arXiv subjects

Jeffrey Lee

Publications and source records attributed to Jeffrey Lee.

7 recordsLinked to original sources

arXiv2026

Adaptive Hybrid Force-Vision Control with EMG-Derived References for Deformable Bottle Manipulation

Robotic automation of laboratory tasks has become a growing interest to remove researchers from hazardous environments and repetitive procedures. However, even equipment as simple as squeeze bottles poses a challenging manipulation problem due to its deformability. This paper presents an adaptive hybrid vision-force controller for robotic grippers to effectively manipulate laboratory squeeze bottles to dispense liquid. This approach integrates force and vision feedback according to the interaction state, enabling effective squeezing while accounting for bottle deformation and recovery. The proposed controller is validated by comparing its performance with a pure force controller on 12 combinations of conditions consisting of 2 target dispense volumes, 2 plastic squeeze bottles, and 1 to 3 squeezes used to dispense. Human demonstrations of these operating conditions were collected using electromyography (EMG) sensing mapped to a desired grip force, which was then used as the force reference for the controllers to squeeze the bottles. Results demonstrated that the proposed adaptive hybrid vision-force controller achieved more accurate liquid dispensing with a mean absolute error (MAE) of 6.54 mL compared to a MAE of 15.92 mL from the pure force controller, while also replicating the human action recorded from the EMG signal more closely.

cs.RO↗

arXiv2026

BrainDyn: A Sheaf Neural ODE for Forecasting and Generating Brain Dynamics

Brain activity unfolds as coordinated patterns across regions over time, and modeling these dynamics is a central goal in computational neuroscience. Because activity is distributed over connected brain regions, it is naturally represented on a graph whose nodes are brain regions or recording electrodes and whose edges capture relationships between these nodes, with message-passing modeling regional communication. However, standard message-passing networks place every node in a single shared feature space and typically aggregate messages from neighbors by scalar summation, which does not align with neurobiological evidence of brain communication. In order to model the heterogeneous nature of the brain, we introduce BrainDyn, a graph ordinary differential equation (ODE) network built on a cellular sheaf with dynamic restriction maps, recomputed from the forecasting context window rather than fixed once, mediating region-to-region communication through learned linear transformations that drive the time evolution of neural activity. We evaluate BrainDyn on resting-state fMRI, EEG, and simulated activity from the NEST simulator. Across these modalities, we test multi-region forecasting and autoregressive generation of brain transients, and, using NEST, prediction of altered transients following controlled perturbations. BrainDyn outperforms competing models across modalities, while analysis of the learned features shows that they are expressive, interpretable, and biologically aligned.

cs.LG↗

arXiv2026

Can LLM Agents Select and Engage with Biological Tools?

Computational tools for biological research (biological tools, or BTs) are advancing rapidly in both quantity and demonstrated capability. While these technologies offer many benefits for scientific advancement, malicious actors may seek to exploit them to design and develop biological weapons (BWs). Generally, BTs require specialized expertise to operate successfully, presenting a barrier to nefarious use by nonexperts. Large language model (LLM) Agents may erode this barrier by enabling actors with limited domain expertise to successfully use BTs. Assessments of how AI could change bioweapons development pathways should examine how LLM Agents interact with BTs on relevant biological design tasks, but such assessments are limited. In this report, we present an assessment of the abilities of LLM Agents in the early stages of interaction with BTs. First, we evaluate seven frontier LLM Agents on their ability to select appropriate BTs for a given task. Second, we assess how these LLM Agents engage with two BTs, EVEscape and ESM3, through targeted case studies. These case studies probe the enabling capabilities required for autonomous BT operation, a potential entry point to a wide range of risk pathways. We identify and assess technical barriers at the intersection of LLM Agents and BTs, offering the biosecurity community and AI developers a foundation for further risk and capability assessments as technologies progress.

cs.CY↗

arXiv2026

Testing Large Language Model Agents on the Use of Biological Tools for Nucleic Acid Synthesis Screening Evasion

