Paper Citation Record · LEDGER

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding

As of 16 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2606.25552.

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

pith.paper-citation-record.v1
2606.25552 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-25T21:06:46.079335Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-25T21:06:46.079335Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-04T19:40:07.042588Z

Reference resolution

37 of 37 outbound references displayed

verified exact7
verified fuzzy0
unresolved29
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7f196f44-acf0-4ac5-815f-b4db87c551fe · outbound

This paper cites I want to know the weather in Taipei.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding I want to know the weather in Taipei

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

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Observation 88635a02-0a3e-4d2a-af74-12551c088dc3 · outbound

This paper cites SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding

Reference 2

Resolution: malformed identifier; local_arxiv, observed 2026-07-04T19:40:07.045672Z

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Observation 36218d87-a713-4a3b-85a0-0f9c82100c42 · outbound

This paper cites Experiment Setting 3.1.1.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding Experiment Setting 3.1.1

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:40:07.042640Z

Source-reported events for the cited work

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Observation 1d71eae7-8e17-491a-b018-809f8aa6669d · outbound

This paper cites It decom- poses predictions into intent-specific frames, applies Hybrid Jaccard slot clustering, and filters unreliable frames via path support scoring.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding It decom- poses predictions into intent-specific frames, applies Hybrid Jaccard slot clustering, and filters unreliable frames via path support scoring

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fc6e9cf-427a-4d3c-9af0-a2ee77369640 · outbound

This paper cites Any findings and implications in the paper do not necessarily reflect those of the sponsors.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding Any findings and implications in the paper do not necessarily reflect those of the sponsors

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 028aa137-e321-4c84-9978-af505e1c9b94 · outbound

This paper cites We also used AI tools to assist with code de- velopment, with strict human review and verification to ensure correctness.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding We also used AI tools to assist with code de- velopment, with strict human review and verification to ensure correctness

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a23f2b3d-5133-4afe-8ea5-7b91614f0af9 · outbound

This paper cites Tur and R.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding Tur and R

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07e09c74-3a06-43d5-984f-fdf4fcd89357 · outbound

This paper cites A survey on spoken language understanding: Recent advances and new frontiers,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding A survey on spoken language understanding: Recent advances and new frontiers,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a2c583a-b323-4ab5-a74e-de47baa353da · outbound

This paper cites A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:40:07.049699Z

Source-reported events for the cited work

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Observation 6c29dbec-c557-4a47-88c6-0502193e13ab · outbound

This paper cites Zero-shot spoken language understanding via large language models: A preliminary study,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding Zero-shot spoken language understanding via large language models: A preliminary study,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 983a5832-4497-4b5f-90ec-61dbbc268087 · outbound

This paper cites CroPrompt: Cross-task Interactive Prompting for Zero- Shot Spoken Language Understanding,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding CroPrompt: Cross-task Interactive Prompting for Zero- Shot Spoken Language Understanding,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

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Observation d1f6863c-c202-43cf-8968-3e2482be7c7e · outbound

This paper cites DXA-Net: Dual- Task Cross-Lingual Alignment Network for Zero-Shot Cross- Lingual Spoken Language Understanding,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding DXA-Net: Dual- Task Cross-Lingual Alignment Network for Zero-Shot Cross- Lingual Spoken Language Understanding,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

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Observation b80431e4-bf05-43ea-b455-63047b0735c1 · outbound

This paper cites How ChatGPT is Robust for Spoken Language Understanding?.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding How ChatGPT is Robust for Spoken Language Understanding?

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 091f693c-6242-4c4c-8409-e663be522b4f · outbound

This paper cites A unified framework for multi-intent spoken language understanding with prompting,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding A unified framework for multi-intent spoken language understanding with prompting,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

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Observation f7452b59-3fcb-47cf-87de-26ce8cc6c9c9 · outbound

This paper cites Intent detection in the age of LLMs,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding Intent detection in the age of LLMs,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 288434ee-2d03-4006-8041-edba41209a2d · outbound

This paper cites WavPrompt: Towards Few-Shot Spoken Language Un- derstanding with Frozen Language Models,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding WavPrompt: Towards Few-Shot Spoken Language Un- derstanding with Frozen Language Models,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d9ac079-142e-4b47-8617-3d4aecffaba9 · outbound

This paper cites Prompting Whisper for QA- driven Zero-shot End-to-end Spoken Language Understanding,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding Prompting Whisper for QA- driven Zero-shot End-to-end Spoken Language Understanding,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

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Observation 33ab8d78-0619-4daf-acd0-355da426d108 · outbound

This paper cites Mac-slu: Multi-intent automo- tive cabin spoken language understanding benchmark.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding Mac-slu: Multi-intent automo- tive cabin spoken language understanding benchmark

