Translation Invariance of Neural Operators for the FitzHugh-Nagumo Model
arXiv:2603.17523v1 Announce Type: new Abstract: Neural Operators (NOs) are a powerful deep learning framework designed to learn the solution operator that arise from partial differential equations. This study investigates NOs ability to capture the stiff spatio-temporal dynamics of the FitzHugh-Nagumo...
Recursive Language Models Meet Uncertainty: The Surprising Effectiveness of Self-Reflective Program Search for Long Context
arXiv:2603.15653v1 Announce Type: new Abstract: Long-context handling remains a core challenge for language models: even with extended context windows, models often fail to reliably extract, reason over, and use the information across long contexts. Recent works like Recursive Language Models...
Resilience Meets Autonomy: Governing Embodied AI in Critical Infrastructure
arXiv:2603.15885v1 Announce Type: new Abstract: Critical infrastructure increasingly incorporates embodied AI for monitoring, predictive maintenance, and decision support. However, AI systems designed to handle statistically representable uncertainty struggle with cascading failures and crisis dynamics that exceed their training assumptions. This...
CraniMem: Cranial Inspired Gated and Bounded Memory for Agentic Systems
arXiv:2603.15642v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed in long running workflows, where they must preserve user and task state across many turns. Many existing agent memory systems behave like external databases with ad hoc...
Theoretical Foundations of Latent Posterior Factors: Formal Guarantees for Multi-Evidence Reasoning
arXiv:2603.15674v1 Announce Type: new Abstract: We present a complete theoretical characterization of Latent Posterior Factors (LPF), a principled framework for aggregating multiple heterogeneous evidence items in probabilistic prediction tasks. Multi-evidence reasoning arises pervasively in high-stakes domains including healthcare diagnosis, financial...
NeuronSpark: A Spiking Neural Network Language Model with Selective State Space Dynamics
arXiv:2603.16148v1 Announce Type: new Abstract: We ask whether a pure spiking backbone can learn large-scale language modeling from random initialization, without Transformer distillation. We introduce NeuronSpark, a 0.9B-parameter SNN language model trained with next-token prediction and surrogate gradients. The model...
NeSy-Route: A Neuro-Symbolic Benchmark for Constrained Route Planning in Remote Sensing
arXiv:2603.16307v1 Announce Type: new Abstract: Remote sensing underpins crucial applications such as disaster relief and ecological field surveys, where systems must understand complex scenes and constraints and make reliable decisions. Current remote-sensing benchmarks mainly focus on evaluating perception and reasoning...
ARISE: Agent Reasoning with Intrinsic Skill Evolution in Hierarchical Reinforcement Learning
arXiv:2603.16060v1 Announce Type: new Abstract: The dominant paradigm for improving mathematical reasoning in language models relies on Reinforcement Learning with verifiable rewards. Yet existing methods treat each problem instance in isolation without leveraging the reusable strategies that emerge and accumulate...
I Know What I Don't Know: Latent Posterior Factor Models for Multi-Evidence Probabilistic Reasoning
arXiv:2603.15670v1 Announce Type: new Abstract: Real-world decision-making, from tax compliance assessment to medical diagnosis, requires aggregating multiple noisy and potentially contradictory evidence sources. Existing approaches either lack explicit uncertainty quantification (neural aggregation methods) or rely on manually engineered discrete predicates...
ClaimFlow: Tracing the Evolution of Scientific Claims in NLP
arXiv:2603.16073v1 Announce Type: new Abstract: Scientific papers do more than report results $-$ they advance $\textit{claims}$ that later work supports, extends, or sometimes refutes. Yet existing methods for citation and claim analysis capture only fragments of this dialogue. In this...
SciZoom: A Large-scale Benchmark for Hierarchical Scientific Summarization across the LLM Era
arXiv:2603.16131v1 Announce Type: new Abstract: The explosive growth of AI research has created unprecedented information overload, increasing the demand for scientific summarization at multiple levels of granularity beyond traditional abstracts. While LLMs are increasingly adopted for summarization, existing benchmarks remain...
PyPhonPlan: Simulating phonetic planning with dynamic neural fields and task dynamics
arXiv:2603.16299v1 Announce Type: new Abstract: We introduce PyPhonPlan, a Python toolkit for implementing dynamical models of phonetic planning using coupled dynamic neural fields and task dynamic simulations. The toolkit provides modular components for defining planning, perception and memory fields, as...
Flood Risk Follows Valleys, Not Grids: Graph Neural Networks for Flash Flood Susceptibility Mapping in Himachal Pradesh with Conformal Uncertainty Quantification
arXiv:2603.15681v1 Announce Type: new Abstract: Flash floods are the most destructive natural hazard in Himachal Pradesh (HP), India, causing over 400 fatalities and $1.2 billion in losses in the 2023 monsoon season alone. Existing risk maps treat every pixel independently,...
Tackling Over-smoothing on Hypergraphs: A Ricci Flow-guided Neural Diffusion Approach
arXiv:2603.15696v1 Announce Type: new Abstract: Hypergraph neural networks (HGNNs) have demonstrated strong capabilities in modeling complex higher-order relationships. However, existing HGNNs often suffer from over-smoothing as the number of layers increases and lack effective control over message passing among nodes....
OMNIFLOW: A Physics-Grounded Multimodal Agent for Generalized Scientific Reasoning
arXiv:2603.15797v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated exceptional logical reasoning capabilities but frequently struggle with the continuous spatiotemporal dynamics governed by Partial Differential Equations (PDEs), often resulting in non-physical hallucinations. Existing approaches typically resort to costly,...
