Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2503.00493.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T23:32:42.611362Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T23:09:00.924175Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 585c329c-d32d-47ab-8fbc-992d7eee9ba9 · inbound
Miipher-2: A Universal Speech Restoration Model for Million-Hour Scale Data Restoration cites this paper.
Miipher-2: A Universal Speech Restoration Model for Million-Hour Scale Data Restoration LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:32:42.611362Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e237341a-0ecd-4eb0-9baa-657a811df6d5 · inbound
ReverbMiipher: Generative Speech Restoration meets Reverberation Characteristics Controllability cites this paper.
ReverbMiipher: Generative Speech Restoration meets Reverberation Characteristics Controllability LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:16:41.756782Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4be90f66-6d7e-4ff2-86a4-0089fa2e26b6 · inbound
From Continuous to Discrete: Cross-Domain Collaborative General Speech Enhancement via Hierarchical Language Models cites this paper.
From Continuous to Discrete: Cross-Domain Collaborative General Speech Enhancement via Hierarchical Language Models LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:04:57.445338Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2b39c493-1a95-4a2a-9427-71333ceffea6 · inbound
UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling cites this paper.
UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T22:07:46.146681Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7306e42e-cc95-4085-b004-a6fac48f0d06 · inbound
UniSE: A Unified Framework for Decoder-Only Autoregressive LM-Based Speech Enhancement cites this paper.
UniSE: A Unified Framework for Decoder-Only Autoregressive LM-Based Speech Enhancement LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T08:29:18.782766Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4341fc9a-414d-489a-be2e-fc50134b89ae · inbound
GenTSE: Enhancing Target Speaker Extraction via a Coarse-to-Fine Generative Language Model cites this paper.
GenTSE: Enhancing Target Speaker Extraction via a Coarse-to-Fine Generative Language Model LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T14:20:49.137953Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 724a89e1-e54b-455c-ad92-246456025c8c · inbound
Discriminative-Generative Target Speaker Extraction with Decoder-Only Language Models cites this paper.
Discriminative-Generative Target Speaker Extraction with Decoder-Only Language Models LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement
Reference 50
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T15:44:14.732250Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 92d132db-ce09-448c-8109-391cd37a2316 · inbound
SEMamba++: A General Speech Restoration Framework Leveraging Global, Local, and Periodic Spectral Patterns cites this paper.
SEMamba++: A General Speech Restoration Framework Leveraging Global, Local, and Periodic Spectral Patterns LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T22:43:13.611383Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 204a1e36-8fbc-47a4-a68c-7aa745e0c694 · inbound
PhASE-Flow: Phonetic-Conditioned Acoustic Flow Matching in SSL Representation Domain for Speech Enhancement cites this paper.
PhASE-Flow: Phonetic-Conditioned Acoustic Flow Matching in SSL Representation Domain for Speech Enhancement LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement
Reference 47
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T23:09:00.925960Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.