Tootfinder

github stopped offering text completions for pull requests https://github.blog/changelog/2025-08-15-deprecating-copilot-text-completion-for-pull-request-descriptions/

Deprecating Copilot text completion for pull request descriptions - GitHub Changelog
On September 12, 2025, we will retire Copilot text completion for pull request descriptions. After this date, when you start writing a description, Copilot will no longer suggest completions. This…

Urban Living III - Secrecy 🙊
都市生活 III - 秘密 🙊
📷 Nikon FE
🎞️Lucky SHD 400
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

Yiding Cao Geld mit PayPal.Me senden
Folgen Sie dem Link PayPal.Me/dingsterdingster und geben Sie den Betrag ein. Es ist bequem und sicher. Sie haben noch kein PayPal-Konto? Kein Problem.

Data Verbalisation: What is Text Doing in a Data Visualisation?
Paul Murrell
https://arxiv.org/abs/2506.15129 https://arxiv.org/pdf/2…

Data Verbalisation: What is Text Doing in a Data Visualisation?
This article discusses the role that text elements play in a data visualisation. We argue that there is a need for a simple, coherent explanation of text elements similar to the understanding that already exists for non-text elements like bars, points, and lines. We explore examples of how text is used within a data visualisation and use existing knowledge and assessment techniques to evaluate when text is effective and when it is not. The result is a framework that aims to be easy to understan…

Four old posts updated this week:
For Google’s AI Edge Gallery:
https://adrianroselli.com/2020/03/i-dont-care-what-google-or-apple-or-whomever-did.html#:~:text=Go…

I Don’t Care What Google or Apple or Whoever Did
Please do not use this post as an excuse to beat up the devs at Apple or Google. If you are doing that, you have missed the point of this post and you are being unnecessarily mean to individuals who may have no control over broader organizational decisions. Do not…

7Bench: a Comprehensive Benchmark for Layout-guided Text-to-image Models
Elena Izzo, Luca Parolari, Davide Vezzaro, Lamberto Ballan
https://arxiv.org/abs/2508.12919 https://

7Bench: a Comprehensive Benchmark for Layout-guided Text-to-image Models
Layout-guided text-to-image models offer greater control over the generation process by explicitly conditioning image synthesis on the spatial arrangement of elements. As a result, their adoption has increased in many computer vision applications, ranging from content creation to synthetic data generation. A critical challenge is achieving precise alignment between the image, textual prompt, and layout, ensuring semantic fidelity and spatial accuracy. Although recent benchmarks assess text alig…

SpecDetect: Simple, Fast, and Training-Free Detection of LLM-Generated Text via Spectral Analysis
Haitong Luo, Weiyao Zhang, Suhang Wang, Wenji Zou, Chungang Lin, Xuying Meng, Yujun Zhang
https://arxiv.org/abs/2508.11343

SpecDetect: Simple, Fast, and Training-Free Detection of LLM-Generated Text via Spectral Analysis
The proliferation of high-quality text from Large Language Models (LLMs) demands reliable and efficient detection methods. While existing training-free approaches show promise, they often rely on surface-level statistics and overlook fundamental signal properties of the text generation process. In this work, we reframe detection as a signal processing problem, introducing a novel paradigm that analyzes the sequence of token log-probabilities in the frequency domain. By systematically analyzing …

RFM-Editing: Rectified Flow Matching for Text-guided Audio Editing
Liting Gao, Yi Yuan, Yaru Chen, Yuelan Cheng, Zhenbo Li, Juan Wen, Shubin Zhang, Wenwu Wang
https://arxiv.org/abs/2509.14003

RFM-Editing: Rectified Flow Matching for Text-guided Audio Editing
Diffusion models have shown remarkable progress in text-to-audio generation. However, text-guided audio editing remains in its early stages. This task focuses on modifying the target content within an audio signal while preserving the rest, thus demanding precise localization and faithful editing according to the text prompt. Existing training-based and zero-shot methods that rely on full-caption or costly optimization often struggle with complex editing or lack practicality. In this work, we p…

Adobe updates its Firefly video model and adds a beta Generate Sound Effects tool that can create custom sounds from text prompts and the user's voice (Sabrina Ortiz/ZDNET)
https://www.zdnet.com/article/adobe-firefly-ca…

Adobe Firefly can now generate AI sound effects for videos - and I'm seriously impressed
In addition to video sound effects, Text to Avatar is launching in beta. It allows you to turn scripts into avatar-led videos.

#DH2025 thanks @flochiff.bsky.social for sharing this link as I wanted to follow up on Pandore! 'Pandore: automating text-processing workflows for humanities researchers' from Sorbonne Université and ObTIC - Observatoire des textes, des idées et des corpus

Qwen-Image-Edit: Image Editing with Higher Quality and Efficiency
https://qwenlm.github.io/blog/qwen-image-edit/?utm_source=www.the…

If you've been paying attention, this is a *very* strong signal that OpenAI is hitting the limits of improved capability with more compute/data and they're (predictably) all out of other ideas. The quiet "exponential model capabilities" lie here is what Altmann promised but is now starting not to be able to deliver, even in cherry-picked demo terms.
https://www.cnbc.com/2025/08/11/sam-altman-says-agi-is-a-pointless-term-experts-agree.html
The "agentic" turn was never going to pan out, because it exposes the unreliability of LLMs too directly, and it turns out that no amount of yelling at your text vending machine to "Be smarter! Think harder!" will actually get you anything more than vended text.
I'm *praying* that we get into this crash sooner rather than later, since the faster it comes, the less painful it will be.
My recent reading in actual research papers corroborates this, for example, asking LLMs to play games exposes their utter lack of anything that can be termed "reasoning":
https://arxiv.org/pdf/2508.08501v1

On $2$-connected graphs avoiding cycles of length $0$ modulo $4$
Hojin Chu, Boram Park, Homoon Ryu
https://arxiv.org/abs/2507.12798 https://

On $2$-connected graphs avoiding cycles of length $0$ modulo $4$
For two integers $k$ and $\ell$, an $(\ell \text{ mod }k)$-cycle means a cycle of length $m$ such that $m\equiv \ell\pmod{k}$. In 1977, Bollobás proved a conjecture of Burr and Erdős by showing that if $\ell$ is even or $k$ is odd, then every $n$-vertex graph containing no $(\ell \text{ mod }k)$-cycles has at most a linear number of edges in terms of $n$. Since then, determining the exact extremal bounds for graphs without $(\ell \text{ mod }k)$-cycles has emerged as an interesting question i…

Thermal-nonthermal transition of the charged particle production in pp collisions
J. Alonso Tlali, D. Rosales Herrera, J. R. Alvarado Garc\'ia, A. Fern\'andez T\'ellez, C. Pajares, J. E. Ram\'irez
https://arxiv.org/abs/2509.14175

