei Liang, Wei Xu, Qing Zhao
https://arxiv.org/abs/2507.08477

ILT-Iterative LoRA Training through Focus-Feedback-Fix for Multilingual Speech Recognition
The deep integration of large language models and automatic speech recognition systems has become a promising research direction with high practical value. To address the overfitting issue commonly observed in Low-Rank Adaptation (LoRA) during the supervised fine-tuning (SFT) stage, this work proposes an innovative training paradigm Iterative LoRA Training (ILT) in combination with an Iterative Pseudo Labeling strategy, effectively enhancing the theoretical upper bound of model performance. Bas…

Using Large Language Models for Legal Decision-Making in Austrian Value-Added Tax Law: An Experimental Study
Marina Luketina, Andrea Benkel, Christoph G. Schuetz
https://arxiv.org/abs/2507.08468

Using Large Language Models for Legal Decision-Making in Austrian Value-Added Tax Law: An Experimental Study
This paper provides an experimental evaluation of the capability of large language models (LLMs) to assist in legal decision-making within the framework of Austrian and European Union value-added tax (VAT) law. In tax consulting practice, clients often describe cases in natural language, making LLMs a prime candidate for supporting automated decision-making and reducing the workload of tax professionals. Given the requirement for legally grounded and well-justified analyses, the propensity of L…

Hidden fully-compensated ferrimagnetism
San-Dong Guo
https://arxiv.org/abs/2507.11118 https://arxiv.org/pdf/2507.11118

Hidden fully-compensated ferrimagnetism
Incorporating zero-net-magnetization magnets that exhibit spin-splitting into spintronics delivers key advantages: faster switching dynamics, greater immunity to destabilizing fields, lower power consumption, and markedly improved overall efficiency. The collinear magnets with net-zero magnetization and spin-splitting mainly include altermagnet and fully-compensated ferrimagnet, which provide possibility to achieve hidden spin polarization (HSP) with net-zero spin polarization in total but non-…

Diagnosing Failures in Large Language Models' Answers: Integrating Error Attribution into Evaluation Framework
Zishan Xu, Shuyi Xie, Qingsong Lv, Shupei Xiao, Linlin Song, Sui Wenjuan, Fan Lin
https://arxiv.org/abs/2507.08459

Diagnosing Failures in Large Language Models' Answers: Integrating Error Attribution into Evaluation Framework
With the widespread application of Large Language Models (LLMs) in various tasks, the mainstream LLM platforms generate massive user-model interactions daily. In order to efficiently analyze the performance of models and diagnose failures in their answers, it is essential to develop an automated framework to systematically categorize and attribute errors. However, existing evaluation models lack error attribution capability. In this work, we establish a comprehensive Misattribution Framework wi…

Replaced article(s) found for math.CT. https://arxiv.org/list/math.CT/new
[1/1]:
- Naive co-contra correspondence for $\mathcal D$-modules
Leonid Positselski

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences
Selina Heller, Mohamed Ibrahim, David Antony Selby, Sebastian Vollmer
https://arxiv.org/abs/2507.08440

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences
Decision conferences are structured, collaborative meetings that bring together experts from various fields to address complex issues and reach a consensus on recommendations for future actions or policies. These conferences often rely on facilitated discussions to ensure productive dialogue and collective agreement. Recently, Large Language Models (LLMs) have shown significant promise in simulating real-world scenarios, particularly through collaborative multi-agent systems that mimic group in…

ChainEdit: Propagating Ripple Effects in LLM Knowledge Editing through Logical Rule-Guided Chains
Zilu Dong, Xiangqing Shen, Zinong Yang, Rui Xia
https://arxiv.org/abs/2507.08427 …

ChainEdit: Propagating Ripple Effects in LLM Knowledge Editing through Logical Rule-Guided Chains
Current knowledge editing methods for large language models (LLMs) struggle to maintain logical consistency when propagating ripple effects to associated facts. We propose ChainEdit, a framework that synergizes knowledge graph-derived logical rules with LLM logical reasoning capabilities to enable systematic chain updates. By automatically extracting logical patterns from structured knowledge bases and aligning them with LLMs' internal logics, ChainEdit dynamically generates and edits logically…

A Survey of Large Language Models in Discipline-specific Research: Challenges, Methods and Opportunities
Lu Xiang, Yang Zhao, Yaping Zhang, Chengqing Zong
https://arxiv.org/abs/2507.08425

A Survey of Large Language Models in Discipline-specific Research: Challenges, Methods and Opportunities
Large Language Models (LLMs) have demonstrated their transformative potential across numerous disciplinary studies, reshaping the existing research methodologies and fostering interdisciplinary collaboration. However, a systematic understanding of their integration into diverse disciplines remains underexplored. This survey paper provides a comprehensive overview of the application of LLMs in interdisciplinary studies, categorising research efforts from both a technical perspective and with reg…

Indium Hydroxide Ceramic Targets: A Breakthrough in High-Mobility Thin-Film Transistor Technology
Hikaru Sadahira, Prashant R. Ghediya, Hyeonjun Kong, Akira Miura, Yasutaka Matsuo, Hiromichi Ohta, Yusaku Magari
https://arxiv.org/abs/2507.11011

