TJS Articles & News

Deloitte sounds alarm as AI agent deployment outruns safety frameworksAI News A new report from Deloitte has warned that businesses are deploying AI agents faster than their safety protocols and safeguards can keep up. Therefore, serious concerns around security, data privacy, and accountability are spreading. According to the survey, agentic systems are moving from pilot to production so quickly that traditional risk controls, which were designed
The post Deloitte sounds alarm as AI agent deployment outruns safety frameworks appeared first on AI News.

 A new report from Deloitte has warned that businesses are deploying AI agents faster than their safety protocols and safeguards can keep up. Therefore, serious concerns around security, data privacy, and accountability are spreading. According to the survey, agentic systems are moving from pilot to production so quickly that traditional risk controls, which were designed
The post Deloitte sounds alarm as AI agent deployment outruns safety frameworks appeared first on AI News. Read More  

Federated Learning, Part 2: Implementation with the Flower Framework 🌼Towards Data Science Implementing cross-silo federated learning step by step
The post Federated Learning, Part 2: Implementation with the Flower Framework 🌼 appeared first on Towards Data Science.

 Implementing cross-silo federated learning step by step
The post Federated Learning, Part 2: Implementation with the Flower Framework 🌼 appeared first on Towards Data Science. Read More  

A Systemic Evaluation of Multimodal RAG Privacycs.AI updates on arXiv.org arXiv:2601.17644v2 Announce Type: replace-cross
Abstract: The growing adoption of multimodal Retrieval-Augmented Generation (mRAG) pipelines for vision-centric tasks (e.g. visual QA) introduces important privacy challenges. In particular, while mRAG provides a practical capability to connect private datasets to improve model performance, it risks the leakage of private information from these datasets during inference. In this paper, we perform an empirical study to analyze the privacy risks inherent in the mRAG pipeline observed through standard model prompting. Specifically, we implement a case study that attempts to infer the inclusion of a visual asset, e.g. image, in the mRAG, and if present leak the metadata, e.g. caption, related to it. Our findings highlight the need for privacy-preserving mechanisms and motivate future research on mRAG privacy.

 arXiv:2601.17644v2 Announce Type: replace-cross
Abstract: The growing adoption of multimodal Retrieval-Augmented Generation (mRAG) pipelines for vision-centric tasks (e.g. visual QA) introduces important privacy challenges. In particular, while mRAG provides a practical capability to connect private datasets to improve model performance, it risks the leakage of private information from these datasets during inference. In this paper, we perform an empirical study to analyze the privacy risks inherent in the mRAG pipeline observed through standard model prompting. Specifically, we implement a case study that attempts to infer the inclusion of a visual asset, e.g. image, in the mRAG, and if present leak the metadata, e.g. caption, related to it. Our findings highlight the need for privacy-preserving mechanisms and motivate future research on mRAG privacy. Read More  

RIFT: Reordered Instruction Following Testbed To Evaluate Instruction Following in Singular Multistep Prompt Structurescs.AI updates on arXiv.org arXiv:2601.18924v1 Announce Type: new
Abstract: Large Language Models (LLMs) are increasingly relied upon for complex workflows, yet their ability to maintain flow of instructions remains underexplored. Existing benchmarks conflate task complexity with structural ordering, making it difficult to isolate the impact of prompt topology on performance. We introduce RIFT, Reordered Instruction Following Testbed, to assess instruction following by disentangling structure from content. Using rephrased Jeopardy! question-answer pairs, we test LLMs across two prompt structures: linear prompts, which progress sequentially, and jumping prompts, which preserve identical content but require non-sequential traversal. Across 10,000 evaluations spanning six state-of-the-art open-source LLMs, accuracy dropped by up to 72% under jumping conditions (compared to baseline), revealing a strong dependence on positional continuity. Error analysis shows that approximately 50% of failures stem from instruction-order violations and semantic drift, indicating that current architectures internalize instruction following as a sequential pattern rather than a reasoning skill. These results reveal structural sensitivity as a fundamental limitation in current architectures, with direct implications for applications requiring non-sequential control flow such as workflow automation and multi-agent systems.

