arXiv Machine Learning
Jul 22
Exposure-Based Reinforcement Learning to Rank
arXiv:2607. 18689v1 Announce Type: new Abstract: Reinforcement learning (RL) methods for learning-to-rank (LTR) can optimize (almost) any ranking goal, e.
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 22
Onboarding Without Forgetting: Hypernetwork Personalization with Data-Free Replay for Personalized Federated Learning
arXiv:2508. 05157v2 Announce Type: replace Abstract: Federated Learning (FL) enables collaborative training across distributed clients without sharing raw data, offering strong privacy benefits.
arXiv Machine Learning
Jul 22
The Tractability Landscape of Sampling with Inexact Scores
arXiv:2607. 19004v1 Announce Type: cross Abstract: We provide a simple and tight characterization of the types of inexact score oracle access that permit sampling with vanishing total variation bias, for a standard, well-behaved target family.
arXiv Machine Learning
Jul 22
Signed Rectified Flow: Negativity-Controlled Generation
arXiv:2607. 18516v1 Announce Type: new Abstract: We introduce Signed Rectified Flow (Signed RF), a generalization of Rectified Flow that targets the signed measure $\pi^{sign} = (1+\alpha)\pi^+ - \alpha\pi^-$, where $\alpha>0$, $\pi^+$ is the distribution to promote, and $\pi^-$ is the distribution to suppress.
arXiv AI
Jul 22
Evaluating medical AI under missing information: same-provider judges and human raters change apparent safety
arXiv:2607. 18828v1 Announce Type: new Abstract: Readiness stress-testing of medical AI has focused on closed-ended and multimodal benchmarks.
arXiv Machine Learning
Jul 22
Estimating near-verbatim extraction risk in language models with decoding-constrained beam search
arXiv:2603. 24917v2 Announce Type: replace-cross Abstract: Recent work shows that standard greedy-decoding extraction methods for quantifying memorization in LLMs miss how extraction risk varies across sequences.
arXiv AI
Jul 22
Trusted Credentials, Untrusted Behavior: Benchmarking LLM-Agent Security in High-Performance Computing
arXiv:2607. 18485v1 Announce Type: cross Abstract: Large language model (LLM) agents are starting to take on routine work in high-performance computing (HPC), including monitoring Slurm jobs, diagnosing failed builds, inspecting simulation output, and coordinating scientific workflows.
arXiv AI
Jul 22
HALLMARK: Diagnosing Three Failure Modes in LLM Citation Verifiers
arXiv:2607. 18360v1 Announce Type: cross Abstract: Large language models (LLMs) now routinely draft literature reviews and assist with academic writing, which means a higher risk of fabricated references: GPTZero found 53 papers with hallucinated citations among NeurIPS 2025's accepted set.
arXiv Machine Learning
Jul 22
CLT-Forge: A Scalable Library for Cross-Layer Transcoders and Attribution Graphs
arXiv:2603. 21014v2 Announce Type: replace Abstract: Mechanistic interpretability seeks to understand how Large Language Models (LLMs) represent and process information.
arXiv AI
Jul 22
ChemHyperMag: Physics-informed magnetic hypergraph learning improves molecular ADMET prediction
arXiv:2607. 18332v1 Announce Type: cross Abstract: Accurate prediction of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) is important for drug discovery.
arXiv AI
Jul 22
Enhancing Transformer-based Routing by Encoding Distance via Relative Positional Encoding
arXiv:2607. 18909v1 Announce Type: new Abstract: This paper explores Relative Positional Encoding (RPE) as an additive bias in Transformer architectures to solve the Team Orienteering Problem.
arXiv AI
Jul 22
Norm or Direction? Decoding Vision Mambas for High-Resolution Vision
arXiv:2607. 18625v1 Announce Type: cross Abstract: Vision Mamba models replace quadratic self-attention with linear complexity selective state space models (SSMs), emerging as efficient visual backbones.
arXiv AI
Jul 22
AutoJourn: Multi-Perspective Summarisation, Bias Detection and Bias Neutralisation for LLM-Generated News in Automated Journalism
arXiv:2607. 18983v1 Announce Type: cross Abstract: We present AutoJourn, a demonstration system for multi-perspective news generation and bias-aware evaluation using large language models (LLMs).
arXiv AI
Jul 22
Bounding Boxes to Improve Small Language Model Performance on Vision-Based Grading Tasks
arXiv:2607. 18767v1 Announce Type: cross Abstract: The deployment of Small Language Models (SLMs) in educational settings offers significant advantages in terms of privacy, cost, and scalability.
arXiv AI
Jul 22
FSDBN: Foreground-Aware EEG--Visual Alignment via Dynamic Brain Networks
arXiv:2607. 18344v1 Announce Type: cross Abstract: EEG-based visual decoding provides a non-invasive pathway for interpreting visual semantics.
arXiv AI
Jul 22
Prompt Design at Scale: How Format, Instruction Count, and Context Length Shape Instruction Adherence and Hallucination in Large Language Models
arXiv:2607. 19257v1 Announce Type: cross Abstract: Practitioners make three prompt-design decisions with almost no controlled evidence behind them: how to format instructions and context (markdown, plain text, prose, or tabular), how many simultaneous instructions a system prompt can carry before compliance degrades, and how much context a model can hold before recall and honesty degrade.
arXiv AI
Jul 22
Breaking the Homogeneity Assumption: Specialized Multi-Generator Adversarial Learning for Rare Failure Detection in Predictive Maintenance
arXiv:2607. 19153v1 Announce Type: cross Abstract: Supervised learning models in the predictive maintenance field are regularly trained on highly imbalanced industrial datasets: machine failures occur rarely but have a disproportionate effect on operations.
arXiv AI
Jul 22
Adversarial Robustness of Phishing Email Detection: A Comparative Study of TF-IDF + Logistic Regression and Fine-Tuned DistilBERT
arXiv:2607. 18429v1 Announce Type: cross Abstract: Phishing emails remain one of the most persistent cybersecurity threats, and machine-learning classifiers are widely used to detect them.
arXiv AI
Jul 22
Agentic Real2Sim: Physics-based World Modeling with Vision-Language Agents
arXiv:2607. 19190v1 Announce Type: cross Abstract: Real-to-sim conversion for robotic interaction with objects remains labor-intensive because it requires more than visual reconstruction: a streamlined real2sim process must recover scene geometries and object states, infer physical parameters, and assemble actors, objects, cameras, poses, and trajectories into a runnable physical simulation.
arXiv AI
Jul 22
BioSecBench-Refusal: A paired metric for performance and alignment in agentic biosecurity risk assessment
arXiv:2607. 05462v2 Announce Type: replace-cross Abstract: As AI agents are incorporated into life science workflows, the capabilities that speed discovery might also enable misuse.