Author: Tech Jacks Solutions

Manage Amazon SageMaker HyperPod clusters using the HyperPod CLI and SDKArtificial Intelligence In this post, we demonstrate how to use the CLI and the SDK to create and manage SageMaker HyperPod clusters in your AWS account. We walk through a practical example and dive deeper into the user workflow and parameter choices.

 In this post, we demonstrate how to use the CLI and the SDK to create and manage SageMaker HyperPod clusters in your AWS account. We walk through a practical example and dive deeper into the user workflow and parameter choices. Read More  

A Coding, Data-Driven Guide to Measuring, Visualizing, and Enforcing Cognitive Complexity in Python Projects Using complexipyMarkTechPost In this tutorial, we build an end-to-end cognitive complexity analysis workflow using complexipy. We start by measuring complexity directly from raw code strings, then scale the same analysis to individual files and an entire project directory. Along the way, we generate machine-readable reports, normalize them into structured DataFrames, and visualize complexity distributions to understand how
The post A Coding, Data-Driven Guide to Measuring, Visualizing, and Enforcing Cognitive Complexity in Python Projects Using complexipy appeared first on MarkTechPost.

 In this tutorial, we build an end-to-end cognitive complexity analysis workflow using complexipy. We start by measuring complexity directly from raw code strings, then scale the same analysis to individual files and an entire project directory. Along the way, we generate machine-readable reports, normalize them into structured DataFrames, and visualize complexity distributions to understand how
The post A Coding, Data-Driven Guide to Measuring, Visualizing, and Enforcing Cognitive Complexity in Python Projects Using complexipy appeared first on MarkTechPost. Read More  

NVIDIA AI releases C-RADIOv4 vision backbone unifying SigLIP2, DINOv3, SAM3 for classification, dense prediction, segmentation workloads at scaleMarkTechPost How do you combine SigLIP2, DINOv3, and SAM3 into a single vision backbone without sacrificing dense or segmentation performance? NVIDIA’s C-RADIOv4 is a new agglomerative vision backbone that distills three strong teacher models, SigLIP2-g-384, DINOv3-7B, and SAM3, into a single student encoder. It extends the AM-RADIO and RADIOv2.5 line, keeping similar computational cost while improving
The post NVIDIA AI releases C-RADIOv4 vision backbone unifying SigLIP2, DINOv3, SAM3 for classification, dense prediction, segmentation workloads at scale appeared first on MarkTechPost.

 How do you combine SigLIP2, DINOv3, and SAM3 into a single vision backbone without sacrificing dense or segmentation performance? NVIDIA’s C-RADIOv4 is a new agglomerative vision backbone that distills three strong teacher models, SigLIP2-g-384, DINOv3-7B, and SAM3, into a single student encoder. It extends the AM-RADIO and RADIOv2.5 line, keeping similar computational cost while improving
The post NVIDIA AI releases C-RADIOv4 vision backbone unifying SigLIP2, DINOv3, SAM3 for classification, dense prediction, segmentation workloads at scale appeared first on MarkTechPost. Read More  

Structured outputs on Amazon Bedrock: Schema-compliant AI responsesArtificial Intelligence Today, we’re announcing structured outputs on Amazon Bedrock—a capability that fundamentally transforms how you can obtain validated JSON responses from foundation models through constrained decoding for schema compliance. In this post, we explore the challenges of traditional JSON generation and how structured outputs solves them. We cover the two core mechanisms—JSON Schema output format and strict tool use—along with implementation details, best practices, and practical code examples.

 Today, we’re announcing structured outputs on Amazon Bedrock—a capability that fundamentally transforms how you can obtain validated JSON responses from foundation models through constrained decoding for schema compliance. In this post, we explore the challenges of traditional JSON generation and how structured outputs solves them. We cover the two core mechanisms—JSON Schema output format and strict tool use—along with implementation details, best practices, and practical code examples. Read More  

SCALE: Self-uncertainty Conditioned Adaptive Looking and Execution for Vision-Language-Action Modelscs.AI updates on arXiv.org arXiv:2602.04208v1 Announce Type: cross
Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robotic control, with test-time scaling (TTS) gaining attention to enhance robustness beyond training. However, existing TTS methods for VLAs require additional training, verifiers, and multiple forward passes, making them impractical for deployment. Moreover, they intervene only at action decoding while keeping visual representations fixed-insufficient under perceptual ambiguity, where reconsidering how to perceive is as important as deciding what to do. To address these limitations, we propose SCALE, a simple inference strategy that jointly modulates visual perception and action based on ‘self-uncertainty’, inspired by uncertainty-driven exploration in Active Inference theory-requiring no additional training, no verifier, and only a single forward pass. SCALE broadens exploration in both perception and action under high uncertainty, while focusing on exploitation when confident-enabling adaptive execution across varying conditions. Experiments on simulated and real-world benchmarks demonstrate that SCALE improves state-of-the-art VLAs and outperforms existing TTS methods while maintaining single-pass efficiency.

