{"total":151,"rows":[{"id":"2501.06186","title":"LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs","created":"2025-01-10","cited_by_count":147,"area":"lr"},{"id":"2503.20752","title":"Reason-RFT: Reinforcement Fine-Tuning for Visual Reasoning of Vision Language Models","created":"2025-03-26","cited_by_count":99,"area":"lr"},{"id":"2108.11451","title":"From Statistical Relational to Neurosymbolic Artificial Intelligence: a Survey","created":"2021-08-25","cited_by_count":72,"area":null},{"id":"2112.15424","title":"A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges","created":"2021-11-12","cited_by_count":69,"area":null},{"id":"2507.03019","title":"Look-Back: Implicit Visual Re-focusing in MLLM Reasoning","created":"2025-07-02","cited_by_count":55,"area":"lr"},{"id":"2207.14526","title":"Leveraging Explanations in Interactive Machine Learning: An Overview","created":"2022-07-29","cited_by_count":53,"area":null},{"id":"2105.03659","title":"Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text","created":"2021-05-08","cited_by_count":46,"area":null},{"id":"2205.00523","title":"Deep Learning with Logical Constraints","created":"2022-05-01","cited_by_count":44,"area":null},{"id":"2205.01128","title":"Neurocompositional computing: From the Central Paradox of Cognition to a new generation of AI systems","created":"2022-05-02","cited_by_count":44,"area":null},{"id":"2108.00648","title":"From LSAT: The Progress and Challenges of Complex Reasoning","created":"2021-08-02","cited_by_count":43,"area":null},{"id":"2102.11965","title":"Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases","created":"2021-02-23","cited_by_count":39,"area":null},{"id":"2111.08164","title":"A Survey on Neural-symbolic Learning Systems","created":"2021-11-10","cited_by_count":39,"area":null},{"id":"2202.12205","title":"Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review","created":"2022-02-24","cited_by_count":38,"area":"lr"},{"id":"2402.12240","title":"BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts","created":"2024-02-19","cited_by_count":37,"area":null},{"id":"2109.09904","title":"Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems","created":"2021-09-21","cited_by_count":36,"area":null},{"id":"2210.15889","title":"Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing","created":"2022-10-28","cited_by_count":35,"area":null},{"id":"2205.14268","title":"NeuPSL: Neural Probabilistic Soft Logic","created":"2022-05-27","cited_by_count":34,"area":"lr"},{"id":"2104.11914","title":"EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case","created":"2021-04-24","cited_by_count":30,"area":null},{"id":"2308.12595","title":"Logic-induced Diagnostic Reasoning for Semi-supervised Semantic Segmentation","created":"2023-08-24","cited_by_count":29,"area":null},{"id":"2202.06851","title":"HAKE: A Knowledge Engine Foundation for Human Activity Understanding","created":"2022-02-14","cited_by_count":28,"area":null},{"id":"2103.01719","title":"Differentiable Inductive Logic Programming for Structured Examples","created":"2021-03-02","cited_by_count":26,"area":null},{"id":"2303.04660","title":"Neural Probabilistic Logic Programming in Discrete-Continuous Domains","created":"2023-03-08","cited_by_count":25,"area":null},{"id":"2401.01040","title":"Towards Cognitive AI Systems: a Survey and Prospective on Neuro-Symbolic AI","created":"2024-01-02","cited_by_count":24,"area":null},{"id":"2110.00804","title":"ProTo: Program-Guided Transformer for Program-Guided Tasks","created":"2021-10-02","cited_by_count":21,"area":null},{"id":"2203.00938","title":"Neuro-Symbolic Verification of Deep Neural Networks","created":"2022-03-02","cited_by_count":21,"area":null},{"id":"2102.11529","title":"Differentiable Logic Machines","created":"2021-02-23","cited_by_count":18,"area":null},{"id":"2103.04854","title":"Injecting Knowledge in Data-driven Vehicle Trajectory