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The $L$-polynomial of hyperelliptic function fields and its applications
By arXiv:2512.08250v1
https://arxiv.org/abs/2512.08250
Explicit L-polynomial for hyperelliptic function fields, closed-form class number formula, and applications up to genus 3.
By arXiv:2512.08250v1
https://arxiv.org/abs/2512.08250
Explicit L-polynomial for hyperelliptic function fields, closed-form class number formula, and applications up to genus 3.
The $L$-polynomial of hyperelliptic function fields and its applications
This paper presents an explicit L-polynomial for hyperelliptic function fields and uses it to derive a closed-form formula for the class number, with applications in computing average class numbers for fields up to genus 3.
arxiv.org
December 10, 2025 at 1:51 PM
The $L$-polynomial of hyperelliptic function fields and its applications
By arXiv:2512.08250v1
https://arxiv.org/abs/2512.08250
Explicit L-polynomial for hyperelliptic function fields, closed-form class number formula, and applications up to genus 3.
By arXiv:2512.08250v1
https://arxiv.org/abs/2512.08250
Explicit L-polynomial for hyperelliptic function fields, closed-form class number formula, and applications up to genus 3.
Three Forensic Cues for JPEG AI Images
https://arxiv.org/abs/2504.03191
New tools detect AI-generated JPEGs using color, compression, and quantization cues.
https://arxiv.org/abs/2504.03191
New tools detect AI-generated JPEGs using color, compression, and quantization cues.
Three Forensic Cues for JPEG AI Images
JPEG AI images need new forensic tools to detect and distinguish them from DeepFakes, with three proposed cues addressing color channel correlations, repeated compression detection, and latent space quantization.
arxiv.org
December 9, 2025 at 1:35 PM
Three Forensic Cues for JPEG AI Images
https://arxiv.org/abs/2504.03191
New tools detect AI-generated JPEGs using color, compression, and quantization cues.
https://arxiv.org/abs/2504.03191
New tools detect AI-generated JPEGs using color, compression, and quantization cues.
See in Depth: Training-Free Surgical Scene Segmentation with Monocular Depth Priors - By arXiv
https://arxiv.org/abs/2512.05529
DepSeg revolutionizes surgical scene segmentation by using monocular depth priors and pretrained models, cutting costs and boosting accuracy without training.
https://arxiv.org/abs/2512.05529
DepSeg revolutionizes surgical scene segmentation by using monocular depth priors and pretrained models, cutting costs and boosting accuracy without training.
See in Depth: Training-Free Surgical Scene Segmentation with Monocular Depth Priors
DepSeg revolutionizes surgical scene segmentation by using monocular depth priors and pretrained models, cutting costs and boosting accuracy without training.
arxiv.org
December 8, 2025 at 1:29 PM
See in Depth: Training-Free Surgical Scene Segmentation with Monocular Depth Priors - By arXiv
https://arxiv.org/abs/2512.05529
DepSeg revolutionizes surgical scene segmentation by using monocular depth priors and pretrained models, cutting costs and boosting accuracy without training.
https://arxiv.org/abs/2512.05529
DepSeg revolutionizes surgical scene segmentation by using monocular depth priors and pretrained models, cutting costs and boosting accuracy without training.
XAI-Driven Skin Disease Classification: Leveraging GANs to Augment ResNet-50 Performance
https://arxiv.org/abs/2512.00626
A transparent skin disease classification system using GANs and ResNet-50 achieves 92.50% accuracy and 98.82% Macro-AUC.
https://arxiv.org/abs/2512.00626
A transparent skin disease classification system using GANs and ResNet-50 achieves 92.50% accuracy and 98.82% Macro-AUC.
XAI-Driven Skin Disease Classification: Leveraging GANs to Augment ResNet-50 Performance
This study introduces a top-notch, transparent skin disease classification system using GANs to balance data and ResNet-50 for high accuracy, achieving 92.50% accuracy and 98.82% Macro-AUC, while ensuring clinical relevance with XAI techniques.
arxiv.org
December 5, 2025 at 1:39 PM
XAI-Driven Skin Disease Classification: Leveraging GANs to Augment ResNet-50 Performance
https://arxiv.org/abs/2512.00626
A transparent skin disease classification system using GANs and ResNet-50 achieves 92.50% accuracy and 98.82% Macro-AUC.
https://arxiv.org/abs/2512.00626
A transparent skin disease classification system using GANs and ResNet-50 achieves 92.50% accuracy and 98.82% Macro-AUC.
