Challenges of Explainability in Continual Learning for Time Series Forecasting
arXiv:2607.19382v1 Announce Type: new
Abstract: Deep learning models have shown strong potential for time series forecasting, yet their deployment in real-world environmental monitoring remains challenging due to non-stationary dynamics and limited explainability. In this work, we investigate expla...
FineServe: A Fine-Grained Dataset and Characterization of Global LLM Serving Workloads
arXiv:2607.19349v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly deployed as always-on online services, making efficient LLM serving a critical systems challenge. Achieving low latency and high throughput under volatile demand requires deep understanding of real-world s...
Hybrid LSTM-Graph Neural Framework for Robust Financial Fraud Detection and Adversarial Resilience
arXiv:2607.19350v1 Announce Type: new
Abstract: Financial institutions face significant challenges in detecting sophisticated money laundering patterns, such as smurfing and layering, due to extreme data imbalance (0.13% fraud rate) and evolving adversarial evasion tactics. This paper proposes Frau...
OpenEvoShield: Dual Non-Stationary Continual Defense for Open-World Multi-Agent System Attacks
arXiv:2607.19351v1 Announce Type: new
Abstract: LLM-based multi-agent systems (LLM-MAS) are increasingly deployed in safety-critical applications, where adversaries inject malicious instructions through inter-agent communication to propagate harmful behaviors. Unlike static threats, these attacks a...
Benchmarking Confidential GPU Inference on NVIDIA H100 under Intel TDX
arXiv:2607.19353v1 Announce Type: new
Abstract: Confidential computing is becoming a practical deployment requirement for AI inference workloads that process sensitive inputs or protect proprietary model assets. However, the performance cost of enabling confidential execution for GPU-accelerated la...
FormulaSPIN: Self-Play Fine-Tuning for Natural Language to Spreadsheet Formula Generation
arXiv:2607.19354v1 Announce Type: new
Abstract: Spreadsheet applications are used by hundreds of millions worldwide, yet writing formulas remains a significant barrier. Existing approaches rely on static supervised data, which quickly saturates on limited annotations. In this paper, we introduce FO...
How AI Is Helping States Cut Through Decades of Red Tape
Stanford researchers scanned 500 million words of state law to reveal the stunning growth of reporting requirements, and are now giving governments a tool to clean them up.
Beyond Single-Dimensional Compression: The Compound Sparsity Frontier of Large Language Models
arXiv:2607.18280v1 Announce Type: new
Abstract: Large language models (LLMs) are often compressed through static parameter pruning or dynamic token-level computation, yet aggressive sparsification can trigger rapid performance degradation beyond an essential sparsity boundary. This work asks \emph{...
ALAS: Additive Learnable Alpha-Stable Kernels for Flexible Bayesian Optimization
arXiv:2607.18282v1 Announce Type: new
Abstract: Bayesian Optimization is widely used for expensive black-box optimization, yet its success often depends on choosing a kernel that matches the objective's unknown structure. In this work, we propose ALAS, a flexible Gaussian Process kernel family buil...
FedCC: A Low-Resource Federated Adaptation of Foundation Models for Robust Corpus Callosum localization in Fetal Ultrasound Images
arXiv:2607.18283v1 Announce Type: new
Abstract: Accurate localization of the corpus callosum (CC) in fetal ultrasound (US) images is crucial for the early identification of neurodevelopmental abnormalities. However, this task remains highly challenging due to the intrinsic limitations of US imaging...
SysAdmin: Measuring Instrumental Power-Seeking in Frontier AI
arXiv:2607.18239v1 Announce Type: new
Abstract: Power-seeking defined as behaviors where AI systems acquire resources, evade oversight, or resist termination beyond task requirements is identified as a key driver of Loss of Control (LoC) risk. In this work, we introduce SysAdmin, a benchmark that p...
