Author: Tech Jacks Solutions

Six Lessons Learned Building RAG Systems in ProductionTowards Data Science Best practices for data quality, retrieval design, and evaluation in production RAG systems
The post Six Lessons Learned Building RAG Systems in Production appeared first on Towards Data Science.

 Best practices for data quality, retrieval design, and evaluation in production RAG systems
The post Six Lessons Learned Building RAG Systems in Production appeared first on Towards Data Science. Read More  

Zara’s use of AI shows how retail workflows are quietly changingAI News Zara is testing how far generative AI can be pushed into everyday retail operations, starting with a part of the business that rarely gets attention in technology discussions: product imagery. Recent reporting shows the retailer using AI to generate new images of real models wearing different outfits, based on existing photoshoots. Models remain involved in
The post Zara’s use of AI shows how retail workflows are quietly changing appeared first on AI News.

 Zara is testing how far generative AI can be pushed into everyday retail operations, starting with a part of the business that rarely gets attention in technology discussions: product imagery. Recent reporting shows the retailer using AI to generate new images of real models wearing different outfits, based on existing photoshoots. Models remain involved in
The post Zara’s use of AI shows how retail workflows are quietly changing appeared first on AI News. Read More  

A Complete Workflow for Automated Prompt Optimization Using Gemini Flash, Few-Shot Selection, and Evolutionary Instruction SearchMarkTechPost In this tutorial, we shift from traditional prompt crafting to a more systematic, programmable approach by treating prompts as tunable parameters rather than static text. Instead of guessing which instruction or example works best, we build an optimization loop around Gemini 2.0 Flash that experiments, evaluates, and automatically selects the strongest prompt configuration. In this
The post A Complete Workflow for Automated Prompt Optimization Using Gemini Flash, Few-Shot Selection, and Evolutionary Instruction Search appeared first on MarkTechPost.

 In this tutorial, we shift from traditional prompt crafting to a more systematic, programmable approach by treating prompts as tunable parameters rather than static text. Instead of guessing which instruction or example works best, we build an optimization loop around Gemini 2.0 Flash that experiments, evaluates, and automatically selects the strongest prompt configuration. In this
The post A Complete Workflow for Automated Prompt Optimization Using Gemini Flash, Few-Shot Selection, and Evolutionary Instruction Search appeared first on MarkTechPost. Read More  

Exploring User Acceptance and Concerns toward LLM-powered Conversational Agents in Immersive Extended Realitycs.AI updates on arXiv.org arXiv:2512.15343v1 Announce Type: cross
Abstract: The rapid development of generative artificial intelligence (AI) and large language models (LLMs), and the availability of services that make them accessible, have led the general public to begin incorporating them into everyday life. The extended reality (XR) community has also sought to integrate LLMs, particularly in the form of conversational agents, to enhance user experience and task efficiency. When interacting with such conversational agents, users may easily disclose sensitive information due to the naturalistic flow of the conversations, and combining such conversational data with fine-grained sensor data may lead to novel privacy issues. To address these issues, a user-centric understanding of technology acceptance and concerns is essential. Therefore, to this end, we conducted a large-scale crowdsourcing study with 1036 participants, examining user decision-making processes regarding LLM-powered conversational agents in XR, across factors of XR setting type, speech interaction type, and data processing location. We found that while users generally accept these technologies, they express concerns related to security, privacy, social implications, and trust. Our results suggest that familiarity plays a crucial role, as daily generative AI use is associated with greater acceptance. In contrast, previous ownership of XR devices is linked to less acceptance, possibly due to existing familiarity with the settings. We also found that men report higher acceptance with fewer concerns than women. Regarding data type sensitivity, location data elicited the most significant concern, while body temperature and virtual object states were considered least sensitive. Overall, our study highlights the importance of practitioners effectively communicating their measures to users, who may remain distrustful. We conclude with implications and recommendations for LLM-powered XR.

