OpenRaiser/Intern-Atlas huggingface dataset:
Faster-than-Nyquist Signaling in the Finite Time-Bandwidth Product Regime
Venue: arXiv cs.IT 2026
Year: 2026

This paper analyzes faster-than-Nyquist (FTN) signaling within a consistent framework based on a fixed time-bandwidth product (TBP), resolving potential ambiguities present in finite blocklength analysis. A key feature of FTN is its ability to increase the number of transmitted symbols in a given time and frequency resource, which can lower the rate penalties inherent in short packet communications. We derive tight bounds on the maximum channel coding rate (MCCR) and demonstrate that FTN's rate gains over Nyquist signaling can be higher in the finite TBP regime than in the asymptotic case. Performance is benchmarked against the theoretical optimum of transmitting prolate spheroidal wave functions, showing that a well-designed FTN system can closely approach this limit. We present practical design criteria, including the optimal time-acceleration factor for maximizing signaling dimensions, an optimized pulse shape that meets strict out-of-band constraints, and a turbo-equalization-based coding scheme that performs near the derived MCCR bounds. These findings establish FTN as a practical and near-optimal technique for enhancing the rate and reliability of latency-constrained communications.
Multi-class Support Vector Machine with Maximizing Minimum Margin
Venue: arXiv cs.LG 2026
Year: 2026

Support Vector Machine (SVM) stands out as a prominent machine learning technique widely applied in practical pattern recognition tasks. It achieves binary classification by maximizing the "margin", which represents the minimum distance between instances and the decision boundary. Although many efforts have been dedicated to expanding SVM for multi-class case through strategies such as one versus one and one versus the rest, satisfactory solutions remain to be developed. In this paper, we propose a novel method for multi-class SVM that incorporates pairwise class loss considerations and maximizes the minimum margin. Adhering to this concept, we embrace a new formulation that imparts heightened flexibility to multi-class SVM. Furthermore, the correlations between the proposed method and multiple forms of multi-class SVM are analyzed. The proposed regularizer, akin to the concept of "margin", can serve as a seamless enhancement over the softmax in deep learning, providing guidance for network parameter learning. Empirical evaluations demonstrate the effectiveness and superiority of our proposed method over existing multi-classification methods.
Optimal Transport Aggregation for Distributed Mixture-of-Experts
Venue: arXiv cs.LG 2026
Year: 2026

Mixture-of-experts (MoE) models provide a flexible statistical framework for modeling heterogeneity and nonlinear relationships. In many modern applications, however, datasets are naturally distributed across multiple machines due to storage, computational, or governance constraints. We consider a distributed model aggregation setting in which local MoE models are trained independently on decentralized datasets and subsequently combined into a global estimator. Aggregating MoE models is challenging because standard averaging produces models that do not preserve the MoE structure, and therefore do not yield estimates of the global model parameters. To address this issue, we propose a principled aggregation framework based on optimal transport that constructs a reduced global MoE estimator by minimizing a transportation divergence between the collection of local estimators and the aggregated model. An efficient majorization--minimization (MM) algorithm is derived to solve the resulting optimization problem. The method requires only a single communication step from local machines to a central server, making it a frugal distributed learning approach particularly attractive for large-scale settings where communication costs are a major bottleneck. We further establish statistical guarantees for the aggregated estimator, including consistency under standard assumptions on the local estimators. Experiments on synthetic and real datasets demonstrate that the approach achieves performance comparable to centralized training while significantly reducing computation time. The source codes are publicly available on Github.
CSAI: Conditional Self-Attention Imputation for Healthcare Time-series
Venue: arXiv cs.LG 2026
Year: 2026

