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Paper Citation Record · LEDGER

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy

As of 4 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 2 inbound Pith citation observations for arXiv:2606.27652.

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

pith.paper-citation-record.v1
2606.27652 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T05:06:09.216428Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T07:20:31.305011Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

  • verified exact13
  • verified fuzzy0
  • unresolved31
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation 9aff33c2-b51f-41bf-9184-c8dfcc767c22 · outbound

This paper cites GPT-4o System Card.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy GPT-4o System Card

Reference 1

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local_arxiv, observed 2026-06-29T18:23:51.448041Z

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Observation de2ba455-3b97-41de-a5d2-8cba257e897d · outbound

This paper cites OpenAI GPT-5 System Card.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy OpenAI GPT-5 System Card

Reference 2

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local_arxiv, observed 2026-06-29T18:23:51.435970Z

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:3e21dcfd9306cc891f6babb677b083225f81e4b2a020f7a26a67058b483adf23

Observation da7dea5b-8fed-471a-b87c-87df051a16da · outbound

This paper cites Qwen2.5-Omni Technical Report.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Qwen2.5-Omni Technical Report

Reference 3

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local_arxiv, observed 2026-06-29T18:23:51.428492Z

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:da79d52bd148b595c54e2492451f107bea9bbe5e865832f088c344e96401c29b

Observation 36891fd2-1076-4ede-97c3-d2a9a493e9ab · outbound

This paper cites MIT press, 2000.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy MIT press, 2000

Reference 4

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:efd4af5d30df7ac56621ee4a6c29e8ef09b8bcb56df8438a759258ef0b5bc913

Observation b1f64b24-c8f1-48b5-8eae-5497ddbf82cd · outbound

This paper cites Mer 2025: When affective computing meets large language models.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Mer 2025: When affective computing meets large language models

Reference 5

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:d3a669ebcfa512e23ad656b85a4dd882d398aa83e47f86c4ed5412ee50b705d7

Observation 739549d3-b542-434e-9569-12c879cf362f · outbound

This paper cites Multimodal large language models meet multimodal emotion recognition and reasoning: A survey.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Multimodal large language models meet multimodal emotion recognition and reasoning: A survey

Reference 6

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arxiv_id, observed 2026-06-29T18:23:51.433738Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:8e1086f1c9138e33b1573cf0d8d2890d6efafa43cc02597626b1a9ab5a2438be

Observation d5a39ae3-1023-44cc-9177-153db0e80cb8 · outbound

This paper cites Mer 2023: Multi-label learning, modality robustness, and semi-supervised learning.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Mer 2023: Multi-label learning, modality robustness, and semi-supervised learning

Reference 7

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:78891b26a9c1814ab19656efedf02946063bdffe97011d74242859e476df8037

Observation 149b0958-c115-4d1c-91a7-31bd1a4c22bb · outbound

This paper cites Mer 2024: Semi-supervised learning, noise robustness, and open-vocabulary multimodal emotion recognition.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Mer 2024: Semi-supervised learning, noise robustness, and open-vocabulary multimodal emotion recognition

Reference 8

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:0cb853479d7d9fd72fbad61c6664d9e0a7e5100f6abe66f9d5ef720c72c5b0a3

Observation 0a3b1def-6148-4c7c-ad5d-31bcae9cf164 · outbound

This paper cites Meld: A multimodal multi-party dataset for emotion recognition in conversa- tions.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Meld: A multimodal multi-party dataset for emotion recognition in conversa- tions

Reference 9

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:021c3f62f9c0aabcaf134653559e5e99b12dc04506a4233984db21f25f445123

Observation a8e8c97f-d239-4dda-a882-8cb2735a49e5 · outbound

This paper cites Iemocap: Interactive emotional dyadic motion capture database.Language resources and evaluation, 42(4):335–359, 2008.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Iemocap: Interactive emotional dyadic motion capture database.Language resources and evaluation, 42(4):335–359, 2008

Reference 10

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:13ae767e228c697d922e47a53ce1794b82ad027b0fc11262de381d5f52f2b893

Observation 89f16994-88af-47a2-b211-073f84fe5263 · outbound

This paper cites Affectgpt: A new dataset, model, and benchmark for emotion understanding with multimodal large language models.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Affectgpt: A new dataset, model, and benchmark for emotion understanding with multimodal large language models

Reference 11

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:b5f3206dd363887bede3c9c8bedf117d80a4b561b663cc411240ae3e5e2d2a2e

Observation c3fadfbc-79e2-4d54-9d54-2c3a397acb34 · outbound

This paper cites Ov-mer: Towards open-vocabulary multimodal emotion recognition.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Ov-mer: Towards open-vocabulary multimodal emotion recognition

