{
  "annotation_id": "c65b8750-bf79-4b42-9a18-66d5aebc2196",
  "date_created": "2026-02-17T05:53:24.218000Z",
  "date_modified": "2026-02-17T05:53:24.218000Z",
  "file_hash": "43858afc2664badb1bb00f242c08fda5fce7ea0e1e044db651f4fe5cc38ddc53",
  "private": false,
  "record": {
    "abstract": "Large Language Models (LLMs) often exhibit a gap between their internal knowledge and their explicit linguistic outputs. In this report, we empirically investigate whether Looped Transformers (LTs)--architectures that increase computational depth by iterating shared layers--can bridge this gap by utilizing their iterative nature as a form of introspection. Our experiments reveal that while increasing loop iterations narrows the gap, it is partly driven by a degradation of their internal knowledge carried by representations. Moreover, another empirical analysis suggests that current LTs\u0027 ability to perceive representations does not improve across loops; it is only present in the final loop. These results suggest that while LTs offer a promising direction for scaling computational depth, they have yet to achieve the introspection required to truly link representation space and natural language.",
    "arxiv_id": "2601.10242",
    "authors": [
      "Guanxu Chen",
      "Dongrui Liu",
      "Jing Shao"
    ],
    "categories": [
      "cs.CL",
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Loop as a Bridge: Can Looped Transformers Truly Link Representation Space and Natural Language Outputs?",
    "url": "https://arxiv.org/abs/2601.10242",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "79e4a0c2-5b98-450a-ae62-e54335473e3d",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}