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Paper Citation Record · LEDGER

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation

As of 11 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2605.29461.

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

pith.paper-citation-record.v1
2605.29461 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T08:05:39.980715Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

  • verified exact4
  • verified fuzzy0
  • unresolved28
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a50cb164-1675-45aa-9219-d9e205126a6a · outbound

This paper cites End-to-end object detection with transformers.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation End-to-end object detection with transformers

Reference 1

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:04c4220cff9aa3e19ab441d755a18b6a8512f3d621a01adf180f041a5658517d

Observation fade93ac-c225-47e0-895a-a621e914018d · outbound

This paper cites F., and Chen, C.-S.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation F., and Chen, C.-S

Reference 2

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:54673f2b0a64cfbb4c246f55716fd326f7579f0f53bcf53bc67d5f270923719f

Observation e7221c23-1ba4-4045-b56e-780cc8470b9b · outbound

This paper cites C., and Kirillov, A.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation C., and Kirillov, A

Reference 3

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:ac7f072fdb21c23898e754a130ebf92a078d483364527c39b86c02c42c73fbaf

Observation 9a6c6b0e-9a0d-4a45-a481-b6577e7679c5 · outbound

This paper cites Per-pixel classification is not all you need for semantic segmentation.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation Per-pixel classification is not all you need for semantic segmentation

Reference 4

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:728642dd80041d3f14754e711750401bf3f1b3f224f19ed5bc7627c618a7048f

Observation 9aae2ee4-6541-45a6-a802-658790dd1701 · outbound

This paper cites G., Kirillov, A., and Girdhar, R.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation G., Kirillov, A., and Girdhar, R

Reference 5

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:ab57954b01bcf07684a8f1677dee91b9fe229d157a5d9457b70a10e393c89a23

Observation 5039f788-fbc9-49e2-ba7b-01f27aeb42ae · outbound

This paper cites Mathematical morphology in image processing.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation Mathematical morphology in image processing

Reference 6

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:b1bf51722761f089b1d869f23589b5921b1ce65230aefb7117489be296295672

Observation 4a607c1e-c9c2-401c-92ca-3731788ce703 · outbound

This paper cites Segmentation from natural language expressions.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation Segmentation from natural language expressions

Reference 7

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:ac5aba3c14f5f2e6ccbb1a0b5bb1ebd8f3809a9c698e75eb48c9af6191643014

Observation 43618a6d-cc23-4b77-b200-54b050498d8b · outbound

This paper cites Referitgame: Referring to objects in photographs of natural scenes.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation Referitgame: Referring to objects in photographs of natural scenes

Reference 8

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:3af8e5dc9e91dd8443015e60432c4df830f045d0c52acfbea2b7e3d2804a5994

Observation 9bf70268-c6b5-4aef-b59e-2bd9e4f615d4 · outbound

This paper cites C., Lo, W.-Y., et al.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation C., Lo, W.-Y., et al

Reference 9

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:48e95f3c8ce2f3d404ebd0624649714f922ff4b26566a11f98f394da205ea71e

Observation 06d0e4b6-e3fb-41de-8a8d-eef18457eab8 · outbound

This paper cites C., Lo, W.-Y., et al.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation C., Lo, W.-Y., et al

Reference 10

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:63710f3c9f923d7c4807e8e6dae0b86945ffc763df632cd1d46e75ec62fb9a85

Observation c36dac7c-ef9d-4e2a-81d0-7c67e3a789d9 · outbound

This paper cites Lisa: Reasoning segmentation via large language model.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation Lisa: Reasoning segmentation via large language model

Reference 11

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:816ed9ce1816715815d7e2d6f6a1d705811f707a229dbe4c04ea1155c74eba92

Observation 4cecd09d-5d52-4479-b4ea-080f122f0c9c · outbound

This paper cites Referring image segmentation via recurrent refinement networks.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation Referring image segmentation via recurrent refinement networks

Reference 12

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:353c283304aec96b3564d2c691ff934d62d53ac75ad2d3d2dd7fa1d3bfc9e0be

Observation d6a89188-f0bc-48e9-8b47-d5a9a4134eb4 · outbound

This paper cites an unresolved cited work.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation Unresolved cited work

Reference 13

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:efbcde32c9e3dc93f0134d94df25ca0dbaea9638a9ab69760febb58577cd43d6

Observation 3d9dbc8f-5878-4aa4-a947-2d7f3ded359b · outbound

This paper cites L., and Murphy, K.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation L., and Murphy, K

Reference 14

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:dde341d8c7f032a73d2cbce7778db515620a0cb6e6995dabe863cf2de3f7f72a

Observation 21a045ef-0a89-443a-b9f8-226f012288ec · outbound

This paper cites Pixellm: Pixel reasoning with large multimodal model.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation Pixellm: Pixel reasoning with large multimodal model

Reference 15

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:6d2b1d12086c7295a2425f5181e7aca11a00a1ecf8046615dacca8d158f7ef9c

Observation 66dc4466-cbf1-4de1-a757-8025cf7fa73c · outbound

This paper cites Morphological gradients.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation Morphological gradients

Reference 16

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:129d32bbc3656bd872e7bc3346d37a7270a182e164838787168cac7ff0828d34

Observation b9b1f842-121e-42bb-ada0-0461f20a76d3 · outbound

This paper cites Segmenter: Transformer for semantic segmentation.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation Segmenter: Transformer for semantic segmentation