Thermal-nonthermal transition of the charged particle production in pp collisions
We determine the internal energy of charged particle production in minimum bias pp collisions using a thermostatistical approach by analyzing the $p_\text{T}$ spectrum reported by the ALICE Collaboration across LHC energies. To do this, we define temperature as the slope of the $p_\text{T}$ spectrum at low $p_\text{T}$ values and Shannon's entropy as the system's entropy, calculated considering the normalized $p_\text{T}$ spectrum. We found that the internal energy for the Hagedorn and Tricomi …

StyleMM: Stylized 3D Morphable Face Model via Text-Driven Aligned Image Translation
Seungmi Lee, Kwan Yun, Junyong Noh
https://arxiv.org/abs/2508.11203 https://

StyleMM: Stylized 3D Morphable Face Model via Text-Driven Aligned Image Translation
We introduce StyleMM, a novel framework that can construct a stylized 3D Morphable Model (3DMM) based on user-defined text descriptions specifying a target style. Building upon a pre-trained mesh deformation network and a texture generator for original 3DMM-based realistic human faces, our approach fine-tunes these models using stylized facial images generated via text-guided image-to-image (i2i) translation with a diffusion model, which serve as stylization targets for the rendered mesh. To pr…

from my link log —
The unreasonable effectiveness of modern sort algorithms.
https://github.com/Voultapher/sort-research-rs/blob/main/writeup/unreasonable/text.md
saved 2025-09-14

sort-research-rs/writeup/unreasonable/text.md at main · Voultapher/sort-research-rs
Test and benchmark suite for sort implementations. - Voultapher/sort-research-rs

Weighted First Order Model Counting for Two-variable Logic with Axioms on Two Relations
Qipeng Kuang, V\'aclav K\r{u}la, Ond\v{r}ej Ku\v{z}elka, Yuanhong Wang, Yuyi Wang
https://arxiv.org/abs/2508.11515

Weighted First Order Model Counting for Two-variable Logic with Axioms on Two Relations
The Weighted First-Order Model Counting Problem (WFOMC) asks to compute the weighted sum of models of a given first-order logic sentence over a given domain. The boundary between fragments for which WFOMC can be computed in polynomial time relative to the domain size lies between the two-variable fragment ($\text{FO}^2$) and the three-variable fragment ($\text{FO}^3$). It is known that WFOMC for \FOthree{} is $\mathsf{\#P_1}$-hard while polynomial-time algorithms exist for computing WFOMC for $…

A universal W-algebra for N=4 super Yang-Mills
Federico Bonetti, Carlo Meneghelli
https://arxiv.org/abs/2506.15678 https://arxiv.org/…

A universal W-algebra for N=4 super Yang-Mills
Using bootstrap methods, we provide evidence for the existence of a non-linear W-algebra, denoted $W_\infty^\text{s,s}$, which contains the small N= 4 super Virasoro algebra and features an infinite tower of additional generators, organized in short supersymmetry multiplets. The algebra $W_\infty^\text{s,s}$ has one free parameter, its central charge c. We claim that the simple quotient of $W_\infty^\text{s,s}$ for c= -3(N^2-1) is isomorphic to the vertex operator algebra associated to 4d N=4 $…

Topological Dissipation as the Missing Link in Multiscale Polymer Dynamics
Xu-Ze Zhang, Rui Shi, Ming-Ji Fang, Zhong-Yuan Lu, Hu-Jun Qian
https://arxiv.org/abs/2508.12359 https:…

Topological Dissipation as the Missing Link in Multiscale Polymer Dynamics
We identify topological dissipation -- momentum transport along the polymer backbone with Ising-type exponential decay ($\sim \exp(-Δn/n_\text{d})$) -- as the missing link connecting atomistic and mesoscale dynamics. Simulations of four polymers reveal that dynamical correlation length $n_\text{d} \approx n_\text{k}/3$ (Kuhn length $n_\text{k}$), enabling a coarse-grained framework that \emph{explicitly separates} topological (intrachain) and spatial (interchain) dissipation channels without t…

Neu auf der Wunschliste: https://www.amazon.de/hz/wishlist/ls/3HYT6TVV6PW2H
Falls jemand was Gebrauchtes hat, bitte nicht wegwerfen, gerne her damit.

Oha, RFCs zu lesen, lohnt sich. 😏
Zumindest, um auf Mastodon zu flexen.
---
I scored 19/21 on https://e-mail.wtf and all I got was this lousy text to share on social media.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts
Heyang Xue, Xuchen Song, Yu Tang, Jianyu Chen, Yanru Chen, Yang Li, Yahui Zhou
https://arxiv.org/abs/2508.11326

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts
Description-based text-to-speech (TTS) models exhibit strong performance on in-domain text descriptions, i.e., those encountered during training. However, in real-world applications, the diverse range of user-generated descriptions inevitably introduces numerous out-of-domain inputs that challenge the text understanding capabilities of these systems. To address this issue, we propose MoE-TTS, a description-based TTS model designed to enhance the understanding of out-of-domain text descriptions.…

I scored 20/21 on https://e-mail.wtf and all I got was this lousy text to share on social media.
This is an entertaining quiz. But RFC 5322 is not the right place to look for an answer to the question of what a (syntactically) valid email address is.

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[5/5]:
- Handwritten Text Recognition of Historical Manuscripts Using Transformer-Based Models
Erez Meoded

CFP-The Text: Vol.8 No.1-January 2026 Issue https://call-for-papers.sas.upenn.edu/cfp/2025/07/18/cfp-the-text-vol8-no1-january-2026-issue

Enhancing Time Awareness in Generative Recommendation
Sunkyung Lee, Seongmin Park, Jonghyo Kim, Mincheol Yoon, Jongwuk Lee
https://arxiv.org/abs/2509.13957 https://

Enhancing Time Awareness in Generative Recommendation
Generative recommendation has emerged as a promising paradigm that formulates the recommendations into a text-to-text generation task, harnessing the vast knowledge of large language models. However, existing studies focus on considering the sequential order of items and neglect to handle the temporal dynamics across items, which can imply evolving user preferences. To address this limitation, we propose a novel model, Generative Recommender Using Time awareness (GRUT), effectively capturing hi…

DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement
Shaoqing Lin, Chong Teng, Fei Li, Donghong Ji, Lizhen Qu, Zhuang Li
https://arxiv.org/abs/2506.15583

DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement
Vision-Language Models (VLMs) now generate discourse-level, multi-sentence visual descriptions, challenging text scene graph parsers originally designed for single-sentence caption-to-graph mapping. Current approaches typically merge sentence-level parsing outputs for discourse input, often missing phenomena like cross-sentence coreference, resulting in fragmented graphs and degraded downstream VLM task performance. To address this, we introduce a new task, Discourse-level text Scene Graph pars…

