arXiv AI
Jun 8
Position: Don't Just "Fix it in Post": A Science of AI Must Study Training Dynamics
arXiv:2606. 06533v1 Announce Type: new Abstract: What would it mean to have a scientific understanding of AI?
AI safety and alignment
Alignment, interpretability, red-teaming, bias and privacy: the research on what these systems do when they misbehave.
arXiv AI
Jun 8
PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams
arXiv:2606. 07454v1 Announce Type: cross Abstract: Scientific paper recommendation is typically evaluated as static ranking over a fixed candidate set, yet real scientific reading unfolds as a daily, longitudinal process in which interests shift and feedback accumulates.
arXiv AI
Jun 8
CARVE-Q: Quantum-Proposed, Classically Certified Interactive Driving Repair
arXiv:2606. 06531v1 Announce Type: new Abstract: The critical question after a correct driving veto is not only whether a maneuver is unsafe, but whether the blocked interaction admits a lawful, auditable, and responsibility-bounded repair.
arXiv AI
Jun 8
How reliable are LLMs when it comes to playing dice?
arXiv:2606. 07515v1 Announce Type: cross Abstract: We investigate the probabilistic reasoning capabilities of large language models through a controlled benchmarking study on discrete probability problems.
arXiv AI
Jun 8
Re-imagining ISO 26262 in the Age of Autonomous Vehicles: Enhancing Controllability through Transferability and Predictability
arXiv:2606. 07437v1 Announce Type: cross Abstract: The ISO 26262 standard defines functional safety for road vehicles through risk assessments based on Severity, Exposure, and Controllability, grounded in a human-driven vehicle paradigm.
arXiv Machine Learning
Jun 8
SecretFan: Synthesizing Realistic Data without Breaking Privacy
arXiv:2602. 05833v2 Announce Type: replace Abstract: There is a need for synthetic training and test datasets that replicate statistical distributions of original datasets without compromising their confidentiality.
arXiv AI
Jun 8
Scalable GANs with Transformers
arXiv:2509. 24935v3 Announce Type: replace-cross Abstract: Scalability has driven recent advances in generative modeling, yet its principles remain underexplored for adversarial learning.
arXiv AI
Jun 8
SafeGene: Reusable Adapters for Transferable Safety Alignment
arXiv:2606. 06519v1 Announce Type: new Abstract: Open-weight LLMs are increasingly fine-tuned into customized assistants, but downstream fine-tuning can weaken safety alignment and make models more vulnerable to malicious prompts, even when the training data is not intentionally harmful.
arXiv AI
Jun 8
Generative Models Erode Human Temporal Learning Through Market Selection
arXiv:2606. 06572v1 Announce Type: cross Abstract: We argue that modern generative models create structural risks for knowledge and cultural production at current, sub-AGI capability levels.
arXiv AI
Jun 8
MSAIC-Net: A Multi-Scale Attention and Imbalance-Aware Contrastive Network for ECG-Based Myocardial Substrate Abnormality Detection
arXiv:2606. 06718v1 Announce Type: cross Abstract: Myocardial substrate abnormalities, such as myocardial scar and myocardial infarction (MI), are associated with adverse cardiovascular outcomes.
arXiv AI
Jun 8
Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting
arXiv:2603. 02220v2 Announce Type: replace-cross Abstract: Time series forecasting remains a challenging problem due to the intricate entanglement of intra-period fluctuations and inter-period trends.
arXiv AI
Jun 8
Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach
arXiv:2510. 09041v3 Announce Type: replace-cross Abstract: Deep reinforcement learning (DRL) has demonstrated remarkable success in developing autonomous driving policies.
arXiv AI
Jun 8
Latent-space Attacks for Refusal Evasion in Language Models
arXiv:2605. 21706v2 Announce Type: replace Abstract: Safety-aligned language models are trained to refuse harmful requests, yet refusal behavior can be suppressed by steering their internal representations.
arXiv Machine Learning
Jun 8
Covariance Shrinkage via Stochastic Interpolation
arXiv:2606. 07382v1 Announce Type: new Abstract: We recast classical shrinkage of high-dimensional covariance estimators as empirical risk minimization over a parametric stochastic interpolant between a source and a target distribution.
arXiv Machine Learning
Jun 8
Unmixing ATR-{\mu}FTIR spectroscopic images of cross-sections of historical oil paintings
arXiv:2603. 06673v2 Announce Type: replace-cross Abstract: Spectroscopic imaging (SI) has become central to heritage science because it enables non-invasive, spatially resolved characterisation of materials in artefacts.
arXiv Machine Learning
Jun 8
Predictable Compression Failures: Order Sensitivity and Information Budgeting for Evidence-Grounded Binary Adjudication
arXiv:2509. 11208v3 Announce Type: replace-cross Abstract: Transformers used for evidence-grounded binary adjudication (e.
arXiv AI
Jun 8
A Temporal Spatial Minimax Rate for Smoothly-Varying Distributions in Wasserstein Space
arXiv:2606. 07325v1 Announce Type: cross Abstract: We study the minimax rate of estimating a future value $\mu_{t_n+h}$ of a curve $t\mapsto\mu_t$ in the $2$-Wasserstein space $\mathcal{P}_2(\mathbb{R}^d)$ from finitely many noisy snapshots of its past, under an adiabatic bound $\|\nabla_t^k v\|\le\varepsilon$ on the $k$-th covariant derivative of the velocity field.
arXiv AI
Jun 8
Socratic-SWE: Self-Evolving Coding Agents via Trace-Derived Agent Skills
arXiv:2606. 07412v1 Announce Type: cross Abstract: LLM-driven software engineering agents have become a central testbed for real-world language-model capability, yet their training remains limited by the availability of high-quality SWE tasks.
arXiv AI
Jun 8
Workflow-to-Skill: Skill Creation via Routing-Workflow-Semantics-Attachments Decomposition
arXiv:2606. 06893v1 Announce Type: new Abstract: Large language model agents increasingly rely on Skills to encode procedural knowledge, yet high-quality Skills remain costly to hand-write.
arXiv AI
Jun 8
Evidence-Based Intelligent Diagnostic and Therapeutic Visualization System with Large Language Models: Multi-Turn Interaction and Multimodal Treatment Plan Generation
arXiv:2606. 06869v1 Announce Type: new Abstract: Aim: Existing AI-assisted traditional Chinese medicine diagnostic tools suffer from opaque reasoning processes, passive interaction, and limited treatment plan presentation.