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            agentic learning <br/> ai lab
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                            agentic learning <br/> ai lab
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                        Agentic Learning AI Lab is a research lab in New York
                        University founded in 2022. We innovate learning algorithms that enable future agentic AI to learn and adapt flexibly in the real world.
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                Key Areas
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                    Learning from Visual Experience
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                    Embodied AI; Self-supervised learning; Continual learning; Representation learning; Multimodal learning.
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                    Adaptive Foundation Models
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                    Personalized AI; Few-shot, continual, meta learning; Memorization and forgetting; Future news forecasting.
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                    Concept Learning and Abstraction
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                    Few-shot concept learning; Visual reasoning; Relational abstraction; Hierarchical abstraction and planning.
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                Recent Works
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                        <h3 class="tw-text-xl tw-font-medium tw-mt-4">Replay Can Provably Increase Forgetting</h3>
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                            We provide a theoretical analysis of sample replay in over-parameterized continual linear regression, and we show that replay can provably increase forgetting in the worst case even though the network has the capacity to memorize all tasks.
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                        Published: 2025-06-04
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                        <h3 class="tw-text-xl tw-font-medium tw-mt-4">Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos</h3>
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                            Memory Storyboard groups recent past frames into temporal segments and provides effective summarization of the past visual streams for memory replay.
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                        Published: 2025-01-21
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                        <h3 class="tw-text-xl tw-font-medium tw-mt-4">Are LLMs Prescient? A Continuous Evaluation using Daily News as Oracle</h3>
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                            Our new benchmark, Daily Oracle, automatically generates question-answer (QA) pairs from daily news, challenging LLMs to predict "future" events based on pre-training data.
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                        Published: 2024-11-13
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                            We propose PooDLe, a self-supervised learning method that combines an invariance-based objective on pooled representations with a dense SSL objective that enforces equivariance to optical flow warping.
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                        Published: 2024-08-20
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                        <h3 class="tw-text-xl tw-font-medium tw-mt-4">ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative Generation</h3>
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                            ProCreate is a simple and easy-to-implement method to improve sample diversity and creativity of diffusion-based image generative models and to prevent training data reproduction.
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                        Published: 2024-08-05
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                        <h3 class="tw-text-xl tw-font-medium tw-mt-4">Integrating Present and Past in Unsupervised Continual Learning</h3>
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                            We formulate Osiris, a unifying framework for unsupervised continual learning (UCL), which disentangles learning objectives that encompass stability, plasticity, and cross-task consolidation.
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                        Published: 2024-04-29
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                        <h3 class="tw-text-xl tw-font-medium tw-mt-4">CoLLEGe: Concept Embedding Generation for Large Language Models</h3>
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                            CoLLEGe is a meta-learning framework capable of generating flexible embeddings for new concepts using a small number of example sentences or definitions.
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                        Published: 2024-03-22
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                        <h3 class="tw-text-xl tw-font-medium tw-mt-4">Reawakening Knowledge: Anticipatory Recovery from Catastrophic Interference via Structured Training</h3>
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                            We discover a curious and remarkable property of LLMs fine-tuned sequentially in this setting: they exhibit anticipatory behavior, recovering from the forgetting on documents before encountering them again.
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                        Published: 2024-03-14
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