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"metadata": { "creators": [ { "affiliations": [ { "name": "Elanare Institute" } ], "person_or_org": { "family_name": "Ishibashi", "given_name": "Ryuhei", "identifiers": [ { "identifier": "0009-0004-7089-5265", "scheme": "orcid" } ], "name": "Ishibashi, Ryuhei", "type": "personal" } } ], "dates": [ { "date": "2025-12-10", "type": { "id": "collected", "title": { "de": "Gesammelt", "en": "Collected" } } } ], "description": "

As Large Language Models (LLMs) become integral to software architecture, the pre-
vailing practice of ”Prompt Engineering” remains largely heuristic. This paper proposes a
shift towards ”Context Engineering”, rigorously defining prompts as a set of constraints
within a design optimization problem. By applying topological analysis to the model’s high-
dimensional possibility space, we demonstrate that context acts as a dimensionality reduc-
tion operator. We identify the phenomenon of ”Contextual Binding”—where excessive or
conflicting constraints (both implicit and explicit) cause the feasible solution manifold to col-
lapse into a null set, forcing the model into undefined behaviors often characterized as hallu-
cinations. Furthermore, we validate this theory by analyzing empirical community heuristics
(such as the ”KERNEL” pattern) alongside recent academic findings on in-context learning
mechanics [?, ?]. Finally, we introduce ”Context Refactoring” as a methodology to manage
constraint density and maintain a healthy solution space.

", "languages": [ { "id": "eng", "title": { "en": "English" } } ], "publication_date": "2025-12-10", "publisher": "Zenodo", "resource_type": { "id": "publication-preprint", "title": { "de": "Preprint", "en": "Preprint" } }, "rights": [ { "description": { "en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited." }, "icon": "cc-by-icon", "id": "cc-by-4.0", "props": { "scheme": "spdx", "url": "https://creativecommons.org/licenses/by/4.0/legalcode" }, "title": { "en": "Creative Commons Attribution 4.0 International" } } ], "subjects": [ { "subject": "LLM" }, { "subject": "Constraints" }, { "id": "euroscivoc:1071", "identifiers": [ { "identifier": "http://data.europa.eu/8mn/euroscivoc/4988e02f-0b78-40f3-a53b-2b67a4aae5aa", "scheme": "url" } ], "props": { "parents": "euroscivoc:23,euroscivoc:51,euroscivoc:359" }, "scheme": "EuroSciVoc", "subject": "Topology" } ], "title": "Constraint-Driven Contexts: Engineering the Solution Space of Large Language Models", "version": "version 0.1 draft" }, "parent": { "access": { "owned_by": { "user": "1462495" }, "settings": { "accept_conditions_text": null, "allow_guest_requests": false, "allow_user_requests": false, "secret_link_expiration": 0 } }, "communities": {}, "id": "17881315", "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.17881315", "provider": "datacite" } } }, "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.17881316", "provider": "datacite" }, "oai": { "identifier": "oai:zenodo.org:17881316", "provider": "oai" } }, "revision_id": 3, "stats": { "all_versions": { "data_volume": 32286182.0, "downloads": 386, "unique_downloads": 318, "unique_views": 430, "views": 464 }, "this_version": { "data_volume": 29813873.0, "downloads": 359, "unique_downloads": 295, "unique_views": 362, "views": 392 } }, "status": "published", "swh": {}, "updated": "2025-12-10T15:21:10.170037+00:00", "versions": { "index": 1, "is_latest": false } }