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Paper Citation Record · LEDGER

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

As of 9 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 2026-08-09T06:31:02.800959+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

No source-named external measurement is stored.

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:ebd9df20d023ee989f5802fa2e3ac5134e7db3f28e6e009b37ab0969cb9954a7

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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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:2041979393c825a52ecf6793c16291199962fdfded5730147d320c45b0e5d729

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:db25ccf036e548a2bee33dd017c9fba1c7a91c3110280636a54a5f4994e8df75

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=sha256:91b5eaead5d9054e5a22b1a4d192eef99dc7c4263b2d515166af691ed2dea8bd

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:8de97b111034c92b617d54c6fc8c3a6a4847081966bd75c4c9c34d1782688d39

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=sha256:724a708364049fa04384c626f2a225908d7938661d18827da3afe24732d895da

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=sha256:8a7bd1ac0347b9bcef63d7e92d9f18c60c423965c6465f13d71d1f871580a59e

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=sha256:8292da395cc400fda2e780e09d5ea672a8b049af2c981e59afce5e09b7883643

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=sha256:6350a4d2077502def69f96ca16d7000108e869e4309b963cfeea4b83578ebaf7

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:a91442eb773aa9613a36317e900359e770d430dc2144854c6bdb59aa748d2a8e

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=sha256:5fc3266493ebc87bbcfd443ec0d86ed7ba4e8d2e8b5ae5597d5a588f07b27105

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=sha256:0eecb32e9da5a966784f95150064bb8a37de4f4aedfffa7256c1ba27cc45e7f7

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=sha256:0d9a4b55ace7a34c9030de5b5724baf648c854874969222c11025ed0d149fc90

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:ef9dbfa319551403795d2162a6e1996bd11d8d0f3fa28a2b3dc932f41d7792d8

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:c493da0dac42fc4318e1fcabf4145eabd223ec1149ef0a6622aa18db1bedbf29

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=sha256:39e7a71531ec75f95400a66c83b61c4a2e3e7a428ea2542a55f5f00fd3c58d39

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:183c50368152e53f1d964c8dc2973da2b9c6e214c214e9f1f54c82e85e06d6fa

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:66c9471a1e9c6d3dee6ce8cca0a14fd506bb4ba0c122d53ba67670141534e386

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=sha256:1227e0de429f98ce3e5bafb58a81a7f92b451d4100040978029ba21cde1d7e95

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:feddcc1c4fc89cb3a54cd248901a10b3ea6e9c1eb24f890b8f08b44ae4340606

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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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:6ffba7a86ef535e8a171429515efe24d0725d2f6959ae478fb6afb9b37221a5d

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=sha256:1bd5e43b4b4be7d975211f31b3de2b77ac51e0873c143b8ca7bcc99caa144f43

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:fe54a198b27a011306dca1d2f1717de0104fbac201e52779ca6fb88cea0c94a6

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=sha256:3ef6f447ba8461cf1903073d25952f26dec7704ad7956013c813e69abdb395eb

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:fa8b7045929d87e82dac60c06177007fb1b82c1d9b0881a04b8c731bd87f3abf

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:f7fb8aebbf227069fd08798f4aa4a1aa2b69c0cb4c1c9b79b69fcbc0b0a82336

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:d6773603d8c819bba5083596c4cd77eb3056fda16c3020e3eb90e620e076c96d

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=sha256:421b47eab83e4008aaddd302c2cc3c9e66ed0adc9a2ed2959bbcdc2d47189c41

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:a475ee855dfbc285e5dc0b82ef9116835ce54ecd46826b915cac4f3c7a32d2fd

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=sha256:9256e0ac391d894ad80909b3f0dbb9f9a4320c95eb39246b977d9399f288ee1e

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=sha256:0317bba2e2422e6f33015697562db7502a92ae657fa1e6b2b8ceee420b4239d3

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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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:10a1415a06840435c843021c0650726bab1bff97a0276903e7b2a5a8c06b07ee

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:f9cc71b6fae50b52b0b15ba08038ab0f2eb92ecb14e625b43edf7b9bd4ee90dc

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=sha256:4f3a2ef25689a489a4a7e961c3624dae39fb6e3c5920c87eeb2069d3bd00c1f8

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

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source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:4c50110354ca561a081032ec303c3010acdec215a2791cc7b54d998a4d66ff1d

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T05:06:09.216428Z digest=sha256:1eac4cf3f3f79407eb377c507e28cd4d4022af3afe5dc1da661e9d80554d6ee4

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=sha256:132a6acb3cfea0567bce0d3500190cf29479747da43558ca9cc5a65f871fd514

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=sha256:82d7a38ad45a28cbe12f6f98c2c34c388052760cca626e95ae5960b98805edc5

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=sha256:992cb7c5aa07f908cee990ee137da81f86ce5ac19a67aa9af4839f9271d716f5

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:b9b17a0fbd91e5aab1c1e342a6738f058b19aa1fdb1d2b6a6b692750498f2591

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:aa1efd4f6a943b4736c0c8559e9a958e05cf1f9ed6c3de705c052899a72883cd

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=sha256:0d612ac1c4a83f3ab40230b97a2af806eebed148647bd780bacf8a44297249f1

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=sha256:9e7bd2eb960e202c08b4739c15f97056a308be8eea64895179a5ff776505bbb0

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

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T11:49:31.595873Z digest=sha256:0b23a43039f9f75b31125b41317658a95753fafdf206428f6d74503b72d76199

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=sha256:616c9a09ea2e2caf558e8d5397edca3ea9560764632c4e843c0a431b39941fe8