Reference 12

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:661bf33dbcbbbdaa21683057d57228daef6729ba563168c3e504629b19208dec

Observation 2f6721db-461d-43d1-8c0a-5fc088aaf701 · outbound

This paper cites Explainable multimodal emotion reasoning.CoRR, 2023.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Explainable multimodal emotion reasoning.CoRR, 2023

Reference 13

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:6cbaeb9411882852ef18437ca2f82673d3b6f1e052df415e4b44ea41b6488f28

Observation a148fa02-2a7b-4c76-8a9c-b6148e7d5db1 · outbound

This paper cites Deepseek-r1 incentivizes reasoning in llms through reinforcement learning.Nature, 645(8081):633–638, 2025.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Deepseek-r1 incentivizes reasoning in llms through reinforcement learning.Nature, 645(8081):633–638, 2025

Reference 14

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:e509cbfcf71f06dea30290209f8c8be9eb484de5f57f454b1dc10d165d296672

Observation 5641f6ad-3fb5-42ed-b9ed-ec256e5c27dc · outbound

This paper cites Affectgpt-r1: Leveraging reinforcement learning for open-vocabulary multimodal emotion recognition.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Affectgpt-r1: Leveraging reinforcement learning for open-vocabulary multimodal emotion recognition

Reference 15

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arxiv_id, observed 2026-06-29T18:23:51.445768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:88a30127a02236407283b59afef9b8fd5c5220873d9bfe01931504a5c50ac2ad

Observation db441dfa-501f-404a-b6d7-55eb6216d76b · outbound

This paper cites HumanOmniV2: From Understanding to Omni-Modal Reasoning with Context.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy HumanOmniV2: From Understanding to Omni-Modal Reasoning with Context

Reference 16

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arxiv_id, observed 2026-06-29T18:23:51.450720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:f025b92d598fa999fc3ab9bca9ef8b23b4153831bc045db165bb95e62792ac5c

Observation 82ae2464-03d4-4626-b2e2-5586145176f3 · outbound

This paper cites From system 1 to system 2: A survey of reasoning large language models.TPAMI, 2026.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy From system 1 to system 2: A survey of reasoning large language models.TPAMI, 2026

Reference 17

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:3ca0037f0893f20f5fa73e485be388d6021f6671b679785becc233748195eaaf

Observation c1ffc9ea-2473-40d3-a7e7-3bcce07f176e · outbound

This paper cites Qwen3-Omni Technical Report.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Qwen3-Omni Technical Report

Reference 18

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local_arxiv, observed 2026-06-29T18:23:51.438414Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:3c9a6f386c79ac63baf61a4959b40c0d31b99e0bfef843cff67a8b3cfc83648e

Observation 32e8cc38-a9dd-45db-afbd-d7a8773aa399 · outbound

This paper cites DeepSeek-V3 Technical Report.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy DeepSeek-V3 Technical Report

Reference 19

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local_arxiv, observed 2026-06-29T18:23:51.440715Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:da86818c6c5e98abe160ad1d8889839f0aee4b1c044711a1e10430859a7ad89a

Observation 36323b14-11e6-4772-aa21-c36b39b7dd61 · outbound

This paper cites Visual instruction tuning.NeurIPS, 2024.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Visual instruction tuning.NeurIPS, 2024

Reference 20

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:947bc569afea76806510041e796f95f260b7774d0efec8e52ea71d20af355941

Observation 4a50014c-ef68-4ab3-ae6c-ac2796c2a43e · outbound

This paper cites Parallel diffusion solver via residual dirichlet policy optimization.IEEE TPAMI, pages 1–17, 2026.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Parallel diffusion solver via residual dirichlet policy optimization.IEEE TPAMI, pages 1–17, 2026

Reference 21

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:9df2252c3da5cc9991ead9c302bc70381d95509379c18b303aa4cea873133d6f

Observation 14bfe3fd-5aa8-4dd0-98d8-9b993342d188 · outbound

This paper cites MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning

Reference 22

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local_arxiv, observed 2026-06-29T18:23:51.426185Z

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:e9e77ac0a58bfa2427b7d35dd9e0f51ae4217264bd5e5feaff147dae822c885c

Observation 8450f34c-dcd4-4cb8-9822-91253341aaaa · outbound

This paper cites Perception-aware policy optimization for multimodal reasoning.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Perception-aware policy optimization for multimodal reasoning

Reference 23

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:59ac5d8a75a726e9036b715caa9cf1bb23b369f9af20c29deab4104ac9f39208