Beyond Top Activations: Efficient and Reliable Crowdsourced Evaluation of Automated Interpretability
https://arxiv.org/abs/2506.07985
MG-IS and BRAgg improve crowdsourced evaluations, reducing costs by up to 40x.
https://arxiv.org/abs/2506.07985
MG-IS and BRAgg improve crowdsourced evaluations, reducing costs by up to 40x.
Beyond Top Activations: Efficient and Reliable Crowdsourced Evaluation of Automated Interpretability
This paper introduces Model-Guided Importance Sampling (MG-IS) and Bayesian Rating Aggregation (BRAgg) to make crowdsourced evaluations of automated interpretability methods more cost-effective and accurate, reducing evaluation costs by up to 40x.
arxiv.org
December 4, 2025 at 1:39 PM
Beyond Top Activations: Efficient and Reliable Crowdsourced Evaluation of Automated Interpretability
https://arxiv.org/abs/2506.07985
MG-IS and BRAgg improve crowdsourced evaluations, reducing costs by up to 40x.
https://arxiv.org/abs/2506.07985
MG-IS and BRAgg improve crowdsourced evaluations, reducing costs by up to 40x.
SAS Decision Builder now on Microsoft Fabric for smarter decisions.
https://www.techzine.eu/news/analytics/136955/sas-decision-builder-generally-available-on-microsoft-fabric/
https://www.techzine.eu/news/analytics/136955/sas-decision-builder-generally-available-on-microsoft-fabric/
SAS Decision Builder generally available on Microsoft Fabric
SAS has made its decision intelligence solution SAS Decision Builder generally available on Microsoft Fabric. The tool combines multiple AI models, rules,
www.techzine.eu
December 3, 2025 at 1:51 PM
SAS Decision Builder now on Microsoft Fabric for smarter decisions.
https://www.techzine.eu/news/analytics/136955/sas-decision-builder-generally-available-on-microsoft-fabric/
https://www.techzine.eu/news/analytics/136955/sas-decision-builder-generally-available-on-microsoft-fabric/
Latent Collaboration in Multi-Agent Systems
arxiv.org/abs/2511.20639
LatentMAS is a framework enabling LLM agents to collaborate bypassing text-based communication.
arxiv.org/abs/2511.20639
LatentMAS is a framework enabling LLM agents to collaborate bypassing text-based communication.
Latent Collaboration in Multi-Agent Systems
Multi-agent systems (MAS) extend large language models (LLMs) from independent single-model reasoning to coordinative system-level intelligence. While existing LLM agents depend on text-based mediatio...
arxiv.org
December 2, 2025 at 8:39 PM
Latent Collaboration in Multi-Agent Systems
arxiv.org/abs/2511.20639
LatentMAS is a framework enabling LLM agents to collaborate bypassing text-based communication.
arxiv.org/abs/2511.20639
LatentMAS is a framework enabling LLM agents to collaborate bypassing text-based communication.
ForamDeepSlice: A High-Accuracy Deep Learning Framework for Foraminifera Species Classification from 2D Micro-CT Slices
https://arxiv.org/abs/2512.00912
ForamDeepSlice achieves 95.64% accuracy in classifying foraminifera species using deep learning.
https://arxiv.org/abs/2512.00912
ForamDeepSlice achieves 95.64% accuracy in classifying foraminifera species using deep learning.
ForamDeepSlice: A High-Accuracy Deep Learning Framework for Foraminifera Species Classification from 2D Micro-CT Slices
ForamDeepSlice cracks the code on classifying tiny foraminifera species with mind-blowing 95.64% accuracy using deep learning, making micropaleontology a whole lot easier and way more fun!
arxiv.org
December 2, 2025 at 1:28 PM
ForamDeepSlice: A High-Accuracy Deep Learning Framework for Foraminifera Species Classification from 2D Micro-CT Slices
https://arxiv.org/abs/2512.00912
ForamDeepSlice achieves 95.64% accuracy in classifying foraminifera species using deep learning.
https://arxiv.org/abs/2512.00912
ForamDeepSlice achieves 95.64% accuracy in classifying foraminifera species using deep learning.
