Tootfinder

Physics-Informed Learning of Proprietary Inverter Models for Grid Dynamic Studies
Kyung-Bin Kwon, Sayak Mukherjee, Ramij R. Hossain, Marcelo Elizondo
https://arxiv.org/abs/2507.15259

Physics-Informed Learning of Proprietary Inverter Models for Grid Dynamic Studies
This letter develops a novel physics-informed neural ordinary differential equations-based framework to emulate the proprietary dynamics of the inverters -- essential for improved accuracy in grid dynamic simulations. In current industry practice, the original equipment manufacturers (OEMs) often do not disclose the exact internal controls and parameters of the inverters, posing significant challenges in performing accurate dynamic simulations and other relevant studies, such as gain tunings fo…

Franca: Nested Matryoshka Clustering for Scalable Visual Representation Learning
Shashanka Venkataramanan, Valentinos Pariza, Mohammadreza Salehi, Lukas Knobel, Spyros Gidaris, Elias Ramzi, Andrei Bursuc, Yuki M. Asano
https://arxiv.org/abs/2507.14137

Franca: Nested Matryoshka Clustering for Scalable Visual Representation Learning
We present Franca (pronounced Fran-ka): free one; the first fully open-source (data, code, weights) vision foundation model that matches and in many cases surpasses the performance of state-of-the-art proprietary models, e.g., DINOv2, CLIP, SigLIPv2, etc. Our approach is grounded in a transparent training pipeline inspired by Web-SSL and uses publicly available data: ImageNet-21K and a subset of ReLAION-2B. Beyond model release, we tackle critical limitations in SSL clustering methods. While mo…

MedReseacher-R1: Expert-Level Medical Deep Researcher via A Knowledge-Informed Trajectory Synthesis Framework
Ailing Yu, Lan Yao, Jingnan Liu, Zhe Chen, Jiajun Yin, Yuan Wang, Xinhao Liao, Zhiling Ye, Ji Li, Yun Yue, Hansong Xiao, Hualei Zhou, Chunxiao Guo, Peng Wei, Jinjie Gu
https://arxiv.org/abs/2508.14880…

MedReseacher-R1: Expert-Level Medical Deep Researcher via A Knowledge-Informed Trajectory Synthesis Framework
Recent developments in Large Language Model (LLM)-based agents have shown impressive capabilities spanning multiple domains, exemplified by deep research systems that demonstrate superior performance on complex information-seeking and synthesis tasks. While general-purpose deep research agents have shown impressive capabilities, they struggle significantly with medical domain challenges, as evidenced by leading proprietary systems achieving limited accuracy on complex medical benchmarks. The ke…

ListenToJESD204B: A Lightweight Open-Source JESD204B IP Core for FPGA-Based Ultrasound Acquisition systems
Soumyo Bhattacharjee, Federico Villani, Christian Vogt, Andrea Cossettini, Luca Benini
https://arxiv.org/abs/2508.14798

ListenToJESD204B: A Lightweight Open-Source JESD204B IP Core for FPGA-Based Ultrasound Acquisition systems
The demand for hundreds of tightly synchronized channels operating at tens of MSPS in ultrasound systems exceeds conventional low-voltage differential signaling links' bandwidth, pin count, and latency. Although the JESD204B serial interface mitigates these limitations, commercial FPGA IP cores are proprietary, costly, and resource-intensive. We present ListenToJESD204B, an open-source receiver IP core released under a permissive Solderpad 0.51 license for AMD Xilinx Zynq UltraScale+ devices. W…

A Disentangled Representation Learning Framework for Low-altitude Network Coverage Prediction
Xiaojie Li, Zhijie Cai, Nan Qi, Chao Dong, Guangxu Zhu, Haixia Ma, Qihui Wu, Shi Jin
https://arxiv.org/abs/2507.14186

A Disentangled Representation Learning Framework for Low-altitude Network Coverage Prediction
The expansion of the low-altitude economy has underscored the significance of Low-Altitude Network Coverage (LANC) prediction for designing aerial corridors. While accurate LANC forecasting hinges on the antenna beam patterns of Base Stations (BSs), these patterns are typically proprietary and not readily accessible. Operational parameters of BSs, which inherently contain beam information, offer an opportunity for data-driven low-altitude coverage prediction. However, collecting extensive low-a…

Automated Route-based Conflation Between Linear Referencing System Maps And OpenStreetMap Using Open-source Tools
Gibran Ali, Neal Feierabend, Prarthana Doshi, Whoibin Chung, Simona Babiceanu, Michael Fontaine
https://arxiv.org/abs/2507.13939

Automated Route-based Conflation Between Linear Referencing System Maps And OpenStreetMap Using Open-source Tools
Transportation researchers and planners utilize a wide range of roadway metrics that are usually associated with different basemaps. Conflation is an important process for transferring these metrics onto a single basemap. However, conflation is often an expensive and time-consuming process based on proprietary algorithms that require manual verification. In this paper, an automated open-source process is used to conflate two basemaps: the linear reference system (LRS) basemap produced by the …

Modern Animal, a veterinary clinic network that uses proprietary AI-powered software to enhance veterinary workflows, raised a $46M Series D (Allie Garfinkle/Fortune)
https://fortune.com/2025/09/16/exclusive-modern-animal-veterinar…

Exclusive: Modern Animal, veterinary clinic network, raises $46 million Series D
Recently, Modern Animal raised $46 million in Series D funding, the company exclusively told Fortune.

PeerTube is a decentralized alternative to YouTube
that doesn't believe in harvesting user data and pushing people into echo chambers via a proprietary algorithm.
Built on open standards and federation,
it lets communities host and share video without depending on a central server.
https://news.itsfoss.com/peertube-7…

Decentralized YouTube Alternative Adds Live Streame Scheduling Option in Latest Release
Also adds multilingual emails, improved admin tools, and session management.

Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends
Zhenhua Xu, Xubin Yue, Zhebo Wang, Qichen Liu, Xixiang Zhao, Jingxuan Zhang, Wenjun Zeng, Wengpeng Xing, Dezhang Kong, Changting Lin, Meng Han
https://arxiv.org/abs/2508.11548

Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends
Copyright protection for large language models is of critical importance, given their substantial development costs, proprietary value, and potential for misuse. Existing surveys have predominantly focused on techniques for tracing LLM-generated content-namely, text watermarking-while a systematic exploration of methods for protecting the models themselves (i.e., model watermarking and model fingerprinting) remains absent. Moreover, the relationships and distinctions among text watermarking, mo…

Evaluating Large Language Models for Functional and Maintainable Code in Industrial Settings: A Case Study at ASML
Yash Mundhra, Max Valk, Maliheh Izadi
https://arxiv.org/abs/2509.12395

Evaluating Large Language Models for Functional and Maintainable Code in Industrial Settings: A Case Study at ASML
Large language models have shown impressive performance in various domains, including code generation across diverse open-source domains. However, their applicability in proprietary industrial settings, where domain-specific constraints and code interdependencies are prevalent, remains largely unexplored. We present a case study conducted in collaboration with the leveling department at ASML to investigate the performance of LLMs in generating functional, maintainable code within a closed, high…

Ok so this piece of proprietary abandoned bricked hardware has a fucking sd card hidden inside it but no UART? Damn :(

I continue to be amazed that anyone uses Amazon AWS, or Microsoft Azure, or Google Cloud Services. Only someone who's very happy to completely lock their business into a 3rd party proprietary stack they don't control, and loves paying 20x (2000%!) more than reasonable commodity infrastructure rates would do so. I'm amazed that these businesses are even viable. Just goes to show how much flab most corporations have. They're ripe for disruption and displacement.

SCoder: Iterative Self-Distillation for Bootstrapping Small-Scale Data Synthesizers to Empower Code LLMs
Xinyu Zhang, Changzhi Zhou, Linmei Hu, Luhao Zhang, Xiancai Chen, Haomin Fu, Yang Yang, Mengdi Zhang
https://arxiv.org/abs/2509.07858

SCoder: Iterative Self-Distillation for Bootstrapping Small-Scale Data Synthesizers to Empower Code LLMs
Existing code large language models (LLMs) often rely on large-scale instruction data distilled from proprietary LLMs for fine-tuning, which typically incurs high costs. In this paper, we explore the potential of small-scale open-source LLMs (e.g., 7B) as synthesizers for high-quality code instruction data construction. We first observe that the data synthesis capability of small-scale LLMs can be enhanced by training on a few superior data synthesis samples from proprietary LLMs. Building on t…

After seeing a workshop using a crappy pseudo/proprietary forensic web capture toolkit for law enforcement, I was disappointed by the lack of open source tooling to have a sane forensic collection for web capture. Following a discussion with @… he did an implementation to add web forensic acquisition in

Narrowing the Gap: Supervised Fine-Tuning of Open-Source LLMs as a Viable Alternative to Proprietary Models for Pedagogical Tools
Lorenzo Lee Solano, Charles Koutcheme, Juho Leinonen, Alexandra Vassar, Jake Renzella
https://arxiv.org/abs/2507.05305

Narrowing the Gap: Supervised Fine-Tuning of Open-Source LLMs as a Viable Alternative to Proprietary Models for Pedagogical Tools
Frontier Large language models (LLMs) like ChatGPT and Gemini can decipher cryptic compiler errors for novice programmers, but their computational scale, cost, and tendency to over-assist make them problematic for widespread pedagogical adoption. This work demonstrates that smaller, specialised language models, enhanced via Supervised Fine-Tuning (SFT), present a more viable alternative for educational tools. We utilise a new dataset of 40,000 C compiler error explanations, derived from real in…

UrgenGo: Urgency-Aware Transparent GPU Kernel Launching for Autonomous Driving
Hanqi Zhu, Wuyang Zhang, Xinran Zhang, Ziyang Tao, Xinrui Lin, Yu Zhang, Jianmin Ji, Yanyong Zhang
https://arxiv.org/abs/2509.12207

UrgenGo: Urgency-Aware Transparent GPU Kernel Launching for Autonomous Driving
The rapid advancements in autonomous driving have introduced increasingly complex, real-time GPU-bound tasks critical for reliable vehicle operation. However, the proprietary nature of these autonomous systems and closed-source GPU drivers hinder fine-grained control over GPU executions, often resulting in missed deadlines that compromise vehicle performance. To address this, we present UrgenGo, a non-intrusive, urgency-aware GPU scheduling system that operates without access to application sou…

SynParaSpeech: Automated Synthesis of Paralinguistic Datasets for Speech Generation and Understanding
Bingsong Bai, Qihang Lu, Wenbing Yang, Zihan Sun, YueRan Hou, Peilei Jia, Songbai Pu, Ruibo Fu, Yingming Gao, Ya Li, Jun Gao
https://arxiv.org/abs/2509.14946

SynParaSpeech: Automated Synthesis of Paralinguistic Datasets for Speech Generation and Understanding
Paralinguistic sounds, like laughter and sighs, are crucial for synthesizing more realistic and engaging speech. However, existing methods typically depend on proprietary datasets, while publicly available resources often suffer from incomplete speech, inaccurate or missing timestamps, and limited real-world relevance. To address these problems, we propose an automated framework for generating large-scale paralinguistic data and apply it to construct the SynParaSpeech dataset. The dataset compr…

Thyme: Think Beyond Images
Yi-Fan Zhang, Xingyu Lu, Shukang Yin, Chaoyou Fu, Wei Chen, Xiao Hu, Bin Wen, Kaiyu Jiang, Changyi Liu, Tianke Zhang, Haonan Fan, Kaibing Chen, Jiankang Chen, Haojie Ding, Kaiyu Tang, Zhang Zhang, Liang Wang, Fan Yang, Tingting Gao, Guorui Zhou
https://arxiv.org/abs/2508.11630

Thyme: Think Beyond Images
Following OpenAI's introduction of the ``thinking with images'' concept, recent efforts have explored stimulating the use of visual information in the reasoning process to enhance model performance in perception and reasoning tasks. However, to the best of our knowledge, no open-source work currently offers a feature set as rich as proprietary models (O3), which can perform diverse image manipulations and simultaneously enhance logical reasoning capabilities through code. In this paper, we make…

This empowers developers worldwide to build intelligent applications without being locked into proprietary systems
Learn More: • GitHub: https://github.com/MoonshotAI/Kimi-K2 • Hugging Face:

Cybersecurity AI: Humanoid Robots as Attack Vectors
V\'ictor Mayoral-Vilches
https://arxiv.org/abs/2509.14139 https://arxiv.org/pdf/2509.14139

Cybersecurity AI: Humanoid Robots as Attack Vectors
We present a systematic security assessment of the Unitree G1 humanoid showing it operates simultaneously as a covert surveillance node and can be purposed as an active cyber operations platform. Partial reverse engineering of Unitree's proprietary FMX encryption reveal a static Blowfish-ECB layer and a predictable LCG mask-enabled inspection of the system's otherwise sophisticated security architecture, the most mature we have observed in commercial robotics. Two empirical case studies expose …

Advancing Retrieval-Augmented Generation for Structured Enterprise and Internal Data
Chandana Cheerla
https://arxiv.org/abs/2507.12425 https://