Observation 45733f55-72e0-47bb-ab4a-99f6fde0ffef · outbound

This paper cites Visual-rft: Visual reinforcement fine-tuning.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Visual-rft: Visual reinforcement fine-tuning

Reference 24

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:a7b076b40bfc4d5956c64c0a35d1a22e93cf9c9770592755ae599a3e6696f431

Observation 6d4eae79-72e5-409c-817b-5be5c45443ab · outbound

This paper cites Vl- rethinker: Incentivizing self-reflection of vision-language models with reinforcement learning.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Vl- rethinker: Incentivizing self-reflection of vision-language models with reinforcement learning

Reference 25

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:c67211f059ccd8501df7a014fe2af05db438a966edd4c13fd0312b4e839fc621

Observation 7fc44035-1d43-4e86-bb2d-0bd7171b9d00 · outbound

This paper cites Videoauto-r1: Video auto reasoning via thinking once, answering twice.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Videoauto-r1: Video auto reasoning via thinking once, answering twice

Reference 26

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arxiv_id, observed 2026-06-29T18:23:51.431143Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:c3ac334d700d5e8316ee576d5c86a07a59a1eb385719af9d48318220adfa2daa

Observation 264cb53c-4288-43a0-8527-c53326148bf7 · outbound

This paper cites Emotion-llama: Multimodal emotion recognition and reasoning with instruction tuning.NeurIPS, 2024.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Emotion-llama: Multimodal emotion recognition and reasoning with instruction tuning.NeurIPS, 2024

Reference 27

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:19d6a59c03827adf5d60025fc5142d4e88c16bb3a2ffc6c85038044fb6f2854a

Observation d053f9a6-071c-4820-9585-8a194434da9f · outbound

This paper cites Benchmarking and bridging emotion conflicts for multimodal emotion reasoning.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Benchmarking and bridging emotion conflicts for multimodal emotion reasoning

Reference 28

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:8d08e94294184cab6a6ad74dee9667591ab911689fe53732b0598dc7b4a51ff8

Observation 8e546f26-7de7-4ed5-9f63-2ad9a93e99a2 · outbound

This paper cites Mme-emotion: A holistic evaluation benchmark for emotional intelligence in multimodal large language models.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Mme-emotion: A holistic evaluation benchmark for emotional intelligence in multimodal large language models

Reference 29

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:559fb616441665b994f51efc3968af6c5883bf23140a207bfc8c096524d27fda

Observation 086d81b5-2dc0-4052-961a-924a3405b370 · outbound

This paper cites R1-Omni: Explainable Omni-Multimodal Emotion Recognition with Reinforcement Learning.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy R1-Omni: Explainable Omni-Multimodal Emotion Recognition with Reinforcement Learning

Reference 30

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arxiv_id, observed 2026-06-29T18:23:51.423928Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:d8b56184612dcba1ae8eb8dda6297bb8dcc285469a607f4b0640e80bc92ff815

Observation 4b282b2f-c161-458b-9b76-c56c0e4435a5 · outbound

This paper cites Emotion-coherent reasoning for multimodal llms via emotional rationale verifier.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Emotion-coherent reasoning for multimodal llms via emotional rationale verifier

Reference 31

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:70fcb4f917fddefb5ba8b32d4029ea765ce6a87126c206d2b9ac285756d0bbca

Observation 83072bef-a268-4225-a828-3e1b98b061ed · outbound

This paper cites Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models

Reference 32

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local_arxiv, observed 2026-06-29T18:23:51.453211Z

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:cfe43133b3ef12d1edb1147c20aaf6bf7b737da30516e1464d299d47d9a2934e

Observation 0122020a-d026-48ab-8b21-2d79d000c8dc · outbound

This paper cites Salmonn: Towards generic hearing abilities for large language models.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Salmonn: Towards generic hearing abilities for large language models

Reference 33

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Observation 859053f4-2207-4caf-baf0-528cce73949f · outbound

This paper cites Mvbench: A comprehensive multi-modal video understanding benchmark.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Mvbench: A comprehensive multi-modal video understanding benchmark

Reference 34

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:d3964dd1752e346197c2fe8ab9a3560f7b9aea52d367d45fc4f0283dc8f91916

Observation 127d14ed-162b-4aa9-bb15-0ca07bac231c · outbound

This paper cites Llama-vid: An image is worth 2 tokens in large language models.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Llama-vid: An image is worth 2 tokens in large language models

Reference 35

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:98b22e7899ef0e2ab18246cb4405b00c476977cefafe8f9eae9a392f8b6300bd

Observation e11dc62f-2d2c-4f7e-b8dc-94eec586f0f6 · outbound

This paper cites Chat-univi: Unified visual representation empowers large language models with image and video understanding.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Chat-univi: Unified visual representation empowers large language models with image and video understanding