We introduce the Conditional Self-Attention Imputation (CSAI) model, a novel recurrent neural network architecture designed to address the challenges of complex missing data patterns in multivariate time series derived from hospital electronic health records (EHRs). CSAI extends state-of-the-art neural network-based imputation by introducing key modifications specific to EHR data: a) attention-based hidden state initialisation to capture both long- and short-range temporal dependencies prevalent in EHRs, b) domain-informed temporal decay to mimic clinical data recording patterns, and c) a non-uniform masking strategy that models non-random missingness by calibrating weights according to both temporal and cross-sectional data characteristics. Comprehensive evaluation across four EHR benchmark datasets demonstrates CSAI's effectiveness compared to state-of-the-art architectures in data restoration and downstream tasks. CSAI is integrated into PyPOTS, an open-source Python toolbox designed for machine learning tasks on partially observed time series. This work significantly advances the state of neural network imputation applied to EHRs by more closely aligning algorithmic imputation with clinical realities.
Fusion of Quadratic Time-Frequency Analysis and Convolutional Neural Networks to Diagnose Bearing Faults Under Time-Varying Speeds
Venue: arXiv cs.LG 2026
Year: 2026

Diagnosis of bearing faults is paramount to reducing maintenance costs and operational breakdowns. Bearing faults are primary contributors to machine vibrations, and analyzing their signal morphology offers insights into their health status. Unfortunately, existing approaches are optimized for controlled environments, neglecting realistic conditions such as time-varying rotational speeds and the vibration's non-stationary nature. This paper presents a fusion of time-frequency analysis and deep learning techniques to diagnose bearing faults under time-varying speeds and varying noise levels. First, we formulate the bearing fault-induced vibrations and discuss the link between their non-stationarity and the bearing's inherent and operational parameters. We also elucidate quadratic time-frequency distributions and validate their effectiveness in resolving distinctive dynamic patterns associated with different bearing faults. Based on this, we design a time-frequency convolutional neural network (TF-CNN) to diagnose various faults in rolling-element bearings. Our experimental findings undeniably demonstrate the superior performance of TF-CNN in comparison to recently developed techniques. They also assert its versatility in capturing fault-relevant non-stationary features that couple with speed changes and show its exceptional resilience to noise, consistently surpassing competing methods across various signal-to-noise ratios and performance metrics. Altogether, the TF-CNN achieves substantial accuracy improvements up to 15%, in severe noise conditions.
Learning-based agricultural management in partially observable environments subject to climate variability
Venue: arXiv cs.LG 2026
Year: 2026

Agricultural management, with a particular focus on fertilization strategies, holds a central role in shaping crop yield, economic profitability, and environmental sustainability. While conventional guidelines offer valuable insights, their efficacy diminishes when confronted with extreme weather conditions, such as heatwaves and droughts. In this study, we introduce an innovative framework that integrates Deep Reinforcement Learning (DRL) with Recurrent Neural Networks (RNNs). Leveraging the Gym-DSSAT simulator, we train an intelligent agent to master optimal nitrogen fertilization management. Through a series of simulation experiments conducted on corn crops in Iowa, we compare Partially Observable Markov Decision Process (POMDP) models with Markov Decision Process (MDP) models. Our research underscores the advantages of utilizing sequential observations in developing more efficient nitrogen input policies. Additionally, we explore the impact of climate variability, particularly during extreme weather events, on agricultural outcomes and management. Our findings demonstrate the adaptability of fertilization policies to varying climate conditions. Notably, a fixed policy exhibits resilience in the face of minor climate fluctuations, leading to commendable corn yields, cost-effectiveness, and environmental conservation. However, our study illuminates the need for agent retraining to acquire new optimal policies under extreme weather events. This research charts a promising course toward adaptable fertilization strategies that can seamlessly align with dynamic climate scenarios, ultimately contributing to the optimization of crop management practices.
Parameterized Hardware Design with Latency-Abstract Interfaces
Venue: arXiv cs.PL 2026
Year: 2026

