World PulseNow
Newsletter

Bayesian Optimization on Networks

arXiv — cs.LG•Monday, November 3, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
A recent paper on arXiv explores Bayesian optimization on networks modeled as metric graphs, addressing the challenges of optimizing expensive-to-evaluate functions. By developing algorithms that update a Gaussian process surrogate model, the authors aim to enhance the efficiency of acquiring query points. This research is significant as it provides a framework that can be applied to various fields where optimization is crucial, potentially leading to advancements in technology and data analysis.
— Curated by the World Pulse Now AI Editorial System
Read Original

Was this article worth reading? Share it

Recommended Readings
An Automated Framework for Strategy Discovery, Retrieval, and Evolution in LLM Jailbreak Attacks
arXiv — cs.LGan hour ago
An Automated Framework for Strategy Discovery, Retrieval, and Evolution in LLM Jailbreak Attacks
PositiveArtificial Intelligence
This article discusses a new automated framework designed to discover, retrieve, and evolve strategies for addressing jailbreak attacks on large language models. It highlights the importance of security in web services and presents a strategy that can bypass existing defenses, shedding light on a critical area of research.
Read full article
via arXiv — cs.LG
Let Multimodal Embedders Learn When to Augment Query via Adaptive Query Augmentation
arXiv — cs.LGan hour ago
Let Multimodal Embedders Learn When to Augment Query via Adaptive Query Augmentation
PositiveArtificial Intelligence
A new study highlights the benefits of query augmentation, which enhances the relevance of search queries by adding useful information. It focuses on Large Language Model-based embedders that improve both representation and generation for better query results. This innovative approach shows promise in making search queries more effective.
Read full article
via arXiv — cs.LG
Verifying LLM Inference to Prevent Model Weight Exfiltration
arXiv — cs.LGan hour ago
Verifying LLM Inference to Prevent Model Weight Exfiltration
PositiveArtificial Intelligence
As AI models gain value, the risk of model weight theft from inference servers increases. This article explores how to verify model responses to prevent such attacks and detect any unusual behavior during inference.
Read full article
via arXiv — cs.LG
ScenicProver: A Framework for Compositional Probabilistic Verification of Learning-Enabled Systems
arXiv — cs.LGan hour ago
ScenicProver: A Framework for Compositional Probabilistic Verification of Learning-Enabled Systems
NeutralArtificial Intelligence
ScenicProver is a new framework designed to tackle the challenges of verifying learning-enabled cyber-physical systems. It addresses the limitations of existing tools by allowing for compositional analysis using various verification techniques, making it easier to work with complex real-world environments.
Read full article
via arXiv — cs.LG
PrivGNN: High-Performance Secure Inference for Cryptographic Graph Neural Networks
arXiv — cs.LGan hour ago
PrivGNN: High-Performance Secure Inference for Cryptographic Graph Neural Networks
PositiveArtificial Intelligence
PrivGNN is a groundbreaking approach that enhances the security of graph neural networks in privacy-sensitive cloud environments. By developing secure inference protocols, it addresses the critical need for protecting sensitive graph-structured data, paving the way for safer and more efficient data analysis.
Read full article
via arXiv — cs.LG
Demo: Statistically Significant Results On Biases and Errors of LLMs Do Not Guarantee Generalizable Results
arXiv — cs.LGan hour ago
Demo: Statistically Significant Results On Biases and Errors of LLMs Do Not Guarantee Generalizable Results
NeutralArtificial Intelligence
Recent research highlights the challenges faced by medical chatbots, particularly regarding biases and errors in their responses. While these systems are designed to provide consistent medical advice, factors like demographic information can impact their performance. This study aims to explore the conditions under which these chatbots may fail, emphasizing the need for improved infrastructure to address these issues.
Read full article
via arXiv — cs.LG
Re-FORC: Adaptive Reward Prediction for Efficient Chain-of-Thought Reasoning
arXiv — cs.LGan hour ago
Re-FORC: Adaptive Reward Prediction for Efficient Chain-of-Thought Reasoning
PositiveArtificial Intelligence