 arXiv:2512.15343v1 Announce Type: cross
Abstract: The rapid development of generative artificial intelligence (AI) and large language models (LLMs), and the availability of services that make them accessible, have led the general public to begin incorporating them into everyday life. The extended reality (XR) community has also sought to integrate LLMs, particularly in the form of conversational agents, to enhance user experience and task efficiency. When interacting with such conversational agents, users may easily disclose sensitive information due to the naturalistic flow of the conversations, and combining such conversational data with fine-grained sensor data may lead to novel privacy issues. To address these issues, a user-centric understanding of technology acceptance and concerns is essential. Therefore, to this end, we conducted a large-scale crowdsourcing study with 1036 participants, examining user decision-making processes regarding LLM-powered conversational agents in XR, across factors of XR setting type, speech interaction type, and data processing location. We found that while users generally accept these technologies, they express concerns related to security, privacy, social implications, and trust. Our results suggest that familiarity plays a crucial role, as daily generative AI use is associated with greater acceptance. In contrast, previous ownership of XR devices is linked to less acceptance, possibly due to existing familiarity with the settings. We also found that men report higher acceptance with fewer concerns than women. Regarding data type sensitivity, location data elicited the most significant concern, while body temperature and virtual object states were considered least sensitive. Overall, our study highlights the importance of practitioners effectively communicating their measures to users, who may remain distrustful. We conclude with implications and recommendations for LLM-powered XR. Read More  

On Assessing the Relevance of Code Reviews Authored by Generative Modelscs.AI updates on arXiv.org arXiv:2512.15466v1 Announce Type: cross
Abstract: The use of large language models like ChatGPT in code review offers promising efficiency gains but also raises concerns about correctness and safety. Existing evaluation methods for code review generation either rely on automatic comparisons to a single ground truth, which fails to capture the variability of human perspectives, or on subjective assessments of “usefulness”, a highly ambiguous concept. We propose a novel evaluation approach based on what we call multi-subjective ranking. Using a dataset of 280 self-contained code review requests and corresponding comments from CodeReview StackExchange, multiple human judges ranked the quality of ChatGPT-generated comments alongside the top human responses from the platform. Results show that ChatGPT’s comments were ranked significantly better than human ones, even surpassing StackExchange’s accepted answers. Going further, our proposed method motivates and enables more meaningful assessments of generative AI’s performance in code review, while also raising awareness of potential risks of unchecked integration into review processes.

 arXiv:2512.15466v1 Announce Type: cross
Abstract: The use of large language models like ChatGPT in code review offers promising efficiency gains but also raises concerns about correctness and safety. Existing evaluation methods for code review generation either rely on automatic comparisons to a single ground truth, which fails to capture the variability of human perspectives, or on subjective assessments of “usefulness”, a highly ambiguous concept. We propose a novel evaluation approach based on what we call multi-subjective ranking. Using a dataset of 280 self-contained code review requests and corresponding comments from CodeReview StackExchange, multiple human judges ranked the quality of ChatGPT-generated comments alongside the top human responses from the platform. Results show that ChatGPT’s comments were ranked significantly better than human ones, even surpassing StackExchange’s accepted answers. Going further, our proposed method motivates and enables more meaningful assessments of generative AI’s performance in code review, while also raising awareness of potential risks of unchecked integration into review processes. Read More  

Control-Augmented Autoregressive Diffusion for Data Assimilationcs.AI updates on arXiv.org arXiv:2510.06637v2 Announce Type: replace-cross
Abstract: Despite recent advances in test-time scaling and finetuning of diffusion models, guidance in Auto-Regressive Diffusion Models (ARDMs) remains underexplored. We introduce an amortized framework that augments a pretrained ARDM with a lightweight controller network, trained offline by previewing future rollouts to output stepwise controls that anticipate upcoming observations under a terminal-cost objective. Our approach is motivated by viewing guided generation as an entropy-regularized stochastic optimal control problem over ARDM trajectories: we learn a reusable policy that injects small control corrections inside each denoising sub-step while remaining anchored to the pretrained dynamics. We evaluate this framework in the context of data assimilation (DA) for chaotic spatiotemporal partial differential equations (PDEs), where existing methods can be computationally prohibitive and prone to forecast drift under sparse observations. At inference, DA reduces to a single causal forward rollout with on-the-fly corrections, requiring neither adjoint computations nor gradient-based optimization, and yields an order-of-magnitude speedup over strong diffusion-based DA baselines. Across two canonical PDEs and six observation regimes, our method consistently improves stability, accuracy, and physics-aware fidelity over state-of-the-art baselines. We will release code and checkpoints publicly.