Vistoria: A Multimodal System to Support Fictional Story Writing through Instrumental Text-Image Co-Editing
Kexue Fu, Jingfei Huang, Long Ling, Sumin Hong, Yihang Zuo, Ray LC, Toby Jia-jun Li
https://arxiv.org/abs/2509.13646

Vistoria: A Multimodal System to Support Fictional Story Writing through Instrumental Text-Image Co-Editing
Humans think visually-we remember in images, dream in pictures, and use visual metaphors to communicate. Yet, most creative writing tools remain text-centric, limiting how authors plan and translate ideas. We present Vistoria, a system for synchronized text-image co-editing in fictional story writing that treats visuals and text as coequal narrative materials. A formative Wizard-of-Oz co-design study with 10 story writers revealed how sketches, images, and annotations serve as essential instrum…

This is such a good idea. The amount of times I meant to text someone…
https://9to5mac.com/2025/08/18/imessage-gets-a-new-drafts-folder-in-ios-26/

I scored 5/21 on https://e-mail.wtf and all I got was this lousy text to share on social media.
Sadly, this means I'm not allowed to Fediverse anymore.
For today.

Email is Easy
Everyone knows what an email address is, right?

I scored 16/21 on https://e-mail.wtf and all I got was this lousy text to share on social media.

Email is Easy
Everyone knows what an email address is, right?

Bye bye Radfreigabe am #Hasenberg! Du wirst mir fehlen! 😢
https://www.tag24.de/dresden/lokales/terrassenufer-wieder-frei-e…

Bustling 🫜
攒动 🫜
📷 Nikon FE
🎞️FOMAPAN Action 400
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

I scored 10/21 on https://e-mail.wtf and all I got was this lousy text to share on social media.

Email is Easy
Everyone knows what an email address is, right?

Handwritten Text Recognition of Historical Manuscripts Using Transformer-Based Models
Erez Meoded
https://arxiv.org/abs/2508.11499 https://arxiv.org/pdf/25…

Handwritten Text Recognition of Historical Manuscripts Using Transformer-Based Models
Historical handwritten text recognition (HTR) is essential for unlocking the cultural and scholarly value of archival documents, yet digitization is often hindered by scarce transcriptions, linguistic variation, and highly diverse handwriting styles. In this study, we apply TrOCR, a state-of-the-art transformer-based HTR model, to 16th-century Latin manuscripts authored by Rudolf Gwalther. We investigate targeted image preprocessing and a broad suite of data augmentation techniques, introducing…

Charge-4$e$ Anyon Superconductor from Doping $\text{SU}(4)_1$ chiral spin liquid
Lu Zhang, Ya-Hui Zhang, Xue-Yang Song
https://arxiv.org/abs/2508.12370 https://

Charge-4$e$ Anyon Superconductor from Doping $\text{SU}(4)_1$ chiral spin liquid
Previous studies have shown that $\text{SU}(4)_1$ chiral spin liquid can emerge in the SU($4$) Hubbard model on triangular lattice. A natural question then arises: What is the phase upon doping? In this work, we show the possibility that hole doping can give rise to an anyon superconductor and propose that both spinons and holons form integer quantum Hall states with opposite chiralities. Using topological field theory we demonstrate that the phase is a topological charge-$4e$ superconductor wi…

Zathras is having Win11 issues. https://mastodon.art/@pentup/115050095384100543

"Zotero is now my go-to app for research and paper management, and I use it every day. It’s a fundamental part of the preparation of future editions of @… and now I’m a happy customer of theirs."
https://

Zotero
Writing articles for De Programmatica Ipsum made me adopt a workflow to read and annotate research papers. Most of the material that I use to write articles for DPI are papers, nearly all in PDF format, some with OCR’d text, some without (particularly older ones).

⭐ 🚂 🌧 ÖPNV: Warum deutsche Zustände nicht "normal" sind | @…
https://heise.de/-10538584#:~:text=Die Problematik digitaler Anzeigen
"Digitale…

Arbeidernes frigjŸring må være arbeidernes eget verk!
⭐ Les mer her: #Anarkisme #Syndikalisme

Here's my latest Prairie Voice post with a video voice excerpt:
https://prairievoice.com/p/prairie-voice-speaks?r=17i29e

Brazil’s Chamber of Deputies Approves Bill Banning Cosmetic Testing on Live Vertebrates https://vegconomist.com/politics-law/brazil-approves-bill-banning-cosmetic-testing-live-vertebrates/

Brazil's Chamber of Deputies Approves Bill Banning Cosmetic Testing on Live Vertebrates - vegconomist - the vegan business magazine
Brazil's Chamber of Deputies has approved the Senate's substitute text for a bill that would ban the testing of cosmetics, personal hygiene products, and

Energy Efficiency in Robotics Software: A Systematic Literature Review (2020-2024)
Aryan Gupta
https://arxiv.org/abs/2508.12170 https://arxiv.org/pdf/2508.…

Energy Efficiency in Robotics Software: A Systematic Literature Review (2020-2024)
This study presents a systematic literature review of software-level approaches to energy efficiency in robotics published from 2020 through 2024, updating and extending pre-2020 evidence. An automated-but-audited pipeline combined Google Scholar seeding, backward/forward snowballing, and large-language-model (LLM) assistance for screening and data extraction, with ~10% human audits at each automated step and consensus-with-tie-breaks for full-text decisions. The final corpus comprises 79 peer-…

Winter Soldier: Backdooring Language Models at Pre-Training with Indirect Data Poisoning
Wassim Bouaziz, Mathurin Videau, Nicolas Usunier, El-Mahdi El-Mhamdi
https://arxiv.org/abs/2506.14913

Winter Soldier: Backdooring Language Models at Pre-Training with Indirect Data Poisoning
The pre-training of large language models (LLMs) relies on massive text datasets sourced from diverse and difficult-to-curate origins. Although membership inference attacks and hidden canaries have been explored to trace data usage, such methods rely on memorization of training data, which LM providers try to limit. In this work, we demonstrate that indirect data poisoning (where the targeted behavior is absent from training data) is not only feasible but also allow to effectively protect a dat…

Diff-TONE: Timestep Optimization for iNstrument Editing in Text-to-Music Diffusion Models
Teysir Baoueb, Xiaoyu Bie, Xi Wang, Ga\"el Richard
https://arxiv.org/abs/2506.15530 …

Diff-TONE: Timestep Optimization for iNstrument Editing in Text-to-Music Diffusion Models
Breakthroughs in text-to-music generation models are transforming the creative landscape, equipping musicians with innovative tools for composition and experimentation like never before. However, controlling the generation process to achieve a specific desired outcome remains a significant challenge. Even a minor change in the text prompt, combined with the same random seed, can drastically alter the generated piece. In this paper, we explore the application of existing text-to-music diffusion …

Folk at #DH2025 might be interested in this article 'From handwriting to searchable text: using AI to make millions of historical documents accessible'