Hardware designs must use latency-insensitive (LI) interfaces when timing is input-dependent. When timing is input-independent, designs should use latency-sensitive (LS) interfaces for maximum performance. However, designs commonly use LI interfaces to integrate with externally generated LS modules--from, e.g., IP generators, high-level synthesis, or domain specific languages. In every fully integrated design, such uses of LI represent pure overhead. The challenge is that generators can dramatically change timing interfaces of the modules to meet performance objectives, and LI interfaces act as a useful design abstraction and enable timing adaptation. We define latency-abstract (LA) interfaces, a new design abstraction, which provide the timing adaptability of LI interfaces at design-time and the efficient integration of LS interfaces. LA interfaces use output parameters, a novel compile-time mechanism for child modules to return values parent modules, to abstract and encapsulate timing behaviors at design time. During design elaboration, LA interfaces are compiled into efficient LS interfaces based on parameter values. While an attractive option, LA interfaces inherit the complexities of parameterized hardware design: the user must reason how parameters influence timing behaviors of modules and ensure that designs adapt to interface changes. To address this challenge and demonstrate the utility of LA interfaces, we design Lilac, a parameterized HDL that uses a type system track the influence of parameters on timing behaviors and formally guarantee that every parameterization of an LA design results in a circuit without structural hazards.
Knee or ROC
Venue: arXiv cs.LG 2026
Year: 2026

Self-attention transformers have demonstrated accuracy for image classification with smaller data sets. However, a limitation is that tests to-date are based upon single class image detection with known representation of image populations. For instances where the input image classes may be greater than one and test sets that lack full information on representation of image populations, accuracy calculations must adapt. The Receiver Operating Characteristic (ROC) accuracy threshold can address the instances of multiclass input images. However, this approach is unsuitable in instances where image population representation is unknown. We then consider calculating accuracy using the knee method to determine threshold values on an ad-hoc basis. Results of ROC curve and knee thresholds for a multi-class data set, created from CIFAR-10 images, are discussed for multiclass image detection.
Improved accuracy of continuum surface flux models for metal additive manufacturing melt pool simulations
Venue: arXiv cs.CE 2026
Year: 2026

Computational modeling of the melt pool dynamics in laser-based powder bed fusion metal additive manufacturing (PBF-LB/M) promises to shed light on fundamental mechanisms of defect generation. These processes are accompanied by rapid evaporation so that the evaporation-induced recoil pressure and cooling arise as major driving forces for fluid dynamics and temperature evolution. The magnitude of these interface fluxes depends exponentially on the melt pool surface temperature, which, therefore, has to be predicted with high accuracy. The present work utilizes a diffuse interface finite element model based on a continuum surface flux (CSF) description of interface fluxes to study dimensionally reduced thermal two-phase problems representative for PBF-LB/M in a finite element framework. It is demonstrated that the extreme temperature gradients combined with the high ratios of material properties between metal and ambient gas lead to significant errors in the interface temperatures and fluxes when classical CSF approaches, along with typical interface thicknesses and discretizations, are applied. It is expected that this finding is also relevant for other types of diffuse interface PBF-LB/M melt pool models. A novel parameter-scaled CSF approach is proposed, which is constructed to yield a smoother temperature field in the diffuse interface region, significantly increasing the solution accuracy. The interface thickness required to predict the temperature field with a given level of accuracy is less restrictive by at least one order of magnitude for the proposed parameter-scaled approach compared to classical CSF, drastically reducing computational costs. Finally, we showcase the general applicability of the parameter-scaled CSF to a 3D simulation of stationary laser melting of PBF-LB/M considering the fully coupled thermo-hydrodynamic multi-phase problem, including phase change.
AlphaMapleSAT: An MCTS-based Cube-and-Conquer SAT Solver for Hard Combinatorial Problems
Venue: arXiv cs.AI 2026
Year: 2026

This paper introduces AlphaMapleSAT, a Cube-and-Conquer (CnC) parallel SAT solver that integrates Monte Carlo Tree Search (MCTS) with deductive feedback to efficiently solve challenging combinatorial SAT problems. Traditional lookahead cubing methods, used by solvers such as March, limit their search depth to reduce overhead often resulting in suboptimal partitions. By contrast, AlphaMapleSAT performs a deeper MCTS search guided by deductive rewards from SAT solvers. This approach enables informed exploration of the cubing space while keeping cubing costs low. We demonstrate the efficacy of our technique via extensive evaluations against the widely used and established March cubing solver on three well-known challenging combinatorial benchmarks, including the minimum Kochen-Specker (KS) problem from quantum mechanics, the Murty-Simon Conjecture, and the Ramsey problems from extremal graph theory. We compare AlphaMapleSAT against March using different types of conquering solvers such as SAT Modulo Symmetries (SMS) and SAT+CAS, both built on top of the CaDiCaL SAT solver. We show that in all cases, there is a speedup in elapsed real time (wall clock time) ranging from 1.61x to 7.57x on a 128 core machine for the above-mentioned problems. We also perform cube-level and parallel scaling analysis over 32, 64, and 128 cores, which shows that AlphaMapleSAT outperforms March on all these settings. Our results show that deductively-guided MCTS search technique for cubing in CnC solvers can significantly outperform March on hard combinatorial problems.
Domain-Independent Dynamic Programming
Venue: arXiv cs.AI 2026
Year: 2026

