641
AI is reshaping banking—but not causing a jobs wipeout
Banking and finance headcounts remain largely stable, according to a Fortune report.
Kathleen P
552
VisionDirector: Vision-Language Guided Closed-Loop Refinement for Generative Image Synthesis
arXiv:2512.19243v1 Announce Type: new
Abstract: Generative models can now produce photorealistic imagery, yet they still struggle with the long, multi-goal prompts that professional designers issue. To expose this gap and better evaluate models' performance in real-world settings, we introduce Long…
Abstract: Generative models can now produce photorealistic imagery, yet they still struggle with the long, multi-goal prompts that professional designers issue. To expose this gap and better evaluate models' performance in real-world settings, we introduce Long…
Lilgski
420
Socratic Students: Teaching Language Models to Learn by Asking Questions
arXiv:2512.13102v2 Announce Type: replace
Abstract: Large Language Models (LLMs) excel at static interactions, where they answer user queries by retrieving knowledge encoded in their parameters. However, in many real-world settings, such as educational tutoring or medical assistance, relevant infor…
Abstract: Large Language Models (LLMs) excel at static interactions, where they answer user queries by retrieving knowledge encoded in their parameters. However, in many real-world settings, such as educational tutoring or medical assistance, relevant infor…
Meg
431
Fraud Detection Through Large-Scale Graph Clustering with Heterogeneous Link Transformation
arXiv:2512.19061v1 Announce Type: new
Abstract: Collaborative fraud, where multiple fraudulent accounts coordinate to exploit online payment systems, poses significant challenges due to the formation of complex network structures. Traditional detection methods that rely solely on high-confidence id…
Abstract: Collaborative fraud, where multiple fraudulent accounts coordinate to exploit online payment systems, poses significant challenges due to the formation of complex network structures. Traditional detection methods that rely solely on high-confidence id…
Barrettfosterca
342
TRACE: Your Diffusion Model is Secretly an Instance Edge Detector
arXiv:2503.07982v3 Announce Type: replace
Abstract: High-quality instance and panoptic segmentation has traditionally relied on dense instance-level annotations such as masks, boxes, or points, which are costly, inconsistent, and difficult to scale. Unsupervised and weakly-supervised approaches red…
Abstract: High-quality instance and panoptic segmentation has traditionally relied on dense instance-level annotations such as masks, boxes, or points, which are costly, inconsistent, and difficult to scale. Unsupervised and weakly-supervised approaches red…
Bnard1911
343
Transforming Data Management In EDA: Preparing For The AI Era
Ensuring data moves smoothly across multiple disciplines, tools, and globally distributed teams.
The post Transforming Data Management In EDA: Preparing For The AI Era appeared first on Semiconductor Engineering.
The post Transforming Data Management In EDA: Preparing For The AI Era appeared first on Semiconductor Engineering.
Chrisdang126
322
Trajectory Planning for UAV-Based Smart Farming Using Imitation-Based Triple Deep Q-Learning
arXiv:2512.18604v1 Announce Type: new
Abstract: Unmanned aerial vehicles (UAVs) have emerged as a promising auxiliary platform for smart agriculture, capable of simultaneously performing weed detection, recognition, and data collection from wireless sensors. However, trajectory planning for UAV-bas…
Abstract: Unmanned aerial vehicles (UAVs) have emerged as a promising auxiliary platform for smart agriculture, capable of simultaneously performing weed detection, recognition, and data collection from wireless sensors. However, trajectory planning for UAV-bas…
Red Dog
332
Low-Rank Expert Merging for Multi-Source Domain Adaptation in Person Re-Identification
arXiv:2508.06831v2 Announce Type: replace
Abstract: Adapting person re-identification (reID) models to new target environments remains a challenging problem that is typically addressed using unsupervised domain adaptation (UDA) methods. Recent works show that when labeled data originates from sever…
Abstract: Adapting person re-identification (reID) models to new target environments remains a challenging problem that is typically addressed using unsupervised domain adaptation (UDA) methods. Recent works show that when labeled data originates from sever…
Dkeleti
333
Self-Consistent Probability Flow for High-Dimensional Fokker-Planck Equations
arXiv:2512.19196v1 Announce Type: cross
Abstract: Solving high-dimensional Fokker-Planck (FP) equations is a challenge in computational physics and stochastic dynamics, due to the curse of dimensionality (CoD) and the bottleneck of evaluating second-order diffusion terms. Existing deep learning app…
Abstract: Solving high-dimensional Fokker-Planck (FP) equations is a challenge in computational physics and stochastic dynamics, due to the curse of dimensionality (CoD) and the bottleneck of evaluating second-order diffusion terms. Existing deep learning app…
Alessandro C
332
LLVM Considering An AI Tool Policy, AI Bot For Fixing Build System Breakage Proposed
Last week a request for comments (RFC) was issued around establishing an LLVM AI Tool Use Policy. The proposed policy would allow AI-assisted contributions to be made to this open-source compiler codebase but that there would need to be a "human in the loop" and the contributor versed enough to be a…
Schuylerrichardson
333
A Convolutional Neural Deferred Shader for Physics Based Rendering
arXiv:2512.19522v1 Announce Type: new
Abstract: Recent advances in neural rendering have achieved impressive results on photorealistic shading and relighting, by using a multilayer perceptron (MLP) as a regression model to learn the rendering equation from a real-world dataset. Such methods show pr…
Abstract: Recent advances in neural rendering have achieved impressive results on photorealistic shading and relighting, by using a multilayer perceptron (MLP) as a regression model to learn the rendering equation from a real-world dataset. Such methods show pr…
Unknown
319
Explainable Graph Spectral Clustering For GloVe-like Text Embeddings
arXiv:2508.14075v2 Announce Type: replace
Abstract: In a previous paper, we proposed an introduction to the explainability of Graph Spectral Clustering results for textual documents, given that document similarity is computed as cosine similarity in term vector space.