From handwriting to searchable text: using AI to make millions of historical documents accessible
In April 2025 the Swedish National Archives made 1,2 million digital images of archival documents into searchable, machine readable text — making the information in the archives more accessible than…

PhantomHunter: Detecting Unseen Privately-Tuned LLM-Generated Text via Family-Aware Learning
Yuhui Shi, Yehan Yang, Qiang Sheng, Hao Mi, Beizhe Hu, Chaoxi Xu, Juan Cao
https://arxiv.org/abs/2506.15683

PhantomHunter: Detecting Unseen Privately-Tuned LLM-Generated Text via Family-Aware Learning
With the popularity of large language models (LLMs), undesirable societal problems like misinformation production and academic misconduct have been more severe, making LLM-generated text detection now of unprecedented importance. Although existing methods have made remarkable progress, a new challenge posed by text from privately tuned LLMs remains underexplored. Users could easily possess private LLMs by fine-tuning an open-source one with private corpora, resulting in a significant performanc…

GenExam: A Multidisciplinary Text-to-Image Exam
Zhaokai Wang, Penghao Yin, Xiangyu Zhao, Changyao Tian, Yu Qiao, Wenhai Wang, Jifeng Dai, Gen Luo
https://arxiv.org/abs/2509.14232

GenExam: A Multidisciplinary Text-to-Image Exam
Exams are a fundamental test of expert-level intelligence and require integrated understanding, reasoning, and generation. Existing exam-style benchmarks mainly focus on understanding and reasoning tasks, and current generation benchmarks emphasize the illustration of world knowledge and visual concepts, neglecting the evaluation of rigorous drawing exams. We introduce GenExam, the first benchmark for multidisciplinary text-to-image exams, featuring 1,000 samples across 10 subjects with exam-st…

$B\rightarrow K \text{axion-like particles}$: effective versus UV-complete models and enhanced two-loop contributions
Xiyuan Gao, Ulrich Nierste
https://arxiv.org/abs/2506.14876

$B\rightarrow K + \text{axion-like particles}$: effective versus UV-complete models and enhanced two-loop contributions
An axion-like particle $a$ (ALP) can explain the excess of $B\rightarrow K + \text{invisible}$ events at Belle-II. However, many analyses of ALP scenarios are over-simplified. We revisit the $B\rightarrow K a$ transition rate in a popular minimal and UV complete model with two Higgs doublets (2HDM) and a complex singlet (DFSZ model). To this end we compare our results with previous studies which derived the $\overline{b}sa$ vertex from the $\overline{b}sA$ vertex, where $A$ is the heavy pseudo-…

Insights Informed Generative AI for Design: Incorporating Real-world Data for Text-to-Image Output
Richa Gupta, Alexander Htet Kyaw
https://arxiv.org/abs/2506.15008

Insights Informed Generative AI for Design: Incorporating Real-world Data for Text-to-Image Output
Generative AI, specifically text-to-image models, have revolutionized interior architectural design by enabling the rapid translation of conceptual ideas into visual representations from simple text prompts. While generative AI can produce visually appealing images they often lack actionable data for designers In this work, we propose a novel pipeline that integrates DALL-E 3 with a materials dataset to enrich AI-generated designs with sustainability metrics and material usage insights. After t…

The column number for 3-modular matrices
Joseph Paat, Zach Walsh, Luze Xu
https://arxiv.org/abs/2509.13463 https://arxiv.org/pdf/2509.13463

The column number for 3-modular matrices
An integer-valued matrix $\mathbf{A}$ is $Δ$-modular if each $\text{rank}(\mathbf{A}) \times \text{rank}(\mathbf{A})$ submatrix has determinant at most $Δ$ in absolute value. The column number problem is to determine the maximum number of pairwise non-parallel columns of a rank-$r$, $Δ$-modular matrix. Exact values for the column number are only known for $r \le 2$ or $Δ\le 2$. We prove that if $r$ is sufficiently large, then the maximum number of pairwise non-parallel columns of a rank-$r$…

City Features VI 🏙️
城市特征 VI 🏙️
📷 Nikon FE
🎞️Ilford FP4 Plus, expired 1994
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

I’m old enough to remember `-ms-high-contrast-adjust: none;` to remove the backplate that Edge added in 2016 (pre-Chromiedge).
Not surprised its `forced-colors` version is frustrating authors:
“forced-color-adjust: none is an unavoidable foot gun”
https://sarahmhigley.com/writing/force

forced-color-adjust: none is an unavoidable foot gun | Sarah Higley
A very long treatise on why text backplates were a bad idea. Most of the time.

MOVER: Multimodal Optimal Transport with Volume-based Embedding Regularization
Haochen You, Baojing Liu
https://arxiv.org/abs/2508.12149 https://arxiv.org/…

MOVER: Multimodal Optimal Transport with Volume-based Embedding Regularization
Recent advances in multimodal learning have largely relied on pairwise contrastive objectives to align different modalities, such as text, video, and audio, in a shared embedding space. While effective in bi-modal setups, these approaches struggle to generalize across multiple modalities and often lack semantic structure in high-dimensional spaces. In this paper, we propose MOVER, a novel framework that combines optimal transport-based soft alignment with volume-based geometric regularization t…

Cut-off scale of quantum gravity
Asya Aynbund, V. V. Kiselev
https://arxiv.org/abs/2508.11293 https://arxiv.org/pdf/2508.11293

Cut-off scale of quantum gravity
Dynamics of gravity interaction with matter at one-loop level of effective quantum field theory naturally sets the cut-off scale $Λ_E$ in a sub-Planckian region through incorporating the gauge coupling constant $α(Λ_E)$ and the reduced Planckian mass $\tilde m_\text{pl}$ into relation $ Λ_E\sim \tilde m_\text{pl}\,α(Λ_E)/(4π)$ numerically yielding $Λ_E\sim 10^{16}$ GeV. Such two scales are empirically inherent for an inflationary cosmology.

