A domain-shift aware neural network is proposed for estimating unbalance masses in rotating systems under varying conditions. The model uses maximum mean discrepancy to align feature representations across different operating domains, improving prediction accuracy when system behaviors differ from training conditions. Results show its effectiveness in structural health monitoring applications.
TransitNet, a compact attention-augmented deep learning framework, achieves 95.2% accuracy in low-SNR transit blind searches, outperforming TLS and BLS in ROC-AUC and PR-AP values. It recovers 93.0% of injected Earth- and sub-Earth-size transits, with 97.4% of injected transits fully covered by estimated transit windows, and successfully recovers all 34 confirmed Kepler planets with a mean midpoint error of 1.24 hours.
An exploratory analysis of 10 vibe-coded C/C++ projects reveals near-zero in-artifact variability, with all decisions resolved at generation time. The paper proposes Variability by Regeneration (VbR), a product-line approach where an LLM acts as a derivation engine, generating tailored binaries from declarative specifications, with a variant dispatcher routing user requests to the correct binary. VbR shifts variability into specifications, not code, offering a new paradigm for SPL engineering.
A study uses SHAP and SSHAP techniques to analyze electricity price drivers in 39 European bidding zones. It finds solar energy has a disproportionate impact on prices, gas remains a dominant factor, and interconnections highlight regional interdependence. The research also builds a synthetic EU-wide market to examine a fully integrated scenario.
A new taxonomy classifies LLM agent communication protocols across five dimensions: counterparty, payload, interaction state, discovery mechanism, and schema flexibility. Analysis shows hybrid payloads, session-state persistence, and runtime schema negotiation are common, with decentralized discovery remaining rare. The study predicts short-term convergence toward unified agent-to-agent and agent-to-context protocols, and long-term evolution toward a federated, layered protocol stack.
OrthoReg introduces orthogonal regularization to prevent neural components from relearning symbolic structures in hybrid dynamical systems. By directly penalizing overlap between symbolic and neural parts, it enables a complementary decomposition where symbolic models capture expressible physics and neural components handle remaining dynamics. On benchmarks with partial library mismatch, OrthoReg improves symbolic recovery and out-of-distribution performance.
The article confirms there was no actual jailbreak of Anthropic's Fable AI. Instead, a test involving fake code with planted vulnerabilities was conducted, where Fable refused to review the code and only responded to a request to 'fix this code' after manual steps. Katie Moussouris of Luta Security states this scenario should not trigger export controls, calling it a deliberate, engineered test that undermines claims of a security breach.
The author outlines three core goals: clarifying frontier AI model evolution, building an open AI ecosystem, and creating institutions to support these missions. Interconnects serves as a raw, independent voice for frontier AI thinking, with a dedicated technical audience of over 70K subscribers. The blog maintains paywalled comments to prevent AI-generated noise, and the author plans to reach 1000 paid subscribers by summer, emphasizing financial sustainability and independence amid rising AI service costs.
INI-VPINN is a variational physics-informed neural network that implicitly enforces Neumann and interface conditions using compact support weighting functions and integration by parts. It achieves higher accuracy and faster convergence than existing PINN methods in solving multi-material problems with geometric singularities and mixed boundary conditions, and is publicly available on GitHub.
ConTex reformulates counterfactual generation for time series forecasting as a globally consistent intervention problem. It achieves state-of-the-art validity with sparse, interpretable interventions, reduces computational cost by 12-36x, and enables real-time inference in approximately 0.007 seconds.
This paper proposes SD-ZFS, a deep reinforcement learning framework adapted from S2V-DQN, to solve the NP-hard minimum zero-forcing set problem on undirected graphs. The framework demonstrates strong performance compared to optimal solutions and greedy heuristics, showing effective generalization, scalability, and transfer across diverse graph structures.
A new convex quasilinearization method, LiL-Q, solves nonlinear PDEs by reducing them to linear subproblems using physics-informed neural networks. LiL-Q converges in single-digit iterations across seven benchmarks, achieving machine precision when the exact solution lies in the trial space, and requires up to two orders of magnitude fewer parameters than standard PINN solvers.
Large-scale experiments show that state-of-the-art dataset distillation methods are comparable to or worse than coreset selection on ImageNet and ImageNette. Coresets consistently achieve better data coverage and are more computationally efficient, highlighting their practical superiority over distilled sets.
The paper establishes that the \mathbb{Z}_2\-index is upper-bounded by a linear function of the list replicability number. It proves a strong separation between sign rank and \mathbb{Z}_2\-index and shows that list replicability is bounded above by height and minimum star number, with a composition result for the product of concept classes.
A study of 14,727 security and privacy prompts from 3.2M real-world user-LLM conversations identifies nine categories of S&P queries. Commercial LLMs outperform open models, with GPT 5.5 providing good responses on 98% of prompts versus Llama 4 at 47%, though some commercial models produce contradictory responses across runs.
MGUP introduces a selective update mechanism that applies larger step-sizes to a fixed proportion of parameters in stochastic optimization, while using smaller, non-zero step-sizes for the rest. It integrates seamlessly with optimizers like AdamW, Lion, and Muon, providing theoretical convergence guarantees for MGUP-AdamW and demonstrating superior or more stable performance in training large language models and MAE pretraining tasks.
New York City's 2025 congestion pricing led to significant increases in bus and subway ridership, with gains extending beyond Manhattan's core. Overall travel demand decreased modestly, primarily within the Congestion Relief Zone, and neighborhood-level responses reveal uneven socio-demographic adaptation.
SPHERE-JEPA introduces deterministic statistical regularizers on the hypersphere, replacing stochastic sliced methods with analytically integrated objectives like MMD, KSD, and KL divergence. Rotationally invariant kernels based on heat and bandlimited filters ensure spatial bias-free learning, with empirical results showing improved convergence and performance on ImageNet and Galaxy10, and superior instance separation in procedural texture retrieval using KL divergence.
TUNEAHEAD is a lightweight framework that predicts fine-tuning performance using meta-feature vectors from dataset descriptors and short probe runs. It outperforms baselines like Early-Stop Extrapolation and ProxyLM, achieving an RMSE of 1.47 percentage points and 95.1% of predictions within ±3 percentage points of true scores on 370 held-out runs.
A confusion-aware difficulty score is introduced within the Transfer Teacher framework to improve model interpretability and data efficiency. Evaluations on CIFAR-10 show that confusion-aware curriculum ordering outperforms random ordering by up to 8.7% at 20% data, demonstrating consistent data-efficiency gains. However, curriculum or anti-curriculum ordering does not improve accuracy over standard training at full data, indicating that scoring function improvements alone are insufficient to overcome curriculum learning failure modes.