activity
20242026
most citedAutomated, LLM enabled extraction of synthesis details for reticular materials from scientific literature

7 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Language Models Refine Mechanical Linkage Designs Through Symbolic Reflection and Modular Optimisation

João Pedro Gandarela, Thiago Rios, Stefan Menzel +1

Designing mechanical linkages involves combinatorial topology selection and continuous parameter fitting. We show that language models can systematically improve linkage designs th…

cs.DB2026

Reflective Reasoning for SQL Generation

Isabelle Mohr, Joao Gandarela, John Dujany +1

Robust text-to-SQL over complex, real-world databases remains brittle even with modern LLMs: iterative refinement often introduces syntactic and semantic drift, corrections tend to…

cs.AI2025

Symbolic Intermediaries as a Linguistic-Numerical Interface for LLM-Driven Geometric Reasoning

João Pedro Gandarela, Thiago Rios, Stefan Menzel +1

Large Language Models (LLMs) display reasoning capabilities over linguistic and symbolic objects but have limited capabilities to directly interpret the continuous numerical output…

cond-mat.mtrl-sci2024★ 7 cited

Automated, LLM enabled extraction of synthesis details for reticular materials from scientific literature

Viviane Torres da Silva, Alexandre Rademaker, Krystelle Lionti +12

Automated knowledge extraction from scientific literature can potentially accelerate materials discovery. We have investigated an approach for extracting synthesis protocols for re…

cs.CL2024

Inductive Learning of Logical Theories with LLMs: An Expressivity-Graded Analysis

João Pedro Gandarela, Danilo S. Carvalho, André Freitas

This work presents a novel systematic methodology to analyse the capabilities and limitations of Large Language Models (LLMs) with feedback from a formal inference engine, on logic…