ZapGPT: Free-form Language Prompting for Simulated Cellular Control

• URL: http://arxiv.org/abs/2509.10660v1
• Date: Fri, 12 Sep 2025 19:38:46 GMT
• Title: ZapGPT: Free-form Language Prompting for Simulated Cellular Control
• Authors: Nam H. Le, Patrick Erickson, Yanbo Zhang, Michael Levin, Josh Bongard,
• Abstract summary: We show that simple agents' collective behavior can be guided by free-form language prompts.<n>By treating natural language as a control layer, the system suggests a future in which spoken or written prompts could direct systems to desired behaviors.
• Score: 3.599327240436075
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human language is one of the most expressive tools for conveying intent, yet most artificial or biological systems lack mechanisms to interpret or respond meaningfully to it. Bridging this gap could enable more natural forms of control over complex, decentralized systems. In AI and artificial life, recent work explores how language can specify high-level goals, but most systems still depend on engineered rewards, task-specific supervision, or rigid command sets, limiting generalization to novel instructions. Similar constraints apply in synthetic biology and bioengineering, where the locus of control is often genomic rather than environmental perturbation. A key open question is whether artificial or biological collectives can be guided by free-form natural language alone, without task-specific tuning or carefully designed evaluation metrics. We provide one possible answer here by showing, for the first time, that simple agents' collective behavior can be guided by free-form language prompts: one AI model transforms an imperative prompt into an intervention that is applied to simulated cells; a second AI model scores how well the prompt describes the resulting cellular dynamics; and the former AI model is evolved to improve the scores generated by the latter. Unlike previous work, our method does not require engineered fitness functions or domain-specific prompt design. We show that the evolved system generalizes to unseen prompts without retraining. By treating natural language as a control layer, the system suggests a future in which spoken or written prompts could direct computational, robotic, or biological systems to desired behaviors. This work provides a concrete step toward this vision of AI-biology partnerships, in which language replaces mathematical objective functions, fixed rules, and domain-specific programming.

Related papers

• Commanding Humanoid by Free-form Language: A Large Language Action Model with Unified Motion Vocabulary [59.98573566227095]
  We introduce Humanoid-LLA, a Large Language Action Model that maps expressive language commands to physically executable whole-body actions for humanoid robots.<n>Our approach integrates three core components: a unified motion vocabulary that aligns human and humanoid motion primitives into a shared discrete space; a vocabulary-directed controller distilled from a privileged policy to ensure physical feasibility; and a physics-informed fine-tuning stage using reinforcement learning with dynamics-aware rewards to enhance robustness and stability.
  arXiv  Detail & Related papers  (2025-11-28T08:11:24Z)
• Executable Analytic Concepts as the Missing Link Between VLM Insight and Precise Manipulation [70.8381970762877]
  Vision-Language Models (VLMs) have demonstrated remarkable capabilities in semantic reasoning and task planning.<n>We introduce GRACE, a novel framework that grounds VLM-based reasoning through executable analytic concepts.<n>G GRACE provides a unified and interpretable interface between high-level instruction understanding and low-level robot control.
  arXiv  Detail & Related papers  (2025-10-09T09:08:33Z)
• Interpretable Robot Control via Structured Behavior Trees and Large Language Models [0.0]
  This paper presents a novel framework that bridges natural language understanding and robotic execution.<n>The proposed approach is practical in real-world scenarios, with an average cognition-to-execution accuracy of approximately 94%.
  arXiv  Detail & Related papers  (2025-08-13T08:53:13Z)
• Giving Simulated Cells a Voice: Evolving Prompt-to-Intervention Models for Cellular Control [1.7056803236939193]
  Large language models (LLMs) have enabled natural language as an interface for interpretable control in AI systems.<n>We present a functional pipeline that translates natural language prompts into spatial vector fields capable of directing simulated cellular collectives.
  arXiv  Detail & Related papers  (2025-05-05T16:21:46Z)
• Large Language Models for Scientific Synthesis, Inference and Explanation [56.41963802804953]
  We show how large language models can perform scientific synthesis, inference, and explanation. We show that the large language model can augment this "knowledge" by synthesizing from the scientific literature. This approach has the further advantage that the large language model can explain the machine learning system's predictions.
  arXiv  Detail & Related papers  (2023-10-12T02:17:59Z)
• Generative Models as a Complex Systems Science: How can we make sense of large language model behavior? [75.79305790453654]
  Coaxing out desired behavior from pretrained models, while avoiding undesirable ones, has redefined NLP. We argue for a systematic effort to decompose language model behavior into categories that explain cross-task performance.
  arXiv  Detail & Related papers  (2023-07-31T22:58:41Z)
• AmadeusGPT: a natural language interface for interactive animal behavioral analysis [65.55906175884748]
  We introduce AmadeusGPT: a natural language interface that turns natural language descriptions of behaviors into machine-executable code. We show we can produce state-of-the-art performance on the MABE 2022 behavior challenge tasks. AmadeusGPT presents a novel way to merge deep biological knowledge, large-language models, and core computer vision modules into a more naturally intelligent system.
  arXiv  Detail & Related papers  (2023-07-10T19:15:17Z)
• "No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared Autonomy [70.45420918526926]
  We present LILAC, a framework for incorporating and adapting to natural language corrections online during execution. Instead of discrete turn-taking between a human and robot, LILAC splits agency between the human and robot. We show that our corrections-aware approach obtains higher task completion rates, and is subjectively preferred by users.
  arXiv  Detail & Related papers  (2023-01-06T15:03:27Z)
• LaTTe: Language Trajectory TransformEr [33.7939079214046]
  This work proposes a flexible language-based framework to modify generic 3D robotic trajectories. We employ an auto-regressive transformer to map natural language inputs and contextual images into changes in 3D trajectories. We show through simulations and real-life experiments that the model can successfully follow human intent.
  arXiv  Detail & Related papers  (2022-08-04T22:43:21Z)
• Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation [80.29069988090912]
  We study the problem of learning a range of vision-based manipulation tasks from a large offline dataset of robot interaction. We propose to leverage offline robot datasets with crowd-sourced natural language labels. We find that our approach outperforms both goal-image specifications and language conditioned imitation techniques by more than 25%.
  arXiv  Detail & Related papers  (2021-09-02T17:42:13Z)
• Language Understanding for Field and Service Robots in a Priori Unknown Environments [29.16936249846063]
  This paper provides a novel learning framework that allows field and service robots to interpret and execute natural language instructions. We use language as a "sensor" -- inferring spatial, topological, and semantic information implicit in natural language utterances. We incorporate this distribution in a probabilistic language grounding model and infer a distribution over a symbolic representation of the robot's action space.
  arXiv  Detail & Related papers  (2021-05-21T15:13:05Z)
• Learning Adaptive Language Interfaces through Decomposition [89.21937539950966]
  We introduce a neural semantic parsing system that learns new high-level abstractions through decomposition. Users interactively teach the system by breaking down high-level utterances describing novel behavior into low-level steps.
  arXiv  Detail & Related papers  (2020-10-11T08:27:07Z)
• Language Conditioned Imitation Learning over Unstructured Data [9.69886122332044]
  We present a method for incorporating free-form natural language conditioning into imitation learning. Our approach learns perception from pixels, natural language understanding, and multitask continuous control end-to-end as a single neural network. We show this dramatically improves language conditioned performance, while reducing the cost of language annotation to less than 1% of total data.
  arXiv  Detail & Related papers  (2020-05-15T17:08:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.