Predictors","created":"2021-03-08","cited_by_count":18,"area":null},{"id":"2307.09437","title":"Grounded Object Centric Learning","created":"2023-07-18","cited_by_count":17,"area":null},{"id":"2309.13556","title":"LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoning","created":"2023-09-24","cited_by_count":17,"area":null},{"id":"2110.09383","title":"Neuro-Symbolic Forward Reasoning","created":"2021-10-18","cited_by_count":16,"area":null},{"id":"2311.17365","title":"Symbol-LLM: Leverage Language Models for Symbolic System in Visual Human Activity Reasoning","created":"2023-11-29","cited_by_count":16,"area":"lr"},{"id":"2402.00854","title":"SymbolicAI: A framework for logic-based approaches combining generative models and solvers","created":"2024-02-01","cited_by_count":16,"area":null},{"id":"2403.00323","title":"Softened Symbol Grounding for Neuro-symbolic Systems","created":"2024-03-01","cited_by_count":16,"area":null},{"id":"2404.04308","title":"Visual Knowledge in the Big Model Era: Retrospect and Prospect","created":"2024-04-05","cited_by_count":16,"area":null},{"id":"2303.10650","title":"Logic of Differentiable Logics: Towards a Uniform Semantics of DL","created":"2023-03-19","cited_by_count":15,"area":null},{"id":"2503.05954","title":"A Survey on Deep Learning Approaches for Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, Diversity, and Beyond","created":"2025-03-07","cited_by_count":15,"area":null},{"id":"2212.13296","title":"VQA and Visual Reasoning: An Overview of Recent Datasets, Methods and Challenges","created":"2022-12-26","cited_by_count":14,"area":"lr"},{"id":"2306.08397","title":"Scalable Neural-Probabilistic Answer Set Programming","created":"2023-06-14","cited_by_count":14,"area":null},{"id":"2205.12735","title":"Neuro-Symbolic Learning of Answer Set Programs from Raw Data","created":"2022-05-25","cited_by_count":12,"area":null},{"id":"2209.14974","title":"Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classification","created":"2022-09-26","cited_by_count":12,"area":null},{"id":"2210.05372","title":"DEPTWEET: A Typology for Social Media Texts to Detect Depression Severities","created":"2022-10-10","cited_by_count":12,"area":null},{"id":"2307.04347","title":"Injecting Logical Constraints into Neural Networks via Straight-Through Estimators","created":"2023-07-10","cited_by_count":12,"area":null},{"id":"2402.01889","title":"The Role of Foundation Models in Neuro-Symbolic Learning and Reasoning","created":"2024-02-02","cited_by_count":12,"area":"lr"},{"id":"2503.19466","title":"A Probabilistic Neuro-symbolic Layer for Algebraic Constraint Satisfaction","created":"2025-03-25","cited_by_count":11,"area":null},{"id":"2107.01877","title":"Faster-LTN: a neuro-symbolic, end-to-end object detection architecture","created":"2021-07-05","cited_by_count":10,"area":null},{"id":"2403.00329","title":"Learning with Logical Constraints but without Shortcut Satisfaction","created":"2024-03-01","cited_by_count":10,"area":null},{"id":"2212.14849","title":"Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression Search","created":"2022-12-30","cited_by_count":9,"area":null},{"id":"2406.04472","title":"On the Hardness of Probabilistic Neurosymbolic Learning","created":"2024-06-06","cited_by_count":9,"area":"lr"},{"id":"2106.11417","title":"Interpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programming","created":"2021-06-21","cited_by_count":8,"area":null},{"id":"2110.03395","title":"SLASH: Embracing Probabilistic Circuits into Neural Answer Set Programming","created":"2021-10-07","cited_by_count":8,"area":null},{"id":"2507.11127","title":"Defining neurosymbolic AI","created":"2025-07-15","cited_by_count":8,"area":null},{"id":"2208.00374","title":"Neuro-Symbolic Learning: Principles and Applications in Ophthalmology","created":"2022-07-31","cited_by_count":7,"area":null},{"id":"2310.09744","title":"Explore