Advancing Retrieval-Augmented Generation for Structured Enterprise and Internal Data
Organizations increasingly rely on proprietary enterprise data, including HR records, structured reports, and tabular documents, for critical decision-making. While Large Language Models (LLMs) have strong generative capabilities, they are limited by static pretraining, short context windows, and challenges in processing heterogeneous data formats. Conventional Retrieval-Augmented Generation (RAG) frameworks address some of these gaps but often struggle with structured and semi-structured data.…

France-based Scintil Photonics, which is developing integrated photonic system-on-chips for AI infrastructure, raised a $58M Series B led by Yotta and NGP (Tamara Djurickovic/Tech.eu)
https://tech.eu/2025/09/09/scintil-photonics-secure…

Scintil Photonics secured $58M to scale integrated photonics for AI factories
Grenoble-based Scintil Photonics, a company developing integrated Photonic System-on-Chip (PSoC) solutions for AI infrastructure, has closed a $58 million Series B round. The funding will drive global hiring, accelerate production, and expand its international footprint as it launches the industry’s first single-chip DWDM light engine. Built on its proprietary SHIP™ (Scintil Heterogeneous Integration Photonics) process, Scintil’s technology integrates lasers, photodiodes, and modula…

It’s not fair to compare a state-of-the-art-AI-assisted proprietary tool from a trillion-dollar corporation with a hand-coded free and open tool from a small not-for-profit cooperative.
(The latter works.)
https://github.com/orgs/community/discussions/17…

The Github website is extremely slow on Safari · community · Discussion #170758
Select Topic Area Bug Body Over the past few months, Github has been getting slower and slower on Safari. It has now reached a point where it is unusable. Displaying any pull request with more than...

I did not know that ffmpeg had va-api encoders, instead had to try and find the proprietary amd encoders for almost an hour(

Civil Servants as Builders: Enabling Non-IT Staff to Develop Secure Python and R Tools
Prashant Sharma
https://arxiv.org/abs/2508.07203 https://arxiv.org/p…

Civil Servants as Builders: Enabling Non-IT Staff to Develop Secure Python and R Tools
Current digital government literature focuses on professional in-house IT teams, specialized digital service teams, vendor-developed systems, or proprietary low-code/no-code tools. Almost no scholarship addresses a growing middle ground: technically skilled civil servants outside formal IT roles who can write real code but lack a sanctioned, secure path to deploy their work. This paper introduces a limits-aware, open-source and replicable platform that enables such public servants to develop, p…

i should make a build of vscodium that can install all of the proprietary extensions
my next best bet for a usable editor is sublime text, and unfortunately st4 doesn't seem like it's particularly actively developed anymore. yes, it's still maintained, but i expect a much better editor today than in 2010

Imagine if #Laravel, the company, actually shared news on their own website instead of X.
It would be like all the people everywhere could read it.
https://oldfriends.live/@paul/11480878

The curse of The vOICe is that it is so mature, safe, globally available and affordable that it is of little interest to investors (big money) or mass media (hype). Better stick proprietary electrodes into the brain: it may cause seizures, hemorrhages, fail beyond repair, etc.
ChatGPT: "It's a curse for The vOICe — but it's a curse that points to its strengths."

ChatGPT - Rozdíl TTL vs CMOS
Shared via ChatGPT

South Korea's CJ ENM, which produced Parasite, unveils a comprehensive AI content strategy at the premiere of its first fully AI-generated animation series (Naman Ramachandran/Variety)
https://variety.com/2025/tv/news/cj-enm-ai-strategy-cat-biggie-12…

CJ ENM Debuts AI Animation 'Cat Biggie' as Part of New AI Strategy
CJ ENM unveils AI content strategy with debut of 'Cat Biggie,' a 100% AI-generated animation series, plus new proprietary production tools.

Improving and Evaluating Open Deep Research Agents
Doaa Allabadi, Kyle Bradbury, Jordan M. Malof
https://arxiv.org/abs/2508.10152 https://arxiv.org/pdf/250…

Improving and Evaluating Open Deep Research Agents
We focus here on Deep Research Agents (DRAs), which are systems that can take a natural language prompt from a user, and then autonomously search for, and utilize, internet-based content to address the prompt. Recent DRAs have demonstrated impressive capabilities on public benchmarks however, recent research largely involves proprietary closed-source systems. At the time of this work, we only found one open-source DRA, termed Open Deep Research (ODR). In this work we adapt the challenging recen…

How Robust are LLM-Generated Library Imports? An Empirical Study using Stack Overflow
Jasmine Latendresse, SayedHassan Khatoonabadi, Emad Shihab
https://arxiv.org/abs/2507.10818

How Robust are LLM-Generated Library Imports? An Empirical Study using Stack Overflow
Software libraries are central to the functionality, security, and maintainability of modern code. As developers increasingly turn to Large Language Models (LLMs) to assist with programming tasks, understanding how these models recommend libraries is essential. In this paper, we conduct an empirical study of six state-of-the-art LLMs, both proprietary and open-source, by prompting them to solve real-world Python problems sourced from Stack Overflow. We analyze the types of libraries they import…

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test?
Bhakti Khera, Rezvan Alamian, Pascal A. Scherz, Stephan M. Goetz
https://arxiv.org/abs/2507.10576

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test?
The legal field already uses various large language models (LLMs) in actual applications, but their quantitative performance and reasons for it are underexplored. We evaluated several open-source and proprietary LLMs -- including GPT-series, Anthropic, Deepseek and Llama-3, variants -- on parts of the European Qualifying Examination (EQE) for future European Patent Attorneys. OpenAI o1 led with 0.82 accuracy and 0.81 F1 score, whereas (Amazon Web Services) AWS Llama 3.1 8B lagged at 0.50 accura…

Open-Source Agentic Hybrid RAG Framework for Scientific Literature Review
Aditya Nagori, Ricardo Accorsi Casonatto, Ayush Gautam, Abhinav Manikantha Sai Cheruvu, Rishikesan Kamaleswaran
https://arxiv.org/abs/2508.05660

Open-Source Agentic Hybrid RAG Framework for Scientific Literature Review
The surge in scientific publications challenges traditional review methods, demanding tools that integrate structured metadata with full-text analysis. Hybrid Retrieval Augmented Generation (RAG) systems, combining graph queries with vector search offer promise but are typically static, rely on proprietary tools, and lack uncertainty estimates. We present an agentic approach that encapsulates the hybrid RAG pipeline within an autonomous agent capable of (1) dynamically selecting between GraphRA…

Generating Inputs for Grammar Mining using Dynamic Symbolic Execution
Andreas Pointner (University of Applied Sciences Upper Austria, Austria), Josef Pichler (University of Applied Sciences Upper Austria, Austria), Herbert Pr\"ahofer (Johannes Kepler University Linz, Austria)
https://arxiv.org/abs/2508.03832

