transfer-learning

The paper analyzes the safety of per-agent policy composition in multi-agent reinforcement learning, proving that independent composition can fail, and proposes MA-USFA, a hierarchical method that enables safe and flexible successor-feature transfer in cooperative MARL.

This paper proposes Hierarchical Federated Transfer Learning (HFTL) for Digital Twin-based Vehicular Ad hoc Networks, addressing data heterogeneity and sparsity via vehicle clustering and a data quality score mechanism to defend against malicious vehicles.

This paper proposes a market-information-aware gated LoRA framework to adapt the Chronos-2 time-series foundation model for day-ahead electricity price forecasting, improving cross-market transferability on Chinese provincial markets.

This paper proposes an LLM-assisted semantic stop embedding approach for reinforcement-learning-based bus holding control, reducing headway variability, bunching events, and passenger waiting time in simulations.

This paper proposes a transferable autologistic model for predicting rare equipment failures across heterogeneous sensor configurations, evaluating it on a synthetic refrigerator dataset.

This paper studies whether prompt-side agent playbooks (procedures distilled from prior runs) transfer across settings, finding that frozen transfer works only conditionally depending on compatibility with target deployment, with mixed results on ALFWorld, TAU2-Bench, and XBench-DeepSearch.

This paper introduces EddyFlow, a deep learning framework for kilometer-scale sea surface temperature downscaling that balances predictive accuracy, scale-dependent structure, and regional generalization. It achieves strong zero-shot performance and near-ideal spectral fidelity across multiple ocean regions.

This paper describes Team uOttawa's system for the EvaLatin 2026 shared task on Named Entity Recognition for Classical Latin, using prompt engineering of commercial LLMs (Gemini 2.5 Pro and Claude Sonnet 4-5) to achieve first place in both coarse- and fine-grained NER subtasks.

A research paper evaluating CHAP, a CNN-BiLSTM model, for classifying sedentary behavior from wearable accelerometer data, focusing on transfer from hip to wrist placement and the benefits of finetuning.

This paper proposes Frontier Learning, a framework that combines representations and predictions from multiple black-box and white-box pretrained models to construct a unified target-domain representation, guaranteeing performance no worse than any individual reuse baseline under distribution shift. Evaluations on visual domain adaptation and clinical mortality prediction show consistent gains over strong baselines.

This paper introduces a Capability-Driven Multimodal Scaling Law that predicts VLM performance from LLM textual benchmark capabilities, enabling principled backbone selection across model families without expensive training sweeps.

This arXiv paper introduces ProGFM, a Propagation-aware Graph Foundation Model that treats propagation relationships between edges and feature dimensions as transferable knowledge units, enabling adaptive aggregation and improved cross-domain generalization.

This paper introduces Knowledge-Geometry Decoupling (KGD), a method for pretrain-then-transfer in streaming recommendation systems. It separates pretrained behavioral knowledge from task-specific geometry, enabling continual model refresh without interference, and reports 4-12% improvements over baselines plus successful deployment at Shopee.

Tweet highlights the 4th edition of the classic AI textbook 'Artificial Intelligence: A Modern Approach,' covering deep learning, transfer learning, multi-agent systems, robotics, and causality, with a link to Amazon.

Presents NEXUS, a lightweight foundation model with ~3M parameters pre-trained on LHC collision data, demonstrating improved downstream performance and cross-domain transfer to gravitational waves, flood forecasting, and neural activity.

This paper investigates whether temporally drifting data streams can be partitioned into discrete regimes by fitting a hidden Markov model to the trajectory of neural network weights trained on successive time windows, showing that recovered latent states correlate with transfer performance across two datasets.

AgentGFM proposes a graph foundation model where each node acts as an agent with a shared trainable policy, enabling adaptive information-flow control through a predict–act–observe–correct process. It achieves strong performance across node-level, graph-level, and large-scale transfer tasks.

This paper proposes a diagnosis and optimization framework for cross-domain and cross-lingual generalization in offensive language detection, decomposing performance degradation into dataset and language effects, and quantifying the trade-off between multilingual capability and source-task performance.

Introduces Building2Building (B2B), a large-scale benchmark for studying generalization and transfer in reinforcement learning using realistic HVAC control environments built on EnergyPlus, compatible with Gymnasium.

Inertia-1 is a research project that systematically explores the full lifecycle of motion models—data, sensing, objectives, and scale—to produce a unified representation that transfers across body placements, devices, and tasks without retraining, leveraging self-supervised pretraining on 18 million hours of accelerometry data.