Qualifire AI Releases Rogue: An End-to-End Agentic AI Testing Framework, Evaluating the Performance of AI AgentsMarkTechPost Agentic systems are stochastic, context-dependent, and policy-bounded. Conventional QA—unit tests, static prompts, or scalar “LLM-as-a-judge” scores—fails to expose multi-turn vulnerabilities and provides weak audit trails. Developer teams need protocol-accurate conversations, explicit policy checks, and machine-readable evidence that can gate releases with confidence. Qualifire AI has open-sourced Rogue, a Python framework that evaluates AI agents over
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Agentic systems are stochastic, context-dependent, and policy-bounded. Conventional QA—unit tests, static prompts, or scalar “LLM-as-a-judge” scores—fails to expose multi-turn vulnerabilities and provides weak audit trails. Developer teams need protocol-accurate conversations, explicit policy checks, and machine-readable evidence that can gate releases with confidence. Qualifire AI has open-sourced Rogue, a Python framework that evaluates AI agents over
The post Qualifire AI Releases Rogue: An End-to-End Agentic AI Testing Framework, Evaluating the Performance of AI Agents appeared first on MarkTechPost. Read More
A Coding Guide to Build an AI-Powered Cryptographic Agent System with Hybrid Encryption, Digital Signatures, and Adaptive Security IntelligenceMarkTechPost In this tutorial, we build an AI-powered cryptographic agent system that combines the strength of classical encryption with adaptive intelligence. We design agents capable of performing hybrid encryption with RSA and AES, generating digital signatures, detecting anomalies in message patterns, and intelligently recommending key rotations. As we progress, we witness these autonomous agents securely establish
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In this tutorial, we build an AI-powered cryptographic agent system that combines the strength of classical encryption with adaptive intelligence. We design agents capable of performing hybrid encryption with RSA and AES, generating digital signatures, detecting anomalies in message patterns, and intelligently recommending key rotations. As we progress, we witness these autonomous agents securely establish
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Baidu’s PaddlePaddle Team Releases PaddleOCR-VL (0.9B): a NaViT-style + ERNIE-4.5-0.3B VLM Targeting End-to-End Multilingual Document ParsingMarkTechPost How do you convert complex, multilingual documents—dense layouts, small scripts, formulas, charts, and handwriting—into faithful structured Markdown/JSON with state-of-the-art accuracy while keeping inference latency and memory low enough for real deployments?Baidu’s PaddlePaddle group has released PaddleOCR-VL, a 0.9B-parameter vision-language model designed for end-to-end document parsing across text, tables, formulas, charts, and handwriting. The core model
The post Baidu’s PaddlePaddle Team Releases PaddleOCR-VL (0.9B): a NaViT-style + ERNIE-4.5-0.3B VLM Targeting End-to-End Multilingual Document Parsing appeared first on MarkTechPost.
How do you convert complex, multilingual documents—dense layouts, small scripts, formulas, charts, and handwriting—into faithful structured Markdown/JSON with state-of-the-art accuracy while keeping inference latency and memory low enough for real deployments?Baidu’s PaddlePaddle group has released PaddleOCR-VL, a 0.9B-parameter vision-language model designed for end-to-end document parsing across text, tables, formulas, charts, and handwriting. The core model
The post Baidu’s PaddlePaddle Team Releases PaddleOCR-VL (0.9B): a NaViT-style + ERNIE-4.5-0.3B VLM Targeting End-to-End Multilingual Document Parsing appeared first on MarkTechPost. Read More
Google AI Releases C2S-Scale 27B Model that Translate Complex Single-Cell Gene Expression Data into ‘cell sentences’ that LLMs can UnderstandMarkTechPost A team of researchers from Google Research, Google DeepMind, and Yale released C2S-Scale 27B, a 27-billion-parameter foundation model for single-cell analysis built on Gemma-2. The model formalizes single-cell RNA-seq (scRNA-seq) profiles as “cell sentences”—ordered lists of gene symbols—so that a language model can natively parse and reason over cellular states. Beyond benchmarking gains, the research
The post Google AI Releases C2S-Scale 27B Model that Translate Complex Single-Cell Gene Expression Data into ‘cell sentences’ that LLMs can Understand appeared first on MarkTechPost.