Generating Inputs for Grammar Mining using Dynamic Symbolic Execution
A vast number of software systems include components that parse and process structured input. In addition to programming languages, which are analyzed by compilers or interpreters, there are numerous components that process standardized or proprietary data formats of varying complexity. Even if such components were initially developed and tested based on a specification, such as a grammar, numerous modifications and adaptations over the course of software evolution can make it impossible to pre…

Exploiting Timing Side-Channels in Quantum Circuits Simulation Via ML-Based Methods
Ben Dong, Hui Feng, Qian Wang
https://arxiv.org/abs/2509.12535 https://…

Exploiting Timing Side-Channels in Quantum Circuits Simulation Via ML-Based Methods
As quantum computing advances, quantum circuit simulators serve as critical tools to bridge the current gap caused by limited quantum hardware availability. These simulators are typically deployed on cloud platforms, where users submit proprietary circuit designs for simulation. In this work, we demonstrate a novel timing side-channel attack targeting cloud- based quantum simulators. A co-located malicious process can observe fine-grained execution timing patterns to extract sensitive informati…

Gene-R1: Reasoning with Data-Augmented Lightweight LLMs for Gene Set Analysis
Zhizheng Wang, Yifan Yang, Qiao Jin, Zhiyong Lu
https://arxiv.org/abs/2509.10575 https://

Gene-R1: Reasoning with Data-Augmented Lightweight LLMs for Gene Set Analysis
The gene set analysis (GSA) is a foundational approach for uncovering the molecular functions associated with a group of genes. Recently, LLM-powered methods have emerged to annotate gene sets with biological functions together with coherent explanatory insights. However, existing studies primarily focus on proprietary models, which have been shown to outperform their open-source counterparts despite concerns over cost and data privacy. Furthermore, no research has investigated the application …

Em Portugal, provavelmente, também vamos ter de começar pela administração local.
Talvez seja algo que valha a pena pôr na agenda agora em algumas das corridas autšrquicas.
https://mastodon.cloud/@OpenForumEurope/114936538200485234

OpenForum Europe (@OpenForumEurope@mastodon.cloud)
Attached: 1 image The City of Lyon is transitioning from proprietary to open source software, supporting digital independence, sustainable IT, and aligning with EU public sector trends. Read the full article: https://interoperable-europe.ec.europa.eu/collection/open-source-observatory-osor/news/municipality-lyon-moves-towards-open-source

Fair Risk Optimization of Distributed Systems
Aray Almen, Darinka Dentcheva
https://arxiv.org/abs/2509.05737 https://arxiv.org/pdf/2509.05737

Fair Risk Optimization of Distributed Systems
The paper provides a framework for the assessment and optimization of the total risk of complex distributed systems. The framework takes into account the risk of each agent, which may arise from heterogeneous sources, as well as the risk associated with the efficient operation of the system as a whole. The challenges posed by this task are associated with the lack of additivity of risk, the need to evaluate the risk of every agent (unit) using confidential or proprietary information, and the …

JamShield: A Machine Learning Detection System for Over-the-Air Jamming Attacks
Ioannis Panitsas, Yagmur Yigit, Leandros Tassiulas, Leandros Maglaras, Berk Canberk
https://arxiv.org/abs/2507.11483

JamShield: A Machine Learning Detection System for Over-the-Air Jamming Attacks
Wireless networks are vulnerable to jamming attacks due to the shared communication medium, which can severely degrade performance and disrupt services. Despite extensive research, current jamming detection methods often rely on simulated data or proprietary over-the-air datasets with limited cross-layer features, failing to accurately represent the real state of a network and thus limiting their effectiveness in real-world scenarios. To address these challenges, we introduce JamShield, a dynam…

MiDashengLM: Efficient Audio Understanding with General Audio Captions
Heinrich Dinkel, Gang Li, Jizhong Liu, Jian Luan, Yadong Niu, Xingwei Sun, Tianzi Wang, Qiyang Xiao, Junbo Zhang, Jiahao Zhou
https://arxiv.org/abs/2508.03983

MiDashengLM: Efficient Audio Understanding with General Audio Captions
Current approaches for large audio language models (LALMs) often rely on closed data sources or proprietary models, limiting their generalization and accessibility. This paper introduces MiDashengLM, a novel open audio-language model designed for efficient and comprehensive audio understanding through the use of general audio captions using our novel ACAVCaps training dataset. MiDashengLM exclusively relies on publicly available pretraining and supervised fine-tuning (SFT) datasets, ensuring fu…

If you want to show how cool generative AI is, please don't use an astronaut in the desert. This is becoming a bit of a cliché I think.
A skateboarding lion is also a close runner-up in the slop olympics.
(warning, link goes to a page for a proprietary tool that creates three "AI agents" that are told to act as a photography team which is tasked to generate a text prompt that in turn is used to generate an ugly

Arago, which is developing a photonic chip, codenamed JEF, that is designed to cut AI energy use by ten times at a comparable cost, raised a $26M seed (Tamara Djurickovic/Tech.eu)
https://tech.eu/2025/07/08/arago-raises-2…

Arago raises $26M to slash AI energy consumption with breakthrough photonic chip
Arago, a deeptech startup pioneering a new class of energy-efficient AI chips powered by light, has raised $26 million in seed funding to accelerate the commercialisation of its photonic processor, codenamed “JEF”. At the core of “JEF” is a proprietary photonic technology that processes data using lasers instead of transistors. By using photons, light particles that generate much less heat than electrons, this chip can deliver the same performance as top GPUs while consuming 10× less …

Finally a good looking android app for Screen time monitoring. I won't use the proprietary #digitalwellbeing one.
https://gitlab.com/Atharok/ScreenTime

Companies too. Like my gym. https://oldfriends.live/@paul/114808787833782813

@… …we should send all proprietary business secrets to OpenAI

We do need a European Sovereign Tech Fund, yes.
What we don’t need is Microsoft – a trillion-dollar surveillance capitalist from the US that peddles proprietary technology and is helping Israel carry out its genocide of the Palestinian people as we speak – having anything whatsoever to do with it.
#microsoft

#buyitforlife https://feddit.org/post/16331311
This is a great example of proprietary abandonware being saved from landfills and remaining useful tech through open-source software.
Why aren't th…

{\tau}-Ring: A Smart Ring Platform for Multimodal Physiological and Behavioral Sensing
Jiankai Tang, Zhe He, Mingyu Zhang, Wei Geng, Chengchi Zhou, Weinan Shi, Yuanchun Shi, Yuntao Wang
https://arxiv.org/abs/2508.00778