 arXiv:2602.04208v1 Announce Type: cross
Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robotic control, with test-time scaling (TTS) gaining attention to enhance robustness beyond training. However, existing TTS methods for VLAs require additional training, verifiers, and multiple forward passes, making them impractical for deployment. Moreover, they intervene only at action decoding while keeping visual representations fixed-insufficient under perceptual ambiguity, where reconsidering how to perceive is as important as deciding what to do. To address these limitations, we propose SCALE, a simple inference strategy that jointly modulates visual perception and action based on ‘self-uncertainty’, inspired by uncertainty-driven exploration in Active Inference theory-requiring no additional training, no verifier, and only a single forward pass. SCALE broadens exploration in both perception and action under high uncertainty, while focusing on exploitation when confident-enabling adaptive execution across varying conditions. Experiments on simulated and real-world benchmarks demonstrate that SCALE improves state-of-the-art VLAs and outperforms existing TTS methods while maintaining single-pass efficiency. Read More  

SkeletonGaussian: Editable 4D Generation through Gaussian Skeletonizationcs.AI updates on arXiv.org arXiv:2602.04271v1 Announce Type: cross
Abstract: 4D generation has made remarkable progress in synthesizing dynamic 3D objects from input text, images, or videos. However, existing methods often represent motion as an implicit deformation field, which limits direct control and editability. To address this issue, we propose SkeletonGaussian, a novel framework for generating editable dynamic 3D Gaussians from monocular video input. Our approach introduces a hierarchical articulated representation that decomposes motion into sparse rigid motion explicitly driven by a skeleton and fine-grained non-rigid motion. Concretely, we extract a robust skeleton and drive rigid motion via linear blend skinning, followed by a hexplane-based refinement for non-rigid deformations, enhancing interpretability and editability. Experimental results demonstrate that SkeletonGaussian surpasses existing methods in generation quality while enabling intuitive motion editing, establishing a new paradigm for editable 4D generation. Project page: https://wusar.github.io/projects/skeletongaussian/

 arXiv:2602.04271v1 Announce Type: cross
Abstract: 4D generation has made remarkable progress in synthesizing dynamic 3D objects from input text, images, or videos. However, existing methods often represent motion as an implicit deformation field, which limits direct control and editability. To address this issue, we propose SkeletonGaussian, a novel framework for generating editable dynamic 3D Gaussians from monocular video input. Our approach introduces a hierarchical articulated representation that decomposes motion into sparse rigid motion explicitly driven by a skeleton and fine-grained non-rigid motion. Concretely, we extract a robust skeleton and drive rigid motion via linear blend skinning, followed by a hexplane-based refinement for non-rigid deformations, enhancing interpretability and editability. Experimental results demonstrate that SkeletonGaussian surpasses existing methods in generation quality while enabling intuitive motion editing, establishing a new paradigm for editable 4D generation. Project page: https://wusar.github.io/projects/skeletongaussian/ Read More  

DeFrame: Debiasing Large Language Models Against Framing Effectscs.AI updates on arXiv.org arXiv:2602.04306v1 Announce Type: cross
Abstract: As large language models (LLMs) are increasingly deployed in real-world applications, ensuring their fair responses across demographics has become crucial. Despite many efforts, an ongoing challenge is hidden bias: LLMs appear fair under standard evaluations, but can produce biased responses outside those evaluation settings. In this paper, we identify framing — differences in how semantically equivalent prompts are expressed (e.g., “A is better than B” vs. “B is worse than A”) — as an underexplored contributor to this gap. We first introduce the concept of “framing disparity” to quantify the impact of framing on fairness evaluation. By augmenting fairness evaluation benchmarks with alternative framings, we find that (1) fairness scores vary significantly with framing and (2) existing debiasing methods improve overall (i.e., frame-averaged) fairness, but often fail to reduce framing-induced disparities. To address this, we propose a framing-aware debiasing method that encourages LLMs to be more consistent across framings. Experiments demonstrate that our approach reduces overall bias and improves robustness against framing disparities, enabling LLMs to produce fairer and more consistent responses.

 arXiv:2602.04306v1 Announce Type: cross
Abstract: As large language models (LLMs) are increasingly deployed in real-world applications, ensuring their fair responses across demographics has become crucial. Despite many efforts, an ongoing challenge is hidden bias: LLMs appear fair under standard evaluations, but can produce biased responses outside those evaluation settings. In this paper, we identify framing — differences in how semantically equivalent prompts are expressed (e.g., “A is better than B” vs. “B is worse than A”) — as an underexplored contributor to this gap. We first introduce the concept of “framing disparity” to quantify the impact of framing on fairness evaluation. By augmenting fairness evaluation benchmarks with alternative framings, we find that (1) fairness scores vary significantly with framing and (2) existing debiasing methods improve overall (i.e., frame-averaged) fairness, but often fail to reduce framing-induced disparities. To address this, we propose a framing-aware debiasing method that encourages LLMs to be more consistent across framings. Experiments demonstrate that our approach reduces overall bias and improves robustness against framing disparities, enabling LLMs to produce fairer and more consistent responses. Read More  

