arXiv AI
Jul 13
Inside the Skill Market: From Software Engineering Activities to Reusable Agent Skills
arXiv:2607. 09065v1 Announce Type: cross Abstract: Software engineering (abbrev.
AI safety and alignment
Alignment, interpretability, red-teaming, bias and privacy: the research on what these systems do when they misbehave.
arXiv Machine Learning
Jul 13
Pitfalls and Remedies for Multi-Task Bayesian Optimization
arXiv:2607. 09073v1 Announce Type: new Abstract: Bayesian optimization routinely warm-starts a target experiment with data from related source tasks, and the multi-task Gaussian process is the textbook surrogate for the job.
arXiv Machine Learning
Jul 13
Quantum-Enhanced Synthetic Data Generation Using Quantum Circuit Born Machines for Imbalanced Tabular Learning
arXiv:2607. 09113v1 Announce Type: cross Abstract: Data scarcity and class imbalance are persistent challenges in machine learning that degrade model generalization and introduce predictive bias.
arXiv Machine Learning
Jul 13
Leveraging Interpretable Tsetlin Machine for PDF Malware Detection
arXiv:2607. 09290v1 Announce Type: cross Abstract: In the digital era, Portable Document Format (PDF) is one of the most widely used file formats for storing and exchanging digital documents due to its platform independence and rich functionality.
arXiv Machine Learning
Jul 13
SYNRARE: Synthetic Rare Disease EHR Generation for ML Benchmarking
arXiv:2607. 09404v1 Announce Type: new Abstract: Motivation: Rare disease (RD) diagnosis is frequently delayed due to the similarities in symptoms to common disease variants.
arXiv AI
Jul 13
When Routes Run Out: Adversarial Co-Learning and Explainable Robustness in Quantum Repeater Networks
arXiv:2607. 09378v1 Announce Type: cross Abstract: We study an adversarial bandit problem for entanglement-based quantum-network routing over a modest graph corpus.
arXiv Machine Learning
Jul 13
Optimizing Against Safety Representations: Activation-Guided Adversarial Suffixes and the Geometry of Refusal
arXiv:2607. 08883v1 Announce Type: new Abstract: Behavioral alignment in large language models often masks fragile internal safety representations.
arXiv Machine Learning
Jul 13
Ruby: Unmasking Unsafe Rust in Stripped Binaries via Machine Learning
arXiv:2211. 00111v3 Announce Type: replace-cross Abstract: Rust, as an emerging system programming language, introduces $\texttt{unsafe}$ to allow developers to bypass safety checks during compilation.
arXiv AI
Jul 13
Multi-Attribute Steering of Language Models via Targeted Intervention
arXiv:2502. 12446v3 Announce Type: replace-cross Abstract: Inference-time intervention (ITI) has emerged as a promising method for steering large language model (LLM) behavior in a particular direction (e.
arXiv AI
Jul 13
Evolution of Accuracy and Visual-Cognitive Errors in a Decade of Vision-Language AI Models
arXiv:2607. 09654v1 Announce Type: cross Abstract: Vision language models (VLMs) have made remarkable progress in visual reasoning during the last decade.
arXiv AI
Jul 13
REFORGE: A Method for Benchmarking LLMs' Reverse Engineering Capabilities in Decompiled Binary Function Naming
arXiv:2607. 07738v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly applied to reverse-engineering tasks, and recent threat-intelligence reporting shows them operating inside live offensive-security workflows.
arXiv AI
Jul 13
Empowering 9-1-1 Calltaking Training with Generative AI: Experiences and Lessons Learned
arXiv:2602. 13241v3 Announce Type: replace-cross Abstract: Emergency call-takers form the first operational link in public safety response, handling over 240 million calls annually while facing a sustained training crisis: staffing shortages exceed 25\% in many centers, and preparing a single new hire can require up to 720 hours of one-on-one instruction that removes experienced personnel from active duty.
arXiv AI
Jul 13
PACE: A Personalized Adaptive Curriculum Engine for 9-1-1 Call-taker Training
arXiv:2603. 05361v2 Announce Type: replace Abstract: 9-1-1 call-taking training requires mastery of over a thousand interdependent skills, covering diverse incident types and protocol-specific nuances.
arXiv Machine Learning
Jul 13
Present but Rescaled: Chat-to-Agent Transfer of Additive Activation Steering
arXiv:2607. 09156v1 Announce Type: new Abstract: Additive activation steering (injecting a scaled residual-stream direction during generation) is calibrated almost entirely in single-turn chat, yet the models it targets are increasingly deployed as tool-using ReAct agents.
arXiv Machine Learning
Jul 13
Contrastive Learning on Multimodal Analysis of Electronic Health Records
arXiv:2403. 14926v3 Announce Type: replace-cross Abstract: Electronic health record (EHR) systems capture a wealth of multimodal clinical data, encompassing both structured clinical codes and unstructured clinical notes.
arXiv AI
Jul 13
Tuning Derivatives for Causal Fairness in Machine Learning
arXiv:2605. 05882v2 Announce Type: replace-cross Abstract: Artificial-intelligence systems are becoming ubiquitous in society, yet their predictions typically inherit biases with respect to protected attributes such as race, gender, or age.
arXiv AI
Jul 13
Git-Assistant: Planning-Based Support for Updating Git Repositories
arXiv:2607. 09224v1 Announce Type: cross Abstract: Version control systems are essential for collaborative software development, yet tools like git remain challenging for many practitioners.
arXiv AI
Jul 13
STEEL: Sparsity-Aware Fused Attention for Energy-Efficient Long-Sequence Inference on AMD's XDNA NPU
arXiv:2607. 09385v1 Announce Type: cross Abstract: The growing adoption of large language model-based agents within operating system workflows has increased the importance of energy-efficient inference on laptop-class systems-on-chip (SoCs).
arXiv AI
Jul 13
Parameter-Efficient Vision-Language Adaptation with Continuous Metadata Conditioning for Animal Re-Identification
arXiv:2607. 09443v1 Announce Type: cross Abstract: Long-term animal re-identification (ReID) must remain robust to gradual morphological evolution and seasonal appearance shifts.
arXiv AI
Jul 13
REAL: REtrieval-reAsoning and Logic-constructed Attention Behaviors for Long-Context KV Cache Compression
arXiv:2508. 15806v2 Announce Type: replace-cross Abstract: The growing sequence length of large language models poses significant challenges for key-value (KV) caches.