Indium Hydroxide Ceramic Targets: A Breakthrough in High-Mobility Thin-Film Transistor Technology
Thin-film transistors composed of a hydrogen-containing indium oxide active layer are promising candidates for backplane devices in next-generation flat panel displays, offering higher definition and faster operation. However, the hydrogen incorporation process during film deposition poses challenges for scalable and industrial development due to both safety and controllability issues. Here, we demonstrate that using indium hydroxide ceramic as the target material for film deposition overcomes …

The Curious Case of Factuality Finetuning: Models' Internal Beliefs Can Improve Factuality
Benjamin Newman, Abhilasha Ravichander, Jaehun Jung, Rui Xin, Hamish Ivison, Yegor Kuznetsov, Pang Wei Koh, Yejin Choi
https://arxiv.org/abs/2507.08371

The Curious Case of Factuality Finetuning: Models' Internal Beliefs Can Improve Factuality
Language models are prone to hallucination - generating text that is factually incorrect. Finetuning models on high-quality factual information can potentially reduce hallucination, but concerns remain; obtaining factual gold data can be expensive and training on correct but unfamiliar data may potentially lead to even more downstream hallucination. What data should practitioners finetune on to mitigate hallucinations in language models? In this work, we study the relationship between the factu…

[2025-07-09 Wed (UTC), 2 new articles found for math.CT Category Theory]
toXiv_bot_toot

Exploring Design of Multi-Agent LLM Dialogues for Research Ideation
Keisuke Ueda, Wataru Hirota, Takuto Asakura, Takahiro Omi, Kosuke Takahashi, Kosuke Arima, Tatsuya Ishigaki
https://arxiv.org/abs/2507.08350

Exploring Design of Multi-Agent LLM Dialogues for Research Ideation
Large language models (LLMs) are increasingly used to support creative tasks such as research idea generation. While recent work has shown that structured dialogues between LLMs can improve the novelty and feasibility of generated ideas, the optimal design of such interactions remains unclear. In this study, we conduct a comprehensive analysis of multi-agent LLM dialogues for scientific ideation. We compare different configurations of agent roles, number of agents, and dialogue depth to underst…

Beyond N-Grams: Rethinking Evaluation Metrics and Strategies for Multilingual Abstractive Summarization
Itai Mondshine, Tzuf Paz-Argaman, Reut Tsarfaty
https://arxiv.org/abs/2507.08342

Beyond N-Grams: Rethinking Evaluation Metrics and Strategies for Multilingual Abstractive Summarization
Automatic n-gram based metrics such as ROUGE are widely used for evaluating generative tasks such as summarization. While these metrics are considered indicative (even if imperfect) of human evaluation for English, their suitability for other languages remains unclear. To address this, we systematically assess evaluation metrics for generation both n-gram-based and neural based to evaluate their effectiveness across languages and tasks. Specifically, we design a large-scale evaluation suite acr…

What Factors Affect LLMs and RLLMs in Financial Question Answering?
Peng Wang, Xuesi Hu, Jiageng Wu, Yuntao Zou, Qiancheng Zhang, Dagang Li
https://arxiv.org/abs/2507.08339

What Factors Affect LLMs and RLLMs in Financial Question Answering?
Recently, the development of large language models (LLMs) and reasoning large language models (RLLMs) have gained considerable attention from many researchers. RLLMs enhance the reasoning capabilities of LLMs through Long Chain-of-Thought (Long CoT) processes, significantly improving the performance of LLMs in addressing complex problems. However, there are few works that systematically explore what methods can fully unlock the performance of LLMs and RLLMs within the financial domain. To inves…

Weak low-temperature ferromagnetism and linear magnetoresistance in Lu$_{0.75}$Fe$_6$Sn$_6$ with a disordered HfFe$_6$Ge$_6$-type structure
Chenfei Shi, Zhaodi Lin, Qiyuan Liu, Junai Lv, Xiaofan Xu, Baojuan Kang, Jin-Hu Yang, Yi Liu, Jian Zhang, Shixun Cao, Jin-Ke Bao
https://arxiv.org/abs/2507.10964

Weak low-temperature ferromagnetism and linear magnetoresistance in Lu$_{0.75}$Fe$_6$Sn$_6$ with a disordered HfFe$_6$Ge$_6$-type structure
We report the synthesis of Lu$_{0.75}$Fe$_6$Sn$_6$ single crystals with a Fe-kagome lattice using a self-flux method. The crystal structure, magnetic, thermodynamic and electrical transport properties were investigated. Structure refinement reveals that Lu$_{0.75}$Fe$_6$Sn$_6$ has a HfFe$_6$Ge$_6$-type structure as the major framework intergrown with a CoSn-type structure, leading to a vacancy of 25% on the Lu-site and disorder on the Sn-site. It exhibits a significant magnetic anisotropy with …

Distillation versus Contrastive Learning: How to Train Your Rerankers
Zhichao Xu, Zhiqi Huang, Shengyao Zhuang, Ashim Gupta, Vivek Srikumar
https://arxiv.org/abs/2507.08336