 arXiv:2601.18924v1 Announce Type: new
Abstract: Large Language Models (LLMs) are increasingly relied upon for complex workflows, yet their ability to maintain flow of instructions remains underexplored. Existing benchmarks conflate task complexity with structural ordering, making it difficult to isolate the impact of prompt topology on performance. We introduce RIFT, Reordered Instruction Following Testbed, to assess instruction following by disentangling structure from content. Using rephrased Jeopardy! question-answer pairs, we test LLMs across two prompt structures: linear prompts, which progress sequentially, and jumping prompts, which preserve identical content but require non-sequential traversal. Across 10,000 evaluations spanning six state-of-the-art open-source LLMs, accuracy dropped by up to 72% under jumping conditions (compared to baseline), revealing a strong dependence on positional continuity. Error analysis shows that approximately 50% of failures stem from instruction-order violations and semantic drift, indicating that current architectures internalize instruction following as a sequential pattern rather than a reasoning skill. These results reveal structural sensitivity as a fundamental limitation in current architectures, with direct implications for applications requiring non-sequential control flow such as workflow automation and multi-agent systems. Read More  

AutoGameUI: Constructing High-Fidelity GameUI via Multimodal Correspondence Matchingcs.AI updates on arXiv.org arXiv:2411.03709v2 Announce Type: replace-cross
Abstract: Game UI development is essential to the game industry. However, the traditional workflow requires substantial manual effort to integrate pairwise UI and UX designs into a cohesive game user interface (GameUI). The inconsistency between the aesthetic UI design and the functional UX design typically results in mismatches and inefficiencies. To address the issue, we present an automatic system, AutoGameUI, for efficiently and accurately constructing GameUI. The system centers on a two-stage multimodal learning pipeline to obtain the optimal correspondences between UI and UX designs. The first stage learns the comprehensive representations of UI and UX designs from multimodal perspectives. The second stage incorporates grouped cross-attention modules with constrained integer programming to estimate the optimal correspondences through top-down hierarchical matching. The optimal correspondences enable the automatic GameUI construction. We create the GAMEUI dataset, comprising pairwise UI and UX designs from real-world games, to train and validate the proposed method. Besides, an interactive web tool is implemented to ensure high-fidelity effects and facilitate human-in-the-loop construction. Extensive experiments on the GAMEUI and RICO datasets demonstrate the effectiveness of our system in maintaining consistency between the constructed GameUI and the original designs. When deployed in the workflow of several mobile games, AutoGameUI achieves a 3$times$ improvement in time efficiency, conveying significant practical value for game UI development.

 arXiv:2411.03709v2 Announce Type: replace-cross
Abstract: Game UI development is essential to the game industry. However, the traditional workflow requires substantial manual effort to integrate pairwise UI and UX designs into a cohesive game user interface (GameUI). The inconsistency between the aesthetic UI design and the functional UX design typically results in mismatches and inefficiencies. To address the issue, we present an automatic system, AutoGameUI, for efficiently and accurately constructing GameUI. The system centers on a two-stage multimodal learning pipeline to obtain the optimal correspondences between UI and UX designs. The first stage learns the comprehensive representations of UI and UX designs from multimodal perspectives. The second stage incorporates grouped cross-attention modules with constrained integer programming to estimate the optimal correspondences through top-down hierarchical matching. The optimal correspondences enable the automatic GameUI construction. We create the GAMEUI dataset, comprising pairwise UI and UX designs from real-world games, to train and validate the proposed method. Besides, an interactive web tool is implemented to ensure high-fidelity effects and facilitate human-in-the-loop construction. Extensive experiments on the GAMEUI and RICO datasets demonstrate the effectiveness of our system in maintaining consistency between the constructed GameUI and the original designs. When deployed in the workflow of several mobile games, AutoGameUI achieves a 3$times$ improvement in time efficiency, conveying significant practical value for game UI development. Read More