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Paper Citation Record · LEDGER

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation

As of 23 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 2026-08-22T06:32:14.747728+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=sha256:24de0939fcdb235cde268b44caba71b38729ab3bb6237b57b75348da42f1e881

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:da88a8f96ac5141b9b3dacab0b7f66c1b8084d98745a74e156f49a1f4dbd486a

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=sha256:413bb2f0352939309606c324c206641ed6c222b71241a5002d5d1bdfd0d64202

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=sha256:42809582a52fb25969d18eda6abcd98d36ab5f241d2d27a056b6845f20342bbe

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:ab0e09543e15da31da302be1cb0dbe8f91189a7cf45290341953b2fe9422bda6

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=sha256:302d437b2422c30f9e04ed8a203ca866fcc9ce1223f2eb5a7f15f846f1223b1e

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:cfbbe92f28395c2fd99e1185381caa9e99dacfa30ab10f966933914cc81d2213

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=sha256:27cb8813283899945e29039fc2025767ce6c7c149f6fc1cfbe6a54de4a39c8c3

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=sha256:9257fcfa7fe5d0a9719a3888ae5bee9742e0fa29353f18b26da3c05edff9dde0

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:d3314093462a554646bdb7972c76d7eb1faf64e99394387cd983c03c10680d1b

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:bcb502269b400b39ec91ff0dbd26c6e86c1aac1f1a235f32cb00744cf717fcde

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=sha256:100166e224b1d36936c3e3e1483f15b5a1a9c3228831996adb67aae0b5616163

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=sha256:4f53ca446e2429906610f81716b44d44224a371ff86997eff947d999bfed80af

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=sha256:7141080fd2a314081888731f623e907a76d4b91d063de119747ef2f8109f65cd

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=sha256:0f4cf5de04cb964dcdc7691082857693ffca1701fc0bfcdb186c7dcb303f9c08

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=sha256:8c2b05a99f92bfe59d77e325fd3249efa4b721a94a52fde647774513ad7165c6

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=sha256:6f803178c77f7497e7affa6744cbb88d616b3f958206885910e7a9714edc0854

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:ffe9de20fddab0d795b07c3e0f92c0b1f0524f3283ea0a7e567df2affd2cf440

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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source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:67091311f2016b3ead26736077dfe527884ff5dcb5ac7d8c6f11e475c779e00d

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:f5bee212d8f4befa0ac6ab65ab82532f1accede2145b9e415947527367aaee90

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=sha256:77b82aea4ef99f150c3937d123f5c16923fd76ec0ec2748a63f7b0d11d63a54c

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=sha256:4784d9479f45f0312b5c3a6ad7ab7417d143507235b216ddb5c5f19317a99dd2

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 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:b8400f59c0962309471f06b061d03f8c3b6c5e3fcf8a629b2f02bd71b884b5ae

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=sha256:0fdc8316f6d3e29549a3de699f5660a9943db10c4a2ae90564ca082c1e8193ac

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=sha256:154a334d6bd1e54794925fa1d060dafe12a9fc99415a42bbfc4237f04f7284a2

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

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-06-29T08:05:39.980715Z digest=sha256:5bad9681f672e78cfd2bb2cbb0e83ee472a3945ec303bf50756db9b5ef39688f

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=sha256:4c3a680fcbd11c5f59a3653cc1496a27eb10bc36fd42a8a7bfe79848b7d9e7c2

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=sha256:58780fefef763fbfdfa55f7e20980b5bd9823d8c1048029e00cd8119f785389d

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:f8c72c8797e8be2ac44509fc9e65dab49d8c41cf549af5cfbeb5463eb2060fdd

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=sha256:45c279f365d7f550868d78d5a27f9e47be5fb16c769675e4315b23380e156fa2

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=sha256:361da37b74b10f45d7575caa1848b71afbec84b6f4800db1c9f2663f7ddd50e1

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=sha256:528236239ea794759b774f8370654bd9b16d3955d0c0d37c2f978a4c5ee02e4c

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:bf68deff8098741a232032cd33518160bf02f18342cc5c68ce3808445bf12bc9

Pith citing papers

No inbound Pith citation observations are available.