FashionPose: Text to Pose to Relight Image Generation for Personalized Fashion Visualization
Chuancheng Shi, Yixiang Chen, Burong Lei, Jichao Chen
https://arxiv.org/abs/2507.13311

FashionPose: Text to Pose to Relight Image Generation for Personalized Fashion Visualization
Realistic and controllable garment visualization is critical for fashion e-commerce, where users expect personalized previews under diverse poses and lighting conditions. Existing methods often rely on predefined poses, limiting semantic flexibility and illumination adaptability. To address this, we introduce FashionPose, the first unified text-to-pose-to-relighting generation framework. Given a natural language description, our method first predicts a 2D human pose, then employs a diffusion mo…

Integrating Text and Time-Series into (Large) Language Models to Predict Medical Outcomes
Iyadh Ben Cheikh Larbi, Ajay Madhavan Ravichandran, Aljoscha Burchardt, Roland Roller
https://arxiv.org/abs/2509.13696

Integrating Text and Time-Series into (Large) Language Models to Predict Medical Outcomes
Large language models (LLMs) excel at text generation, but their ability to handle clinical classification tasks involving structured data, such as time series, remains underexplored. In this work, we adapt instruction-tuned LLMs using DSPy-based prompt optimization to process clinical notes and structured EHR inputs jointly. Our results show that this approach achieves performance on par with specialized multimodal systems while requiring less complexity and offering greater adaptability acros…

Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey
Rui Shao, Wei Li, Lingsen Zhang, Renshan Zhang, Zhiyang Liu, Ran Chen, Liqiang Nie
https://arxiv.org/abs/2508.13073

Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey
Robotic manipulation, a key frontier in robotics and embodied AI, requires precise motor control and multimodal understanding, yet traditional rule-based methods fail to scale or generalize in unstructured, novel environments. In recent years, Vision-Language-Action (VLA) models, built upon Large Vision-Language Models (VLMs) pretrained on vast image-text datasets, have emerged as a transformative paradigm. This survey provides the first systematic, taxonomy-oriented review of large VLM-based V…

City Features VII 🏙️
城市特征 VII 🏙️
📷 Nikon FE
🎞️Ilford FP4 Plus, expired 1994
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

I confounded a British friend by telling them a popular local morning radio jingle of my childhood was “Danny moves your fanny.”
Attached is one variant, but this is the one I recall best (no audio): https://www.reddit.com/r/Buffalo/comments/1618sls/comment/jxttpyn/…

Spectral Tur\'an-type problem in non-$r$-partite graphs: Forbidden generalized book graph $B_{r,k}$
Yuantian Yu, Shuchao Li
https://arxiv.org/abs/2508.12034 https://

Spectral Turán-type problem in non-$r$-partite graphs: Forbidden generalized book graph $B_{r,k}$
Given a graph $H$, a graph is said to be $H$-free if it does not contain $H$ as a subgraph. A graph is color-critical when it has an edge whose removal leads to a reduction in its chromatic number. For a graph $H$ with a chromatic number of \(r + 1\), we use \(\text{spex}_{r + 1}(n, H)\) to represent the maximum spectral radius among non-$r$-partite $H$-free graphs of order $n$. The set of all non-$r$-partite $H$-free graphs of order $n$ that have a spectral radius of \(\text{spex}_{r + 1}(n, H…

A Computational Framework to Identify Self-Aspects in Text
Jaya Caporusso, Matthew Purver, Senja Pollak
https://arxiv.org/abs/2507.13115 https://

A Computational Framework to Identify Self-Aspects in Text
This Ph.D. proposal introduces a plan to develop a computational framework to identify Self-aspects in text. The Self is a multifaceted construct and it is reflected in language. While it is described across disciplines like cognitive science and phenomenology, it remains underexplored in natural language processing (NLP). Many of the aspects of the Self align with psychological and other well-researched phenomena (e.g., those related to mental health), highlighting the need for systematic NLP-…

E3RG: Building Explicit Emotion-driven Empathetic Response Generation System with Multimodal Large Language Model
Ronghao Lin, Shuai Shen, Weipeng Hu, Qiaolin He, Aolin Xiong, Li Huang, Haifeng Hu, Yap-peng Tan
https://arxiv.org/abs/2508.12854

E3RG: Building Explicit Emotion-driven Empathetic Response Generation System with Multimodal Large Language Model
Multimodal Empathetic Response Generation (MERG) is crucial for building emotionally intelligent human-computer interactions. Although large language models (LLMs) have improved text-based ERG, challenges remain in handling multimodal emotional content and maintaining identity consistency. Thus, we propose E3RG, an Explicit Emotion-driven Empathetic Response Generation System based on multimodal LLMs which decomposes MERG task into three parts: multimodal empathy understanding, empathy memory r…

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TTSOps: A Closed-Loop Corpus Optimization Framework for Training Multi-Speaker TTS Models from Dark Data
Kentaro Seki, Shinnosuke Takamichi, Takaaki Saeki, Hiroshi Saruwatari
https://arxiv.org/abs/2506.15614

TTSOps: A Closed-Loop Corpus Optimization Framework for Training Multi-Speaker TTS Models from Dark Data
This paper presents TTSOps, a fully automated closed-loop framework for constructing multi-speaker text-to-speech (TTS) systems from noisy, uncurated web-scale speech data, often referred to as ``dark data,'' such as online videos. Conventional TTS training pipelines require well-curated corpora with high acoustic quality and accurate text-speech alignment, which severely limits scalability, speaker diversity, and real-world applicability. While recent studies have proposed acoustic-quality-bas…

ICDAR 2025 Competition on FEw-Shot Text line segmentation of ancient handwritten documents (FEST)
Silvia Zottin, Axel De Nardin, Giuseppe Branca, Claudio Piciarelli, Gian Luca Foresti
https://arxiv.org/abs/2509.12965

ICDAR 2025 Competition on FEw-Shot Text line segmentation of ancient handwritten documents (FEST)
Text line segmentation is a critical step in handwritten document image analysis. Segmenting text lines in historical handwritten documents, however, presents unique challenges due to irregular handwriting, faded ink, and complex layouts with overlapping lines and non-linear text flow. Furthermore, the scarcity of large annotated datasets renders fully supervised learning approaches impractical for such materials. To address these challenges, we introduce the Few-Shot Text Line Segmentation of …

AgentCTG: Harnessing Multi-Agent Collaboration for Fine-Grained Precise Control in Text Generation
Xinxu Zhou, Jiaqi Bai, Zhenqi Sun, Fanxiang Zeng, Yue Liu
https://arxiv.org/abs/2509.13677

AgentCTG: Harnessing Multi-Agent Collaboration for Fine-Grained Precise Control in Text Generation
Although significant progress has been made in many tasks within the field of Natural Language Processing (NLP), Controlled Text Generation (CTG) continues to face numerous challenges, particularly in achieving fine-grained conditional control over generation. Additionally, in real scenario and online applications, cost considerations, scalability, domain knowledge learning and more precise control are required, presenting more challenge for CTG. This paper introduces a novel and scalable frame…

Say It, See It: A Systematic Evaluation on Speech-Based 3D Content Generation Methods in Augmented Reality
Yanming Xiu, Joshua Chilukuri, Shunav Sen, Maria Gorlatova
https://arxiv.org/abs/2508.12498

Say It, See It: A Systematic Evaluation on Speech-Based 3D Content Generation Methods in Augmented Reality
As augmented reality (AR) applications increasingly require 3D content, generative pipelines driven by natural input such as speech offer an alternative to manual asset creation. In this work, we design a modular, edge-assisted architecture that supports both direct text-to-3D and text-image-to-3D pathways, enabling interchangeable integration of state-of-the-art components and systematic comparison of their performance in AR settings. Using this architecture, we implement and evaluate four rep…

