arXiv Machine Learning

Learning from flowsheets: A generative transformer model for autocompletion of flowsheets

arXiv:2208. 00859v2 Announce Type: replace Abstract: We propose a novel method enabling autocompletion of chemical flowsheets.

arXiv AI

Jun 9

Toward autocorrection of chemical process flowsheets using large language models

arXiv:2312. 02873v2 Announce Type: replace-cross Abstract: The process engineering domain widely uses Process Flow Diagrams (PFDs) and Process and Instrumentation Diagrams (P&IDs) to represent process flows and equipment configurations.

By Lukas Schulze Balhorn, Marc Caballero, Artur M. Schweidtmann

llms safety

arXiv AI

Jun 2

Prototype Transformer: Towards Language Model Architectures Interpretable by Design

arXiv:2602. 11852v2 Announce Type: replace Abstract: While state-of-the-art language models (LMs) surpass most humans in certain domains, their reasoning remains largely opaque, reducing trust and increasing the risk of deception and hallucination.

By Yordan Yordanov, Matteo Forasassi, Bayar Menzat, Ruizhi Wang, Chang Qi, Markus Kaltenberger, Amine M'Charrak, Tommaso Salvatori, Thomas Lukasiewicz

llms benchmarks safety

arXiv AI

Aug 11

Transformer Explainer: Learning LLM Transformers with Interactive Visual Explanation and Experimentation

arXiv:2408. 04619v2 Announce Type: replace-cross Abstract: The Transformer architecture underpins modern large language models powering state-of-the-art text generation and AI applications.

By Aeree Cho, Grace C. Kim, Alexander Karpekov, Seongmin Lee, Alec Helbling, Benjamin Hoover, Zijie J. Wang, Minsuk Kahng, Duen Horng Chau

llms benchmarks

arXiv AI

Jun 9

Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence

arXiv:2412. 00508v2 Announce Type: replace-cross Abstract: Control structure design is an important but tedious step in P&ID development.

By Lukas Schulze Balhorn, Kevin Degens, Artur M. Schweidtmann

arXiv Machine Learning

Aug 3

Simulation Code Generation for Fluid Systems using Large Language Models: Benchmarking Models and Prompting Strategies

arXiv:2607. 29389v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated a strong ability to generate syntactically correct code from natural-language specifications.

By Jan Marius St\"urmer, Jascha Knack, Tobias Koch, Andreas Weinmann

llms benchmarks

arXiv Machine Learning

Jun 9

SFILES 2.0: An extended text-based flowsheet representation

arXiv:2208. 00778v2 Announce Type: replace-cross Abstract: SFILES are a text-based notation for chemical process flowsheets.

By Gabriel Vogel, Edwin Hirtreiter, Lukas Schulze Balhorn, Artur M. Schweidtmann

arXiv Machine Learning

Jul 15

Learning Mechanistic Reasoning for Chemical Reactions with Large Language Models

arXiv:2607. 12771v1 Announce Type: new Abstract: Reaction mechanisms consist of the step-by-step sequences of elementary reactions that explain chemical transformations.

By Xingyu Dang, Haocheng Tang, Junmei Wang, Yanjun Li

llms fine-tuning benchmarks safety

arXiv Computation and Language

Sep 2

Suffix-Constrained Greedy Search Algorithms for Causal Language Models

arXiv:2603.01243v3 Announce Type: replace Abstract: Large language models (LLMs) are powerful tools that have found applications beyond human-machine interfaces and chatbots. Beside free-form generat...

By Ayoub Hammal, Pierre Zweigenbaum, Caio Corro

llms

arXiv Machine Learning

Jul 17

FlowBot: Inducing LLM Workflows with Bilevel Optimization and Textual Gradients

arXiv:2604. 26258v3 Announce Type: replace-cross Abstract: LLM workflows, which coordinate structured calls to individual LLMs/agents to achieve a particular goal, offer a promising path towards building powerful AI systems that can tackle diverse tasks.

By Hongyeon Yu, Young-Bum Kim, Yoon Kim

llms agents

arXiv AI

Aug 21

MidTool: Mid-training Data Synthesis for Agentic Tool Use

arXiv:2608. 20314v1 Announce Type: new Abstract: Mid-training is increasingly recognized as a critical stage for shaping the capabilities of large language models.

By Fengqing Jiang, Yite Wang, Boyi Liu, Zhaoyang Wang, Canwen Xu, Zhewei Yao, Radha Poovendran, Yuxiong He

llms agents reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 15

LapidaryEngine: Fully Conversational Crystal Generation

arXiv:2606. 14215v1 Announce Type: new Abstract: The emergence of Large Language Models (LLMs) has inspired the vision of generating bespoke crystal materials directly from natural-language instructions, enabling users to design materials through intuitive, conversational interaction.

By Yusei Ito, Yuta Suzuki, Tomoya Murata, Masaki Adachi

llms safety

arXiv Computation and Language

Aug 31

CoFrGeNet: Continued Fraction Architectures for Language Generation

CoFrGeNet introduces Continued Fraction Generative Networks, a new function class that replaces Multi-head Attention and Feed-Forward Networks in Transformer blocks with fewer parameters. The architecture includes custom gradient formulations for efficient optimization and can be plugged into existing Transformer workflows with minimal changes. Experiments on GPT2‑xl and Llama3 show competitive or superior performance on downstream tasks while using 1/2 to 2/3 of the original parameters and shorter pre‑training time.

By Amit Dhurandhar, Vijil Chenthamarakshan, Dennis Wei, Tejaswini Pedapati, Karthikeyan Natesan Ramamurthy, Rahul Nair

llms

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