Reference 36

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unresolved
no resolver link, observed 2026-06-29T05:06:09.216428Z

Source-reported events for the cited work

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:cc43edc35138ebc4a989fb1ea24572937a6bcf973d42505c2f9d491173244856

Observation 143b3b6d-b76f-414e-84ef-d5e764626aae · outbound

This paper cites mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality

Reference 37

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verified exact
local_arxiv, observed 2026-06-29T18:23:51.443168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:a160090e911514d696b5bb78d3e2ec3f8cb8a759f1bf4fd8ef7075ba2d089c4d

Observation 2e5ccf09-1a84-4ab5-b844-cb47b6bbecfc · outbound

This paper cites Pandagpt: One model to instruction-follow them all.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Pandagpt: One model to instruction-follow them all

Reference 38

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no resolver link, observed 2026-06-29T05:06:09.216428Z

Source-reported events for the cited work

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:d8dbdf4fb04c278feb8f683e1379a60258b769f4c20f3529367d77a4d95b1ce8

Observation b38c01f2-033a-483f-9cdf-c17841a2db29 · outbound

This paper cites Mosi: multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Mosi: multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos

Reference 39

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unresolved
no resolver link, observed 2026-06-29T05:06:09.216428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:489c35fd86f93b783ac1a7b696684a39a9cb0d4eadf138b7221e2cf72266ec9d

Observation f93caa1b-a62c-4188-9bcf-52416996f7c9 · outbound

This paper cites Multimodal language analysis in the wild: Cmu-mosei dataset and interpretable dynamic fusion graph.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Multimodal language analysis in the wild: Cmu-mosei dataset and interpretable dynamic fusion graph

Reference 40

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unresolved
no resolver link, observed 2026-06-29T05:06:09.216428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:f611cf2d9bc59844399e2632888de45518370a9c5f4b1264edd3143e6b777ce2

Observation 01e3eb2d-5342-4afd-b2c0-e78bc5e9ccd6 · outbound

This paper cites Ch-sims: A chinese multimodal sentiment analysis dataset with fine-grained annotation of modality.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Ch-sims: A chinese multimodal sentiment analysis dataset with fine-grained annotation of modality

Reference 41

Resolution
unresolved
no resolver link, observed 2026-06-29T05:06:09.216428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:330f15eb2cba6af961dfe96415e3ea2db20cc24b244fa3bfa767b6b3a3cbbc77

Observation 80d024b5-3ef5-4372-9754-565d282e052a · outbound

This paper cites Make acoustic and visual cues matter: Ch-sims v2.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Make acoustic and visual cues matter: Ch-sims v2

Reference 42

Resolution
unresolved
no resolver link, observed 2026-06-29T05:06:09.216428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:ba7a55200e5bff396489fedc34fb40d6275dd6f8d80fbe7e95cee90d1808c30f

Observation ee359edf-d1b4-4b92-be34-6913f13c6e36 · outbound

This paper cites Unsupervised visual chain-of-thought reasoning via preference optimization.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy Unsupervised visual chain-of-thought reasoning via preference optimization

Reference 43

Resolution
unresolved
no resolver link, observed 2026-06-29T05:06:09.216428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:8db2d0b0a08df600381ce9f23ac5ed7e99881b5ae9883a367ef6d643c7daf8a8

Observation f91d2f90-53d9-4785-8c3c-dfd0ee759886 · outbound

This paper cites I’ll tell you, it’s not easy for a woman who has divorced and is raising a child to find a partner.

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy I’ll tell you, it’s not easy for a woman who has divorced and is raising a child to find a partner

Reference 44

Resolution
unresolved
no resolver link, observed 2026-06-29T05:06:09.216428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:511c90864462a7004adad4c03a5f6ab31aa994963c366d7c3bcff48643b08ec5

Pith citing papers

Observation 68266c49-5a3a-4795-bd62-ebf0f98734f8 · inbound

Enjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging cites this paper.

Enjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy

Reference 3

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unresolved
no resolver link, observed 2026-07-14T11:49:31.595873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T11:49:31.595873Z digest=sha256:84d89adfba804a85617780a14922b5083877db7baa964d043f7aecac152f2789

Observation 2bbb0d5f-756b-4635-9656-5a9a272bd2bb · inbound

Enjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging cites this paper.

Enjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy

Reference 3

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unresolved
no resolver link, observed 2026-08-02T07:20:31.305011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T07:20:31.305011Z digest=sha256:3a2e3011d115996d22eeeb5fce69d58e017560b6db96c32a2abb8a298ad2a448