When Correctness Feedback Erases Evidence: Phase-Separated Completeness Auditing in Verus
arXiv:2610.20676v1 Announce Type: new
Abstract: Correctness feedback suppresses the very evidence that specification-completeness auditing is designed to uncover: programs that satisfy a written contract but violate intended behavior. We demonstrate this objective reversal on 12 Verus/Rust tasks ac...
Expanding Attention: A Taylor-Series Route to Linear-Time Transformers with a Fixed Number of Recurrent States
arXiv:2610.20637v1 Announce Type: new
Abstract: We expand the exponential kernel of attention into a truncated Taylor series of $n$ terms and factorize each term into a tensor-power inner product, which collapses the causal history into exactly $n$ recurrent states, each updated by one rank-one out...
Smart Alone, Blind Together: Why Accuracy Fails to CertifyTheory of Mind in Multi-Agent Teams
arXiv:2610.19051v1 Announce Type: new
Abstract: Coordinators that act on what they infer about teammates are usually certified by the accuracy of those inferences. We argue that accuracy is the wrong certificate, for two reasons. First, the accuracy needed to act depends on what a wrong action cost...
Beyond Equation Search: Conjecture-Guided Discovery with SCOPE and ClosureBench
arXiv:2610.18938v1 Announce Type: new
Abstract: Most symbolic regression methods optimize candidate equations, but improving an equation's fit does not establish whether its underlying scientific conjecture should be retained, revised, or rejected. A conjecture may admit multiple mathematical reali...
Token Utility for On-Policy Distillation via Local Trust-Region Optimization
arXiv:2610.18883v1 Announce Type: new
Abstract: Token-selection methods for on-policy distillation (OPD) prioritize student-visited positions using diverse signals, including uncertainty, teacher-student disagreement, and mismatch direction. We instead derive token importance from the distillation ...
Equal-Budget Monte Carlo Tree Search versus Randomized-Restart List Scheduling for DAG Workflows: A Controlled Equal-Budget Computational Study
arXiv:2610.12542v1 Announce Type: new
Abstract: Monte Carlo Tree Search (MCTS) is often proposed for scheduling directed-acyclic-graph (DAG) workflows, yet most studies never run the comparison that could refute it - giving a much simpler method the same search effort. We run that comparison under ...
Behind-the-Meter Photovoltaic Visibility from Net Smart Meters: Deployment Regimes and Operational Insights
arXiv:2610.12527v1 Announce Type: new
Abstract: Distribution operators need visibility of behind-the-meter photovoltaic (PV) generation, yet most residential customers are observed only through net smart meters. Deep-learning-based PV disaggregation can restore this visibility, but its operational ...
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...
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...
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 ...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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 ...
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 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...