adaptability
Adaptability in AI refers to the model's ability to adjust to new data or changing environments without requiring complete retraining. This is crucial for applications where the input data distribution may evolve over time.
- Active Target Discovery under Uninformative Priors: The Power of Permanent and Transient Memory
- Adaptable Safe Policy Learning from Multi-task Data with Constraint Prioritized Decision Transformer
- Feature-aware Modulation for Learning from Temporal Tabular Data
- Geometry-Aware Collaborative Multi-Solutions Optimizer for Model Fine-Tuning with Parameter Efficiency
- Large Language Models Think Too Fast To Explore Effectively
- LawShift: Benchmarking Legal Judgment Prediction Under Statute Shifts
- Learning Parameterized Skills from Demonstrations
- Less is More: Local Intrinsic Dimensions of Contextual Language Models
- MINGLE: Mixture of Null-Space Gated Low-Rank Experts for Test-Time Continual Model Merging
- MedChain: Bridging the Gap Between LLM Agents and Clinical Practice with Interactive Sequence
- Multi-agent KTO: Enhancing Strategic Interactions of Large Language Model in Language Game
- OpenHype: Hyperbolic Embeddings for Hierarchical Open-Vocabulary Radiance Fields
- Praxis-VLM: Vision-Grounded Decision Making via Text-Driven Reinforcement Learning
- Predictive Coding Enhances Meta-RL To Achieve Interpretable Bayes-Optimal Belief Representation Under Partial Observability
- R$^2$ec: Towards Large Recommender Models with Reasoning
- RelationAdapter: Learning and Transferring Visual Relation with Diffusion Transformers
- Robust and Diverse Multi-Agent Learning via Rational Policy Gradient
- SQL-R1: Training Natural Language to SQL Reasoning Model By Reinforcement Learning
- Uni-RL: Unifying Online and Offline RL via Implicit Value Regularization
- UniTraj: Learning a Universal Trajectory Foundation Model from Billion-Scale Worldwide Traces