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:40:07.055717Z

Source-reported events for the cited work

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Observation f56d66a6-2b5e-42d3-b3e4-0ac94992b631 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding Self-Consistency Improves Chain of Thought Reasoning in Language Models,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

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Observation 4605922f-2a25-40eb-bf9d-41b30ea05733 · outbound

This paper cites Confidence Improves Self- Consistency in LLMs,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding Confidence Improves Self- Consistency in LLMs,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

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Observation 80237316-80b6-4071-8ada-7d8723107772 · outbound

This paper cites Universal Self-Consistency for Large Language Models,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding Universal Self-Consistency for Large Language Models,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

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Observation 7ea4dfbb-506e-46ba-a51a-a0b20c72f2af · outbound

This paper cites Better patching using llm prompting, via self-consistency,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding Better patching using llm prompting, via self-consistency,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

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Observation b5385353-005c-4b2f-93ac-821ee8f55132 · outbound

This paper cites Estimating the self-consistency of LLMs,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding Estimating the self-consistency of LLMs,

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:40:07.040730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 91cc016b-0ad1-4bbc-80b7-a89013b210cb · outbound

This paper cites HIT- SCIR at MMNLU-22: Consistency Regularization for Multilin- gual Spoken Language Understanding,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding HIT- SCIR at MMNLU-22: Consistency Regularization for Multilin- gual Spoken Language Understanding,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

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Observation c231a7a1-e9f9-43ac-9ad7-0d285dd53a19 · outbound

This paper cites A survey on LLM-as-a-judge,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding A survey on LLM-as-a-judge,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

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Observation 19d4427d-cff4-4b53-b87f-da4c17604e9f · outbound

This paper cites From generation to judgment: Opportunities and chal- lenges of LLM-as-a-judge,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding From generation to judgment: Opportunities and chal- lenges of LLM-as-a-judge,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

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Observation 207f0ad2-2ac4-4e3a-9acb-549698e11b0e · outbound

This paper cites Limitations of the LLM-as-a-Judge approach for evaluating LLM outputs in expert knowledge tasks,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding Limitations of the LLM-as-a-Judge approach for evaluating LLM outputs in expert knowledge tasks,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

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Observation 1d1edc1b-fe2f-4d03-87a5-6bf2aa9ba1ea · outbound

This paper cites Association rule mining: A sur- vey,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding Association rule mining: A sur- vey,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

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Observation e605e901-d54a-4cae-8657-a9c5134de961 · outbound

This paper cites Negative association rule,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding Negative association rule,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

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Observation 66c160ac-c889-443e-acda-213c469e8071 · outbound

This paper cites Qwen3 Technical Report.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding Qwen3 Technical Report

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-07-04T19:40:07.049876Z

Source-reported events for the cited work

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Observation 6f86bb5b-7cb1-4c81-aa3a-0781fd95791f · outbound

This paper cites Robust speech recognition via large-scale weak su- pervision,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding Robust speech recognition via large-scale weak su- pervision,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

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Observation decaae31-c70f-4e6a-a5a4-f164197aaf2c · outbound

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SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding Qwen2.5-Omni Technical Report

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-07-04T19:40:07.046011Z

Source-reported events for the cited work

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Observation ad7a0e0c-a5dd-4b13-907a-d6c47e36dd28 · outbound

This paper cites A Graph Model for Unsupervised Lexical Acquisition,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding A Graph Model for Unsupervised Lexical Acquisition,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

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Observation c40886b9-e003-4978-bbff-71a9ea1590ae · outbound

This paper cites ´Etude comparative de la distribution florale dans une portion des alpes et des jura,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding ´Etude comparative de la distribution florale dans une portion des alpes et des jura,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

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Observation 463ebb51-db5c-41bf-bf59-770423ffc0d4 · outbound

This paper cites Reasoning aware self- consistency: Leveraging reasoning paths for efficient LLM sam- pling,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding Reasoning aware self- consistency: Leveraging reasoning paths for efficient LLM sam- pling,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

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Observation 5850c5aa-ba67-4f5a-b620-479745af8406 · outbound

This paper cites Efficient memory management for large language model serving with PagedAttention,.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding Efficient memory management for large language model serving with PagedAttention,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-25T21:06:46.079335Z

Source-reported events for the cited work

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Observation b70fc1b2-e6cc-492a-a090-47b61c40cbb0 · outbound

This paper cites vllm-omni: Fully disaggregated serving for any-to-any multimodal models.

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding vllm-omni: Fully disaggregated serving for any-to-any multimodal models

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:40:07.018405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

Observation 88635a02-0a3e-4d2a-af74-12551c088dc3 · inbound