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Paper Citation Record · LEDGER

BrepLLM: Enabling Large Language Models to Understand Boundary Representations

As of 23 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 1 inbound Pith citation observation for arXiv:2512.16413.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2512.16413 v2

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T15:37:21.746869Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T20:55:09.818614Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

24 of 24 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved24
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2c1a653b-e98e-4d41-8ca6-acf5389530f2 · outbound

This paper cites Phi-2: The surprising power of small language models, 2023.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations Phi-2: The surprising power of small language models, 2023

Reference 1

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source=pdf_text observed=2026-08-03T15:37:19.636053Z digest=sha256:baad42a52e606b14b98b30c5c523a19f7753437e87866800a7e0a3da8eeddfa8

Observation 89aebcec-691f-42a1-912b-12deecce327a · outbound

This paper cites Query2CAD: Generating CAD models using natural language queries.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations Query2CAD: Generating CAD models using natural language queries

Reference 2

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source=pdf_text observed=2026-08-03T15:37:19.716950Z digest=sha256:904ddb5a8a9be65a4927638bdcfbaa17de1e9e0ef710846fd59e02e2e6e0f64f

Observation 2772d65d-750f-4eef-8e26-37cfc4b0a10b · outbound

This paper cites Text2shape: Generating shapes from natural language by learning joint embeddings.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations Text2shape: Generating shapes from natural language by learning joint embeddings

Reference 3

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source=pdf_text observed=2026-08-03T15:37:19.863650Z digest=sha256:b5ed5861f10e3eb8054348c2d227eb2e300bd18f956d0bcea2b96c5d003c764c

Observation e796e6a2-178e-4587-b848-5e1a5bb318e3 · outbound

This paper cites An investigation on utilizing large language model for in- dustrial computer-aided design automation.Procedia CIRP, 128:221–226, 2024.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations An investigation on utilizing large language model for in- dustrial computer-aided design automation.Procedia CIRP, 128:221–226, 2024

Reference 4

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source=pdf_text observed=2026-08-03T15:37:19.967713Z digest=sha256:de75b60560d1552c7ef155c362963dd584a9de86c40e830bd1747dc2bc1e3392

Observation fe99df47-86ad-4627-a0ff-fb3d15b08884 · outbound

This paper cites A Solver-Aided Hierarchical Language for LLM-Driven CAD Design.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations A Solver-Aided Hierarchical Language for LLM-Driven CAD Design

Reference 5

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source=pdf_text observed=2026-08-03T15:37:20.083481Z digest=sha256:d2f6ef73ab533892c90ab2f6dc75a7f12c3fda19968e80556915d5f724b8ea91

Observation 80064c85-904f-45d6-b922-13e3714a0306 · outbound

This paper cites Text2cad: Generating sequential cad designs from beginner- to-expert level text prompts.Advances in Neural Information Processing Systems, 37:7552–7579, 2024.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations Text2cad: Generating sequential cad designs from beginner- to-expert level text prompts.Advances in Neural Information Processing Systems, 37:7552–7579, 2024

Reference 6

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source=pdf_text observed=2026-08-03T15:37:20.171552Z digest=sha256:a639ceaa86ca3b474cb52b95302ebd46929c3a5859fffb0a9f5ec02efe2b8fe7

Observation c3be2a57-c51f-4003-8361-00e2d681c6c0 · outbound

This paper cites LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models

Reference 7

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source=pdf_text observed=2026-08-03T15:37:20.281619Z digest=sha256:fe9bcd19ed13bae48a9c4d08fc533c572309e554287f7d454c81825197a63117

Observation e05f214c-2ad7-4e43-a385-cc7fc082d6d7 · outbound

This paper cites Llm4cad: Multi-modal large language models for 3d computer-aided design generation.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations Llm4cad: Multi-modal large language models for 3d computer-aided design generation

Reference 8

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source=pdf_text observed=2026-08-03T15:37:20.394352Z digest=sha256:2212d2068c1489b39a5fafb7baf82a23a707ab8944af7ca1f5ed1e5e82d49477