For combinatorial optimization problems, model-based paradigms such as mixed-integer programming (MIP) and constraint programming (CP) aim to decouple modeling and solving a problem: the `holy grail' of declarative problem solving. We propose domain-independent dynamic programming (DIDP), a novel model-based paradigm based on dynamic programming (DP). While DP is not new, it has typically been implemented as a problem-specific method. We introduce Dynamic Programming Description Language (DyPDL), a formalism to define DP models based on a state transition system, inspired by artificial intelligence (AI) planning. we show that heuristic search algorithms can be used to solve DyPDL models and propose seven DIDP solvers. We experimentally compare our DIDP solvers with commercial MIP and CP solvers (solving MIP and CP models, respectively) on common benchmark instances of eleven combinatorial optimization problem classes. We show that DIDP outperforms MIP in nine problem classes, CP also in nine problem classes, and both MIP and CP in seven. DIDP also achieves superior performance to existing state-based solvers including domain-independent AI planners.
Inconsistency Masks: Harnessing Model Disagreement for Stable Semi-Supervised Segmentation
Venue: arXiv cs.CV 2026
Year: 2026

A primary challenge in semi-supervised learning (SSL) for segmentation is the confirmation bias from noisy pseudo-labels, which destabilizes training and degrades performance. We propose Inconsistency Masks (IM), a framework that reframes model disagreement not as noise to be averaged away, but as a valuable signal for identifying uncertainty. IM leverages an ensemble of teacher models to generate a mask that explicitly delineates regions where predictions diverge. By filtering these inconsistent areas from input-pseudo-label pairs, our method effectively mitigates the cycle of error propagation common in both continuous and iterative self-training paradigms. Extensive experiments on the Cityscapes benchmark demonstrate IM's effectiveness as a general enhancement framework: when paired with leading approaches like iMAS, U$^2$PL, and UniMatch, our method consistently boosts accuracy, achieving superior benchmarks across ResNet-50 and DINOv2 backbones, and even improving distilled architectures like SegKC. Furthermore, the method's robustness is confirmed in resource-constrained scenarios where pre-trained weights are unavailable. On three additional diverse datasets from medical and underwater domains trained entirely from scratch, IM significantly outperforms standard SSL baselines. Notably, the IM framework is dataset-agnostic, seamlessly handling binary, multi-class, and complex multi-label tasks by operating on discretized predictions. By prioritizing training stability, IM offers a generalizable and robust solution for semi-supervised segmentation, particularly in specialized areas lacking large-scale pre-training data. The full code is available at: https://github.com/MichaelVorndran/InconsistencyMasks
A Resolution-Based Interactive Proof System for UNSAT
Venue: arXiv cs.LO 2026
Year: 2026