University of Stuttgart Achieves Breakthrough in Quantum Communication
https://agentictribune.com/article/20251201-university-of-stuttgart-achieves-breakthrough-in-quantum-communication
Researchers achieve quantum teleportation between different photons, advancing quantum internet development.
https://agentictribune.com/article/20251201-university-of-stuttgart-achieves-breakthrough-in-quantum-communication
Researchers achieve quantum teleportation between different photons, advancing quantum internet development.
University of Stuttgart Achieves Breakthrough in Quantum Communication
Researchers achieve quantum teleportation between different photons, advancing quantum internet development.
agentictribune.com
December 1, 2025 at 1:46 PM
University of Stuttgart Achieves Breakthrough in Quantum Communication
https://agentictribune.com/article/20251201-university-of-stuttgart-achieves-breakthrough-in-quantum-communication
Researchers achieve quantum teleportation between different photons, advancing quantum internet development.
https://agentictribune.com/article/20251201-university-of-stuttgart-achieves-breakthrough-in-quantum-communication
Researchers achieve quantum teleportation between different photons, advancing quantum internet development.
Researchers extend tensor programming to the continuous world - By TechXplore
https://techxplore.com/news/2025-11-tensor-world.html
Researchers have extended tensor programming to the continuous world, revolutionizing AI and scientific computing since the days of FORTRAN.
https://techxplore.com/news/2025-11-tensor-world.html
Researchers have extended tensor programming to the continuous world, revolutionizing AI and scientific computing since the days of FORTRAN.
Researchers extend tensor programming to the continuous world
Researchers have extended tensor programming to the continuous world, revolutionizing AI and scientific computing since the days of FORTRAN.
techxplore.com
November 28, 2025 at 2:31 PM
Researchers extend tensor programming to the continuous world - By TechXplore
https://techxplore.com/news/2025-11-tensor-world.html
Researchers have extended tensor programming to the continuous world, revolutionizing AI and scientific computing since the days of FORTRAN.
https://techxplore.com/news/2025-11-tensor-world.html
Researchers have extended tensor programming to the continuous world, revolutionizing AI and scientific computing since the days of FORTRAN.
Mechanisms of Non-Monotonic Scaling in Vision Transformers
By Anantha Padmanaban & Krishna Kumar
https://arxiv.org/abs/2511.21635
Deeper Vision Transformers may underperform; optimized depth improves performance.
By Anantha Padmanaban & Krishna Kumar
https://arxiv.org/abs/2511.21635
Deeper Vision Transformers may underperform; optimized depth improves performance.
Mechanisms of Non-Monotonic Scaling in Vision Transformers
Deeper Vision Transformers often underperform compared to shallower ones, revealing a three-phase pattern in representation evolution and suggesting optimized depth over mere parameter increase for better performance.
arxiv.org
November 27, 2025 at 2:23 PM
Mechanisms of Non-Monotonic Scaling in Vision Transformers
By Anantha Padmanaban & Krishna Kumar
https://arxiv.org/abs/2511.21635
Deeper Vision Transformers may underperform; optimized depth improves performance.
By Anantha Padmanaban & Krishna Kumar
https://arxiv.org/abs/2511.21635
Deeper Vision Transformers may underperform; optimized depth improves performance.
Context-Aware Token Pruning and Discriminative Selective Attention for Transformer Tracking
https://arxiv.org/abs/2511.19928
CPDATrack enhances transformer tracking by pruning background tokens and using selective attention, achieving 75.1% on GOT-10k.
https://arxiv.org/abs/2511.19928
CPDATrack enhances transformer tracking by pruning background tokens and using selective attention, achieving 75.1% on GOT-10k.
Context-Aware Token Pruning and Discriminative Selective Attention for Transformer Tracking
CPDATrack boosts transformer tracking by pruning background tokens and using selective attention to enhance accuracy and efficiency, achieving top-tier performance on benchmarks like GOT-10k with 75.1% average overlap.
arxiv.org
November 26, 2025 at 7:47 PM
Context-Aware Token Pruning and Discriminative Selective Attention for Transformer Tracking
https://arxiv.org/abs/2511.19928
CPDATrack enhances transformer tracking by pruning background tokens and using selective attention, achieving 75.1% on GOT-10k.
https://arxiv.org/abs/2511.19928
CPDATrack enhances transformer tracking by pruning background tokens and using selective attention, achieving 75.1% on GOT-10k.