τ-Ring: A Smart Ring Platform for Multimodal Physiological and Behavioral Sensing
Smart rings have emerged as uniquely convenient devices for continuous physiological and behavioral sensing, offering unobtrusive, constant access to metrics such as heart rate, motion, and skin temperature. Yet most commercial solutions remain proprietary, hindering reproducibility and slowing innovation in wearable research. We introduce τ-Ring, a commercial-ready platform that bridges this gap through: (i) accessible hardware combining time-synchronized multi-channel PPG, 6-axis IMU, temper…

I feel invincible.
I feel like I can make everything.
I finally squeezed some time to lean #freecad. It's awesome.
This is a project that I try to get rid of a proprietary plug from a Technics turntable my grandpa left us.
It's not perfect, but man I feel powerful right now

Taiwanese prosecutors say two ex-TSMC employees and a third suspect were arrested for allegedly stealing proprietary tech; investigators searched Tokyo Electron (Kathrin Hille/Financial Times)
https://www.ft.com/content/ad9d7b68-3654-426d-90ea-332713a7b2c6

Evaluating Large Multimodal Models for Nutrition Analysis: A Benchmark Enriched with Contextual Metadata
Bruce Coburn, Jiangpeng He, Megan E. Rollo, Satvinder S. Dhaliwal, Deborah A. Kerr, Fengqing Zhu
https://arxiv.org/abs/2507.07048

Evaluating Large Multimodal Models for Nutrition Analysis: A Benchmark Enriched with Contextual Metadata
Large Multimodal Models (LMMs) are increasingly applied to meal images for nutrition analysis. However, existing work primarily evaluates proprietary models, such as GPT-4. This leaves the broad range of LLMs underexplored. Additionally, the influence of integrating contextual metadata and its interaction with various reasoning modifiers remains largely uncharted. This work investigates how interpreting contextual metadata derived from GPS coordinates (converted to location/venue type), timesta…

The Role of Exploration Modules in Small Language Models for Knowledge Graph Question Answering
Yi-Jie Cheng, Oscar Chew, Yun-Nung Chen
https://arxiv.org/abs/2509.07399 https://…

The Role of Exploration Modules in Small Language Models for Knowledge Graph Question Answering
Integrating knowledge graphs (KGs) into the reasoning processes of large language models (LLMs) has emerged as a promising approach to mitigate hallucination. However, existing work in this area often relies on proprietary or extremely large models, limiting accessibility and scalability. In this study, we investigate the capabilities of existing integration methods for small language models (SLMs) in KG-based question answering and observe that their performance is often constrained by their l…

Understanding discrepancies in the coverage of OpenAlex: the case of China
Mengxue Zheng, Lili Miao, Yi Bu, Vincent Lariviere
https://arxiv.org/abs/2507.19302 https://

Understanding discrepancies in the coverage of OpenAlex: the case of China
Citation indexes play a crucial role for understanding how science is produced, disseminated, and used. However, these databases often face a critical trade-off: those offering extensive and high-quality coverage are typically proprietary, whereas publicly accessible datasets frequently exhibit fragmented coverage and inconsistent data quality. OpenAlex was developed to address this challenge, providing a freely available database with broad open coverage, with a particular emphasis on non-Engl…

Empirical Study of Code Large Language Models for Binary Security Patch Detection
Qingyuan Li, Binchang Li, Cuiyun Gao, Shuzheng Gao, Zongjie Li
https://arxiv.org/abs/2509.06052

Empirical Study of Code Large Language Models for Binary Security Patch Detection
Security patch detection (SPD) is crucial for maintaining software security, as unpatched vulnerabilities can lead to severe security risks. In recent years, numerous learning-based SPD approaches have demonstrated promising results on source code. However, these approaches typically cannot be applied to closed-source applications and proprietary systems that constitute a significant portion of real-world software, as they release patches only with binary files, and the source code is inaccessi…

CosinorAge: Unified Python and Web Platform for Biological Age Estimation from Wearable- and Smartwatch-Based Activity Rhythms
Jinjoo Shim, Jacob Hunecke, Elgar Fleisch, Filipe Barata
https://arxiv.org/abs/2509.01089

CosinorAge: Unified Python and Web Platform for Biological Age Estimation from Wearable- and Smartwatch-Based Activity Rhythms
Every day, millions of people worldwide track their steps, sleep, and activity rhythms with smartwatches and fitness trackers. These continuously collected data streams present a remarkable opportunity to transform routine self-tracking into meaningful health insights that enable individuals to understand -- and potentially influence -- their biological aging. Yet most tools for analyzing wearable data remain fragmented, proprietary, and inaccessible, creating a major barrier between this vast …

ContractEval: Benchmarking LLMs for Clause-Level Legal Risk Identification in Commercial Contracts
Shuang Liu, Zelong Li, Ruoyun Ma, Haiyan Zhao, Mengnan Du
https://arxiv.org/abs/2508.03080

ContractEval: Benchmarking LLMs for Clause-Level Legal Risk Identification in Commercial Contracts
The potential of large language models (LLMs) in specialized domains such as legal risk analysis remains underexplored. In response to growing interest in locally deploying open-source LLMs for legal tasks while preserving data confidentiality, this paper introduces ContractEval, the first benchmark to thoroughly evaluate whether open-source LLMs could match proprietary LLMs in identifying clause-level legal risks in commercial contracts. Using the Contract Understanding Atticus Dataset (CUAD),…

Principle-Guided Verilog Optimization: IP-Safe Knowledge Transfer via Local-Cloud Collaboration
Jing Wang, Zheng Li, Lei Li, Fan He, Liyu Lin, Yao Lai, Yan Li, Xiaoyang Zeng, Yufeng Guo
https://arxiv.org/abs/2508.05675

Principle-Guided Verilog Optimization: IP-Safe Knowledge Transfer via Local-Cloud Collaboration
Recent years have witnessed growing interest in adopting large language models (LLMs) for Register Transfer Level (RTL) code optimization. While powerful cloud-based LLMs offer superior optimization capabilities, they pose unacceptable intellectual property (IP) leakage risks when processing proprietary hardware designs. In this paper, we propose a new scenario where Verilog code must be optimized for specific attributes without leaking sensitive IP information. We introduce the first IP-preser…

Checklist Engineering Empowers Multilingual LLM Judges
Mohammad Ghiasvand Mohammadkhani, Hamid Beigy
https://arxiv.org/abs/2507.06774 https://

Checklist Engineering Empowers Multilingual LLM Judges
Automated text evaluation has long been a central issue in Natural Language Processing (NLP). Recently, the field has shifted toward using Large Language Models (LLMs) as evaluators-a trend known as the LLM-as-a-Judge paradigm. While promising and easily adaptable across tasks, this approach has seen limited exploration in multilingual contexts. Existing multilingual studies often rely on proprietary models or require extensive training data for fine-tuning, raising concerns about cost, time, a…

