LEAD: Breaking the No-Recovery Bottleneck in Long-Horizon Reasoning
Long-horizon execution in Large Language Models (LLMs) remains unstable even when high-level strategies are provided. Evaluating on controlled algorithmic puzzles, we demonstrate that while decomposition is essential for stability, extreme decomposition creates a “no-recovery bottleneck”. We show th...
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...
Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions
arXiv:2607.19378v1 Announce Type: new
Abstract: Subquadratic alternatives to attention require compromises when applied to multi-dimensional data: standard convolutions lack global receptive fields and input dependency, while recurrent models require rasterizing data such as images, volumes, and pa...
arXiv:2607.19379v1 Announce Type: new
Abstract: Prior work has shown that transformers can perform exact Bayesian filtering within a fixed
hypothesis class. Can they also perform Bayesian model selection -- identifying the correct
hypothesis class from data? We introduce model-selection Bayesia...
CruiseBench: A Real-Flight-Aligned N-CMAPSS Benchmark for Engine RUL Prediction
arXiv:2607.19380v1 Announce Type: new
Abstract: Remaining useful life (RUL) prediction estimates how long an engine can continue safe operation and is central to maintenance planning. N-CMAPSS extends C-MAPSS by simulating run-to-failure aero-engine trajectories using recorded real-flight profiles ...
Air Quality Arena: A Large-Scale Multi-Region Ground Monitoring Dataset and Benchmark for Air Quality Forecasting with Time-Series Foundation Models
arXiv:2607.19381v1 Announce Type: new
Abstract: Air pollution causes an estimated 7.9 million premature deaths annually, making accurate forecasting a critical public health priority. Machine learning is increasingly being applied to forecast air pollution levels, yet existing benchmarks remain nar...
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...
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.
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....
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...
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...
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...
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...
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...