A team of researchers from Google Research, Google DeepMind, and Yale released C2S-Scale 27B, a 27-billion-parameter foundation model for single-cell analysis built on Gemma-2. The model formalizes single-cell RNA-seq (scRNA-seq) profiles as “cell sentences”—ordered lists of gene symbols—so that a language model can natively parse and reason over cellular states. Beyond benchmarking gains, the research
The post Google AI Releases C2S-Scale 27B Model that Translate Complex Single-Cell Gene Expression Data into ‘cell sentences’ that LLMs can Understand appeared first on MarkTechPost. Read More
Big Reasoning with Small Models: Instruction Retrieval at Inference Timecs.AI updates on arXiv.org arXiv:2510.13935v1 Announce Type: cross
Abstract: Can we bring large-scale reasoning to local-scale compute? Small language models (SLMs) are increasingly attractive because they run efficiently on local hardware, offering strong privacy, low cost, and reduced environmental impact. Yet they often struggle with tasks that require multi-step reasoning or domain-specific knowledge. We address this limitation through instruction intervention at inference time, where an SLM retrieves structured reasoning procedures rather than generating them from scratch. Our method builds an Instruction Corpus by grouping similar training questions and creating instructions via GPT-5. During inference, the SLM retrieves the most relevant instructions and follows their steps. Unlike retrieval-augmented generation, which retrieves text passages, instruction retrieval gives the model structured guidance for reasoning. We evaluate this framework on MedQA (medical board exams), MMLU Professional Law, and MathQA using models from 3B to 14B parameters without any additional fine-tuning. Instruction retrieval yields consistent gains: 9.4% on MedQA, 7.9% on MMLU Law, and 5.1% on MathQA. Concise instructions outperform longer ones, and the magnitude of improvement depends strongly on model family and intrinsic reasoning ability.
arXiv:2510.13935v1 Announce Type: cross
Abstract: Can we bring large-scale reasoning to local-scale compute? Small language models (SLMs) are increasingly attractive because they run efficiently on local hardware, offering strong privacy, low cost, and reduced environmental impact. Yet they often struggle with tasks that require multi-step reasoning or domain-specific knowledge. We address this limitation through instruction intervention at inference time, where an SLM retrieves structured reasoning procedures rather than generating them from scratch. Our method builds an Instruction Corpus by grouping similar training questions and creating instructions via GPT-5. During inference, the SLM retrieves the most relevant instructions and follows their steps. Unlike retrieval-augmented generation, which retrieves text passages, instruction retrieval gives the model structured guidance for reasoning. We evaluate this framework on MedQA (medical board exams), MMLU Professional Law, and MathQA using models from 3B to 14B parameters without any additional fine-tuning. Instruction retrieval yields consistent gains: 9.4% on MedQA, 7.9% on MMLU Law, and 5.1% on MathQA. Concise instructions outperform longer ones, and the magnitude of improvement depends strongly on model family and intrinsic reasoning ability. Read More
What if AI is the next dot-com bubble?AI News The surge of multi-billion-dollar investments in AI has sparked growing debate over whether the industry is heading for a bubble similar to the dot-com boom. Investors are watching closely for signs that enthusiasm might be fading or that the heavy spending on infrastructure and chips is failing to deliver expected returns. A recent survey by
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The surge of multi-billion-dollar investments in AI has sparked growing debate over whether the industry is heading for a bubble similar to the dot-com boom. Investors are watching closely for signs that enthusiasm might be fading or that the heavy spending on infrastructure and chips is failing to deliver expected returns. A recent survey by
The post What if AI is the next dot-com bubble? appeared first on AI News. Read More
Where are the Whales: A Human-in-the-loop Detection Method for Identifying Whales in High-resolution Satellite Imagerycs.AI updates on arXiv.org arXiv:2510.14709v1 Announce Type: cross
Abstract: Effective monitoring of whale populations is critical for conservation, but traditional survey methods are expensive and difficult to scale. While prior work has shown that whales can be identified in very high-resolution (VHR) satellite imagery, large-scale automated detection remains challenging due to a lack of annotated imagery, variability in image quality and environmental conditions, and the cost of building robust machine learning pipelines over massive remote sensing archives. We present a semi-automated approach for surfacing possible whale detections in VHR imagery using a statistical anomaly detection method that flags spatial outliers, i.e. “interesting points”. We pair this detector with a web-based labeling interface designed to enable experts to quickly annotate the interesting points. We evaluate our system on three benchmark scenes with known whale annotations and achieve recalls of 90.3% to 96.4%, while reducing the area requiring expert inspection by up to 99.8% — from over 1,000 sq km to less than 2 sq km in some cases. Our method does not rely on labeled training data and offers a scalable first step toward future machine-assisted marine mammal monitoring from space. We have open sourced this pipeline at https://github.com/microsoft/whales.