 arXiv:2510.06637v2 Announce Type: replace-cross
Abstract: Despite recent advances in test-time scaling and finetuning of diffusion models, guidance in Auto-Regressive Diffusion Models (ARDMs) remains underexplored. We introduce an amortized framework that augments a pretrained ARDM with a lightweight controller network, trained offline by previewing future rollouts to output stepwise controls that anticipate upcoming observations under a terminal-cost objective. Our approach is motivated by viewing guided generation as an entropy-regularized stochastic optimal control problem over ARDM trajectories: we learn a reusable policy that injects small control corrections inside each denoising sub-step while remaining anchored to the pretrained dynamics. We evaluate this framework in the context of data assimilation (DA) for chaotic spatiotemporal partial differential equations (PDEs), where existing methods can be computationally prohibitive and prone to forecast drift under sparse observations. At inference, DA reduces to a single causal forward rollout with on-the-fly corrections, requiring neither adjoint computations nor gradient-based optimization, and yields an order-of-magnitude speedup over strong diffusion-based DA baselines. Across two canonical PDEs and six observation regimes, our method consistently improves stability, accuracy, and physics-aware fidelity over state-of-the-art baselines. We will release code and checkpoints publicly. Read More  

Cooperative Retrieval-Augmented Generation for Question Answering: Mutual Information Exchange and Ranking by Contrasting Layerscs.AI updates on arXiv.org arXiv:2512.10422v3 Announce Type: replace-cross
Abstract: Since large language models (LLMs) have a tendency to generate factually inaccurate output, retrieval-augmented generation (RAG) has gained significant attention as a key means to mitigate this downside of harnessing only LLMs. However, existing RAG methods for simple and multi-hop question answering (QA) are still prone to incorrect retrievals and hallucinations. To address these limitations, we propose CoopRAG, a novel RAG framework for the question answering task in which a retriever and an LLM work cooperatively with each other by exchanging informative knowledge, and the earlier and later layers of the retriever model work cooperatively with each other to accurately rank the retrieved documents relevant to a given query. In this framework, we (i) unroll a question into sub-questions and a reasoning chain in which uncertain positions are masked, (ii) retrieve the documents relevant to the question augmented with the sub-questions and the reasoning chain, (iii) rerank the documents by contrasting layers of the retriever, and (iv) reconstruct the reasoning chain by filling the masked positions via the LLM. Our experiments demonstrate that CoopRAG consistently outperforms state-of-the-art QA methods on three multi-hop QA datasets as well as a simple QA dataset in terms of both the retrieval and QA performances. Our code is available.

 arXiv:2512.10422v3 Announce Type: replace-cross
Abstract: Since large language models (LLMs) have a tendency to generate factually inaccurate output, retrieval-augmented generation (RAG) has gained significant attention as a key means to mitigate this downside of harnessing only LLMs. However, existing RAG methods for simple and multi-hop question answering (QA) are still prone to incorrect retrievals and hallucinations. To address these limitations, we propose CoopRAG, a novel RAG framework for the question answering task in which a retriever and an LLM work cooperatively with each other by exchanging informative knowledge, and the earlier and later layers of the retriever model work cooperatively with each other to accurately rank the retrieved documents relevant to a given query. In this framework, we (i) unroll a question into sub-questions and a reasoning chain in which uncertain positions are masked, (ii) retrieve the documents relevant to the question augmented with the sub-questions and the reasoning chain, (iii) rerank the documents by contrasting layers of the retriever, and (iv) reconstruct the reasoning chain by filling the masked positions via the LLM. Our experiments demonstrate that CoopRAG consistently outperforms state-of-the-art QA methods on three multi-hop QA datasets as well as a simple QA dataset in terms of both the retrieval and QA performances. Our code is available. Read More  

Chain-of-Evidence Multimodal Reasoning for Few-shot Temporal Action Localizationcs.AI updates on arXiv.org arXiv:2504.13460v4 Announce Type: replace-cross
Abstract: Traditional temporal action localization (TAL) methods rely on large amounts of detailed annotated data, whereas few-shot TAL reduces this dependence by using only a few training samples to identify unseen action categories. However, existing few-shot TAL methods typically focus solely on video-level information, neglecting textual information, which can provide valuable semantic support for the action localization task. To address these issues, in this work, we propose a new few-shot temporal action localization method by Chain-of-Evidence multimodal reasoning to improve localization performance. Specifically, we design a novel few-shot learning framework to capture action commonalities and variations, which includes a semantic-aware text-visual alignment module designed to align the query and support videos at different levels. Meanwhile, to better express the temporal dependencies and causal relationships between actions at the textual level, we design a Chain-of-Evidence (CoE) reasoning method that progressively guides the Vision Language Model (VLM) and Large Language Model (LLM) to generate CoE text descriptions for videos. The generated texts can capture more variance of action than visual features. We conduct extensive experiments on the publicly available ActivityNet1.3, THUMOS14 and our newly collected Human-related Anomaly Localization Dataset. The experimental results demonstrate that our proposed method significantly outperforms existing methods in single-instance and multi-instance scenarios. Our source code and data are available at https://github.com/MICLAB-BUPT/VAL-VLM.