Observation 830b595b-cb50-498b-a9c2-2ded29214a7f · outbound

This paper cites CAD-Assistant: Tool-Augmented VLLMs as Generic CAD Task Solvers.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations CAD-Assistant: Tool-Augmented VLLMs as Generic CAD Task Solvers

Reference 9

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source=pdf_text observed=2026-08-03T15:37:20.477779Z digest=sha256:d2fe1d17f5b3b4c655f3201f1fab47cae87f1b4f78e35415ce3707690ffddd5f

Observation 0e52c526-9977-469f-bc56-db09b73b243b · outbound

This paper cites Shapellm: Universal 3d object understanding for embodied interaction,.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations Shapellm: Universal 3d object understanding for embodied interaction,

Reference 10

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source=pdf_text observed=2026-08-03T15:37:20.555500Z digest=sha256:56fc4fe2f81c5d6e24132fc43023a5925b2bc4876289a906511b7672e41990e0

Observation 93cdc62b-110a-4cf3-8d7b-aa047f427405 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations Learning transferable visual models from natural language supervi- sion

Reference 11

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source=pdf_text observed=2026-08-03T15:37:20.668706Z digest=sha256:80d432479edf82c78f442c55871ddc67b60cbd482dec447b64db466c08ea26e9

Observation 39275633-e027-4f18-8a2a-ab9b67e22615 · outbound

This paper cites Clip-forge: Towards zero-shot text-to-shape genera- tion.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations Clip-forge: Towards zero-shot text-to-shape genera- tion

Reference 12

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source=pdf_text observed=2026-08-03T15:37:20.782457Z digest=sha256:2568a30bcdafb15796f430cd744a157989842d6cb410400ead4ae7613e741e52

Observation 4b289705-d9ff-4a20-bd4a-93c745c6e89f · outbound

This paper cites Meshclip: Efficient cross-modal infor- mation processing for 3d mesh data in zero/few-shot learn- ing.Information Processing & Management, 60(6):103497,.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations Meshclip: Efficient cross-modal infor- mation processing for 3d mesh data in zero/few-shot learn- ing.Information Processing & Management, 60(6):103497,

Reference 13

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source=pdf_text observed=2026-08-03T15:37:20.865420Z digest=sha256:75b58b3db339c15ca77396b3263bd34e6b2bd911898b9f4e9607b21ad400f5d3

Observation f6299a1e-8d1e-4159-840b-1da6e45d8749 · outbound

This paper cites Minigpt-3d: Efficiently aligning 3d point clouds with large language models using 2d priors, 2024.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations Minigpt-3d: Efficiently aligning 3d point clouds with large language models using 2d priors, 2024

Reference 14

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source=pdf_text observed=2026-08-03T15:37:20.925267Z digest=sha256:b541ed55b10f3a3630e671b4453afdd43b741ac96a98929bc8dceaf1a55da59c

Observation c7085ba9-3f28-4c1a-81b4-1ab66b6a95a8 · outbound

This paper cites Cad-gpt: Synthesising cad construction sequence with spatial reasoning-enhanced mul- timodal llms.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations Cad-gpt: Synthesising cad construction sequence with spatial reasoning-enhanced mul- timodal llms

Reference 15

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source=pdf_text observed=2026-08-03T15:37:21.047096Z digest=sha256:35e86d8e3ee5f6641ce3543e47f692f3c12b00e6800f33feb9453f2b66745065

Observation 597f31d2-fcd6-4777-9382-7ee6b48ee71e · outbound

This paper cites Cad-llm: Large language model for cad generation.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations Cad-llm: Large language model for cad generation

Reference 16

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source=pdf_text observed=2026-08-03T15:37:21.121228Z digest=sha256:60abe803da6855467402ae0e183dcf39622a5d286b36902f51bdfaa347a5d558