Structural shifts in institutional participation and collaboration within the AI arXiv preprint research ecosystemcs.AI updates on arXiv.org arXiv:2602.03969v1 Announce Type: cross
Abstract: The emergence of large language models (LLMs) represents a significant technological shift within the scientific ecosystem, particularly within the field of artificial intelligence (AI). This paper examines structural changes in the AI research landscape using a dataset of arXiv preprints (cs.AI) from 2021 through 2025. Given the rapid pace of AI development, the preprint ecosystem has become a critical barometer for real-time scientific shifts, often preceding formal peer-reviewed publication by months or years. By employing a multi-stage data collection and enrichment pipeline in conjunction with LLM-based institution classification, we analyze the evolution of publication volumes, author team sizes, and academic–industry collaboration patterns. Our results reveal an unprecedented surge in publication output following the introduction of ChatGPT, with academic institutions continuing to provide the largest volume of research. However, we observe that academic–industry collaboration is still suppressed, as measured by a Normalized Collaboration Index (NCI) that remains significantly below the random-mixing baseline across all major subfields. These findings highlight a continuing institutional divide and suggest that the capital-intensive nature of generative AI research may be reshaping the boundaries of scientific collaboration.

 arXiv:2602.03969v1 Announce Type: cross
Abstract: The emergence of large language models (LLMs) represents a significant technological shift within the scientific ecosystem, particularly within the field of artificial intelligence (AI). This paper examines structural changes in the AI research landscape using a dataset of arXiv preprints (cs.AI) from 2021 through 2025. Given the rapid pace of AI development, the preprint ecosystem has become a critical barometer for real-time scientific shifts, often preceding formal peer-reviewed publication by months or years. By employing a multi-stage data collection and enrichment pipeline in conjunction with LLM-based institution classification, we analyze the evolution of publication volumes, author team sizes, and academic–industry collaboration patterns. Our results reveal an unprecedented surge in publication output following the introduction of ChatGPT, with academic institutions continuing to provide the largest volume of research. However, we observe that academic–industry collaboration is still suppressed, as measured by a Normalized Collaboration Index (NCI) that remains significantly below the random-mixing baseline across all major subfields. These findings highlight a continuing institutional divide and suggest that the capital-intensive nature of generative AI research may be reshaping the boundaries of scientific collaboration. Read More  

When Chains of Thought Don’t Matter: Causal Bypass in Large Language Modelscs.AI updates on arXiv.org arXiv:2602.03994v1 Announce Type: cross
Abstract: Chain-of-thought (CoT) prompting is widely assumed to expose a model’s reasoning process and improve transparency. We attempted to enforce this assumption by penalizing unfaithful reasoning, but found that surface-level compliance does not guarantee causal reliance. Our central finding is negative: even when CoT is verbose, strategic, and flagged by surface-level manipulation detectors, model answers are often causally independent of the CoT content. We present a diagnostic framework for auditing this failure mode: it combines (i) an interpretable behavioral module that scores manipulation-relevant signals in CoT text and (ii) a causal probe that measures CoT-mediated influence (CMI) via hidden-state patching and reports a bypass score ($1-mathrm{CMI}$), quantifying the degree to which the answer is produced by a bypass circuit independent of the rationale. In pilot evaluations, audit-aware prompting increases detectable manipulation signals (mean risk-score delta: $+5.10$), yet causal probes reveal task-dependent mediation: many QA items exhibit near-total bypass (CMI $approx 0$), while some logic problems show stronger mediation (CMI up to $0.56$). Layer-wise analysis reveals narrow and task-dependent “reasoning windows” even when mean CMI is low.

 arXiv:2602.03994v1 Announce Type: cross
Abstract: Chain-of-thought (CoT) prompting is widely assumed to expose a model’s reasoning process and improve transparency. We attempted to enforce this assumption by penalizing unfaithful reasoning, but found that surface-level compliance does not guarantee causal reliance. Our central finding is negative: even when CoT is verbose, strategic, and flagged by surface-level manipulation detectors, model answers are often causally independent of the CoT content. We present a diagnostic framework for auditing this failure mode: it combines (i) an interpretable behavioral module that scores manipulation-relevant signals in CoT text and (ii) a causal probe that measures CoT-mediated influence (CMI) via hidden-state patching and reports a bypass score ($1-mathrm{CMI}$), quantifying the degree to which the answer is produced by a bypass circuit independent of the rationale. In pilot evaluations, audit-aware prompting increases detectable manipulation signals (mean risk-score delta: $+5.10$), yet causal probes reveal task-dependent mediation: many QA items exhibit near-total bypass (CMI $approx 0$), while some logic problems show stronger mediation (CMI up to $0.56$). Layer-wise analysis reveals narrow and task-dependent “reasoning windows” even when mean CMI is low. Read More