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ARXIV:2605.13773 · LLM UNDERSTANDING · SUBMITTED 14 MAY · 20:10 UTC · FRESHNESS FRESH
ARXIV:2605.13773LLM UNDERSTANDINGSUBMITTED 14 MAY · 20:10 UTCFRESHNESS FRESHMohammad Reza Mousavi · arXiv
This paper evaluates the semantic understanding of LLMs on High-Level Message Sequence Charts, revealing modest capabilities with significant struggles in complex reasoning tasks.
Opportunity summary
Pain This paper evaluates the semantic understanding of LLMs on High-Level Message Sequence Charts, revealing modest capabilities with significant struggles in complex reasoning tasks.
Evidence 0 refs | 0 sources | 0% coverage
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This paper evaluates the semantic understanding of LLMs on High-Level Message Sequence Charts, revealing modest capabilities with significant struggles in complex reasoning tasks. It is, however, unclear whether these tasks are performed consistently with…
Large Language Models (LLMs) are being employed widely to automate tasks across the software development life-cycle. It is, however, unclear whether these tasks are performed consistently with respect to the semantics of the artefacts…
ScienceToStartup currently rates this 2.0/10 on the public viability pass. The results show that LLMs only have a modest understanding of the formal semantics of HMSCs (ca.
LLM Understanding moved forward this cycle; last verified May 2026. Public score 2.0/10.
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This paper evaluates the semantic understanding of LLMs on High-Level Message Sequence Charts, revealing modest capabilities with significant struggles in complex reasoning tasks.
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10.48550/arXiv.2605.13773This paper evaluates the semantic understanding of LLMs on High-Level Message Sequence Charts, revealing modest capabilities with significant struggles in complex reasoning tasks.
Abstract
Large Language Models (LLMs) are being employed widely to automate tasks across the software development life-cycle. It is, however, unclear whether these tasks are performed consistently with respect to the semantics of the artefacts being handled. This question is particularly under-researched concerning architectural design specification. In this paper, we address this question for High-Level Message Sequence Charts (HMSCs). These are visual models with a rigorous formal semantics that have been used for various purposes, including as a foundation for Sequence Diagrams in the Unified Modelling Language (UML). We examine whether LLMs "understand" the semantics of HMSCs by examining three LLMs (Gemini-3, GPT-5.4, and Qwen-3.6) on how they perform 129 semantic tasks ranging from querying basic semantic constructs in HMSCs (i.e., events and their ordering) to semantic-preserving abstractions and compositions, and calculating the set of traces and trace-equivalent labelled transition systems. The results show that LLMs only have a modest understanding of the formal semantics of HMSCs (ca. 52% overall accuracy), with great variability across different semantic concepts: while LLMs seem to understand the basic semantic concepts of MSCs (ca. 88% accuracy), they struggle with semantic reasoning in tasks involving abstraction and composition (ca. 36% accuracy) and traces and LTSs (ca. 42% accuracy). In particular, all three LLMs struggle with the notions of co-region and explicit causal dependencies and never employed them in semantic-preserving transformations.
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PROBLEM
This paper evaluates the semantic understanding of LLMs on High-Level Message Sequence Charts, revealing modest capabilities with significant struggles in complex reasoning tasks. It is, however, unclear whether these tasks are performed consistently with respect to the semantic...
METHOD
Large Language Models (LLMs) are being employed widely to automate tasks across the software development life-cycle. It is, however, unclear whether these tasks are performed consistently with respect to the semantics of the artefacts being handled.
RESULT
ScienceToStartup currently rates this 2.0/10 on the public viability pass. The results show that LLMs only have a modest understanding of the formal semantics of HMSCs (ca.
WHY NOW
LLM Understanding moved forward this cycle; last verified May 2026. Public score 2.0/10.
Abstract-backed public claims while anchored extraction refreshes.
This paper evaluates the semantic understanding of LLMs on High-Level Message Sequence Charts, revealing modest capabilities with significant struggles in complex reasoning tasks. It is, however, unclear whether these tasks are performed consistently with respect to the semantics of the artefacts being handled.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large Language Models (LLMs) are being employed widely to automate tasks across the software development life-cycle. It is, however, unclear whether these tasks are performed consistently with respect to the semantics of the artefacts being handled.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. The results show that LLMs only have a modest understanding of the formal semantics of HMSCs (ca.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Understanding moved forward this cycle; last verified May 2026. Public score 2.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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This paper evaluates the semantic understanding of LLMs on High-Level Message Sequence Charts, revealing modest capabilities with significant struggles in complex reasoning tasks.
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LLM Understanding
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