arXiv:2510.14709v1 Announce Type: cross
Abstract: Effective monitoring of whale populations is critical for conservation, but traditional survey methods are expensive and difficult to scale. While prior work has shown that whales can be identified in very high-resolution (VHR) satellite imagery, large-scale automated detection remains challenging due to a lack of annotated imagery, variability in image quality and environmental conditions, and the cost of building robust machine learning pipelines over massive remote sensing archives. We present a semi-automated approach for surfacing possible whale detections in VHR imagery using a statistical anomaly detection method that flags spatial outliers, i.e. “interesting points”. We pair this detector with a web-based labeling interface designed to enable experts to quickly annotate the interesting points. We evaluate our system on three benchmark scenes with known whale annotations and achieve recalls of 90.3% to 96.4%, while reducing the area requiring expert inspection by up to 99.8% — from over 1,000 sq km to less than 2 sq km in some cases. Our method does not rely on labeled training data and offers a scalable first step toward future machine-assisted marine mammal monitoring from space. We have open sourced this pipeline at https://github.com/microsoft/whales. Read More
Beyond Seeing: Evaluating Multimodal LLMs on Tool-Enabled Image Perception, Transformation, and Reasoningcs.AI updates on arXiv.org arXiv:2510.12712v2 Announce Type: replace-cross
Abstract: Multimodal Large Language Models (MLLMs) are increasingly applied in real-world scenarios where user-provided images are often imperfect, requiring active image manipulations such as cropping, editing, or enhancement to uncover salient visual cues. Beyond static visual perception, MLLMs must also think with images: dynamically transforming visual content and integrating it with other tools to solve complex tasks. However, this shift from treating vision as passive context to a manipulable cognitive workspace remains underexplored. Most existing benchmarks still follow a think about images paradigm, where images are regarded as static inputs. To address this gap, we introduce VisualToolBench, a visual tool-use reasoning benchmark that rigorously evaluates MLLMs’ ability to perceive, transform, and reason across complex visual-textual tasks under the think-with-images paradigm. VisualToolBench comprises 1,204 challenging, open-ended vision tasks (603 single-turn, 601 multi-turn) spanning across five diverse domains, each paired with detailed rubrics to enable systematic evaluation. Our evaluation shows that current MLLMs struggle with tasks requiring effective integration of vision and general-purpose tools. Even the strongest model (GPT-5-think) reaches only 18.68% pass rate. We further observe divergent tool-use behaviors, with OpenAI models benefiting from diverse image manipulations while Gemini-2.5-pro shows no improvement. By introducing the first benchmark centered on think with images, VisualToolBench offers critical insights for advancing visual intelligence in MLLMs.