the Effect of Data Selection on Poison Efficiency in Backdoor Attacks","created":"2023-10-15","cited_by_count":7,"area":null},{"id":"2409.13153","title":"Towards Efficient Neuro-Symbolic AI: From Workload Characterization to Hardware Architecture","created":"2024-09-20","cited_by_count":7,"area":null},{"id":"2601.03509","title":"Evolving Programmatic Skill Networks","created":"2026-01-07","cited_by_count":7,"area":null},{"id":"2103.06854","title":"A conditional, a fuzzy and a probabilistic interpretation of self-organising maps","created":"2021-03-11","cited_by_count":6,"area":null},{"id":"2105.10709","title":"Inclusion of Domain-Knowledge into GNNs using Mode-Directed Inverse Entailment","created":"2021-05-22","cited_by_count":6,"area":null},{"id":"2305.08876","title":"Neurosymbolic AI and its Taxonomy: a survey","created":"2023-05-12","cited_by_count":6,"area":null},{"id":"2505.19825","title":"Position: Foundation Models for Tabular Data within Systemic Contexts Need Grounding","created":"2025-05-26","cited_by_count":6,"area":null},{"id":"2506.23908","title":"Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence","created":"2025-06-30","cited_by_count":6,"area":"lr"},{"id":"2403.17853","title":"Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic","created":"2024-03-26","cited_by_count":5,"area":null},{"id":"2408.11367","title":"Towards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxation","created":"2024-08-21","cited_by_count":5,"area":null},{"id":"2501.16368","title":"Foundation Models for CPS-IoT: Opportunities and Challenges","created":"2025-01-22","cited_by_count":5,"area":null},{"id":"2503.18213","title":"A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectives","created":"2025-03-23","cited_by_count":5,"area":null},{"id":"2505.21743","title":"Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happen","created":"2025-05-27","cited_by_count":5,"area":null},{"id":"2601.10520","title":"Breaking Up with Normatively Monolithic Agency with GRACE: A Reason-Based Neuro-Symbolic Architecture for Safe and Ethical AI Alignment","created":"2026-01-15","cited_by_count":5,"area":"lr"},{"id":"2602.21628","title":"RuCL: Stratified Rubric-Based Curriculum Learning for Multimodal Large Language Model Reasoning","created":"2026-02-25","cited_by_count":5,"area":"lr"},{"id":"2107.01238","title":"Solving Machine Learning Problems","created":"2021-07-02","cited_by_count":4,"area":null},{"id":"2111.14547","title":"LiVLR: A Lightweight Visual-Linguistic Reasoning Framework for Video Question Answering","created":"2021-11-29","cited_by_count":4,"area":null},{"id":"2505.24874","title":"The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models","created":"2025-05-30","cited_by_count":4,"area":null},{"id":"2506.06856","title":"Vision-EKIPL: External Knowledge-Infused Policy Learning for Visual Reasoning","created":"2025-06-07","cited_by_count":4,"area":"lr"},{"id":"2508.02320","title":"Zero-shot Compositional Action Recognition with Neural Logic Constraints","created":"2025-08-04","cited_by_count":4,"area":null},{"id":"2601.13387","title":"Confidence over Time: Confidence Calibration with Temporal Logic for Large Language Model Reasoning","created":"2026-01-19","cited_by_count":4,"area":"lr"},{"id":"2002.10373","title":"Symbolic Learning and Reasoning with Noisy Data for Probabilistic Anchoring","created":"2020-02-24","cited_by_count":3,"area":"wm"},{"id":"2312.11651","title":"Bridging Logic and Learning: A Neural-Symbolic Approach for Enhanced Reasoning in Neural Models (ASPER)","created":"2023-12-18","cited_by_count":3,"area":"lr"},{"id":"2403.03203","title":"CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments","created":"2024-03-05","cited_by_count":3,"area":"lr"},{"id":"2502.05843","title":"From Objects to Events: Unlocking Complex Visual Understanding in Object Detectors via LLM-guided Symbolic