Trust-region Filter Algorithms utilising Hessian Information for Grey-Box Optimisation
Gul Hameed, Tao Chen, Antonio del Rio Chanona, Lorenz T. Biegler, Michael Short
https://arxiv.org/abs/2509.01651

Trust-region Filter Algorithms utilising Hessian Information for Grey-Box Optimisation
Optimising industrial processes is essential for enhancing profitability, efficiency, and sustainability. Conventional optimisation relies on equation-oriented (EO) glass-box models with fully specified equations and accessible derivatives. However, many real-world systems contain black-box components (such as high-fidelity computational fluid dynamics simulators or proprietary unit models) that lack explicit formulations or analytic gradients. This limits the applicability of derivative-based …

Maisa AI, which offers an enterprise agentic automation service that uses a proprietary system to limit hallucinations, raised $25M seed led by Creandum (Anna Heim/TechCrunch)
https://techcrunch.com/2025/08/27/maisa-ai-gets-25m-to-fix-enterprise-ais…

Maisa AI gets $25M to fix enterprise AI's 95% failure rate | TechCrunch
Maisa AI is built on the premise that enterprise automation requires accountable AI agents, not opaque black boxes.

Beyond Patents: R&D, Capital, and the Productivity Puzzle in Early-Stage High-Tech Firms
Victor (Xucheng), CHEN
https://arxiv.org/abs/2507.18227 https://

Beyond Patents: R&D, Capital, and the Productivity Puzzle in Early-Stage High-Tech Firms
This study investigates the relationship between innovation activities and firm-level productivity among early-stage high-tech startups in China. Using a proprietary dataset encompassing patent records, R&D expenditures, capital valuation, and firm performance from 2020 to 2024, we examine whether and how innovation, measured by patents and R&D input, translates into economic output. Contrary to established literature, we find that patent output does not significantly contribute to either incom…

Optimal Calibrated Signaling in Digital Auctions
Zhicheng Du, Wei Tang, Zihe Wang, Shuo Zhang
https://arxiv.org/abs/2507.17187 https://arxiv.org/pdf/2507.1…

Optimal Calibrated Signaling in Digital Auctions
In digital advertising, online platforms allocate ad impressions through real-time auctions, where advertisers typically rely on autobidding agents to optimize bids on their behalf. Unlike traditional auctions for physical goods, the value of an ad impression is uncertain and depends on the unknown click-through rate (CTR). While platforms can estimate CTRs more accurately using proprietary machine learning algorithms, these estimates/algorithms remain opaque to advertisers. This information as…

DynaMark: A Reinforcement Learning Framework for Dynamic Watermarking in Industrial Machine Tool Controllers
Navid Aftabi, Abhishek Hanchate, Satish Bukkapatnam, Dan Li
https://arxiv.org/abs/2508.21797

DynaMark: A Reinforcement Learning Framework for Dynamic Watermarking in Industrial Machine Tool Controllers
Industry 4.0's highly networked Machine Tool Controllers (MTCs) are prime targets for replay attacks that use outdated sensor data to manipulate actuators. Dynamic watermarking can reveal such tampering, but current schemes assume linear-Gaussian dynamics and use constant watermark statistics, making them vulnerable to the time-varying, partly proprietary behavior of MTCs. We close this gap with DynaMark, a reinforcement learning framework that models dynamic watermarking as a Markov decision p…

WatchWitch: Interoperability, Privacy, and Autonomy for the Apple Watch
Nils Rollshausen, Alexander Heinrich, Matthias Hollick, Jiska Classen
https://arxiv.org/abs/2507.07210

WatchWitch: Interoperability, Privacy, and Autonomy for the Apple Watch
Smartwatches such as the Apple Watch collect vast amounts of intimate health and fitness data as we wear them. Users have little choice regarding how this data is processed: The Apple Watch can only be used with Apple's iPhones, using their software and their cloud services. We are the first to publicly reverse-engineer the watch's wireless protocols, which led to discovering multiple security issues in Apple's proprietary implementation. With WatchWitch, our custom Android reimplementation, we…

Ensemble Debates with Local Large Language Models for AI Alignment
Ephraiem Sarabamoun
https://arxiv.org/abs/2509.00091 https://arxiv.org/pdf/2509.00091

Ensemble Debates with Local Large Language Models for AI Alignment
As large language models (LLMs) take on greater roles in high-stakes decisions, alignment with human values is essential. Reliance on proprietary APIs limits reproducibility and broad participation. We study whether local open-source ensemble debates can improve alignmentoriented reasoning. Across 150 debates spanning 15 scenarios and five ensemble configurations, ensembles outperform single-model baselines on a 7-point rubric (overall: 3.48 vs. 3.13), with the largest gains in reasoning depth …

OleSpeech-IV: A Large-Scale Multispeaker and Multilingual Conversational Speech Dataset with Diverse Topics
Wei Chu, Yuanzhe Dong, Ke Tan, Dong Han, Xavier Menendez-Pidal, Ruchao Fan, Chenfeng Miao, Chanwoo Kim, Bhiksha Raj, Rita Singh
https://arxiv.org/abs/2509.04702

OleSpeech-IV: A Large-Scale Multispeaker and Multilingual Conversational Speech Dataset with Diverse Topics
OleSpeech-IV dataset is a large-scale multispeaker and multilingual conversational speech dataset with diverse topics. The audio content comes from publicly-available English podcasts, talk shows, teleconferences, and other conversations. Speaker names, turns, and transcripts are human-sourced and refined by a proprietary pipeline, while additional information such as timestamps and confidence scores is derived from the pipeline. The IV denotes its position as Tier IV in the Olewave dataset ser…

Sources: Pinecone, which provides an AI-compatible vector database, is exploring a sale after receiving takeover interest; Pinecone was valued at $750M in 2023 (The Information)
https://www.theinformation.com/articles/top-funded-ai-database-star…

Top-Funded AI Database Startup Pinecone Considers a Sale
When the artificial intelligence boom began a few years ago, investors flocked to startups that helped businesses transform and store proprietary data in a format AI could understand, known as a vector database. The technology allowed businesses to use a customized AI chatbot to search or ...