 arXiv:2504.13460v4 Announce Type: replace-cross
Abstract: Traditional temporal action localization (TAL) methods rely on large amounts of detailed annotated data, whereas few-shot TAL reduces this dependence by using only a few training samples to identify unseen action categories. However, existing few-shot TAL methods typically focus solely on video-level information, neglecting textual information, which can provide valuable semantic support for the action localization task. To address these issues, in this work, we propose a new few-shot temporal action localization method by Chain-of-Evidence multimodal reasoning to improve localization performance. Specifically, we design a novel few-shot learning framework to capture action commonalities and variations, which includes a semantic-aware text-visual alignment module designed to align the query and support videos at different levels. Meanwhile, to better express the temporal dependencies and causal relationships between actions at the textual level, we design a Chain-of-Evidence (CoE) reasoning method that progressively guides the Vision Language Model (VLM) and Large Language Model (LLM) to generate CoE text descriptions for videos. The generated texts can capture more variance of action than visual features. We conduct extensive experiments on the publicly available ActivityNet1.3, THUMOS14 and our newly collected Human-related Anomaly Localization Dataset. The experimental results demonstrate that our proposed method significantly outperforms existing methods in single-instance and multi-instance scenarios. Our source code and data are available at https://github.com/MICLAB-BUPT/VAL-VLM. Read More  

Explainable AI for Classifying UTI Risk Groups Using a Real-World Linked EHR and Pathology Lab Datasetcs.AI updates on arXiv.org arXiv:2411.17645v4 Announce Type: replace-cross
Abstract: The use of machine learning and AI on electronic health records (EHRs) holds substantial potential for clinical insight. However, this approach faces challenges due to data heterogeneity, sparsity, temporal misalignment, and limited labeled outcomes. In this context, we leverage a linked EHR dataset of approximately one million de-identified individuals from Bristol, North Somerset, and South Gloucestershire, UK, to characterize urinary tract infections (UTIs). We implemented a data pre-processing and curation pipeline that transforms the raw EHR data into a structured format suitable for developing predictive models focused on data fairness, accountability and transparency. Given the limited availability and biases of ground truth UTI outcomes, we introduce a UTI risk estimation framework informed by clinical expertise to estimate UTI risk across individual patient timelines. Pairwise XGBoost models are trained using this framework to differentiate UTI risk categories with explainable AI techniques applied to identify key predictors and support interpretability. Our findings reveal differences in clinical and demographic predictors across risk groups. While this study highlights the potential of AI-driven insights to support UTI clinical decision-making, further investigation of patient sub-strata and extensive validation are needed to ensure robustness and applicability in clinical practice.

 arXiv:2411.17645v4 Announce Type: replace-cross
Abstract: The use of machine learning and AI on electronic health records (EHRs) holds substantial potential for clinical insight. However, this approach faces challenges due to data heterogeneity, sparsity, temporal misalignment, and limited labeled outcomes. In this context, we leverage a linked EHR dataset of approximately one million de-identified individuals from Bristol, North Somerset, and South Gloucestershire, UK, to characterize urinary tract infections (UTIs). We implemented a data pre-processing and curation pipeline that transforms the raw EHR data into a structured format suitable for developing predictive models focused on data fairness, accountability and transparency. Given the limited availability and biases of ground truth UTI outcomes, we introduce a UTI risk estimation framework informed by clinical expertise to estimate UTI risk across individual patient timelines. Pairwise XGBoost models are trained using this framework to differentiate UTI risk categories with explainable AI techniques applied to identify key predictors and support interpretability. Our findings reveal differences in clinical and demographic predictors across risk groups. While this study highlights the potential of AI-driven insights to support UTI clinical decision-making, further investigation of patient sub-strata and extensive validation are needed to ensure robustness and applicability in clinical practice. Read More