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HiddenLens: A Multi-Layer LLM Framework for Detecting and Resolving Hidden Assumptions in Software Requirements

Paper recorded by Signals 4 on 2026-10-21 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-21 on arXiv · recorded by Signals 4 on 2026-10-22

Category: cs.CL · 自然语言处理 · first seen 2026-10-22

Abstract

Natural-language software requirements often omit assumptions about performance, operating conditions, user behaviour, and domain constraints, affecting architecture, testing, and deployment. If undiscovered until implementation, resolving them is far more costly than addressing them early. Although ambiguity and vagueness are widely studied, less attention has been given to assumptions embedded i

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#181 most recent of 660 cs.CL papers we have recorded · ↑ newer: Building Auditable AI Text Detectors with Sparse Autoencoders · ↓ older: Remembering Through Change: Provenance-Preserving Hypergraph Memory fo
Cite this page: HiddenLens: A Multi-Layer LLM Framework for Detecting and Resolving Hidden Assumptions in Software Requirements: the #181 most recent of 660 cs.CL papers we have recorded (as of 2026-10-21). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/hiddenlens-a-multi-layer-llm-framework-for-detecting-and-resolving-hidden-assump.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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