NeurIPS 2025 Past Generative models
NeurIPS 2025 Workshop: AI and ML for Next-Generation Wireless Communications and Networking
AI4NextG @ NeurIPS 25
- Submission deadline
- Aug 30, 2025, 18:59 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (61)
Fetched from OpenReview (v2) on 2026-06-10.
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A Compression Algorithm for Distributed LMMs with Different Information Fusion Techniques
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A Probabilistic Approach to Pose Synchronization for Multi-Reference Alignment with applications to MIMO wireless communication systems
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A Study of Neural Polar Decoders for Communication
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Adaptive Cooperative Transmission Design for Ultra-Reliable Low-Latency Communications via Deep Reinforcement Learning
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Adaptive GNN-based Proportional-Fair Scheduling in MIMO Networks for Non-stationary Channels
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Asynchronous Unsupervised Online Learning of Bayesian Deep Receivers
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AURA: Adaptive Unified Reasoning and Automation with LLM-Guided MARL for NextG Cellular Networks
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Beyond Prompts: Preserving Semantics in Diffusion-based Communication
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Block ModShift: Model Privacy via Dynamic Designed Shifts
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Building the Cognitive Network: Pillars of AI-Native Wireless ecosystem
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Channel Simulation and Distributed Compression with Ensemble Rejection Sampling
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CHAST: Attention Aided SISO OFDM Channel Estimation
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Conditional Denoising Diffusion Autoencoders for Wireless Semantic Communications
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Conformal Sparsification for Bandwidth-Efficient Edge–Cloud Speculative Decoding
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Constrained Multi-Agent Reinforcement Learning for Task Offloading in Wireless Edge Networks
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Constrained Network Slice Assignment via Large Language Models
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ConTwin: Contrastive Learning for Robust Digital Twin CSI Prediction
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Cross-Layer Design for Near-Field mmWave Beam Management and Scheduling under Delay-Sensitive Traffic
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Data-Free Quantization of Neural Receivers: When 4-Bit Succeeds, Why 6-Bit Matters for 6G
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Dynamic Features Adaptation in Networking: Toward Flexible training and Explainable inference
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End-to-End Waveform Design for Nonlinear Satellite Links with a Convolutional Autoencoder
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Fairness-Oracular MARL with Competitor-Aware Signals for Collaborative Inference
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Federated learning over physical channels: adaptive algorithms with near-optimal guarantees
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Federated Model-Based Offline Multi-Agent Reinforcement Learning for Wireless Networks
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Few Features are Enough: Communication-Efficient AI-RAN
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Foundation Model-aided Multi-agent Reinforcement Learning for Random Access Network Optimization
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Frequency Extrapolation for Carrier Aggregation as a Super-Resolution Problem: Rethinking Conventional Forecasting Methods
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From Simulation to Practice: Generalizable Deep Reinforcement Learning for Cellular Schedulers
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In-Context Radio Map Estimation via Ripple Autoregressive Modeling
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LLM Agent Communication Protocol (LACP) Requires Urgent Standardization: A Telecom-Inspired Protocol is Necessary
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Long-term Wireless Link Scheduling with State-Augmented Graph Neural Networks
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Managing Conflicts Among Black-Box RAN Apps via Multi-Fidelity Game-Theoretic Optimization
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MAR-FL: A Communication Efficient Peer-to-Peer Federated Learning System
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Masked Symbol Modeling for Demodulation of Oversampled Baseband Communication Signals in Impulsive Noise-Dominated Channels
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Mixture-of-Experts for Multi-Task Semantic Communications with CSI-Free Multiple Access
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Multi-Task Transformer Receiver for OFDM Channel Estimation and Symbol Detection
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Network Traffic Foundation Model with Adaptation via In-Context Learning and Mixture-of-Experts
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Neuro-Cognitive Radios for Dynamic Spectrum Access
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Optimal Neural Compressors for the Rate-Distortion-Perception Tradeoff
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PEARL: Peer-Enhanced Adaptive Radio via On-Device LLM
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Physics-based Meta Learning for Channel Transformation
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Physics-Informed Neural Networks for Wireless Channel Estimation with Limited Pilot Signals
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Position: There Is No Ground Truth -- Rethinking Evaluation in AI-Driven Channel Prediction
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Preference-centric Bandits in Wireless Communications: Theory and Applications
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Privacy via Scheduling and Connectivity Design in Decentralized Federated Learning
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Realism and Fidelity: Two Sides of a Coin in Deep Joint Source-Channel Coding
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Reasoning Meets Representation: Envisioning Neuro-Symbolic Wireless Foundation Models
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Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulations
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Robust Channel Representation for Wireless: A Multi-Task Masked Contrastive Approach
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SafeCOMM: Investigating Safety Degradation in Fine-Tuned Telecom Large Language Models
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Satisficing with Binary Feedback: Multi-User mmWave Beam and Rate Adaptation via Combinatorial Bandits
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Single-Step Online Adaptation of Modular Bayesian Deep Receivers with Streaming Data
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Tele-LLM-Hub: Building Context-Aware Multi-Agent LLM Systems for Telecom Networks
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The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network
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The Pathway to Adaptive Lightweight AI Transceivers (Vision Paper)
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Through the telecom lens: Are all training samples important?
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Towards Achieving Integer and Load-balancing User Association in Wireless Networks with a Reparameterized Attention-based GNN
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Towards Building a Foundation Model for Wireless Sensing: A Pilot Study
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VLF-MSC: Vision-Language Feature-Based Multimodal Semantic Communication System
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WISE: Wireless Analog Computing at Radio Frequency for Disaggregated Deep Learning Inference
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XAI-on-RAN: Explainable, AI-native, and GPU-Accelerated RAN Towards 6G