This report is a continuation of previous efforts to test the ability of large language model (LLM)-driven artificial intelligence (AI) agents to interface with AI-enabled biological tools (BTs). While rapid advancements in BTs in recent years have brought promise to accelerate scientific discovery, they also raise significant biosecurity concerns about potential misuse. The biosecurity community is particularly interested in the extent to which LLMs can lower technical barriers and assist non-expert users in accessing and operating BTs. Despite this interest, few evaluations have focused on LLM-BT interactions in the context of a defined threat model. To address this gap, this report describes a test of frontier LLM-driven AI Agents on their ability to use BTs to redesign peptides and proteins to evade nucleic acid synthesis screening measures. Highly relevant to biorisk, this task assesses a potential capability of AI agents that could enable a breach of a critical early defensive layer designed to prevent a multitude of biological misuse scenarios. The findings presented here intend to offer a foundation for biosecurity researchers and AI developers to conduct or further risk and capability assessments as these technologies progress.

cs.CY↗

arXiv2026

Measuring Biological Capabilities and Risks of AI Agents

This paper addresses a rapidly emerging policy challenge: how to generate and interpret credible evidence about the biological capabilities and risks of AI scientists, or agentic AI systems capable of autonomously or collaboratively performing multi-step scientific tasks. As these systems enter real research workflows, decision-makers increasingly face evaluation results whose meaning depends on underlying design choices that are often implicit or under-documented. We synthesize current evidence on AI-enabled biological risks and introduce biological agentic evaluations as a promising, but interpretation-sensitive, tool for assessing these systems. Our central contribution is a set of practical, experience-grounded considerations -- drawing from our own evaluations -- that show how choices around defining, designing, running, scoring, and documenting evaluations materially shape what results do and do not imply about risk. The analysis is intended to help policymakers interpret biological evaluation outputs with appropriate caution; guide public and private funders toward high-leverage investments in AI-biology evaluation research; and support biosecurity practitioners assessing emerging AI systems. A secondary audience includes researchers designing or conducting agentic evaluations within frontier AI labs, AI providers, scientific institutions, and third-party evaluation organizations.

cs.CY↗

arXiv2025

LVLM-Composer's Explicit Planning for Image Generation

The burgeoning field of generative artificial intelligence has fundamentally reshaped our approach to content creation, with Large Vision-Language Models (LVLMs) standing at its forefront. While current LVLMs have demonstrated impressive capabilities in text-to-image generation, they often falter when confronted with complex textual descriptions demanding precise compositional understanding and visual planning. This limitation particularly impacts the accurate rendering of multiple objects, their attributes, spatial relationships, and specific poses within intricate scenes, as evidenced by benchmarks like LongBench-T2I. To address these challenges, we introduce LVLM-Composer, a novel 10-billion parameter scale LVLM specifically engineered for enhanced compositional image synthesis. Our method incorporates a Hierarchical Semantic Planning Module for structured prompt decomposition and a Fine-Grained Feature Alignment Mechanism for precise visual guidance during generation. We propose a multi-stage training paradigm, featuring Hierarchical Semantic-Visual Grounding Pre-training and Compositional Planning Reinforcement Learning with Self-Correction, to instill robust compositional reasoning. Extensive experiments on the LongBench-T2I benchmark, utilizing automatic evaluation by Gemini-2.0-Flash and InternVL3-78B, demonstrate LVLM-Composer's superior performance across critical compositional dimensions including object accuracy, composition fidelity, and pose accuracy, significantly outperforming state-of-the-art baselines. An in-depth ablation study further validates the indispensable contribution of our proposed modules, while human evaluations confirm the perceptual superiority of our generated images. LVLM-Composer represents a significant step towards truly controllable and compositionally accurate open-ended text-to-image generation.

cs.CV↗

arXiv2025

Cross-Cultural Fashion Design via Interactive Large Language Models and Diffusion Models

Fashion content generation is an emerging area at the intersection of artificial intelligence and creative design, with applications ranging from virtual try-on to culturally diverse design prototyping. Existing methods often struggle with cultural bias, limited scalability, and alignment between textual prompts and generated visuals, particularly under weak supervision. In this work, we propose a novel framework that integrates Large Language Models (LLMs) with Latent Diffusion Models (LDMs) to address these challenges. Our method leverages LLMs for semantic refinement of textual prompts and introduces a weak supervision filtering module to effectively utilize noisy or weakly labeled data. By fine-tuning the LDM on an enhanced DeepFashion+ dataset enriched with global fashion styles, the proposed approach achieves state-of-the-art performance. Experimental results demonstrate that our method significantly outperforms baselines, achieving lower Frechet Inception Distance (FID) and higher Inception Scores (IS), while human evaluations confirm its ability to generate culturally diverse and semantically relevant fashion content. These results highlight the potential of LLM-guided diffusion models in driving scalable and inclusive AI-driven fashion innovation.

cs.CL↗