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From Abstraction to Action: DSLs for Natural Language Abstraction Interpretation and Execution — arXiv:2610.28311

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.28311
License (as archived): CC0-1.0
Archived: 2026-10-21T07:09:07.738Z
SHA-256 of the archived text: af07d37fe73319bd71a9476109c7a494ac84f57634745ebcb84412a72bff7ae8
Stable URL of this page: https://metraipolis.com/documento/dc_7d30ef7eaeaf

How to cite

From Abstraction to Action: DSLs for Natural Language Abstraction Interpretation and Execution — arXiv:2610.28311 — arXiv (en), https://arxiv.org/abs/2610.28311 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7d30ef7eaeaf (Metraipolis archive dc_7d30ef7eaeaf, sha256 af07d37fe73319bd71a9476109c7a494ac84f57634745ebcb84412a72bff7ae8)

BibTeX

@misc{dc7d30ef7eaeaf,
  title = {From Abstraction to Action: DSLs for Natural Language Abstraction Interpretation and Execution — arXiv:2610.28311},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.28311},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7d30ef7eaeaf; sha256 af07d37fe73319bd71a9476109c7a494ac84f57634745ebcb84412a72bff7ae8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.28311. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Natural-language instructions often rely on high-level abstractions, such as asking an agent to ``stack blocks in a pyramid.'' Interpreting abstract instructions requires models to both identify the abstraction intended by the speaker, and unroll it into the many concrete actions that instantiate it. Language models struggle here: prior work has shown that performance degrades as the level of abstraction in the instruction rises. To bridge the gap between natural language abstraction and concrete actions, we propose to borrow from coding environments the notion of a {\em domain-specific language} (DSL), an explicit representation layer whose documented primitives name recurring abstractions that expand into concrete actions. We demonstrate the efficacy of DSLs for NL interpretation on \textsc{Hexagons}, a visually-grounded instruction-following task graded for abstraction. We further ask whether language models can induce such DSLs themselves from domain data. Our results show that while code generation consistently improves over direct inference, code generation over a human-curated DSL yields dramatic gains, up to 10 absolute F1 points, reaching the human skyline, with gains concentrated at {\em higher} abstraction levels. We confirm this pattern on the \textsc{Text2CAD} benchmark. At the same time, model-induced DSLs recover only part of this gain, remaining inconsistent across runs and short of their human-curated counterpart. Overall, the right representation closes much of the gap caused by high abstraction --- but inducing that layer of representation automatically is where models still fall short, and where we see a strong opportunity for the community to make progress.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.28311 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-21T07:05:58.150Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).