Renting GPUs felt smart in 2023. The math has moved
Many enterprise AI initiatives started the same way over the past several years: renting cloud capacity, running pilots, and scaling successful projects. That approach made sense when nobody knew if these use cases would actually hold up in a live production environment...
Freeze the Decoder, Heal the Encoder: Parameter-Efficient Adaptation for SVD-Based KV-Cache Compression
arXiv:2610.10552v1 Announce Type: new
Abstract: Comparing parameter-efficient fine-tuning recipes under a single, shared learning rate is a common but flawed practice: when the arms being compared have very different trainable-parameter counts, a shared rate can simultaneously depress the larger ar...
An Empirical Study of Agent Skills' Downstream Utility
arXiv:2610.08875v1 Announce Type: new
Abstract: Agent Skills package procedural guidance and resources for reuse, but a relevant Skill does not necessarily improve task performance. Existing studies characterize Skill content and evaluate downstream performance, yet provide limited explanations of ...
Accelerating Floating-Point Satisfiability Solving via Gradient Normalization
arXiv:2610.08808v1 Announce Type: new
Abstract: Satisfiability Modulo Theories (SMT) solvers are foundational to software verification, program analysis, and compiler testing, particularly over the theory of Quantifier-Free Floating-Point (QF_FP). While recent optimization-based SMT solvers have su...
Anchor Divergence for Semantic Geometry in Contrastive Learning
arXiv:2610.06919v1 Announce Type: new
Abstract: This paper concerns how semantic context determines geometry in learned vector representations. Similarity is typically measured using cosine similarity, which provides a single fixed geometry. Semantic similarity, however, is inherently context depen...
Learning When to Refine: Long-Horizon Reinforcement Learning for Budgeted Neural-Operator PDE Solvers
arXiv:2610.06883v1 Announce Type: new
Abstract: Neural operators provide fast surrogates for time-dependent PDEs, but autoregressive deployment creates a refinement-allocation problem: prediction errors vary over space and time, while only a finite number of local corrections can be committed along...
Comparative review of hybrid forecasting models for short-term prediction of building thermal load
arXiv:2610.06881v1 Announce Type: new
Abstract: In this paper, a comparative review of different hybrid models for short-term forecasting of building thermal demand is carried out. Particularly, the assessment tackles the comparison of data-driven models enhanced with other state-of-the-art techniq...
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.
The post What AI gets wrong and wh...
The AI Risk Observatory: What Can We Learn from AI Disclosures in Annual Reports About Societal Resilience?
arXiv:2610.02281v1 Announce Type: new
Abstract: Societal resilience research relies on access to useful and actionable data, which motivates our main research question: Can annual reports, processed at scale with LLMs, provide a useful signal about how companies disclose their response to AI? We te...
The Price of Greenwashing: Algorithmic Verification and Market Discipline using Conformal Machine Learning
arXiv:2610.02225v1 Announce Type: new
Abstract: While corporate sustainability mandates are expanding, the systemic reliance on self-reported emissions data exposes financial markets to pervasive greenwashing. Current literature relies heavily on subjective ESG ratings or textual sentiment analysis...
Rank-Aware Speculative Sampling for Diffusion Draft Trees
arXiv:2610.02251v1 Announce Type: new
Abstract: Speculative sampling accelerates diffusion generation by verifying inexpensive draft states in parallel while preserving the target law. Recent tree-based methods allocate the parallel compute budget more effectively than single-chain drafts, as demon...
Keep It CALM: Analyzing the Limits of Global Unsafety in Text-to-Image Generation
arXiv:2610.02300v1 Announce Type: new
Abstract: Training-free safeguards for text-to-image generation often rely on a reusable safety signal, such as an unsafe direction or global toxic subspace, applied broadly across prompts. We provide a controlled geometric analysis of this global-unsafety assu...
FourierQK: Filter Shape, Admissibility and the Leakage-Coverage Law
arXiv:2610.00009v1 Announce Type: new
Abstract: Frequency-collapse attention [Zeris, 2026e] achieves large gains over standard dot-product attention by replacing the Q/K dot product with a bandpass-filtered inner product at a learned frequency. A natural follow-up question is: which filter shape wo...
arXiv:2610.00004v1 Announce Type: new
Abstract: Adam is the standard optimizer in deep learning, yet its geometric relationship to natural gradient descent (NGD) contains unresolved questions. We study Adam's full update rule, including momentum, as a diagonal empirical Fisher approximation subject...
Reverse Item Response Theory for Sparsity-Robust Ranking in Fragmented Cancer Drug-Response Matrices
arXiv:2610.00002v1 Announce Type: new
Abstract: We introduce reverse Item Response Theory (IRT) to pharmacogenomic drug-response analysis by treating cancer types as latent "subjects" with resistance ability and drugs as "items" with evasion difficulty. Applied to 242,036 drug sensitivity measureme...
What Do Rationales Communicate? A Message-Intervention Study in Role-Specialized QA
arXiv:2610.00018v1 Announce Type: new
Abstract: Role-specialized QA pipelines increasingly pass rationales from a reasoner to a verifier, but it is unclear what this message actually buys: better answers, stronger support assessment, or a new failure surface. We introduce a message-intervention dia...
Travel Time Prediction in Supply Chain Management Using Machine Learning
arXiv:2609.38190v1 Announce Type: new
Abstract: The purpose of this research is to find data and methods using machine learning and deep learning to correctly predict the estimated travel time for transportation and logistics in a supply chain system. The supply chain ecosystem is very complex and ...
A Moving-Horizon Approximate Branch-and-Reduce Method for Deep Classification Trees
arXiv:2609.38194v1 Announce Type: new
Abstract: Despite the importance for interpretability, decision trees face severe scalability challenges. Existing global optimal methods are often limited by binary feature selection and shallow tree depths, whereas traditional heuristic approaches frequently ...
A Data-Free Physics-Informed Neural Operator for Level-Set Interface Advection
arXiv:2609.38195v1 Announce Type: new
Abstract: Operators for interfacial problems are trained on reference solutions produced by the solver they are intended to replace. This work develops a data-free physics-informed neural operator for level-set interface advection, in which the interface is the...
arXiv:2609.38196v1 Announce Type: new
Abstract: Accurate time series forecasting is critical across various domains, yet traditional ensemble methods often suffer from the disproportionate influence of extreme forecasts. We introduce the Conformal Adversarial Generative Ensemble (CAGE), a novel fra...