Distillation versus Contrastive Learning: How to Train Your Rerankers
Training text rerankers is crucial for information retrieval. Two primary strategies are widely used: contrastive learning (optimizing directly on ground-truth labels) and knowledge distillation (transferring knowledge from a larger reranker). While both have been studied in the literature, a clear comparison of their effectiveness for training cross-encoder rerankers under practical conditions is needed. This paper empirically compares these strategies by training rerankers of different size…

MK2 at PBIG Competition: A Prompt Generation Solution
Yuzheng Xu, Tosho Hirasawa, Seiya Kawano, Shota Kato, Tadashi Kozuno
https://arxiv.org/abs/2507.08335

MK2 at PBIG Competition: A Prompt Generation Solution
The Patent-Based Idea Generation task asks systems to turn real patents into product ideas viable within three years. We propose MK2, a prompt-centric pipeline: Gemini 2.5 drafts and iteratively edits a prompt, grafting useful fragments from weaker outputs; GPT-4.1 then uses this prompt to create one idea per patent, and an Elo loop judged by Qwen3-8B selects the best prompt-all without extra training data. Across three domains, two evaluator types, and six criteria, MK2 topped the automatic le…

Frequency comb in twisted magnonic crystals
Minghao Li, Zhejunyu Jin, Zhaozhuo Zeng, Peng Yan
https://arxiv.org/abs/2507.10922 https://

Frequency comb in twisted magnonic crystals
While twisted magnonic crystals (MCs) have recently gained attention for their intriguing linear phenomena, such as magnon flat bands, their nonlinear dynamics -- particularly the generation of magnonic frequency combs (MFCs) -- have remained largely unexplored. In this work, we demonstrate the creation of MFCs in twisted MCs using two-tone microwave excitation. We find that finite twist angles significantly enhance three-magnon interactions, driven by the non-collinear ground-state magnetic co…

Optical Spin Sensing and Metamagnetic Phase Control in the 2D Van der Waals Magnet Yb3 -Doped CrPS4
Jacob T. Baillie, Kimo Pressler, Nick J. Adams, Faris Horani, Thom J. Snoeren, R\'emi Beaulac, Daniel R. Gamelin
https://arxiv.org/abs/2507.10889

Optical Spin Sensing and Metamagnetic Phase Control in the 2D Van der Waals Magnet Yb3+-Doped CrPS4
The emergence of two-dimensional magnets within the van der Waals toolkit has introduced unprecedented opportunities to develop ultrathin spintronic technologies. Strong coupling between spin and optical properties in such materials can further enable novel spin-photonic capabilities of both fundamental and technological interest. Here, we investigate the optical and spin properties of the air-stable, layered A-type antiferromagnet chromium thiophosphate (CrPS4) when doped with Yb3+. We show th…

Spin ordering-induced fully-compensated ferrimagnetism
San-Dong Guo, Shaobo Chen, Guangzhao Wang
https://arxiv.org/abs/2507.10848 https://

Spin ordering-induced fully-compensated ferrimagnetism
Fully-compensated ferrimagnets exhibit zero net magnetic moment yet display non-relativistic global spin splitting, making them highly advantageous for constructing high-performance spintronic devices. The general strategy is to break the inversion symmetry of conventional antiferromagnets or the rotational/mirror symmetry of altermagnets to achieve fully-compensated ferrimagnets. Here, we propose to induce fully-compensated ferrimagnetism by engineering the spin ordering rather than modifying …

Solid-State Dewetting of Polycrystalline Thin Films: a Phase Field Approach
Paul Hoffrogge, Nils Becker, Daniel Schneider, Britta Nestler, Axel Voigt, Marco Salvalaglio
https://arxiv.org/abs/2507.10811

Solid-State Dewetting of Polycrystalline Thin Films: a Phase Field Approach
Solid-state dewetting (SSD) is the process by which thin solid films break up and retract on a substrate, resulting in the formation of nanostructures and islands. While SSD in single-crystalline films is generally understood as a surface-energy-driven process mediated by surface diffusion, polycrystalline films feature additional complexity due to the presence of grain boundaries. Theoretical investigations and simulation frameworks have mainly focused on single-crystalline SSD. In this work, …

Raman signature of cation vacancies in rare-earth nitrides
M. Markwitz, K. Van Koughnet, K. Kneisel, W. F. Holmes-Hewett, F. Natali, E. X. M. Trewick, L. Porteous, B. J. Ruck, H. J. Trodahl
https://arxiv.org/abs/2507.10796

Raman signature of cation vacancies in rare-earth nitrides
We report a coordinated Raman/computation study of the rare-earth nitrides, a series of intrinsic ferromagnetic semiconductors, to reveal the presence of cation vacancies. Their presence is signaled by a Raman-active vibrational mode at 1100-1400 cm$^{-1}$, rising steadily as the lattice contracts across the series. The mode's frequency is in excellent agreement with the computed breathing-mode vibration of the six nitrogen ions surrounding cation vacancies. The discovery of such cation vacanci…