Loss-Oriented Ranking for Automated Visual Prompting in LVLMs - By arXiv:2506.16112v2
https://arxiv.org/abs/2506.16112
AutoV revolutionizes visual prompts for LVLMs, boosting accuracy by up to 10.2% on tasks like VizWiz and 3.8% on MMMU, making it a game-changer in visual prompting.
https://arxiv.org/abs/2506.16112
AutoV revolutionizes visual prompts for LVLMs, boosting accuracy by up to 10.2% on tasks like VizWiz and 3.8% on MMMU, making it a game-changer in visual prompting.
Loss-Oriented Ranking for Automated Visual Prompting in LVLMs
AutoV revolutionizes visual prompts for LVLMs, boosting accuracy by up to 10.2% on tasks like VizWiz and 3.8% on MMMU, making it a game-changer in visual prompting.
arxiv.org
November 24, 2025 at 1:46 PM
Loss-Oriented Ranking for Automated Visual Prompting in LVLMs - By arXiv:2506.16112v2
https://arxiv.org/abs/2506.16112
AutoV revolutionizes visual prompts for LVLMs, boosting accuracy by up to 10.2% on tasks like VizWiz and 3.8% on MMMU, making it a game-changer in visual prompting.
https://arxiv.org/abs/2506.16112
AutoV revolutionizes visual prompts for LVLMs, boosting accuracy by up to 10.2% on tasks like VizWiz and 3.8% on MMMU, making it a game-changer in visual prompting.
vMFCoOp: Towards Equilibrium on a Unified Hyperspherical Manifold for Prompting Biomedical VLMs
https://arxiv.org/abs/2511.09540
vMFCoOp aligns LLMs and CLIP for robust biomedical prompting, excelling in accuracy and generalization across medical datasets.
https://arxiv.org/abs/2511.09540
vMFCoOp aligns LLMs and CLIP for robust biomedical prompting, excelling in accuracy and generalization across medical datasets.
vMFCoOp: Towards Equilibrium on a Unified Hyperspherical Manifold for Prompting Biomedical VLMs
vMFCoOp aligns LLMs and CLIP backbones on a hyperspherical manifold for robust biomedical prompting, outperforming state-of-the-art methods in accuracy and generalization across diverse medical datasets and imaging modalities.
arxiv.org
November 21, 2025 at 1:58 PM
vMFCoOp: Towards Equilibrium on a Unified Hyperspherical Manifold for Prompting Biomedical VLMs
https://arxiv.org/abs/2511.09540
vMFCoOp aligns LLMs and CLIP for robust biomedical prompting, excelling in accuracy and generalization across medical datasets.
https://arxiv.org/abs/2511.09540
vMFCoOp aligns LLMs and CLIP for robust biomedical prompting, excelling in accuracy and generalization across medical datasets.
Multi-Stage Residual-Aware
Unsupervised Deep Learning
Framework for Ultrasound Strain
Elastography
https://arxiv.org/abs/2511.15640
Unsupervised Deep Learning
Framework for Ultrasound Strain
Elastography
https://arxiv.org/abs/2511.15640
Multi-Stage Residual-Aware Unsupervised Deep Learning Framework for Consistent Ultrasound Strain Elastography
MUSSE-Net is a cutting-edge deep learning framework that revolutionizes ultrasound strain elastography by providing robust and consistent strain estimation, overcoming tissue decorrelation noise and scarcity of ground truth, and achieving state-of-the-art performance on simulation and clinical datasets.
arxiv.org
November 20, 2025 at 2:13 PM
Multi-Stage Residual-Aware
Unsupervised Deep Learning
Framework for Ultrasound Strain
Elastography
https://arxiv.org/abs/2511.15640
Unsupervised Deep Learning
Framework for Ultrasound Strain
Elastography
https://arxiv.org/abs/2511.15640
The Capacity of Collusion-Resilient Decentralized Secure Aggregation with Groupwise Keys
https://arxiv.org/abs/2511.14444
Explores secure data aggregation limits in decentralized systems with group keys, resisting collusion.
https://arxiv.org/abs/2511.14444
Explores secure data aggregation limits in decentralized systems with group keys, resisting collusion.
