transfer-learning

PixelJev is introduced as a native-image decision interface using small open multimodal models to map images, instructions, and candidate sets to structured choices. The study demonstrates adaptation improves accuracy on benchmarks like Pets and highlights challenges in calibration and generalization.

This paper proposes a framework to learn cross-task relationships in multi-task models by approximating joint label distributions, improving performance in YouTube's recommendation systems through transfer learning.

This paper combines transfer learning with conformalized quantile regression to improve solar PV forecasting accuracy and uncertainty quantification under data scarcity caused by load-shedding in Bangladesh, demonstrating significant performance gains over baseline methods.

The paper introduces TopoSIGN, a topology-guided graph pre-training and prompt learning framework for signed graphs, which combines structural encoding and persistent homology to improve transfer learning in tasks like link prediction and node classification.

The paper proposes EviGDA, a framework that enhances graph domain adaptation by combining graph-aware and graph-free experts to improve prediction under structural shifts.

This paper presents RoboDawn, a method to transfer Vision-Language Model intelligence to robotic control, achieving state-of-the-art results on benchmarks with zero-shot and one-shot learning and successful real-world applications.

This paper introduces a post-hoc weight rectification framework called JANUS to mitigate catastrophic forgetting in fine-tuning foundation models, achieving parameter space orthogonality for preserving historical performance while adapting to new tasks.

This paper investigates component roles in warm-start transfer for grokking in neural networks, showing that transferring internal weights improves early performance but risks instability, and proposes methods to stabilize the process.

The paper introduces a three-stage training pipeline using procedural pretraining to improve molecular property prediction, showing enhanced performance under data scarcity by learning inductive biases from abstract generated data.

This survey paper organizes AI applications in games into six roles based on foundation models, discussing transferability and evaluation challenges across the game lifecycle.

The paper presents SAAC-JEPA, a schema-adaptive action-conditioned JEPA model for transferring predictive representations across CNC machines with partial sensor overlap, demonstrating that cross-machine adaptation requires distinct evaluation beyond source-domain accuracy.

This paper investigates portable semantics and negative transfer in latent communication between language-model cells, showing that independently trained societies share semantic interfaces but not raw language, and that inherited interfaces can cause severe negative transfer.

This paper uses Fisher–Rao geometry to show that large language models share behavioral structures that are learned and controllable, enabling transfer learning and minimal-disturbance interventions.

This paper introduces LatentDDM, a method that pretrains neural operators on small subdomains and composes them to improve accuracy and reduce adaptation cost for physical simulations in varying domains.

SMart is a new time series representation learning framework that uses multi-phase recurrence plots recovery and a source dataset selector to enhance representation transfer from multiple datasets, showing improved performance in classification and regression tasks.

This research investigates repurposing the backbone from YOLO26's depth estimation model for image deraining, demonstrating that depth-trained initialization provides a consistent improvement over random initialization in controlled experiments.

StudyBench introduces a controlled physics benchmark to measure how efficiently self-evolution methods convert training material into transferable problem-solving ability, revealing gaps in guidance and compute.

SimCast-S2S is a generative latent-diffusion framework for probabilistic subseasonal precipitation forecasting that leverages transfer learning from climate simulations to outperform deep learning baselines and compete with operational systems.

AgentMercury shows that training AI agents in simulated business environments generated from plain descriptions can transfer effectively to evaluation benchmarks, even if the training worlds are unrelated. The system improved performance through fine-tuning on construction traces.

This paper investigates AI learning and conceptual transfer in the Game of Hidden Rules, focusing on reinforcement learning with Transformer-based A2C framework, rule difficulty analysis, transfer learning, and generalization.