arXiv:2510.12712v2 Announce Type: replace-cross
Abstract: Multimodal Large Language Models (MLLMs) are increasingly applied in real-world scenarios where user-provided images are often imperfect, requiring active image manipulations such as cropping, editing, or enhancement to uncover salient visual cues. Beyond static visual perception, MLLMs must also think with images: dynamically transforming visual content and integrating it with other tools to solve complex tasks. However, this shift from treating vision as passive context to a manipulable cognitive workspace remains underexplored. Most existing benchmarks still follow a think about images paradigm, where images are regarded as static inputs. To address this gap, we introduce VisualToolBench, a visual tool-use reasoning benchmark that rigorously evaluates MLLMs’ ability to perceive, transform, and reason across complex visual-textual tasks under the think-with-images paradigm. VisualToolBench comprises 1,204 challenging, open-ended vision tasks (603 single-turn, 601 multi-turn) spanning across five diverse domains, each paired with detailed rubrics to enable systematic evaluation. Our evaluation shows that current MLLMs struggle with tasks requiring effective integration of vision and general-purpose tools. Even the strongest model (GPT-5-think) reaches only 18.68% pass rate. We further observe divergent tool-use behaviors, with OpenAI models benefiting from diverse image manipulations while Gemini-2.5-pro shows no improvement. By introducing the first benchmark centered on think with images, VisualToolBench offers critical insights for advancing visual intelligence in MLLMs. Read More
LLMs’ Suitability for Network Security: A Case Study of STRIDE Threat Modelingcs.AI updates on arXiv.org arXiv:2505.04101v2 Announce Type: replace-cross
Abstract: Artificial Intelligence (AI) is expected to be an integral part of next-generation AI-native 6G networks. With the prevalence of AI, researchers have identified numerous use cases of AI in network security. However, there are very few studies that analyze the suitability of Large Language Models (LLMs) in network security. To fill this gap, we examine the suitability of LLMs in network security, particularly with the case study of STRIDE threat modeling. We utilize four prompting techniques with five LLMs to perform STRIDE classification of 5G threats. From our evaluation results, we point out key findings and detailed insights along with the explanation of the possible underlying factors influencing the behavior of LLMs in the modeling of certain threats. The numerical results and the insights support the necessity for adjusting and fine-tuning LLMs for network security use cases.
arXiv:2505.04101v2 Announce Type: replace-cross
Abstract: Artificial Intelligence (AI) is expected to be an integral part of next-generation AI-native 6G networks. With the prevalence of AI, researchers have identified numerous use cases of AI in network security. However, there are very few studies that analyze the suitability of Large Language Models (LLMs) in network security. To fill this gap, we examine the suitability of LLMs in network security, particularly with the case study of STRIDE threat modeling. We utilize four prompting techniques with five LLMs to perform STRIDE classification of 5G threats. From our evaluation results, we point out key findings and detailed insights along with the explanation of the possible underlying factors influencing the behavior of LLMs in the modeling of certain threats. The numerical results and the insights support the necessity for adjusting and fine-tuning LLMs for network security use cases. Read More
Do Large Language Models Show Biases in Causal Learning? Insights from Contingency Judgmentcs.AI updates on arXiv.org arXiv:2510.13985v1 Announce Type: new
Abstract: Causal learning is the cognitive process of developing the capability of making causal inferences based on available information, often guided by normative principles. This process is prone to errors and biases, such as the illusion of causality, in which people perceive a causal relationship between two variables despite lacking supporting evidence. This cognitive bias has been proposed to underlie many societal problems, including social prejudice, stereotype formation, misinformation, and superstitious thinking. In this work, we examine whether large language models are prone to developing causal illusions when faced with a classic cognitive science paradigm: the contingency judgment task. To investigate this, we constructed a dataset of 1,000 null contingency scenarios (in which the available information is not sufficient to establish a causal relationship between variables) within medical contexts and prompted LLMs to evaluate the effectiveness of potential causes. Our findings show that all evaluated models systematically inferred unwarranted causal relationships, revealing a strong susceptibility to the illusion of causality. While there is ongoing debate about whether LLMs genuinely understand causality or merely reproduce causal language without true comprehension, our findings support the latter hypothesis and raise concerns about the use of language models in domains where accurate causal reasoning is essential for informed decision-making.
arXiv:2510.13985v1 Announce Type: new
Abstract: Causal learning is the cognitive process of developing the capability of making causal inferences based on available information, often guided by normative principles. This process is prone to errors and biases, such as the illusion of causality, in which people perceive a causal relationship between two variables despite lacking supporting evidence. This cognitive bias has been proposed to underlie many societal problems, including social prejudice, stereotype formation, misinformation, and superstitious thinking. In this work, we examine whether large language models are prone to developing causal illusions when faced with a classic cognitive science paradigm: the contingency judgment task. To investigate this, we constructed a dataset of 1,000 null contingency scenarios (in which the available information is not sufficient to establish a causal relationship between variables) within medical contexts and prompted LLMs to evaluate the effectiveness of potential causes. Our findings show that all evaluated models systematically inferred unwarranted causal relationships, revealing a strong susceptibility to the illusion of causality. While there is ongoing debate about whether LLMs genuinely understand causality or merely reproduce causal language without true comprehension, our findings support the latter hypothesis and raise concerns about the use of language models in domains where accurate causal reasoning is essential for informed decision-making. Read More