Transform Before You Query: A Privacy-Preserving Approach for Vector Retrieval with Embedding Space Alignment
Ruiqi He, Zekun Fei, Jiaqi Li, Xinyuan Zhu, Biao Yi, Siyi Lv, Weijie Liu, Zheli Liu
https://arxiv.org/abs/2507.18518

Transform Before You Query: A Privacy-Preserving Approach for Vector Retrieval with Embedding Space Alignment
Vector Database (VDB) can efficiently index and search high-dimensional vector embeddings from unstructured data, crucially enabling fast semantic similarity search essential for modern AI applications like generative AI and recommendation systems. Since current VDB service providers predominantly use proprietary black-box models, users are forced to expose raw query text to them via API in exchange for the vector retrieval services. Consequently, if query text involves confidential records fro…

GPT-IMAGE-EDIT-1.5M: A Million-Scale, GPT-Generated Image Dataset
Yuhan Wang, Siwei Yang, Bingchen Zhao, Letian Zhang, Qing Liu, Yuyin Zhou, Cihang Xie
https://arxiv.org/abs/2507.21033

GPT-IMAGE-EDIT-1.5M: A Million-Scale, GPT-Generated Image Dataset
Recent advancements in large multimodal models like GPT-4o have set a new standard for high-fidelity, instruction-guided image editing. However, the proprietary nature of these models and their training data creates a significant barrier for open-source research. To bridge this gap, we introduce GPT-IMAGE-EDIT-1.5M, a publicly available, large-scale image-editing corpus containing more than 1.5 million high-quality triplets (instruction, source image, edited image). We systematically construct …

Scalable Offline ASR for Command-Style Dictation in Courtrooms
Kumarmanas Nethil, Vaibhav Mishra, Kriti Anandan, Kavya Manohar
https://arxiv.org/abs/2507.01021

Scalable Offline ASR for Command-Style Dictation in Courtrooms
We propose an open-source framework for Command-style dictation that addresses the gap between resource-intensive Online systems and high-latency Batch processing. Our approach uses Voice Activity Detection (VAD) to segment audio and transcribes these segments in parallel using Whisper models, enabling efficient multiplexing across audios. Unlike proprietary systems like SuperWhisper, this framework is also compatible with most ASR architectures, including widely used CTC-based models. Our mult…

What's Taboo for You? - An Empirical Evaluation of LLMs Behavior Toward Sensitive Content
Alfio Ferrara, Sergio Picascia, Laura Pinnavaia, Vojimir Ranitovic, Elisabetta Rocchetti, Alice Tuveri
https://arxiv.org/abs/2507.23319

What's Taboo for You? - An Empirical Evaluation of LLMs Behavior Toward Sensitive Content
Proprietary Large Language Models (LLMs) have shown tendencies toward politeness, formality, and implicit content moderation. While previous research has primarily focused on explicitly training models to moderate and detoxify sensitive content, there has been limited exploration of whether LLMs implicitly sanitize language without explicit instructions. This study empirically analyzes the implicit moderation behavior of GPT-4o-mini when paraphrasing sensitive content and evaluates the extent o…

MAGPIE: A dataset for Multi-AGent contextual PrIvacy Evaluation
Gurusha Juneja, Alon Albalak, Wenyue Hua, William Yang Wang
https://arxiv.org/abs/2506.20737

MAGPIE: A dataset for Multi-AGent contextual PrIvacy Evaluation
The proliferation of LLM-based agents has led to increasing deployment of inter-agent collaboration for tasks like scheduling, negotiation, resource allocation etc. In such systems, privacy is critical, as agents often access proprietary tools and domain-specific databases requiring strict confidentiality. This paper examines whether LLM-based agents demonstrate an understanding of contextual privacy. And, if instructed, do these systems preserve inference time user privacy in non-adversarial m…

Empowering Manufacturers with Privacy-Preserving AI Tools: A Case Study in Privacy-Preserving Machine Learning to Solve Real-World Problems
Xiaoyu Ji, Jessica Shorland, Joshua Shank, Pascal Delpe-Brice, Latanya Sweeney, Jan Allebach, Ali Shakouri
https://arxiv.org/abs/2507.01808

Empowering Manufacturers with Privacy-Preserving AI Tools: A Case Study in Privacy-Preserving Machine Learning to Solve Real-World Problems
Small- and medium-sized manufacturers need innovative data tools but, because of competition and privacy concerns, often do not want to share their proprietary data with researchers who might be interested in helping. This paper introduces a privacy-preserving platform by which manufacturers may safely share their data with researchers through secure methods, so that those researchers then create innovative tools to solve the manufacturers' real-world problems, and then provide tools that execu…

Quantum-Cognitive Tunnelling Neural Networks for Military-Civilian Vehicle Classification and Sentiment Analysis
Milan Maksimovic, Anna Bohdanets, Immaculate Motsi-Omoijiade, Guido Governatori, Ivan S. Maksymov
https://arxiv.org/abs/2507.18645

Quantum-Cognitive Tunnelling Neural Networks for Military-Civilian Vehicle Classification and Sentiment Analysis
Prior work has demonstrated that incorporating well-known quantum tunnelling (QT) probability into neural network models effectively captures important nuances of human perception, particularly in the recognition of ambiguous objects and sentiment analysis. In this paper, we employ novel QT-based neural networks and assess their effectiveness in distinguishing customised CIFAR-format images of military and civilian vehicles, as well as sentiment, using a proprietary military-specific vocabulary…

Investigating Training Data Detection in AI Coders
Tianlin Li, Yunxiang Wei, Zhiming Li, Aishan Liu, Qing Guo, Xianglong Liu, Dongning Sun, Yang Liu
https://arxiv.org/abs/2507.17389

Investigating Training Data Detection in AI Coders
Recent advances in code large language models (CodeLLMs) have made them indispensable tools in modern software engineering. However, these models occasionally produce outputs that contain proprietary or sensitive code snippets, raising concerns about potential non-compliant use of training data, and posing risks to privacy and intellectual property. To ensure responsible and compliant deployment of CodeLLMs, training data detection (TDD) has become a critical task. While recent TDD methods have…

Fast Feeder Reconfiguration via Mesh Adaptive Direct Search in Black-Box Distribution System Environments
Junyuan Zheng, Wenlong Shi, Zhaoyu Wang
https://arxiv.org/abs/2507.16027 …

Fast Feeder Reconfiguration via Mesh Adaptive Direct Search in Black-Box Distribution System Environments
Feeder reconfiguration is a critical operational strategy in power distribution systems. However, existing optimization approaches typically rely on explicit mathematical formulations and analytical models, which are often infeasible in practical utility environments characterized by heterogeneous, proprietary, and black-box simulation modules. To address this challenge, this paper proposes a fast feeder reconfiguration framework based on Mesh Adaptive Direct Search (MADS). The proposed approac…

Beyond the Cloud: Assessing the Benefits and Drawbacks of Local LLM Deployment for Translators
Peter Sandrini
https://arxiv.org/abs/2507.23399 https://arxi…

