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A Bounded Evaluation Protocol for Representational Systematicity — arXiv:2611.01755

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01755
License (as archived): CC0-1.0
Archived: 2026-11-03T11:10:37.616Z
SHA-256 of the archived text: f652bc5dacd3e426f276be8c085e5ad2d8830812a711ec65cb6cb09105c86735
Stable URL of this page: https://metraipolis.com/documento/dc_c0f008ecbd2f

How to cite

A Bounded Evaluation Protocol for Representational Systematicity — arXiv:2611.01755 — arXiv (en), https://arxiv.org/abs/2611.01755 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c0f008ecbd2f (Metraipolis archive dc_c0f008ecbd2f, sha256 f652bc5dacd3e426f276be8c085e5ad2d8830812a711ec65cb6cb09105c86735)

BibTeX

@misc{dcc0f008ecbd2f,
  title = {A Bounded Evaluation Protocol for Representational Systematicity — arXiv:2611.01755},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01755},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c0f008ecbd2f; sha256 f652bc5dacd3e426f276be8c085e5ad2d8830812a711ec65cb6cb09105c86735},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01755. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Multiple studies have shown that neural networks can be behaviorally systematic, producing correct outputs for novel combinations of familiar parts. Whether they are also representationally systematic, that is, whether they represent these parts and their roles and use them to produce the outputs, remains hard to test, because each kind of evidence can mislead on its own: correct outputs can come from shortcuts, a decodable signal need not be used, an intervention can act through unintended pathways, and a failed search does not show absence. We propose a bounded evaluation protocol for representational systematicity (Bounded-RS): for an explanation that attributes a systematic behavior to a declared representation, it tests on a matched behavioral contrast whether that representation is recoverable and whether it is causally used, checks the validity of this evidence, and assigns the explanation one of four bounded states, Unsupported, Present-only, Causal-use, or Undetermined, through control-calibrated scores, gates, and stop rules. On a semantic-parsing testbed of 96 matched sets that we release, the tested role-filler Binding explanation of fine-tuned T5 models is Causal-use and the distributed Geometry explanation is Unsupported within its declared search space, verdicts that hold across calibration-compatible parameter configurations, training seeds, and T5-base. On 41 cases with known causal status, full adjudication licenses a false Causal-use in 1 of 34 noncausal cases, against 13 when recoverability is taken as use, without missing any causal case. Bounded-RS thus turns the question of representational systematicity, debated since Fodor and Pylyshyn, into comparable and falsifiable verdicts on declared explanations, each reported with the evidence pattern that licenses it and, for a negative, the search space that bounds it.

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/2611.01755 — 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 2026-11-03T10:59:52.314Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).