7 citations · 9 across the 13 of their papers we have counts for
14 papers
Integrating Simulation and Chain-of-thought Reasoning in Multimodal-Language Models For Physical Reasoning
YingQiao Wang, Eric Bigelow, Tomer Ullman +2
Multimodal language models often need to reason about the physical dynamics of the world in order to answer questions appropriately. Various methods have emerged for solving physic…
Recursive Harness Self-Improvement
Hyunin Lee, Jinglue Xu, Jeffrey Seely +3
Under model--harness co-evolution, harnesses are not merely inference-time scaffolds but data-generating components whose execution traces can shape future foundation models. This…
DMV-Bench: Diagnosing Long-Horizon Multimodal Agents' Visual Memory with Incidental Cue Injection
Yujin Tang, Chenming Shang, Ruize Xu +1
Agent benchmarks for measuring memory largely study textual cases, in which information is deliberately extracted from the environment, written down, and then later retrieved. In o…
Evolutionary Context Search for Automated Skill Acquisition
Qi Sun, Stefan Nielsen, Rio Yokota +1
Large Language Models cannot reliably acquire new knowledge post-deployment -- even when relevant text resources exist, models fail to transform them into actionable knowledge with…
TRINITY: An Evolved LLM Coordinator
Jinglue Xu, Qi Sun, Peter Schwendeman +3
Combining diverse foundation models is promising, but weight-merging is limited by mismatched architectures and closed APIs. Trinity addresses this with a lightweight coordinator t…
Learning to Orchestrate Agents in Natural Language with the Conductor
Stefan Nielsen, Edoardo Cetin, Peter Schwendeman +3
Powerful large language models (LLMs) from different providers have been expensively trained and finetuned to specialize across varying domains. In this work, we introduce a new ki…
Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
Yifan Hu, Jie Yang, Tian Zhou +4
Although contrastive and other representation-learning methods have long been explored in vision and NLP, their adoption in modern time series forecasters remains limited. We belie…
Text-to-LoRA: Instant Transformer Adaption
Rujikorn Charakorn, Edoardo Cetin, Yujin Tang +1
While Foundation Models provide a general tool for rapid content creation, they regularly require task-specific adaptation. Traditionally, this exercise involves careful curation o…
Reinforcement Learning Teachers of Test Time Scaling
Edoardo Cetin, Tianyu Zhao, Yujin Tang
Training reasoning language models (LMs) with reinforcement learning (RL) for one-hot correctness inherently relies on the LM being able to explore and solve its task with some cha…
From Hallucination to Reliability: Generative Modeling and the Structure of Scientific Inference
Charles Rathkopf
Generative AI is increasingly used in science, but is unavoidably prone to hallucination. I develop a reliabilist account of how generative AI nevertheless gives rise to new scient…
Transformer-Squared: Self-adaptive LLMs
Qi Sun, Edoardo Cetin, Yujin Tang
Self-adaptive large language models (LLMs) aim to solve the challenges posed by traditional fine-tuning methods, which are often computationally intensive and static in their abili…
Large Language Models to Diffusion Finetuning
Edoardo Cetin, Tianyu Zhao, Yujin Tang
We propose a new finetuning method to provide pre-trained large language models (LMs) the ability to scale test-time compute through the diffusion framework. By increasing the numb…
An Evolved Universal Transformer Memory
Edoardo Cetin, Qi Sun, Tianyu Zhao +1
Prior methods propose to offset the escalating costs of modern foundation models by dropping specific parts of their contexts with hand-designed rules, while attempting to preserve…
LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views
Yuji Roh, Qingyun Liu, Huan Gui +8
Fine-tuning is becoming widely used for leveraging the power of pre-trained foundation models in new downstream tasks. While there are many successes of fine-tuning on various task…