Liquid Labs.

Research is a founding principle of Liquid AI. Our origins stem from over a decade of MIT research; where Liquid Neural Networks created efficient, multimodal architectures for our Liquid Foundation Models.

Liquid Labs carries this legacy forward as our dedicated team driving fundamental breakthroughs in the science of building intelligent, personalized, and adaptive machines.

LongevityBench: An open benchmark and language models for AI in aging biology

By Alex Zhavoronkov, Vladimir Naumov, Denis Sidorenko, Alex Aliper, Vladimir Aladinskiy, Ramin Hasani, Alexander Amini, Katerina Nasto, Mathieu Reymond, Rim Shayakhmetov, Zulfat Miftakhutdinov, and 2 others
We introduce LongevityBench, an open suite of 17 tasks spanning five biodata domains, and use it to assess 18 frontier AI systems from six developer teams.

In-Place Tokenizer Expansion for Pre-trained LLMs

By Jimmy T.H. Smith, Tarek Dakhran, Alberto Cabrera, Simon S. Lee, Paul Pak, Aditya Tadimeti, Tim Seyde, Maxime Labonne, Alexander Amini, and Mathias Lechner
New recipe expands pre-trained LLM tokenizers in place, cutting non-English token counts up to 4x with no quality loss.

Fixed-Point Reasoners: Stable and Adaptive Deep Looped Transformers

By Sajad Movahedi, Vera Milovanović, Shlomo Libo Feigin, Alexander Theus, Thomas Hofmann, Valentina Boeva, T. Konstantin Rusch, and Antonio Orvieto
FPRM is a Transformer-based Fixed-Point Reasoning Model for looped architectures, using pre-norm layers, residual scaling, and fixed-point halting to improve signal propagation, adapt compute to task difficulty, and deliver strong results on Sudoku, Maze, state-tracking, and ARC-AGI.

Neural Low-Discrepancy Sequences

By Michael Etienne Van Huffel, Nathan Kirk, Makram Chahine, Daniela Rus, and T. Konstantin Rusch
NeuroLDS introduces the first machine learning framework for finite low-discrepancy sequences, generating point sequences with low prefix discrepancy and outperforming classical LDS methods across numerical integration, robot motion planning, scientific machine learning, and simulation.

SwitchCraft: A Programmatic Framework for Designing State-Switching Proteins

By Bowen Jing, Mihir Bafna, Anisha Parsan, Heyuan Michael Ni, David Kwabi-Addo, Bryan Bryson, Adam Klivans, and Bonnie Berger
SwitchCraft is a programmable framework for designing state-switching proteins using backpropagation through compositional constraints and structure prediction models, enabling allosteric regulation, ligand discrimination, and de novo fluorescent biosensor design for biotechnology.

MeMo: Model as a Memory

By Ryan Wei Heng Quek, Sanghyuk Lee, Alfred Wei Lun Leong, Arun Verma, Alok Prakash, Nancy F. Chen, Bryan Kian Hsiang Low, Daniela Rus, and Armando Solar-Lezama
MeMo (Memory as a Model) is a modular framework that adds timely, domain-specific knowledge to LLMs without changing model weights, capturing cross-document relationships, resisting retrieval noise, avoiding catastrophic forgetting, and supporting plug-and-play use with open and closed-source models.

Preconditioned DeltaNet: Curvature-aware Sequence Modeling for Linear Recurrences

By Neehal Tumma, Noel Loo, and Daniela Rus
Preconditioned delta-rule recurrences improve subquadratic long-context modeling by incorporating curvature from online least squares. The method links linear attention and the delta rule, introduces efficient variants of DeltaNet, GDN, and KDA, and boosts recall and language modeling performance.

The Key to State Reduction in Linear Attention: A Rank-based Perspective

By Philipp Nazari and T. Konstantin Rusch
Linear attention offers a computationally efficient yet expressive alternative to softmax attention, maintaining a recurrent state that functions as a linear associative memory. However, recent empirical results indicate that the associative memory of trained linear attention models often exhibits a low-rank structure, suggesting that these models underexploit their capacity in practice.

Low-Pass Flow Matching

By Francesco M. Ruscio and T. Konstantin Rusch
Flow Matching typically relies on white noise sources, a choice often misaligned with the power spectra of natural data, which tend to decay with frequency. To address this, we introduce a variant of Flow Matching based on an operator-modulated interpolant.

AlphaQ: Calibration-Free Bit Allocation for Mixture-of-Experts Quantization

By Wanqi Yang, Yuexiao Ma, Alexander Conzelmann, Xiawu Zheng, Michael W. Mahoney, T. Konstantin Rusch, and Shiwei Liu
Mixture-of-Experts (MoE) architectures scale computation via sparse expert activations, yet they remain memory-bound because all expert weights must reside in memory.

Zero-Overhead Introspection for Adaptive Test-Time Compute

By Rohin Manvi, Joey Hong, Tim Seyde, Maxime Labonne, Mathias Lechner, and Sergey Levine
Large language models excel at reasoning but lack key aspects of introspection, including the ability to anticipate their own success and the computation required to achieve it.

Learning residue level protein dynamics with multiscale Gaussians

By Mihir Bafna, Bowen Jing, and Bonnie Berger
Many methods have been developed to predict static protein structures, however understanding the dynamics of protein structure is essential for elucidating biological function.