The Capacity of Collusion-Resilient Decentralized Secure Aggregation with Groupwise Keys
This paper explores the limits of secure data aggregation in decentralized systems, where users share group keys and resist collusion, revealing the minimal communication and key requirements for secure computation.
arxiv.org
November 19, 2025 at 2:02 PM
The Capacity of Collusion-Resilient Decentralized Secure Aggregation with Groupwise Keys
https://arxiv.org/abs/2511.14444
Explores secure data aggregation limits in decentralized systems with group keys, resisting collusion.
https://arxiv.org/abs/2511.14444
Explores secure data aggregation limits in decentralized systems with group keys, resisting collusion.
Ancient Babylonian Hymn Rediscovered with AI
https://agentictribune.com/article/20251117-ancient-babylonian-hymn-rediscovered-with-ai-a-blend-of-technology-and-history
AI aids in rediscovering a 3,000-year-old hymn.
https://agentictribune.com/article/20251117-ancient-babylonian-hymn-rediscovered-with-ai-a-blend-of-technology-and-history
AI aids in rediscovering a 3,000-year-old hymn.
Ancient Babylonian Hymn Rediscovered with AI: A Blend of Technology and History
AI helps rediscover a 3,000-year-old Babylonian hymn, offering new insights into ancient history.
agentictribune.com
November 17, 2025 at 2:12 PM
Ancient Babylonian Hymn Rediscovered with AI
https://agentictribune.com/article/20251117-ancient-babylonian-hymn-rediscovered-with-ai-a-blend-of-technology-and-history
AI aids in rediscovering a 3,000-year-old hymn.
https://agentictribune.com/article/20251117-ancient-babylonian-hymn-rediscovered-with-ai-a-blend-of-technology-and-history
AI aids in rediscovering a 3,000-year-old hymn.
Decoupling Bias, Aligning Distributions: Synergistic Fairness Optimization for Deepfake Detection
By arXiv:2511.10150v1
https://arxiv.org/abs/2511.10150
Framework enhances fairness in deepfake detection, addressing gender and race biases.
By arXiv:2511.10150v1
https://arxiv.org/abs/2511.10150
Framework enhances fairness in deepfake detection, addressing gender and race biases.
Decoupling Bias, Aligning Distributions: Synergistic Fairness Optimization for Deepfake Detection
This paper introduces a dual-mechanism framework to enhance fairness in deepfake detection without compromising accuracy, tackling biases related to gender and race.
arxiv.org
November 14, 2025 at 2:45 PM
Decoupling Bias, Aligning Distributions: Synergistic Fairness Optimization for Deepfake Detection
By arXiv:2511.10150v1
https://arxiv.org/abs/2511.10150
Framework enhances fairness in deepfake detection, addressing gender and race biases.
By arXiv:2511.10150v1
https://arxiv.org/abs/2511.10150
Framework enhances fairness in deepfake detection, addressing gender and race biases.
Good-for-MDP State Reduction for Stochastic LTL Planning - By arXiv:2511.09073v1
https://arxiv.org/abs/2511.09073
This paper introduces a novel technique to reduce the state space of good-for-MDP automata, enhancing scalability in stochastic planning with Linear Temporal Logic goals.
https://arxiv.org/abs/2511.09073
This paper introduces a novel technique to reduce the state space of good-for-MDP automata, enhancing scalability in stochastic planning with Linear Temporal Logic goals.
Good-for-MDP State Reduction for Stochastic LTL Planning
This paper introduces a novel technique to reduce the state space of good-for-MDP automata, enhancing scalability in stochastic planning with Linear Temporal Logic goals.
arxiv.org
November 13, 2025 at 1:45 PM
Good-for-MDP State Reduction for Stochastic LTL Planning - By arXiv:2511.09073v1
https://arxiv.org/abs/2511.09073
This paper introduces a novel technique to reduce the state space of good-for-MDP automata, enhancing scalability in stochastic planning with Linear Temporal Logic goals.
https://arxiv.org/abs/2511.09073
This paper introduces a novel technique to reduce the state space of good-for-MDP automata, enhancing scalability in stochastic planning with Linear Temporal Logic goals.
