Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-03T14:41:13.930130Z
Paper Citation Record · LEDGER
As of 13 August 2026, this Paper Citation Record lists 99 of 99 outbound references and 0 inbound Pith citation observations for arXiv:2607.29048.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-03T14:41:13.930130Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
99 of 99 outbound references displayed
External citation measurements
No source-named external measurement is stored.
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Parameter-Efficient Fine-Tuning for Spiking Point Cloud Models Annual Meeting of the Association for Computational Linguistics (ACL) , year=
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Parameter-Efficient Fine-Tuning for Spiking Point Cloud Models Expert Systems With Applications (ESWA) , year=
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Observation 7053e410-e46b-4c62-b29c-b229a461cba2 · outbound
Parameter-Efficient Fine-Tuning for Spiking Point Cloud Models Not All Errors Are Created Equal: ASCoT Addresses Late-Stage Fragility in Efficient LLM Reasoning
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Parameter-Efficient Fine-Tuning for Spiking Point Cloud Models IEEE Transactions on Multimedia (TMM) , year=
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Observation f5cfc820-3b77-4310-93ad-a355a05375c3 · outbound
Parameter-Efficient Fine-Tuning for Spiking Point Cloud Models International Conference on Artificial Neural Networks (ICANN) , year=
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Observation 01e2b27e-feba-480e-b92c-1d287273cfe3 · outbound
Parameter-Efficient Fine-Tuning for Spiking Point Cloud Models Diffusion Masked Pretraining for Dynamic Point Cloud
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Observation fe16f163-0b74-4484-b5ff-018eb130ac1b · outbound
Parameter-Efficient Fine-Tuning for Spiking Point Cloud Models GaussFusion: Towards Multimodal 3D Gaussian Pretraining
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Observation 32dfe4dd-bbca-44be-962b-b2327961019f · outbound
Parameter-Efficient Fine-Tuning for Spiking Point Cloud Models SPARK: Susceptibility-Guided Profiling and Steering of Latent Reasoning States in Large Language Models
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Observation 0b522729-85e4-4bef-a449-d402f18919ec · outbound
Parameter-Efficient Fine-Tuning for Spiking Point Cloud Models SpikingMOT: A Spike-Driven Multi-Object Tracker
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