Re-FORC is an innovative adaptive reward prediction method that enhances reasoning models by predicting future rewards based on thinking tokens. It allows for early stopping of ineffective reasoning chains, leading to a 26% reduction in compute while preserving accuracy. This advancement showcases the potential for more efficient AI reasoning.
Read full article
via arXiv — cs.LG
AutoAdv: Automated Adversarial Prompting for Multi-Turn Jailbreaking of Large Language Models
arXiv — cs.LGan hour ago
AutoAdv: Automated Adversarial Prompting for Multi-Turn Jailbreaking of Large Language Models
PositiveArtificial Intelligence
AutoAdv is a groundbreaking framework designed to enhance the security of large language models against jailbreaking attacks. By focusing on multi-turn interactions, it achieves an impressive 95% success rate in eliciting harmful outputs, marking a significant improvement over traditional single-turn evaluations.
Read full article
via arXiv — cs.LG
Latest from Artificial Intelligence
Tool-to-Agent Retrieval: Bridging Tools and Agents for Scalable LLM Multi-Agent Systems
arXiv — cs.CLan hour ago
Tool-to-Agent Retrieval: Bridging Tools and Agents for Scalable LLM Multi-Agent Systems
PositiveArtificial Intelligence
Recent advancements in LLM Multi-Agent Systems are making it easier to manage numerous tools and sub-agents effectively. The introduction of Tool-to-Agent Retrieval aims to enhance agent selection by providing a clearer understanding of tool functionalities, leading to better orchestration and improved performance.
Read full article
via arXiv — cs.CL
Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratch
arXiv — cs.LGan hour ago
Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratch
PositiveArtificial Intelligence
Tool Zero introduces an innovative approach to training language models using pure reinforcement learning from scratch. This method aims to enhance the capabilities of language models for complex tasks, overcoming the limitations of traditional supervised fine-tuning that often struggles with unfamiliar scenarios.
Read full article
via arXiv — cs.LG
Why and When Deep is Better than Shallow: An Implementation-Agnostic State-Transition View of Depth Supremacy
arXiv — stat.MLan hour ago
Why and When Deep is Better than Shallow: An Implementation-Agnostic State-Transition View of Depth Supremacy
NeutralArtificial Intelligence
This article explores the advantages of deep models over shallow ones in a framework that doesn't depend on specific network implementations. It discusses how deep models can be understood as abstract state-transition semigroups and presents a bias-variance decomposition that highlights the role of depth in determining variance.
Read full article
via arXiv — stat.ML
Structural Plasticity as Active Inference: A Biologically-Inspired Architecture for Homeostatic Control
arXiv — cs.LGan hour ago
Structural Plasticity as Active Inference: A Biologically-Inspired Architecture for Homeostatic Control
PositiveArtificial Intelligence
This article presents a groundbreaking model called the Structurally Adaptive Predictive Inference Network (SAPIN), which draws inspiration from biological neural cultures. Unlike traditional neural networks that use global backpropagation, SAPIN employs active inference principles to enhance learning and adaptability, showcasing a promising direction for future computational models.
Read full article
via arXiv — cs.LG
Overcoming Non-stationary Dynamics with Evidential Proximal Policy Optimization
arXiv — cs.LGan hour ago
Overcoming Non-stationary Dynamics with Evidential Proximal Policy Optimization
PositiveArtificial Intelligence
A new approach to deep reinforcement learning tackles the challenges posed by non-stationary environments. By focusing on maintaining the flexibility of the critic network and enhancing exploration strategies, this method aims to improve stability and performance in dynamic settings.
Read full article
via arXiv — cs.LG
VidEmo: Affective-Tree Reasoning for Emotion-Centric Video Foundation Models
arXiv — cs.CVan hour ago
VidEmo: Affective-Tree Reasoning for Emotion-Centric Video Foundation Models
PositiveArtificial Intelligence
VidEmo introduces a new approach to understanding emotions in videos, leveraging advancements in video large language models. This innovative method aims to tackle the complexities of emotional analysis, addressing the dynamic nature of emotions and their dependence on various cues.
Read full article
via arXiv — cs.CV