MIRA: Empowering One-Touch AI Services on Smartphones with MLLM-based Instruction Recommendation
Zhipeng Bian, Jieming Zhu, Xuyang Xie, Quanyu Dai, Zhou Zhao, Zhenhua Dong
https://arxiv.org/abs/2509.13773

MIRA: Empowering One-Touch AI Services on Smartphones with MLLM-based Instruction Recommendation
The rapid advancement of generative AI technologies is driving the integration of diverse AI-powered services into smartphones, transforming how users interact with their devices. To simplify access to predefined AI services, this paper introduces MIRA, a pioneering framework for task instruction recommendation that enables intuitive one-touch AI tasking on smartphones. With MIRA, users can long-press on images or text objects to receive contextually relevant instruction recommendations for exe…

CoreEditor: Consistent 3D Editing via Correspondence-constrained Diffusion
Zhe Zhu, Honghua Chen, Peng Li, Mingqiang Wei
https://arxiv.org/abs/2508.11603 https://

CoreEditor: Consistent 3D Editing via Correspondence-constrained Diffusion
Text-driven 3D editing seeks to modify 3D scenes according to textual descriptions, and most existing approaches tackle this by adapting pre-trained 2D image editors to multi-view inputs. However, without explicit control over multi-view information exchange, they often fail to maintain cross-view consistency, leading to insufficient edits and blurry details. We introduce CoreEditor, a novel framework for consistent text-to-3D editing. The key innovation is a correspondence-constrained attentio…

Using AI for User Representation: An Analysis of 83 Persona Prompts
Joni Salminen, Danial Amin, Bernard Jansen
https://arxiv.org/abs/2508.13047 https://arx…

Using AI for User Representation: An Analysis of 83 Persona Prompts
We analyzed 83 persona prompts from 27 research articles that used large language models (LLMs) to generate user personas. Findings show that the prompts predominantly generate single personas. Several prompts express a desire for short or concise persona descriptions, which deviates from the tradition of creating rich, informative, and rounded persona profiles. Text is the most common format for generated persona attributes, followed by numbers. Text and numbers are often generated together, a…

Text-ADBench: Text Anomaly Detection Benchmark based on LLMs Embedding
Feng Xiao, Jicong Fan
https://arxiv.org/abs/2507.12295 https://

Text-ADBench: Text Anomaly Detection Benchmark based on LLMs Embedding
Text anomaly detection is a critical task in natural language processing (NLP), with applications spanning fraud detection, misinformation identification, spam detection and content moderation, etc. Despite significant advances in large language models (LLMs) and anomaly detection algorithms, the absence of standardized and comprehensive benchmarks for evaluating the existing anomaly detection methods on text data limits rigorous comparison and development of innovative approaches. This work pe…

CRED-SQL: Enhancing Real-world Large Scale Database Text-to-SQL Parsing through Cluster Retrieval and Execution Description
Shaoming Duan, Zirui Wang, Chuanyi Liu, Zhibin Zhu, Yuhao Zhang, Peiyi Han, Liang Yan, Zewu Penge
https://arxiv.org/abs/2508.12769

CRED-SQL: Enhancing Real-world Large Scale Database Text-to-SQL Parsing through Cluster Retrieval and Execution Description
Recent advances in large language models (LLMs) have significantly improved the accuracy of Text-to-SQL systems. However, a critical challenge remains: the semantic mismatch between natural language questions (NLQs) and their corresponding SQL queries. This issue is exacerbated in large-scale databases, where semantically similar attributes hinder schema linking and semantic drift during SQL generation, ultimately reducing model accuracy. To address these challenges, we introduce CRED-SQL, a fr…

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RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns
Xin Chen, Junchao Wu, Shu Yang, Runzhe Zhan, Zeyu Wu, Ziyang Luo, Di Wang, Min Yang, Lidia S. Chao, Derek F. Wong
https://arxiv.org/abs/2508.13152

RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns
Detecting content generated by large language models (LLMs) is crucial for preventing misuse and building trustworthy AI systems. Although existing detection methods perform well, their robustness in out-of-distribution (OOD) scenarios is still lacking. In this paper, we hypothesize that, compared to features used by existing detection methods, the internal representations of LLMs contain more comprehensive and raw features that can more effectively capture and distinguish the statistical patte…

LoRAtorio: An intrinsic approach to LoRA Skill Composition
Niki Foteinopoulou, Ignas Budvytis, Stephan Liwicki
https://arxiv.org/abs/2508.11624 https://arx…

LoRAtorio: An intrinsic approach to LoRA Skill Composition
Low-Rank Adaptation (LoRA) has become a widely adopted technique in text-to-image diffusion models, enabling the personalisation of visual concepts such as characters, styles, and objects. However, existing approaches struggle to effectively compose multiple LoRA adapters, particularly in open-ended settings where the number and nature of required skills are not known in advance. In this work, we present LoRAtorio, a novel train-free framework for multi-LoRA composition that leverages intrinsic…

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HOIDiNi: Human-Object Interaction through Diffusion Noise Optimization
Roey Ron, Guy Tevet, Haim Sawdayee, Amit H. Bermano
https://arxiv.org/abs/2506.15625

HOIDiNi: Human-Object Interaction through Diffusion Noise Optimization
We present HOIDiNi, a text-driven diffusion framework for synthesizing realistic and plausible human-object interaction (HOI). HOI generation is extremely challenging since it induces strict contact accuracies alongside a diverse motion manifold. While current literature trades off between realism and physical correctness, HOIDiNi optimizes directly in the noise space of a pretrained diffusion model using Diffusion Noise Optimization (DNO), achieving both. This is made feasible thanks to our ob…

SemCSE: Semantic Contrastive Sentence Embeddings Using LLM-Generated Summaries For Scientific Abstracts
Marc Brinner, Sina Zarriess
https://arxiv.org/abs/2507.13105

SemCSE: Semantic Contrastive Sentence Embeddings Using LLM-Generated Summaries For Scientific Abstracts
We introduce SemCSE, an unsupervised method for learning semantic embeddings of scientific texts. Building on recent advances in contrastive learning for text embeddings, our approach leverages LLM-generated summaries of scientific abstracts to train a model that positions semantically related summaries closer together in the embedding space. This resulting objective ensures that the model captures the true semantic content of a text, in contrast to traditional citation-based approaches that do…

VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning
Senqiao Yang, Junyi Li, Xin Lai, Bei Yu, Hengshuang Zhao, Jiaya Jia
https://arxiv.org/abs/2507.13348

VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning
Recent advancements in vision-language models (VLMs) have improved performance by increasing the number of visual tokens, which are often significantly longer than text tokens. However, we observe that most real-world scenarios do not require such an extensive number of visual tokens. While the performance drops significantly in a small subset of OCR-related tasks, models still perform accurately in most other general VQA tasks with only 1/4 resolution. Therefore, we propose to dynamically proc…

Chat-Driven Text Generation and Interaction for Person Retrieval
Zequn Xie, Chuxin Wang, Sihang Cai, Yeqiang Wang, Shulei Wang, Tao Jin
https://arxiv.org/abs/2509.12662 https://…

Chat-Driven Text Generation and Interaction for Person Retrieval
Text-based person search (TBPS) enables the retrieval of person images from large-scale databases using natural language descriptions, offering critical value in surveillance applications. However, a major challenge lies in the labor-intensive process of obtaining high-quality textual annotations, which limits scalability and practical deployment. To address this, we introduce two complementary modules: Multi-Turn Text Generation (MTG) and Multi-Turn Text Interaction (MTI). MTG generates rich p…

AD-DINOv3: Enhancing DINOv3 for Zero-Shot Anomaly Detection with Anomaly-Aware Calibration
Jingyi Yuan, Jianxiong Ye, Wenkang Chen, Chenqiang Gao
https://arxiv.org/abs/2509.14084

AD-DINOv3: Enhancing DINOv3 for Zero-Shot Anomaly Detection with Anomaly-Aware Calibration
Zero-Shot Anomaly Detection (ZSAD) seeks to identify anomalies from arbitrary novel categories, offering a scalable and annotation-efficient solution. Traditionally, most ZSAD works have been based on the CLIP model, which performs anomaly detection by calculating the similarity between visual and text embeddings. Recently, vision foundation models such as DINOv3 have demonstrated strong transferable representation capabilities. In this work, we are the first to adapt DINOv3 for ZSAD. However, …

Precise Action-to-Video Generation Through Visual Action Prompts
Yuang Wang, Chao Wen, Haoyu Guo, Sida Peng, Minghan Qin, Hujun Bao, Xiaowei Zhou, Ruizhen Hu
https://arxiv.org/abs/2508.13104

Precise Action-to-Video Generation Through Visual Action Prompts
We present visual action prompts, a unified action representation for action-to-video generation of complex high-DoF interactions while maintaining transferable visual dynamics across domains. Action-driven video generation faces a precision-generality trade-off: existing methods using text, primitive actions, or coarse masks offer generality but lack precision, while agent-centric action signals provide precision at the cost of cross-domain transferability. To balance action precision and dyna…

Conan-Embedding-v2: Training an LLM from Scratch for Text Embeddings
Shiyu Li, Yang Tang, Ruijie Liu, Shi-Zhe Chen, Xi Chen
https://arxiv.org/abs/2509.12892 https://

Conan-Embedding-v2: Training an LLM from Scratch for Text Embeddings
Large language models (LLMs) have recently demonstrated excellent performance in text embedding tasks. Previous work usually use LoRA to fine-tune existing LLMs, which are limited by the data and training gap between LLMs and embedding models. In this work, we introduce Conan-embedding-v2, a new 1.4B-parameter LLM trained from scratch and fine-tuned as a text embedder. First, we add news data and multilingual pairs for LLM pretraining to bridge the data gap. Based on this, we propose a cross-li…

CTFlow: Video-Inspired Latent Flow Matching for 3D CT Synthesis
Jiayi Wang, Hadrien Reynaud, Franciskus Xaverius Erick, Bernhard Kainz
https://arxiv.org/abs/2508.12900 https://

CTFlow: Video-Inspired Latent Flow Matching for 3D CT Synthesis
Generative modelling of entire CT volumes conditioned on clinical reports has the potential to accelerate research through data augmentation, privacy-preserving synthesis and reducing regulator-constraints on patient data while preserving diagnostic signals. With the recent release of CT-RATE, a large-scale collection of 3D CT volumes paired with their respective clinical reports, training large text-conditioned CT volume generation models has become achievable. In this work, we introduce CTFlo…

VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding
Shihao Wang, Guo Chen, De-an Huang, Zhiqi Li, Minghan Li, Guilin Li, Jose M. Alvarez, Lei Zhang, Zhiding Yu
https://arxiv.org/abs/2507.13353

VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding
Recent studies have revealed that selecting informative and relevant video frames can significantly improve the performance of Video Large Language Models (Video-LLMs). Current methods, such as reducing inter-frame redundancy, employing separate models for image-text relevance assessment, or utilizing temporal video grounding for event localization, substantially adopt unsupervised learning paradigms, whereas they struggle to address the complex scenarios in long video understanding. We propose…

Novel Parasitic Dual-Scale Modeling for Efficient and Accurate Multilingual Speech Translation
Chenyang Le, Yinfeng Xia, Huiyan Li, Manhong Wang, Yutao Sun, Xingyang Ma, Yanmin Qian
https://arxiv.org/abs/2508.11189

Novel Parasitic Dual-Scale Modeling for Efficient and Accurate Multilingual Speech Translation
Recent advancements in speech-to-text translation have led to the development of multilingual models capable of handling multiple language pairs simultaneously. However, these unified models often suffer from large parameter sizes, making it challenging to balance inference efficiency and performance, particularly in local deployment scenarios. We propose an innovative Parasitic Dual-Scale Approach, which combines an enhanced speculative sampling method with model compression and knowledge dist…

DiffClean: Diffusion-based Makeup Removal for Accurate Age Estimation
Ekta Balkrishna Gavas, Chinmay Hegde, Nasir Memon, Sudipta Banerjee
https://arxiv.org/abs/2507.13292

DiffClean: Diffusion-based Makeup Removal for Accurate Age Estimation
Accurate age verification can protect underage users from unauthorized access to online platforms and e-commerce sites that provide age-restricted services. However, accurate age estimation can be confounded by several factors, including facial makeup that can induce changes to alter perceived identity and age to fool both humans and machines. In this work, we propose DiffClean which erases makeup traces using a text-guided diffusion model to defend against makeup attacks. DiffClean improves ag…

Audio-Based Crowd-Sourced Evaluation of Machine Translation Quality
Sami Ul Haq, Sheila Castilho, Yvette Graham
https://arxiv.org/abs/2509.14023 https://ar…

Audio-Based Crowd-Sourced Evaluation of Machine Translation Quality
Machine Translation (MT) has achieved remarkable performance, with growing interest in speech translation and multimodal approaches. However, despite these advancements, MT quality assessment remains largely text centric, typically relying on human experts who read and compare texts. Since many real-world MT applications (e.g Google Translate Voice Mode, iFLYTEK Translator) involve translation being spoken rather printed or read, a more natural way to assess translation quality would be through…

Reference Points in LLM Sentiment Analysis: The Role of Structured Context
Junichiro Niimi
https://arxiv.org/abs/2508.11454 https://arxiv.org/pdf/2508.1145…