Calibrated Selective Fact-Checking via Evidence Chain Evaluation
arXiv:2607.18240v1 Announce Type: new
Abstract: Large language models (LLMs) can achieve strong fact-checking accuracy, yet forced binary decisions conceal a critical reliability problem: systems may issue confident verdicts even when supporting evidence is weak, sparse, or internally inconsistent....
BatchDAG: LLM-Planned Execution Graphs for Scalable Ad-Hoc Analysis Over Enterprise Data
arXiv:2607.18241v1 Announce Type: new
Abstract: Large language models (LLMs) excel at analyzing individual documents but break down on exhaustive, cross-entity analytical questions over enterprise-scale datasets due to context overflow, loss of per-entity attribution, and linear latency from sequen...
arXiv:2607.18242v1 Announce Type: new
Abstract: The coming era of autonomous AI agents demands a discovery mechanism capable of navigating millions of tools, yet existing solutions buckle under O(N) complexity and centralized governance. Instead of building another fragile overlay, we propose ToolD...
From Agent Failure Paths to Quantified Residual Risk: A Compositional Framework for Resilient Agentic AI
arXiv:2607.18243v1 Announce Type: new
Abstract: Agentic AI is crossing trust boundaries faster than current risk models can represent. Existing approaches provide one of two partial views. They either describe failure mechanisms without producing a transferable residual-risk estimate, or they produ...
New programmable photonic chip can control how fast light moves
Scientists have created a programmable optical chip that can slow light on demand, giving engineers far greater control over how optical signals propagate through a circuit. The technology could provide the delays, synchronization, and buffering functions needed to make light-based computing more pr...
DocOCR-Eval: A Correction-Based Framework for OCR Tool Selection Without Ground Truth
arXiv:2607.16203v1 Announce Type: new
Abstract: Document parsing is a foundational step for document understanding tasks such as visual question answering and key information extraction, as it transforms unstructured scanned images into structured representations by extracting textual, visual, and ...
Fully-sensorized smart-eyewear platform for on-device Machine Learning
arXiv:2607.16222v1 Announce Type: new
Abstract: This paper presents ARGO, a smart eyewear platform designed to bridge ergonomic comfort, high computational throughput, and energy efficiency. Unlike cloud-dependent solutions, ARGO leverages the STM32N6 microcontroller and its integrated Neural Proce...
LLM Unlearning for Cyber Defense: A Survey on Methods, Challenges, and Emerging Threats
arXiv:2607.16227v1 Announce Type: new
Abstract: LLMs are increasingly deployed in security-critical systems across healthcare, finance, education, and decision support, yet their inability to forget creates serious cybersecurity, privacy, and safety risks. Sensitive personal information, copyrighte...
Rater State Bias in RLHF Preference Data: An Audit Framework
arXiv:2607.16195v1 Announce Type: new
Abstract: We identify a structured confound in Reinforcement Learning from Human Feedback (RLHF). Pairwise preference labels are intended to reflect the compared outputs, but they may also reflect the rater's state during annotation. Under sustained stressful o...
Design and Validation of a Lightweight 1D CNN for Affective Touch Classification in Soft Plush Companions
arXiv:2607.16196v1 Announce Type: new
Abstract: Soft, sensorized companions offer a physically safe and emotionally intuitive interface for socially assistive technologies, yet their deformability and multichannel tactile sensing complicate the robust interpretation of human affect. This study pres...
Some Large Language Models Exhibit Consistent Risk Attitudes
arXiv:2607.16197v1 Announce Type: new
Abstract: As artificial intelligence systems are deployed in open-ended, high-stakes settings, a critical dimension remains unmeasured: how perceived risk is translated into action. We test whether large language models (LLMs) exhibit systematic and consistent ...
PlanFlip: Attacking Multi-Agent LLM Systems via Planning-Phase Prompt Injection
arXiv:2607.16199v1 Announce Type: new
Abstract: Multi-agent LLM systems increasingly rely on a Planner to decompose goals into sub-task sequences that downstream Executor and Critic agents execute and audit. We identify the planning phase as a critical attack surface: a single injection into the Pl...