Reasoning","created":"2025-02-09","cited_by_count":3,"area":"lr"},{"id":"2504.00615","title":"Towards Responsible and Trustworthy Educational Data Mining: Comparing Symbolic, Sub-Symbolic, and Neural-Symbolic AI Methods","created":"2025-04-01","cited_by_count":3,"area":null},{"id":"2505.11694","title":"Neural Networks as Universal Finite-State Machines: A Constructive Deterministic Finite Automaton Theory","created":"2025-05-16","cited_by_count":3,"area":null},{"id":"2508.13171","title":"Cognitive Workspace: Active Memory Management for LLMs -- An Empirical Study of Functional Infinite Context","created":"2025-08-08","cited_by_count":3,"area":null},{"id":"2204.00624","title":"Explainable and Interpretable Diabetic Retinopathy Classification Based on Neural-Symbolic Learning","created":"2022-04-01","cited_by_count":2,"area":null},{"id":"2208.06838","title":"Reduced Implication-bias Logic Loss for Neuro-Symbolic Learning","created":"2022-08-14","cited_by_count":2,"area":"lr"},{"id":"2310.16273","title":"Deep Learning for Plant Identification and Disease Classification from Leaf Images: Multi-prediction Approaches","created":"2023-10-25","cited_by_count":2,"area":null},{"id":"2402.18285","title":"PiShield: A PyTorch Package for Learning with Requirements","created":"2024-02-28","cited_by_count":2,"area":null},{"id":"2406.00938","title":"A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI","created":"2024-06-03","cited_by_count":2,"area":null},{"id":"2412.19847","title":"Symbolic Disentangled Representations for Images","created":"2024-12-25","cited_by_count":2,"area":null},{"id":"2508.21618","title":"Physics-Informed Spectral Modeling for Hyperspectral Imaging","created":"2025-08-29","cited_by_count":2,"area":null},{"id":"2510.02855","title":"Constraint Satisfaction Approaches to Wordle: Novel Heuristics and Cross-Lexicon Validation","created":"2025-10-03","cited_by_count":2,"area":null},{"id":"2512.23036","title":"Problems With Large Language Models for Learner Modelling: Why LLMs Alone Fall Short for Responsible Tutoring in K--12 Education","created":"2025-12-28","cited_by_count":2,"area":null},{"id":"2210.02095","title":"ChemAlgebra: Algebraic Reasoning on Chemical Reactions","created":"2022-10-05","cited_by_count":1,"area":null},{"id":"2211.12006","title":"Differentiable Fuzzy $\\mathcal{ALC}$: A Neural-Symbolic Representation Language for Symbol Grounding","created":"2022-11-22","cited_by_count":1,"area":"lr"},{"id":"2305.00304","title":"A preferential interpretation of MultiLayer Perceptrons in a conditional logic with typicality","created":"2023-04-29","cited_by_count":1,"area":null},{"id":"2305.15374","title":"ASPER: Answer Set Programming Enhanced Neural Network Models for Joint Entity-Relation Extraction","created":"2023-05-24","cited_by_count":1,"area":null},{"id":"2312.12878","title":"Rule-Extraction Methods From Feedforward Neural Networks: A Systematic Literature Review","created":"2023-12-20","cited_by_count":1,"area":null},{"id":"2401.09651","title":"Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning","created":"2024-01-17","cited_by_count":1,"area":null},{"id":"2403.02933","title":"Fuzzy Datalog$^\\exists$ over Arbitrary t-Norms","created":"2024-03-05","cited_by_count":1,"area":"lr"},{"id":"2406.19897","title":"FI-CBL: A Probabilistic Method for Concept-Based Learning with Expert Rules","created":"2024-06-28","cited_by_count":1,"area":null},{"id":"2407.13382","title":"Open-World Visual Reasoning by a Neuro-Symbolic Program of Zero-Shot Symbols","created":"2024-07-18","cited_by_count":1,"area":"lr"},{"id":"2502.02135","title":"Standard Neural Computation Alone Is Insufficient for Logical Intelligence","created":"2025-02-04","cited_by_count":1,"area":null},{"id":"2503.06917","title":"Sample-Efficient Optimization over Generative Priors via Coarse Learnability","created":"2025-03-10","cited_by_count":1,"area":null}],"next_cursor":"100"}