Tootfinder

Security Tensors as a Cross-Modal Bridge: Extending Text-Aligned Safety to Vision in LVLM
Shen Li, Liuyi Yao, Wujia Niu, Lan Zhang, Yaliang Li
https://arxiv.org/abs/2507.20994 h…

Security Tensors as a Cross-Modal Bridge: Extending Text-Aligned Safety to Vision in LVLM
Large visual-language models (LVLMs) integrate aligned large language models (LLMs) with visual modules to process multimodal inputs. However, the safety mechanisms developed for text-based LLMs do not naturally extend to visual modalities, leaving LVLMs vulnerable to harmful image inputs. To address this cross-modal safety gap, we introduce security tensors - trainable input vectors applied during inference through either the textual or visual modality. These tensors transfer textual safety al…

Does simple trump complex? Comparing strategies for adversarial robustness in DNNs
William Brooks, Marelie H. Davel, Coenraad Mouton
https://arxiv.org/abs/2508.18019 https://

Does simple trump complex? Comparing strategies for adversarial robustness in DNNs
Deep Neural Networks (DNNs) have shown substantial success in various applications but remain vulnerable to adversarial attacks. This study aims to identify and isolate the components of two different adversarial training techniques that contribute most to increased adversarial robustness, particularly through the lens of margins in the input space -- the minimal distance between data points and decision boundaries. Specifically, we compare two methods that maximize margins: a simple approach w…

SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models
Dipayan Saha, Shams Tarek, Hasan Al Shaikh, Khan Thamid Hasan, Pavan Sai Nalluri, Md. Ajoad Hasan, Nashmin Alam, Jingbo Zhou, Sujan Kumar Saha, Mark Tehranipoor, Farimah Farahmandi
https://arxiv.org/abs/2506.20415…

SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models
Ensuring the security of complex system-on-chips (SoCs) designs is a critical imperative, yet traditional verification techniques struggle to keep pace due to significant challenges in automation, scalability, comprehensiveness, and adaptability. The advent of large language models (LLMs), with their remarkable capabilities in natural language understanding, code generation, and advanced reasoning, presents a new paradigm for tackling these issues. Moving beyond monolithic models, an agentic ap…

Frozen in Time: Parameter-Efficient Time Series Transformers via Reservoir-Induced Feature Expansion and Fixed Random Dynamics
Pradeep Singh, Mehak Sharma, Anupriya Dey, Balasubramanian Raman
https://arxiv.org/abs/2508.18130

Frozen in Time: Parameter-Efficient Time Series Transformers via Reservoir-Induced Feature Expansion and Fixed Random Dynamics
Transformers are the de-facto choice for sequence modelling, yet their quadratic self-attention and weak temporal bias can make long-range forecasting both expensive and brittle. We introduce FreezeTST, a lightweight hybrid that interleaves frozen random-feature (reservoir) blocks with standard trainable Transformer layers. The frozen blocks endow the network with rich nonlinear memory at no optimisation cost; the trainable layers learn to query this memory through self-attention. The design cu…

Analyst: China summoning Nvidia over alleged H20 security risks shows it "wants to use the Nvidia case to show China is a buyer, but it won't be a blind buyer" (Bloomberg)
https://www.bloomberg.com/news/articles/20

China Draws Red Lines on US Chip Tracking With Nvidia Meeting
As the US and China look for any sort of leverage in a prolonged trade fight, Beijing sees an opportunity to win over the world by taking a stand against the Trump administration’s plans to track high-end chips.

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https://www.reddit.com/r/LeopardsAteMyFace/comments/1lunch9/alex_jones_on_the_verge_of_tears_after_admitting/

VerilogDB: The Largest, Highest-Quality Dataset with a Preprocessing Framework for LLM-based RTL Generation
Paul E. Calzada, Zahin Ibnat, Tanvir Rahman, Kamal Kandula, Danyu Lu, Sujan Kumar Saha, Farimah Farahmandi, Mark Tehranipoor
https://arxiv.org/abs/2507.13369

VerilogDB: The Largest, Highest-Quality Dataset with a Preprocessing Framework for LLM-based RTL Generation
Large Language Models (LLMs) are gaining popularity for hardware design automation, particularly through Register Transfer Level (RTL) code generation. In this work, we examine the current literature on RTL generation using LLMs and identify key requirements for training and fine-tuning datasets. We construct a robust Verilog dataset through an automated three-pronged process involving database (DB) creation and management with PostgreSQL, data collection from code hosting sites like OpenCores …

LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking
Vahid Azizi, Fatemeh Koochaki
https://arxiv.org/abs/2506.07449

LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking
Recent advances in Large Language Models (LLMs) have driven their adoption in recommender systems through Retrieval-Augmented Generation (RAG) frameworks. However, existing RAG approaches predominantly rely on flat, similarity-based retrieval that fails to leverage the rich relational structure inherent in user-item interactions. We introduce LlamaRec-LKG-RAG, a novel single-pass, end-to-end trainable framework that integrates personalized knowledge graph context into LLM-based recommendation r…

Machine learning the single-$\Lambda$ hypernuclei with neural-network quantum states
Zi-Xiao Zhang, Yi-Long Yang, Wan-Bing He, Peng-Wei Zhao, Bing-Nan Lu, Yu-Gang Ma
https://arxiv.org/abs/2508.03575

Machine learning the single-$Λ$ hypernuclei with neural-network quantum states
Single-$Λ$ hypernuclei are the most straightforward extension of atomic nuclei. A thorough description of baryonic system beyond first-generation quark sector is indispensable for the maturation of nuclear $ab$ $initio$ methods. This study pioneers the application of neural-network quantum states to hypernuclei, with trainable parameters determined by variational Monte Carlo approach (VMC-NQS). In order to reduce the numerical uncertainty and treat the nucleons and hyperons in a unified manner…