Hypothesis Class Determines Explanation: Why Accurate Models Disagree on Feature Attribution
arXiv:2603.15821v1 Announce Type: new Abstract: The assumption that prediction-equivalent models produce equivalent explanations underlies many practices in explainable AI, including model selection, auditing, and regulatory evaluation. In this work, we show that this assumption does not hold. Through a large-scale...
Federated Learning for Privacy-Preserving Medical AI
arXiv:2603.15901v1 Announce Type: new Abstract: This dissertation investigates privacy-preserving federated learning for Alzheimer's disease classification using three-dimensional MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Existing methodologies often suffer from unrealistic data partitioning, inadequate privacy guarantees, and insufficient benchmarking,...
The Agentic Researcher: A Practical Guide to AI-Assisted Research in Mathematics and Machine Learning
arXiv:2603.15914v1 Announce Type: new Abstract: AI tools and agents are reshaping how researchers work, from proving theorems to training neural networks. Yet for many, it remains unclear how these tools fit into everyday research practice. This paper is a practical...
Generative Inverse Design with Abstention via Diagonal Flow Matching
arXiv:2603.15925v1 Announce Type: new Abstract: Inverse design aims to find design parameters $x$ achieving target performance $y^*$. Generative approaches learn bidirectional mappings between designs and labels, enabling diverse solution sampling. However, standard conditional flow matching (CFM), when adapted to inverse...
Data-Local Autonomous LLM-Guided Neural Architecture Search for Multiclass Multimodal Time-Series Classification
arXiv:2603.15939v1 Announce Type: new Abstract: Applying machine learning to sensitive time-series data is often bottlenecked by the iteration loop: Performance depends strongly on preprocessing and architecture, yet training often has to run on-premise under strict data-local constraints. This is a...
Determinism in the Undetermined: Deterministic Output in Charge-Conserving Continuous-Time Neuromorphic Systems with Temporal Stochasticity
arXiv:2603.15987v1 Announce Type: new Abstract: Achieving deterministic computation results in asynchronous neuromorphic systems remains a fundamental challenge due to the inherent temporal stochasticity of continuous-time hardware. To address this, we develop a unified continuous-time framework for spiking neural networks (SNNs)...
A Depth-Aware Comparative Study of Euclidean and Hyperbolic Graph Neural Networks on Bitcoin Transaction Systems
arXiv:2603.16080v1 Announce Type: new Abstract: Bitcoin transaction networks are large scale socio- technical systems in which activities are represented through multi-hop interaction patterns. Graph Neural Networks(GNNs) have become a widely adopted tool for analyzing such systems, supporting tasks such as...
Functorial Neural Architectures from Higher Inductive Types
arXiv:2603.16123v1 Announce Type: new Abstract: Neural networks systematically fail at compositional generalization -- producing correct outputs for novel combinations of known parts. We show that this failure is architectural: compositional generalization is equivalent to functoriality of the decoder, and this...
The ARC of Progress towards AGI: A Living Survey of Abstraction and Reasoning
arXiv:2603.13372v1 Announce Type: new Abstract: The Abstraction and Reasoning Corpus (ARC-AGI) has become a key benchmark for fluid intelligence in AI. This survey presents the first cross-generation analysis of 82 approaches across three benchmark versions and the ARC Prize 2024-2025...
Human Attribution of Causality to AI Across Agency, Misuse, and Misalignment
arXiv:2603.13236v1 Announce Type: new Abstract: AI-related incidents are becoming increasingly frequent and severe, ranging from safety failures to misuse by malicious actors. In such complex situations, identifying which elements caused an adverse outcome, the problem of cause selection, is a...
Executable Archaeology: Reanimating the Logic Theorist from its IPL-V Source
arXiv:2603.13514v1 Announce Type: new Abstract: The Logic Theorist (LT), created by Allen Newell, J. C. Shaw, and Herbert Simon in 1955-1956, is widely regarded as the first artificial intelligence program. While the original conceptual model was described in 1956, it...
DyACE: Dynamic Algorithm Co-evolution for Online Automated Heuristic Design with Large Language Model
arXiv:2603.13344v1 Announce Type: new Abstract: The prevailing paradigm in Automated Heuristic Design (AHD) typically relies on the assumption that a single, fixed algorithm can effectively navigate the shifting dynamics of a combinatorial search. This static approach often proves inadequate for...
PMIScore: An Unsupervised Approach to Quantify Dialogue Engagement
arXiv:2603.13796v1 Announce Type: new Abstract: High dialogue engagement is a crucial indicator of an effective conversation. A reliable measure of engagement could help benchmark large language models, enhance the effectiveness of human-computer interactions, or improve personal communication skills. However, quantifying...
Spatially Aware Deep Learning for Microclimate Prediction from High-Resolution Geospatial Imagery
arXiv:2603.13273v1 Announce Type: new Abstract: Microclimate models are essential for linking climate to ecological processes, yet most physically based frameworks estimate temperature independently for each spatial unit and rely on simplified representations of lateral heat exchange. As a result, the...
A Stability-Aware Frozen Euler Autoencoder for Physics-Informed Tracking in Continuum Mechanics (SAFE-PIT-CM)
arXiv:2603.13280v1 Announce Type: new Abstract: We introduce a Stability-Aware Frozen Euler autoencoder for Physics-Informed Tracking in Continuum Mechanics (SAFE-PIT-CM) that recovers material parameters and temporal field evolution from videos of physical processes. The architecture is an autoencoder whose latent-space transition...