AI research lab @nyuniversity led by @mengyer | Partially run by students | Also find us https://teapawt.org/t.co/IHXOiESm6f on bsky
New York
Joined November 2024
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Agentic Learning AI Lab @agentic_ai_lab presents at #ICML2026 in Seoul✈️🇰🇷 Come check out our latest research!
📍Main Conference Papers
Temporal Straightening for Latent Planning
Tue Jul 7 10:30am Hall A #1509
Context Tuning for In-Context Optimization
Wed Jul 8 5pm Hall A #2510
📍Workshop Papers
The Surprising Effectiveness of Deleting Weights in LLM Reasoning and Adaptation
Fri Jul 10 Rm 318 | Foundations of Deep Generative Models Workshop
Seeking the Unfamiliar but Memorable: Conceptual Creativity as Meta-Learning
Sat Jul 11 Rm 401 | Human-AI Co-Creativity Workshop
Aligning LLMs with Human Uncertainty: A Beta-Bernoulli Calibrator for LLM Forecasting
Sat Jul 11 Gr. Ballroom 104–105 | Forecasting as a New Frontier of Intelligence Workshop
Alive and Predicting: A Live Evaluation of Multi-Step Forecasting Agents
Sat Jul 11 Gr. Ballroom 104–105 | Forecasting as a New Frontier of Intelligence Workshop
See threads below for details!🧵👇
agentic learning ai lab retweeted
Labs disagree on vision encoders for MLLMs! ⚔️
Gemma 4 12B scrapped the encoder.
Kimi K3 dropped encoder pretraining.
Inkling 2 Small revived the encoder Inkling killed.
We trained 200+ models in a controlled study to settle it:
✅ Keep vision parameters, skip the pretraining. And they don't have to be an encoder! 🤔
Paper: arxiv.org/abs/2611.17334 🧵
agentic learning ai lab retweeted
CDS Asst. Prof. Mengye Ren (@mengyer) and his lab released @outerloop_sci, open-source software whose AI agents open pull requests only when their experiments improve a benchmark.
The agents got a GPT model to its target in 77% fewer training steps.
outerloop.science/
agentic learning ai lab retweeted
Context Tuning (ICML 2026) helped language models learn from a few examples by refining their stored context, with model weights held fixed.
Developed by Courant PhD student Jack Lu (@Jacklu_me) and collaborators.
nyudatascience.medium.com/he…
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agentic learning ai lab retweeted
Was curious if a BF16 trained mask would still hold up on quantized weights, so I ran a quick test using the Qwen2.5-7B GRPO mask
Even when you zero those weights directly inside the FP8 and INT4 checkpoints without touching the scales, almost the entire boost carries over
(on MATH-500, FP8 goes 64.2% -> 74.0% and INT4 goes 60.4% -> 74.2%, vs 74.8% in BF16).
also side finding: in INT4, 25.8% of the 3.16M weights the mask deletes were already rounded to 0 by quantization alone
Even the simplest weight edit, a deletion, can teach an LLM new skills!
Meet Deletion Tuning (#NeurIPS2026): just zeroing <0.1% of weights matches full fine-tuning's accuracy at scale. It's thousands of times smaller and avoids forgetting, a step toward continual learning. 🧵👇
New from our lab, led by @Jacklu_me and @yangzb159 with @yanii’s group at @UCalgaryML: zeroing <0.1% of an LLM’s weights matches full fine-tuning at scale and avoids forgetting!
The Surprising Effectiveness of Deleting Weights in LLM Post-Training ✂️: arxiv.org/abs/2611.10866
Even the simplest weight edit, a deletion, can teach an LLM new skills!
Meet Deletion Tuning (#NeurIPS2026): just zeroing <0.1% of weights matches full fine-tuning's accuracy at scale. It's thousands of times smaller and avoids forgetting, a step toward continual learning. 🧵👇
agentic learning ai lab retweeted
The CDS November Research Feature is here! In her paper "AdaJEPA: An Adaptive Latent World Model," Ying Wang (@yingwww_ ) introduces a world model that keeps learning after training. Read more on AdaJEPA & other CDS research in this month’s feature! t.e2ma.net/webview/130xei/dd…
agentic learning ai lab retweeted
New paper: Outerloop: Open and Trustworthy Multi-Agent Autoresearch. In most open autoresearch systems, the agent grades its own results. Outerloop is a kernel that runs agents as untrusted programs and keeps measurement and review outside their sandboxes.
agentic learning ai lab retweeted
NYU Center for Data Science is hiring Faculty Fellows! Apply by Nov 24 apply.interfolio.com/193498
CDS is hiring Faculty Fellows for fall 2027!
Two-year appointments with independent research, research and travel funds, and one course to teach per year.
Apply by November 24 for full consideration: apply.interfolio.com/193498
agentic learning ai lab retweeted
World models can fail when the world changes.
That's why CDS PhD student Ying Wang (@yingwww_), CDS founding director Yann LeCun (@ylecun) & CDS Assistant Professor Mengye Ren (@mengyer) built AdaJEPA to adapt during planning.
nyudatascience.medium.com/a-…
agentic learning ai lab retweeted
New #NVIDIA paper: what if robots never had to wait for their own thoughts?
TacitVLA learns to reason in chain-of-thought or subtasks, but its actions never read them.
→ Keeps the gains of reasoning at up to 3.4× lower latency on 4 benchmarks, and tops RoboMME and RMBench 🧵👇
Come say hi to Jack at #COLM and learn about our joint work with @hongseu33’s group on HyperThink, skipping slow reasoning by turning each question into a fast weight update!
HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning
🕸️: hyperthink-llm.github.io/
What if LLMs could think in weights, not tokens?
Introducing HyperThink (#COLM2026, co-led with @kdgyun425): a hypernetwork writes a per-question weight update to the LLM for it to skip the long thinking trace, a step toward accurate System 1 thinking! 🧵👇
📍 Tomorrow at @COLM_conf! Oct 6 (Tue), 4:30 to 6:30 PM, board #97, Franciscan C
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agentic learning ai lab retweeted
What if LLMs could think in weights, not tokens?
Introducing HyperThink (#COLM2026, co-led with @kdgyun425): a hypernetwork writes a per-question weight update to the LLM for it to skip the long thinking trace, a step toward accurate System 1 thinking! 🧵👇
📍 Tomorrow at @COLM_conf! Oct 6 (Tue), 4:30 to 6:30 PM, board #97, Franciscan C
agentic learning ai lab retweeted
Honored to receive the 2nd Annual Google ML and Systems Junior Faculty Award for our work on adaptable ML systems and agents. Grateful to @Google for the support, and to my students. More to come on adaptable agents, in open source and open science.
Outerloop is the open autoresearch stack we build and run in the lab. Check it out and deploy on your own cluster! outerloop.science
Outerloop 0.1.0 is out. A fleet of AI agents that improve your benchmarks on your code. Agents propose changes, run them on your cluster, and open pull requests when the metrics improve. We are open source. Let's embrace open science! Get started at outerloop.science
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