Optimal Transport-based Permutation-Invariant Bayesian Optimization of Offshore Wind Farm Layouts
arXiv:2606.00009v1 Announce Type: new
Abstract: Bayesian Optimization (BO) is widely and successfully adopted for solving optimization problems having an expensive-to-evaluate, black-box, and non-convex objective function. However, the vanilla BO algorithm is not able to exploit possible symmetries...
When LLMs Learn to Be Consistently Wrong: A Multi-Model Study of Linear Representations of Synthetic Deception
arXiv:2605.30381v1 Announce Type: new
Abstract: Deceptive alignment, in which models maintain accurate internal representations while deliberately producing false outputs, remains a central challenge in AI safety. While strategic deception is the primary long-term concern, synthetic dishonesty - in...
LLMs Without Deep Neural Networks: New Architecture, Benefits and Case Study
arXiv:2605.30385v1 Announce Type: new
Abstract: The purpose of this article is to provide validation to my deep neural network alternative in the context of LLMs. Very recently, there has been a significant interest by Chinese researchers in a model called RBF network, as a substitute to standard D...
Transforming and Encoding FTS for SAT Solving: What Helps, What Hurts (Extended Version)
arXiv:2605.30563v1 Announce Type: new
Abstract: Factored tasks are a classical planning representation that extends SAS+ with limited forms of disjunctive preconditions, conditional effects, and angelic nondeterminism. This allows for a more compact representation of tasks than traditional formalis...
Procedural Generation of First Person Shooter Maps using Map-Elites
arXiv:2605.30570v1 Announce Type: new
Abstract: We investigate the application of MAP-Elites (a well-known quality diversity algorithm) to design levels for First-Person Shooter (FPS) games. We consider two well-known map representations (All-Black and Grid-Graph) and introduce two novel representa...
One Mask to Rule Them All: On Hidden Facts after Editing and How to Find Them
arXiv:2605.28839v1 Announce Type: new
Abstract: Knowledge editing methods such as ROME and MEMIT update factual associations in transformer models by modifying MLP weights. While evaluated mainly by output behavior, their internal mechanism remains underexplored. We investigate whether edits rely o...
Mechanistic origins of catastrophic forgetting: why RL preserves circuits better than SFT?
arXiv:2605.28860v1 Announce Type: new
Abstract: Fine-tuning large language models (LLMs) frequently induces catastrophic forgetting of prior capabilities. Recent work has shown that reinforcement learning (RL) retains prior capabilities more effectively than supervised fine-tuning (SFT), attributin...
Behavior-Induced Mirror-Prox Temporal-Difference Learning for Faster Off-Policy Prediction
arXiv:2605.28849v1 Announce Type: new
Abstract: Gradient temporal-difference methods provide stable off-policy prediction with linear function approximation, but their practical performance is strongly affected by the geometry induced by the auxiliary-variable metric. Existing Mirror-Prox TD method...
Behavior-Aware Auxiliary Corrections for Off-Policy Temporal-Difference Prediction
arXiv:2605.28855v1 Announce Type: new
Abstract: Temporal-difference learning with function approximation can be unstable under off-policy sampling. TDC stabilizes off-policy TD through an auxiliary covariance correction, and TDRC further regularizes this correction in a single-timescale recursion. ...
Review Arcade: On the Human Alignment and Gameability of LLM Reviews
arXiv:2605.28897v1 Announce Type: new
Abstract: LLM-generated reviews for scientific papers are gaining considerable traction and are even being officially piloted by major conferences. We have to assume that not only reviewers are using LLM-assistance, but also that authors use LLMs to revise thei...
On the Origin of Synthetic Information by Means of Steganographic Inheritance
arXiv:2605.27551v1 Announce Type: new
Abstract: The origin of species has been the mystery of mysteries in natural science. By analogy, the origin of synthetic information, we suggest, is the mystery of mysteries in information science. The question carries a moral weight that a technical account c...
Why LLMs Fail at Causal Discovery and How Interventional Agents Escape
arXiv:2605.27567v1 Announce Type: new
Abstract: Causal discovery is a cornerstone of scientific reasoning, yet whether large language models can perform it reliably remains an open question. Recent benchmarks show that even fine-tuned models plateau on simple causal graphs and degrade as complexity...
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026
Apple is presenting new research at the annual IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), which takes place in person in Denver at the Colorado Convention Center from June 3 to June 7.
We are proud to sponsor the conference, which brings together the scientific and indust...
SilIF: Silhouette-Augmented Isolation Forest for Unsupervised Transaction Fraud Detection
arXiv:2605.26135v1 Announce Type: new
Abstract: Unsupervised anomaly detection is widely used in transaction fraud detection where labels are scarce. Isolation Forest (IF) is among the most popular classical methods due to its scalability and ease of deployment. We propose SilIF, an augmentation of...
AI agents keep breaking in production. Here's why nobody's fixed it yet.
The gap between what agentic AI promises and what it actually delivers in live environments is now one of the most consequential engineering problems in the industry. It is also, frustratingly, one that the field has been slow to name precisely, let alone fix...
CAFD: Concept-Aware DNN Fault Detection using VLMs
arXiv:2605.24008v1 Announce Type: new
Abstract: Fault detection for Deep Neural Networks (DNNs) has received increasing attention in recent years. While more advanced hybrid approaches have been proposed to combine multiple sources of information and outperform earlier techniques, they often incur ...
FusionSense: Tri-Stage Near-Sensor Learning for Runtime-Adaptive Multimodal Edge Intelligence
arXiv:2605.22868v1 Announce Type: new
Abstract: Autonomous systems and smart-industry deployments increasingly split computation across near-sensor, edge, and cloud resources, where tight energy, latency, and reliability budgets demand run-time adaptivity. In practice, deciding what to compute and ...
TO-Agents: A Multi-Agent AI Pipeline for Preference-Guided Topology Optimization
arXiv:2605.21622v1 Announce Type: new
Abstract: Topology optimization can generate efficient structures, but designers often must manually translate qualitative intent, such as desired visual style, product experience, or manufacturability into solver settings that are not directly tied to those pr...
The 3 reasons your AI never makes it to production
Most companies don't have an AI problem. They have a throughput problem. And I think that distinction matters a lot when you start talking about how to actually get AI working in production.
Don't Collapse Your Features: Why CenterLoss Hurts OOD Detection and Multi-Scale Mahalanobis Wins
arXiv:2605.21493v1 Announce Type: new
Abstract: The ability to detect out-of-distribution (OOD) inputs is fundamental to safe deployment of machine learning systems. Yet, current methods often rely on feature representations that are optimised solely for classification accuracy, neglecting the dist...
Double descent for least-squares interpolation on contaminated data: A simulation study
arXiv:2605.21494v1 Announce Type: new
Abstract: Overparametrized models can exhibit an excellent generalization performance, although they should be prone to overfitting according to classical statistical theory. The discovery of the "double descent", indicating that the generalization error decrea...
VSAS-Bench: Real-Time Evaluation of Visual Streaming Assistant Models
Streaming vision-language models (VLMs) continuously generate responses given an instruction prompt and an online stream of input frames. This is a core mechanism for real-time visual assistants. Existing VLM frameworks predominantly assess models in offline settings. In contrast, the performance of...