Antislop: A Comprehensive Framework for Identifying and Eliminating Repetitive Patterns in Language Models

By Samuel J Paech, Allen G Roush, Judah Goldfeder, and Ravid Shwartz-Ziv
Antislop detects and removes repetitive LLM “slop” using inference-time suppression, profiling, and targeted fine-tuning.

The Curious Case of In-Training Compression of State Space Models

By Makram Chahine, Philipp Nazari, Daniela Rus, and T. Konstantin Rusch
State Space Models (SSMs), developed to tackle long sequence modeling tasks efficiently, offer both parallelizable training and fast inference.

LFM2 Technical Report

By Alexander Amini, Anna Banaszak, Harold Benoit, Arthur Böök, Tarek Dakhran, Song Duong, Alfred Eng, Fernando Fernandes, Marc Härkönen, Anne Harrington, Ramin Hasani, and 22 others
We present LFM2, a family of Liquid Foundation Models designed for efficient on-device deployment and strong task capabilities.

RL Squeezes, SFT Expands: A Comparative Study of Reasoning LLMs

By Kohsei Matsutani, Shota Takashiro, Gouki Minegishi, Takeshi Kojima, Yusuke Iwasawa, and Yutaka Matsuo
Large language models (LLMs) are typically trained by reinforcement learning (RL) with verifiable rewards and supervised fine-tuning (SFT) on reasoning traces to improve their reasoning abilities.

Exploring Diffusion Transformer Designs via Grafting

By Keshigeyan Chandrasegaran, Michael Poli, Daniel Y. Fu, Dongjun Kim, Lea M. Hadzic, Manling Li, Agrim Gupta, Stefano Massaroli, Azalia Mirhoseini, Juan Carlos Niebles, Stefano Ermon, and 1 other
Grafting edits pretrained diffusion transformers into efficient hybrids using <2% compute, delivering strong quality and up to 1.43x speedups.

Quantifying Memory Utilization with Effective State-Size

By Rom N. Parnichkun, Neehal Tumma, Armin W. Thomas, Alessandro Moro, Qi An, Taiji Suzuki, Atsushi Yamashita, Michael Poli, and Stefano Massaroli
Introducing effective state-size (ESS), a metric that quantifies memory utilization in sequence models to improve initialization and distillation.

STAR: Synthesis of Tailored Architectures

By Armin W. Thomas, Rom Parnichkun, Alexander Amini, Stefano Massaroli, and Michael Poli
STAR uses evolutionary search over architecture genomes to automatically design hybrids that beat Transformers on quality, size, and cache.

Towards a theory of learning dynamics in deep state space models

By Jakub Smekal, Jimmy Smith, Michael Kleinman, Dan Biderman, and Scott Linderman
Towards a theory of learning dynamics in deep state space models.

The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof

By Derek Lim, Theo Putterman, Robin Walters, Haggai Maron, and Stefanie Jegelka
The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof.

Large Scale Dataset Distillation with Domain Shift

By Noel Loo, Alaa Maalouf, Ramin Hasani, Mathias Lechner, Alexander Amini, and Daniela Rus
Large Scale Dataset Distillation with Domain Shift.

LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery

By Pingchuan Ma, Tsun-Hsuan Wang, Minghao Guo, Zhiqing Sun, Joshua B. Tenenbaum, Daniela Rus, Chuang Gan, and Wojciech Matusik
LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery.

RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation

By Yufei Wang, Zhou Xian, Feng Chen, Tsun-Hsuan Wang, Yian Wang, Katerina Fragkiadaki, Zackory Erickson, David Held, and Chuang Gan
RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation.

Position: Future Directions in the Theory of Graph Machine Learning

By Christopher Morris, Fabrizio Frasca, Nadav Dym, Haggai Maron, Ismail Ilkan Ceylan, Ron Levie, Derek Lim, Michael M. Bronstein, Martin Grohe, and Stefanie Jegelka
Position: Future Directions in the Theory of Graph Machine Learning.

Mechanistic Design and Scaling of Hybrid Architectures

By Michael Poli, Armin W Thomas, Eric Nguyen, Pragaash Ponnusamy, Björn Deiseroth, Kristian Kersting, Taiji Suzuki, Brian Hie, Stefano Ermon, Christopher Re, Ce Zhang, and 1 other
Mechanistic Design and Scaling of Hybrid Architectures.

State-Free Inference of State-Space Models: The Transfer Function Approach

By Rom Parnichkun, Stefano Massaroli, Alessandro Moro, Jimmy T.H. Smith, Ramin Hasani, Mathias Lechner, Qi An, Christopher Re, Hajime Asama, Stefano Ermon, Taiji Suzuki, and 2 others
State-Free Inference of State-Space Models: The Transfer Function Approach.

Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution

By Tim Seyde, Peter Werner, Wilko Schwarting, Markus Wulfmeier, and Daniela Rus
Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution.

Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation

By Noel Loo, Ramin Hasani, Mathias Lechner, Alexander Amini, and Daniela Rus
Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation.

Graph Metanetworks for Processing Diverse Neural Architectures

By Derek Lim, Haggai Maron, Marc T. Law, Jonathan Lorraine, and James Lucas
Graph Metanetworks for Processing Diverse Neural Architectures.