Observation 8f532cda-a6b4-4ffe-8dd8-08c45af23b92 · outbound

This paper cites Cad- vlm: Bridging language and vision in the generation of para- metric cad sketches.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations Cad- vlm: Bridging language and vision in the generation of para- metric cad sketches

Reference 17

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source=pdf_text observed=2026-08-03T15:37:21.175570Z digest=sha256:0bf64dbea2bf5f4381f3938b81e42b6eb75c03a4ae4f4f2b2a4dc0bf98b4e407

Observation 04249605-aff2-4a81-bbbc-9e52994b9585 · outbound

This paper cites Point transformer v3: Simpler, faster, stronger.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations Point transformer v3: Simpler, faster, stronger

Reference 18

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source=pdf_text observed=2026-08-03T15:37:21.246223Z digest=sha256:554e48f163dbfff0b48c3c1b2f890a685c3593a55ccecba0727a7c0118b135b0

Observation c5ebc0d1-9c92-44dd-86af-b54d515ff429 · outbound

This paper cites CAD-MLLM: Unifying Multimodality-Conditioned CAD Generation With MLLM.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations CAD-MLLM: Unifying Multimodality-Conditioned CAD Generation With MLLM

Reference 19

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source=pdf_text observed=2026-08-03T15:37:21.317204Z digest=sha256:8e2bf38c4a7cfef875a27574747f1703605e2d1bba2ee8affe3deb52873dce3e

Observation 0ab70cdc-da1b-4c2b-bcb2-5aa2cf3c7367 · outbound

This paper cites Pointllm: Empowering large language models to understand point clouds, 2024.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations Pointllm: Empowering large language models to understand point clouds, 2024

Reference 20

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source=pdf_text observed=2026-08-03T15:37:21.423904Z digest=sha256:a8c1fa19d9db4043e6137581bdc42b5ec09807cfca4387c6b33ef15bcc305e19

Observation 66ece010-6a88-42d0-80e1-f3fa2661fed1 · outbound

This paper cites Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding

Reference 21

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source=pdf_text observed=2026-08-03T15:37:21.537218Z digest=sha256:d35b9a5f1b1dfc71604a0070a0df921f16b12e65fabd3f36db7d1ad1354f4db4

Observation 4cbf5b11-4ad4-4ad9-b934-323106e9ff84 · outbound

This paper cites Cadtalk: An algorithm and benchmark for semantic commenting of cad programs.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations Cadtalk: An algorithm and benchmark for semantic commenting of cad programs

Reference 22

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source=pdf_text observed=2026-08-03T15:37:21.611131Z digest=sha256:35304b30cf5794567421c8cac3814696e894ddb45865ac20b538857fdbe95f5a

Observation 32f088bd-f298-4e1e-9f68-bee6dd94f225 · outbound

This paper cites Cad-editor: Text-based cad editing through adapting large language mod- els with synthetic data.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations Cad-editor: Text-based cad editing through adapting large language mod- els with synthetic data

Reference 23

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source=pdf_text observed=2026-08-03T15:37:21.688890Z digest=sha256:58c32d6535c76d50c5658fd3ca1b173d3f6fe11b39f34f041268dea8cd49da1c

Observation d1d79f79-c536-432d-a64f-3026e0b3549a · outbound

This paper cites Pointclip: Point cloud understanding by clip.

BrepLLM: Enabling Large Language Models to Understand Boundary Representations Pointclip: Point cloud understanding by clip

Reference 24

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source=pdf_text observed=2026-08-03T15:37:21.746869Z digest=sha256:14a82c7194ec6046299444702a75cb87b7a45b3b0fa18543b0a8558a4520c2be

Pith citing papers

Observation e433228f-7285-4397-86e7-6f8c1943e193 · inbound

BrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning cites this paper.

BrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning BrepLLM: Enabling Large Language Models to Understand Boundary Representations

Reference 9

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source=pdf_text observed=2026-08-02T20:55:09.818614Z digest=sha256:cb7d25ca9947a2b7d9126ffba314e95a594cddebe88c874bb3b4903af4bb06ed