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Lacuna Inc. unveils novel model for narrative similarity at SemEval-2026

By PulseAugur Editorial · [1 sources] ·

Lacuna Inc. has developed the Invariant-Variant Disentangled State-Space Model (IVD-SSM) for the SemEval-2026 Task 4, which focuses on narrative similarity. This model utilizes a hybrid State-Space Model, Jamba-1.5-Mini, to avoid the computational bottlenecks of standard Transformers. A novel component called the Structurally Gated Alignment (SGA) head is introduced, which disentangles structural invariants from lexical variants to improve deep narrative understanding. AI

IMPACT Introduces a novel approach to disentangling narrative structure from lexical elements, potentially advancing AI's ability to understand complex stories.

RANK_REASON The item is a research paper detailing a novel model for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Lacuna Inc. unveils novel model for narrative similarity at SemEval-2026

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The item is a research paper detailing a novel model for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Aleksey Kudelya, Rafif Alshawi, Alexander Shirnin ·

    Lacuna Inc. at SemEval-2026 Task 4: Structurally Gated State-Space Models for Disentangling Narrative Similarity

    arXiv:2607.03482v1 Announce Type: new Abstract: In this paper, we present the Invariant-Variant Disentangled State-Space Model (IVD-SSM), our submission to SemEval-2026 Task 4 on Narrative Story Similarity and Narrative Representation Learning. Evaluating narrative similarity is …

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