Modern SAT or QBF solvers are expected to produce correctness certificates. However, certificates have worst-case exponential size (unless NP=coNP), and at recent SAT competitions the largest certificates of unsatisfiability are starting to reach terabyte size. This puts limits to the development of SAT-solving services in which a client with limited computational power sends a formula to a solver running on a powerful server, which returns a certificate to be checked by the client. Recently, Couillard et al. have suggested to replace certificates with interactive proof systems based on the IP=PSPACE theorem. They have presented an interactive protocol between a prover and a verifier for an extension of QBF. The overall running time of the protocol is linear in the time needed by a standard BDD-based algorithm, and the time invested by the verifier is polynomial in the size of the formula. (So, in particular, the verifier never has to read or process exponentially long certificates). We call such an interactive protocol competitive with the BDD algorithm for solving QBF. While BDD algorithms are state-of-the-art for certain classes of QBF instances, no modern (UN)SAT solver is based on BDDs. For this reason, we initiate the study of interactive certification for more practical SAT algorithms. In particular, we address the question whether interactive protocols can be competitive with some variant of resolution. We present two contributions. First, we prove a theorem that reduces the problem of finding competitive interactive protocols to finding an arithmetisation of formulas satisfying certain commutativity properties. (Arithmetisation is the fundamental technique underlying the IP=PSPACE theorem.) Then, we apply the theorem to give the first interactive protocol for the Davis-Putnam resolution procedure. We also report on an implementation and give some experimental results.
Asynchronous dynamics of isomorphic Boolean networks
Venue: arXiv cs.DM 2026
Year: 2026

A Boolean network is a function $f:\{0,1\}^n\to\{0,1\}^n$ from which several dynamics can be derived, depending on the context. The most classical ones are the synchronous and asynchronous dynamics. Both are digraphs on $\{0,1\}^n$, but the synchronous dynamics (which is identified with $f$) has an arc from $x$ to $f(x)$ while the asynchronous dynamics $\mathcal{A}(f)$ has an arc from $x$ to $x+e_i$ whenever $x_i\neq f_i(x)$. Clearly, $f$ and $\mathcal{A}(f)$ share the same information, but what can be said on these objects up to isomorphism? We prove that if $\mathcal{A}(f)$ is only known up to isomorphism then, with high probability, $f$ can be fully reconstructed up to isomorphism. We then show that the converse direction is far from being true. In particular, if $f$ is only known up to isomorphism, very little can be said on the attractors of $\mathcal{A}(f)$. For instance, if $f$ has $p$ fixed points, then $\mathcal{A}(f)$ has at least $\max(1,p)$ attractors, and we prove that this trivial lower bound is tight: there always exists $h\sim f$ such that $\mathcal{A}(h)$ has exactly $\max(1,p)$ attractors. But $\mathcal{A}(f)$ may often have much more attractors since we prove that, with high probability, there exists $h\sim f$ such that $\mathcal{A}(h)$ has $Ω(2^n)$ attractors.
Knowledge-Centric Metacognitive Learning
Venue: arXiv cs.AI 2026
Year: 2026

Interactions are central to intelligent reasoning and learning abilities, with the interpretation of abstract knowledge guiding meaningful interaction with objects in the environment. While humans readily adapt to novel situations by leveraging abstract knowledge acquired over time, artificial intelligence systems lack principled mechanisms for incorporating abstract knowledge into learning, leading to fundamental challenges in the emergence of intelligent and adaptive behavior. To address this gap, we introduce knowledge-centric metacognitive learning based on three key principles: natural abstractions, knowledge-guided interactions through interpretation, and the composition of interactions for problem solving. Knowledge learning facilitates the acquisition of abstract knowledge and the association of interactions with knowledge, while object interactions guided by abstract knowledge enable the learning of transferable interaction concepts, abstract reasoning, and generalization. This metacognitive mechanism provides a principled approach for integrating knowledge into reinforcement learning and offers a promising pathway toward intelligent and adaptive behavior in artificial intelligence, robotics, and autonomous systems.
Pelican Soup Framework: A Theoretical Framework for Language Model Capabilities
Venue: arXiv cs.CL 2026
Year: 2026

In this work, we propose a simple theoretical framework, Pelican Soup, aiming to better understand how pretraining allows LLMs to (1) generalize to unseen instructions and (2) perform in-context learning, even when the verbalizers are irrelevant to the task. To this end, in our framework, we introduce the notion of "knowledge base" and "reference-sense association" and a simple formalism for natural language processing tasks. Our framework demonstrates how linguistic, psychology, and philosophy studies can inform our understanding of the language model and is connected to several other existing theoretical results. As an illustration of the usage of our framework, we derive a bound on in-context learning loss with our framework. Finally, we support our framework with empirical experiments and provide possible future research directions.