Reference 17

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:37a86050c54e8201a0fb4d1a0b74fda2ea45e28b62d776b5e003ad122129a3e3

Observation 8e95e93d-1bc6-4c59-8439-93ab99044ae7 · outbound

This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 18

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local_arxiv, observed 2026-06-29T08:13:15.857262Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:be7eca6125afdc5d03667197b6420bb58a5f65583e2e7fd2df3c6aa5e55c155a

Observation 18cb95ab-5eeb-4e85-ac76-733203cbd41c · outbound

This paper cites X-sam: From segment anything to any segmentation.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation X-sam: From segment anything to any segmentation

Reference 19

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arxiv_id, observed 2026-06-29T08:13:15.859715Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:7b64d697725ef78ec90e79663b135dba0cded243754a1c7791496b7d09a848b4

Observation a52b1daa-0dd5-435f-883c-9321a2f63e79 · outbound

This paper cites HyperSeg: Towards Universal Visual Segmentation with Large Language Model.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation HyperSeg: Towards Universal Visual Segmentation with Large Language Model

Reference 20

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arxiv_id, observed 2026-06-29T08:13:15.849992Z

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:d3799f08c60fb3f7578c335015d28fd677aa1db460806b3191d5b2a159b0d9e7

Observation 5c232775-a369-4b90-9f07-4adbfd523267 · outbound

This paper cites Gsva: Generalized segmentation via multimodal large language models.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation Gsva: Generalized segmentation via multimodal large language models

Reference 21

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:b1ff7e4e002cc4d2f9589dba6bac5920cd680aeaaddfe935b837720e4ca4c48c

Observation c31a6871-b820-472a-b9f0-d9d54cccd402 · outbound

This paper cites M., and Luo, P.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation M., and Luo, P

Reference 22

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:5ec4a6f2990485c80068c077e56301264a757b29a783c4fef7d8811c9d13f42a

Observation dfd3f880-f139-47c0-81f1-1695e2215d27 · outbound

This paper cites Qwen3 Technical Report.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation Qwen3 Technical Report

Reference 23

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local_arxiv, observed 2026-06-29T08:13:15.852405Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:52fd3bbb2e6d7439aa868f552a0676430e7790d185957c895afd4fb7fc79e18a

Observation 906f6d2e-38f1-4a5f-a865-bd9d2d9f2edd · outbound

This paper cites Cross-modal self-attention network for referring image segmentation.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation Cross-modal self-attention network for referring image segmentation

Reference 24

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:d273fcd59c2200b620fe3d7a36bfd7eb9a2eed84f1067f581e1dc44d752faedb

Observation 4fe3c79e-8d84-409f-997f-b85a4fe75a17 · outbound

This paper cites an unresolved cited work.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation Unresolved cited work

Reference 25

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:a9f3aa9a01bda05e81070cec10715f2809d5bba58c85a05ad414e458a76af21c

Observation a9f17795-4a89-4997-9904-6ac4c78cb05e · outbound

This paper cites Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos

Reference 26

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verified exact
local_arxiv, observed 2026-06-29T08:13:15.854863Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:9f33c6653742b639dd0214932d9a30d7ab3139d24af52214fcd2cf04e10a8032

Observation 7541cd03-95ef-4cba-9405-eab92b644cc6 · outbound

This paper cites Sigmoid loss for language image pre-training.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation Sigmoid loss for language image pre-training

Reference 27

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:acc4b43f25ba551c8f5372ea0652d27681d145e8ef0029851308cc5ac3db6afc

Observation 0c25034c-3088-4ca7-bb35-f9b2685291f1 · outbound

This paper cites C., and Yan, S.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation C., and Yan, S

Reference 28

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:093a5c37a24d1150d927b9c17a31ea2d0fc9e4335b9f533f8d49543aa664eb61

Observation 43c9570b-af8a-4d1d-8c67-72a2cf956e51 · outbound

This paper cites an unresolved cited work.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation Unresolved cited work

Reference 29

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:55e059e82fe020169787463429ac72382ce8df75d8a7bd1b9420db5e7cd58c2a

Observation cc9af6fc-e17a-4b01-83c7-6cec3012c606 · outbound

This paper cites Psalm: Pixelwise segmentation with large multi-modal model.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation Psalm: Pixelwise segmentation with large multi-modal model

Reference 30

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:b9a733e534b72dfd7b9f45a26617312bed383b543f50180a189cc5aa8e7d055f

Observation b3a878dd-2db7-41dd-ab35-4d032dccd8eb · outbound

This paper cites H., et al.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation H., et al

Reference 31

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:d043f72891c53f11ee5b0ea06ef338157f3e6920623b4eaab09eb1ac2f7a48c1

Observation 0449e2d9-c9a1-4d54-acf6-b494d217c442 · outbound

This paper cites Generalized decoding for pixel, image, and language.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation Generalized decoding for pixel, image, and language

Reference 32

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:c66fbae8177f25bb01b2ee582de1b46e19d649f6e7b64818764914ce863507f3

Observation f0e92224-ac7a-4485-9869-4d592dd29664 · outbound

This paper cites an unresolved cited work.

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation Unresolved cited work

Reference 33

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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:66ccf4c61f3bbab493f82e08a075d022cbabf224df7209f20499f25bd4a2a67b

Pith citing papers

No inbound Pith citation observations are available.