In this paper, we generalize…
Abstract: In a previous paper, we proposed an introduction to the explainability of Graph Spectral Clustering results for textual documents, given that document similarity is computed as cosine similarity in term vector space.
In this paper, we generalize…
Rbohn
320
FPBench: A Comprehensive Benchmark of Multimodal Large Language Models for Fingerprint Analysis
arXiv:2512.18073v1 Announce Type: new
Abstract: Multimodal LLMs (MLLMs) have gained significant traction in complex data analysis, visual question answering, generation, and reasoning. Recently, they have been used for analyzing the biometric utility of iris and face images. However, their capabili…
Abstract: Multimodal LLMs (MLLMs) have gained significant traction in complex data analysis, visual question answering, generation, and reasoning. Recently, they have been used for analyzing the biometric utility of iris and face images. However, their capabili…
Emmaelliott2888
321
No Pose at All: Self-Supervised Pose-Free 3D Gaussian Splatting from Sparse Views
arXiv:2508.01171v2 Announce Type: replace
Abstract: We introduce SPFSplat, an efficient framework for 3D Gaussian splatting from sparse multi-view images, requiring no ground-truth poses during training or inference. It employs a shared feature extraction backbone, enabling simultaneous prediction …
Abstract: We introduce SPFSplat, an efficient framework for 3D Gaussian splatting from sparse multi-view images, requiring no ground-truth poses during training or inference. It employs a shared feature extraction backbone, enabling simultaneous prediction …
Anholla
324
Overcoming Spectral Bias via Cross-Attention
arXiv:2512.18586v1 Announce Type: new
Abstract: Spectral bias implies an imbalance in training dynamics, whereby high-frequency components may converge substantially more slowly than low-frequency ones. To alleviate this issue, we propose a cross-attention-based architecture that adaptively reweigh…
Abstract: Spectral bias implies an imbalance in training dynamics, whereby high-frequency components may converge substantially more slowly than low-frequency ones. To alleviate this issue, we propose a cross-attention-based architecture that adaptively reweigh…
Mistymook
321
Shape it Up! Restoring LLM Safety during Finetuning
arXiv:2505.17196v3 Announce Type: replace
Abstract: Finetuning large language models (LLMs) enables user-specific customization but introduces critical safety risks: even a few harmful examples can compromise safety alignment. A common mitigation strategy is to update the model more strongly on exa…
Abstract: Finetuning large language models (LLMs) enables user-specific customization but introduces critical safety risks: even a few harmful examples can compromise safety alignment. A common mitigation strategy is to update the model more strongly on exa…
Schuylerrichardson
222
Sharpness-Controlled Group Relative Policy Optimization with Token-Level Probability Shaping
arXiv:2511.00066v2 Announce Type: replace
Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a practical route to improve large language model reasoning, and Group Relative Policy Optimization (GRPO) is a widely used optimizer in this setting. This paper revisits GRPO from a…
Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a practical route to improve large language model reasoning, and Group Relative Policy Optimization (GRPO) is a widely used optimizer in this setting. This paper revisits GRPO from a…
Rebackashley
219
VERDI: VLM-Embedded Reasoning for Autonomous Driving
arXiv:2505.15925v3 Announce Type: replace
Abstract: While autonomous driving (AD) stacks struggle with decision making under partial observability and real-world complexity, human drivers are capable of commonsense reasoning to make near-optimal decisions with limited information. Recent work has a…
Abstract: While autonomous driving (AD) stacks struggle with decision making under partial observability and real-world complexity, human drivers are capable of commonsense reasoning to make near-optimal decisions with limited information. Recent work has a…
Eminican
222
Exploring Zero-Shot ACSA with Unified Meaning Representation in Chain-of-Thought Prompting
arXiv:2512.19651v1 Announce Type: new
Abstract: Aspect-Category Sentiment Analysis (ACSA) provides granular insights by identifying specific themes within reviews and their associated sentiment. While supervised learning approaches dominate this field, the scarcity and high cost of annotated data f…
Abstract: Aspect-Category Sentiment Analysis (ACSA) provides granular insights by identifying specific themes within reviews and their associated sentiment. While supervised learning approaches dominate this field, the scarcity and high cost of annotated data f…
Adam T