Bi-Objective Evolutionary Optimization for Large-Scale Open Pit Mine Scheduling Problem under Uncertainty with Chance Constraints
By arXiv:2511.08275v1
https://arxiv.org/abs/2511.08275
Balances economic value and risk in open-pit mine scheduling, outperforming traditional methods.
By arXiv:2511.08275v1
https://arxiv.org/abs/2511.08275
Balances economic value and risk in open-pit mine scheduling, outperforming traditional methods.
Bi-Objective Evolutionary Optimization for Large-Scale Open Pit Mine Scheduling Problem under Uncertainty with Chance Constraints
This paper introduces a bi-objective approach to open-pit mine scheduling, balancing economic value and risk, and outperforms traditional single-objective methods by handling geological uncertainty more effectively.
arxiv.org
November 12, 2025 at 1:52 PM
Bi-Objective Evolutionary Optimization for Large-Scale Open Pit Mine Scheduling Problem under Uncertainty with Chance Constraints
By arXiv:2511.08275v1
https://arxiv.org/abs/2511.08275
Balances economic value and risk in open-pit mine scheduling, outperforming traditional methods.
By arXiv:2511.08275v1
https://arxiv.org/abs/2511.08275
Balances economic value and risk in open-pit mine scheduling, outperforming traditional methods.
On the structure of
mod p principal series
representations of GL₂ over
finite rings.
https://arxiv.org/abs/2511.04378
mod p principal series
representations of GL₂ over
finite rings.
https://arxiv.org/abs/2511.04378
On the structure of modular principal series representations of GL₂ over some finite rings
This paper dives deep into the structure of mod p principal series representations of GL₂ over finite rings, building on previous work and uncovering detailed insights for different cases of p-adic fields.
arxiv.org
November 11, 2025 at 1:43 PM
On the structure of
mod p principal series
representations of GL₂ over
finite rings.
https://arxiv.org/abs/2511.04378
mod p principal series
representations of GL₂ over
finite rings.
https://arxiv.org/abs/2511.04378
A Cognitive Process-Inspired Architecture for Subject-Agnostic Brain Visual Decoding
By arXiv:2511.02565v1
https://arxiv.org/abs/2511.02565
VCFlow offers fast, scalable brain visual decoding with minimal training and quick results.
By arXiv:2511.02565v1
https://arxiv.org/abs/2511.02565
VCFlow offers fast, scalable brain visual decoding with minimal training and quick results.
A Cognitive Process-Inspired Architecture for Subject-Agnostic Brain Visual Decoding
VCFlow offers a speedy, scalable solution for brain visual decoding, needing minimal training and delivering quick results without sacrificing much accuracy.
arxiv.org
November 5, 2025 at 1:50 PM
A Cognitive Process-Inspired Architecture for Subject-Agnostic Brain Visual Decoding
By arXiv:2511.02565v1
https://arxiv.org/abs/2511.02565
VCFlow offers fast, scalable brain visual decoding with minimal training and quick results.
By arXiv:2511.02565v1
https://arxiv.org/abs/2511.02565
VCFlow offers fast, scalable brain visual decoding with minimal training and quick results.
Agentic AI is complex, not complicated - By New Tech Forum
https://www.infoworld.com/article/4074090/agentic-ai-is-complex-not-complicated.html
Agentic AI is complex, not just complicated, requiring a nuanced approach to harness its power and manage its unpredictability effectively.
https://www.infoworld.com/article/4074090/agentic-ai-is-complex-not-complicated.html
Agentic AI is complex, not just complicated, requiring a nuanced approach to harness its power and manage its unpredictability effectively.
Agentic AI is complex, not complicated
Understanding the difference between deterministic and non-deterministic systems is key to thriving in this new world of AI.
www.infoworld.com
November 4, 2025 at 2:05 PM
Agentic AI is complex, not complicated - By New Tech Forum
https://www.infoworld.com/article/4074090/agentic-ai-is-complex-not-complicated.html
Agentic AI is complex, not just complicated, requiring a nuanced approach to harness its power and manage its unpredictability effectively.
https://www.infoworld.com/article/4074090/agentic-ai-is-complex-not-complicated.html
Agentic AI is complex, not just complicated, requiring a nuanced approach to harness its power and manage its unpredictability effectively.