Agentic Learning AI Lab
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agentic learning
ai lab
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.

Key Areas

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Learning from Visual Experience

Embodied AI; Self-supervised learning; Continual learning; Representation learning; Multimodal learning.

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Adaptive Agents and Foundation Models

Personalized AI; Agents; Few-shot, continual, meta learning; Memorization and forgetting; News forecasting.

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Concept Learning and Abstraction

Few-shot concept learning; Visual reasoning; Relational abstraction; Hierarchical abstraction and planning.

Recent Works

design

In-Context Clustering with Large Language Models

In-Context Clustering (ICC) is a flexible LLM-based procedure for clustering data from diverse distributions.

Published: 2025-10-09

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design

Local Reinforcement Learning with Action-Conditioned Root Mean Squared Q-Functions

Action-conditioned Root mean squared Q-Functions (ARQ) is a novel backprop-free value estimation method that applies a goodness function and action conditioning for local reinforcement learning.

Published: 2025-10-08

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design

Midway Network: Learning Representations for Recognition and Motion from Latent Dynamics

Midway Network is a new self-supervised learning architecture that learns strong visual representations for both object recognition and motion understanding solely from natural videos by modeling latent dynamics.

Published: 2025-10-07

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design

StreamMem: Query-Agnostic KV Cache Memory for Streaming Video Understanding

StreamMem is a query-agnostic KV cache memory mechanism for streaming video understanding.

Published: 2025-08-21

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design

Context Tuning for In-Context Optimization

Context Tuning is a simple and effective method to significantly enhance few-shot adaptation of LLMs without fine-tuning model parameters.

Published: 2025-07-06

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design

Discrete JEPA: Learning Discrete Token Representations without Reconstruction

Discrete-JEPA extends the latent predictive coding JEPA framework with semantic tokenization and complementary objectives for symbolic reasoning tasks.

Published: 2025-06-22

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design

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos

Memory Storyboard groups recent past frames into temporal segments and provides effective summarization of the past visual streams for memory replay.

Published: 2025-01-21

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design

Are LLMs Prescient? A Continuous Evaluation using Daily News as Oracle

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.

Published: 2024-11-13

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design

PooDLe: Pooled and Dense Self-Supervised Learning from Naturalistic Videos

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.

Published: 2024-08-20

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design

ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative Generation

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.

Published: 2024-08-05

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