Beyond the Cloud: Assessing the Benefits and Drawbacks of Local LLM Deployment for Translators
The rapid proliferation of Large Language Models presents both opportunities and challenges for the translation field. While commercial, cloud-based AI chatbots have garnered significant attention in translation studies, concerns regarding data privacy, security, and equitable access necessitate exploration of alternative deployment models. This paper investigates the feasibility and performance of locally deployable, free language models as a viable alternative to proprietary, cloud-based AI s…

AI Through the Human Lens: Investigating Cognitive Theories in Machine Psychology
Akash Kundu, Rishika Goswami
https://arxiv.org/abs/2506.18156 https://

AI Through the Human Lens: Investigating Cognitive Theories in Machine Psychology
We investigate whether Large Language Models (LLMs) exhibit human-like cognitive patterns under four established frameworks from psychology: Thematic Apperception Test (TAT), Framing Bias, Moral Foundations Theory (MFT), and Cognitive Dissonance. We evaluated several proprietary and open-source models using structured prompts and automated scoring. Our findings reveal that these models often produce coherent narratives, show susceptibility to positive framing, exhibit moral judgments aligned wi…

RL-Finetuned LLMs for Privacy-Preserving Synthetic Rewriting
Zhan Shi, Yefeng Yuan, Yuhong Liu, Liang Cheng, Yi Fang
https://arxiv.org/abs/2508.19286 https://

RL-Finetuned LLMs for Privacy-Preserving Synthetic Rewriting
The performance of modern machine learning systems depends on access to large, high-quality datasets, often sourced from user-generated content or proprietary, domain-specific corpora. However, these rich datasets inherently contain sensitive personal information, raising significant concerns about privacy, data security, and compliance with regulatory frameworks. While conventional anonymization techniques can remove explicit identifiers, such removal may result in performance drop in downstre…

Leveraging Open-Source Large Language Models for Clinical Information Extraction in Resource-Constrained Settings
Luc Builtjes, Joeran Bosma, Mathias Prokop, Bram van Ginneken, Alessa Hering
https://arxiv.org/abs/2507.20859

Leveraging Open-Source Large Language Models for Clinical Information Extraction in Resource-Constrained Settings
Medical reports contain rich clinical information but are often unstructured and written in domain-specific language, posing challenges for information extraction. While proprietary large language models (LLMs) have shown promise in clinical natural language processing, their lack of transparency and data privacy concerns limit their utility in healthcare. This study therefore evaluates nine open-source generative LLMs on the DRAGON benchmark, which includes 28 clinical information extraction t…

ACT: Bridging the Gap in Code Translation through Synthetic Data Generation & Adaptive Training
Shreya Saxena, Siva Prasad, Zishan Ahmad, Vishal Vaddina
https://arxiv.org/abs/2507.16478

ACT: Bridging the Gap in Code Translation through Synthetic Data Generation & Adaptive Training
Code translation is a crucial process in software development and migration projects, enabling interoperability between different programming languages and enhancing software adaptability and thus longevity. Traditional automated translation methods rely heavily on handcrafted transformation rules, which often lack flexibility and scalability. Meanwhile, advanced language models present promising alternatives but are often limited by proprietary, API-based implementations that raise concerns ov…

Virtual local area network over HTTP for launching an insider attack
Yuksel Arslan
https://arxiv.org/abs/2507.19055 https://arxiv.org/pdf/2507.19055…

Virtual local area network over HTTP for launching an insider attack
Computers and computer networks have become integral to virtually every aspect of modern life, with the Internet playing an indispensable role. Organizations, businesses, and individuals now store vast amounts of proprietary, confidential, and personal data digitally. As such, ensuring the security of this data from unauthorized access is critical. Common security measures, such as firewalls, intrusion detection systems (IDS), intrusion prevention systems (IPS), and antivirus software, are cons…

Automatic integration of SystemC in the FMI standard for Software-defined Vehicle design
Giovanni Pollo, Andrei Mihai Albu, Alessio Burrello, Daniele Jahier Pagliari, Cristian Tesconi, Loris Panaro, Dario Soldi, Fabio Autieri, Sara Vinco
https://arxiv.org/abs/2508.19665

Automatic integration of SystemC in the FMI standard for Software-defined Vehicle design
The recent advancements of the automotive sector demand robust co-simulation methodologies that enable early validation and seamless integration across hardware and software domains. However, the lack of standardized interfaces and the dominance of proprietary simulation platforms pose significant challenges to collaboration, scalability, and IP protection. To address these limitations, this paper presents an approach for automatically wrapping SystemC models by using the Functional Mock-up Int…

ThinkDial: An Open Recipe for Controlling Reasoning Effort in Large Language Models
Qianyu He, Siyu Yuan, Xuefeng Li, Mingxuan Wang, Jiangjie Chen
https://arxiv.org/abs/2508.18773

ThinkDial: An Open Recipe for Controlling Reasoning Effort in Large Language Models
Large language models (LLMs) with chain-of-thought reasoning have demonstrated remarkable problem-solving capabilities, but controlling their computational effort remains a significant challenge for practical deployment. Recent proprietary systems like OpenAI's gpt-oss series have introduced discrete operational modes for intuitive reasoning control, but the open-source community has largely failed to achieve such capabilities. In this paper, we introduce ThinkDial, the first open-recipe end-to…

Security Assessment of DeepSeek and GPT Series Models against Jailbreak Attacks
Xiaodong Wu, Xiangman Li, Jianbing Ni
https://arxiv.org/abs/2506.18543 http…

Security Assessment of DeepSeek and GPT Series Models against Jailbreak Attacks
The widespread deployment of large language models (LLMs) has raised critical concerns over their vulnerability to jailbreak attacks, i.e., adversarial prompts that bypass alignment mechanisms and elicit harmful or policy-violating outputs. While proprietary models like GPT-4 have undergone extensive evaluation, the robustness of emerging open-source alternatives such as DeepSeek remains largely underexplored, despite their growing adoption in real-world applications. In this paper, we present …

TULIP: Adapting Open-Source Large Language Models for Underrepresented Languages and Specialized Financial Tasks
\.Irem Demirta\c{s}, Burak Payzun, Se\c{c}il Arslan
https://arxiv.org/abs/2508.16243

TULIP: Adapting Open-Source Large Language Models for Underrepresented Languages and Specialized Financial Tasks
Thanks to the growing popularity of large language models over the years, there is great potential for their applications in finance. Despite the exceptional performance of larger proprietary models, which are presented as black-box solutions through APIs, smaller models that can be hosted on-premise present opportunities for adaptability and privacy. Especially in cases where the management of sensitive information and application of domain knowledge is important, like finance, enhancing the c…