AI Navigate

Logos: An evolvable reasoning engine for rational molecular design

arXiv cs.AI / 3/11/2026

Models & Research

Key Points

  • Logos is a compact molecular reasoning model that integrates multi-step logical reasoning with strict chemical consistency to improve molecular design.
  • The model is trained through a staged strategy that first teaches explicit reasoning linking molecular descriptions to structural decisions, then aligns these patterns with molecular representations, and finally incorporates chemical rules directly into optimization.
  • Logos achieves strong performance on benchmark datasets, matching or surpassing larger language models with fewer parameters, while maintaining structural accuracy and chemical validity.
  • The model’s explicit reasoning steps enable human inspection and assessment, promoting interpretability and reliability in AI-driven molecular design.
  • Logos demonstrates stable behavior in molecular optimization tasks involving multiple conflicting constraints, offering a practical approach for integrating AI into scientific discovery workflows in chemistry and materials science.

Computer Science > Artificial Intelligence

arXiv:2603.09268 (cs)
[Submitted on 10 Mar 2026]

Title:Logos: An evolvable reasoning engine for rational molecular design

View a PDF of the paper titled Logos: An evolvable reasoning engine for rational molecular design, by Haibin Wen and 6 other authors
View PDF HTML (experimental)
Abstract:The discovery and design of functional molecules remain central challenges across chemistry,biology, and materials science. While recent advances in machine learning have accelerated molecular property prediction and candidate generation, existing models tend to excel either in physical fidelity without transparent reasoning, or in flexible reasoning without guarantees of chemical validity. This imbalance limits the reliability of artificial intelligence systems in real scientific design this http URL we present Logos, a compact molecular reasoning model that integrates multi-step logical reasoning with strict chemical consistency. Logos is trained using a staged strategy that first exposes the model to explicit reasoning examples linking molecular descriptions to structural decisions, and then progressively aligns these reasoning patterns with molecular representations. In a final training phase, chemical rules and invariants are incorporated directly into the optimization objective, guiding the model toward chemically valid outputs. Across multiple benchmark datasets, Logos achieves strong performance in both structural accuracy and chemical validity, matching or surpassing substantially larger general-purpose language models while operating with a fraction of their parameters. Beyond benchmark evaluation, the model exhibits stable behaviour in molecular optimization tasks involving multiple, potentially conflicting constraints. By explicitly exposing intermediate reasoning steps, Logos enables human inspection and assessment of the design logic underlying each generated structure. These results indicate that jointly optimizing for reasoning structure and physical consistency offers a practical pathway toward reliable and interpretable AI systems for molecular science, supporting closer integration of artificial intelligence into scientific discovery processes.
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2603.09268 [cs.AI]
  (or arXiv:2603.09268v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2603.09268
Focus to learn more
arXiv-issued DOI via DataCite

Submission history

From: Fanfu Wang [view email]
[v1] Tue, 10 Mar 2026 06:56:35 UTC (9,248 KB)
Full-text links:

Access Paper:

Current browse context:
cs.AI
< prev   |   next >
Change to browse by:
cs

References & Citations

export BibTeX citation Loading...

BibTeX formatted citation

×
Data provided by:

Bookmark

BibSonomy logo Reddit logo
Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
Links to Code Toggle
Papers with Code (What is Papers with Code?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos

Demos

Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers

Recommenders and Search Tools

Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.