Reference Points in LLM Sentiment Analysis: The Role of Structured Context
Large language models (LLMs) are now widely used across many fields, including marketing research. Sentiment analysis, in particular, helps firms understand consumer preferences. While most NLP studies classify sentiment from review text alone, marketing theories, such as prospect theory and expectation--disconfirmation theory, point out that customer evaluations are shaped not only by the actual experience but also by additional reference points. This study therefore investigates how the conte…

CoDiEmb: A Collaborative yet Distinct Framework for Unified Representation Learning in Information Retrieval and Semantic Textual Similarity
Bowen Zhang, Zixin Song, Chunquan Chen, Qian-Wen Zhang, Di Yin, Xing Sun
https://arxiv.org/abs/2508.11442

CoDiEmb: A Collaborative yet Distinct Framework for Unified Representation Learning in Information Retrieval and Semantic Textual Similarity
Learning unified text embeddings that excel across diverse downstream tasks is a central goal in representation learning, yet negative transfer remains a persistent obstacle. This challenge is particularly pronounced when jointly training a single encoder for Information Retrieval (IR) and Semantic Textual Similarity (STS), two essential but fundamentally disparate tasks for which naive co-training typically yields steep performance trade-offs. We argue that resolving this conflict requires sys…

Dataset Creation for Visual Entailment using Generative AI
Rob Reijtenbach, Suzan Verberne, Gijs Wijnholds
https://arxiv.org/abs/2508.11605 https://arxiv.o…

Dataset Creation for Visual Entailment using Generative AI
In this paper we present and validate a new synthetic dataset for training visual entailment models. Existing datasets for visual entailment are small and sparse compared to datasets for textual entailment. Manually creating datasets is labor-intensive. We base our synthetic dataset on the SNLI dataset for textual entailment. We take the premise text from SNLI as input prompts in a generative image model, Stable Diffusion, creating an image to replace each textual premise. We evaluate our datas…

PredGen: Accelerated Inference of Large Language Models through Input-Time Speculation for Real-Time Speech Interaction
Shufan Li, Aditya Grover
https://arxiv.org/abs/2506.15556

PredGen: Accelerated Inference of Large Language Models through Input-Time Speculation for Real-Time Speech Interaction
Large Language Models (LLMs) are widely used in real-time voice chat applications, typically in combination with text-to-speech (TTS) systems to generate audio responses. However, their large size often leads to noticeable latency between the end of user input and the start of audio output, resulting in suboptimal user experiences. This latency is particularly evident when LLMs are deployed as single-user voice assistants on consumer-grade hardware with limited computing capacity. We discovered…

AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysis
S M Rafiuddin, Sadia Kamal, Mohammed Rakib, Arunkumar Bagavathi, Atriya Sen
https://arxiv.org/abs/2507.12695

AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysis
We introduce AdaptiSent, a new framework for Multimodal Aspect-Based Sentiment Analysis (MABSA) that uses adaptive cross-modal attention mechanisms to improve sentiment classification and aspect term extraction from both text and images. Our model integrates dynamic modality weighting and context-adaptive attention, enhancing the extraction of sentiment and aspect-related information by focusing on how textual cues and visual context interact. We tested our approach against several baselines, i…

Approximating Language Model Training Data from Weights
John X. Morris, Junjie Oscar Yin, Woojeong Kim, Vitaly Shmatikov, Alexander M. Rush
https://arxiv.org/abs/2506.15553

Approximating Language Model Training Data from Weights
Modern language models often have open weights but closed training data. We formalize the problem of data approximation from model weights and propose several baselines and metrics. We develop a gradient-based approach that selects the highest-matching data from a large public text corpus and show its effectiveness at recovering useful data given only weights of the original and finetuned models. Even when none of the true training data is known, our method is able to locate a small subset of p…

AgentMental: An Interactive Multi-Agent Framework for Explainable and Adaptive Mental Health Assessment
Jinpeng Hu, Ao Wang, Qianqian Xie, Hui Ma, Zhuo Li, Dan Guo
https://arxiv.org/abs/2508.11567

AgentMental: An Interactive Multi-Agent Framework for Explainable and Adaptive Mental Health Assessment
Mental health assessment is crucial for early intervention and effective treatment, yet traditional clinician-based approaches are limited by the shortage of qualified professionals. Recent advances in artificial intelligence have sparked growing interest in automated psychological assessment, yet most existing approaches are constrained by their reliance on static text analysis, limiting their ability to capture deeper and more informative insights that emerge through dynamic interaction and i…

Automated Triaging and Transfer Learning of Incident Learning Safety Reports Using Large Language Representational Models
Peter Beidler, Mark Nguyen, Kevin Lybarger, Ola Holmberg, Eric Ford, John Kang
https://arxiv.org/abs/2509.13706

Automated Triaging and Transfer Learning of Incident Learning Safety Reports Using Large Language Representational Models
PURPOSE: Incident reports are an important tool for safety and quality improvement in healthcare, but manual review is time-consuming and requires subject matter expertise. Here we present a natural language processing (NLP) screening tool to detect high-severity incident reports in radiation oncology across two institutions. METHODS AND MATERIALS: We used two text datasets to train and evaluate our NLP models: 7,094 reports from our institution (Inst.), and 571 from IAEA SAFRON (SF), all of …

Assessing the Reliability of LLMs Annotations in the Context of Demographic Bias and Model Explanation
Hadi Mohammadi, Tina Shahedi, Pablo Mosteiro, Massimo Poesio, Ayoub Bagheri, Anastasia Giachanou
https://arxiv.org/abs/2507.13138

Assessing the Reliability of LLMs Annotations in the Context of Demographic Bias and Model Explanation
Understanding the sources of variability in annotations is crucial for developing fair NLP systems, especially for tasks like sexism detection where demographic bias is a concern. This study investigates the extent to which annotator demographic features influence labeling decisions compared to text content. Using a Generalized Linear Mixed Model, we quantify this inf luence, finding that while statistically present, demographic factors account for a minor fraction ( 8%) of the observed varianc…

Vision-and-Language Training Helps Deploy Taxonomic Knowledge but Does Not Fundamentally Alter It
Yulu Qin, Dheeraj Varghese, Adam Dahlgren Lindstr\"om, Lucia Donatelli, Kanishka Misra, Najoung Kim
https://arxiv.org/abs/2507.13328

Vision-and-Language Training Helps Deploy Taxonomic Knowledge but Does Not Fundamentally Alter It
Does vision-and-language (VL) training change the linguistic representations of language models in meaningful ways? Most results in the literature have shown inconsistent or marginal differences, both behaviorally and representationally. In this work, we start from the hypothesis that the domain in which VL training could have a significant effect is lexical-conceptual knowledge, in particular its taxonomic organization. Through comparing minimal pairs of text-only LMs and their VL-trained coun…