Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Q-Sparse: All Large Language Models can be Fully Sparsely-Activated
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2407.10969.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:03:43.408919Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T19:43:54.756414Z
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 afb8e5e4-14c5-4c1e-ac13-2af7fac8cf2d · inbound
TAT-VPR: Ternary Adaptive Transformer for Dynamic and Efficient Visual Place Recognition cites this paper.
TAT-VPR: Ternary Adaptive Transformer for Dynamic and Efficient Visual Place Recognition Q-Sparse: All Large Language Models can be Fully Sparsely-Activated
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:03:43.408919Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d48a10fb-c0ee-4a3d-8ee3-d0842025ec77 · inbound
Identifying Pre-training Data in LLMs: A Neuron Activation-Based Detection Framework cites this paper.
Identifying Pre-training Data in LLMs: A Neuron Activation-Based Detection Framework Q-Sparse: All Large Language Models can be Fully Sparsely-Activated
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:15:21.387745Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d50ee72f-5857-4cc9-a87f-a04627505cc0 · inbound
Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models cites this paper.
Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Q-Sparse: All Large Language Models can be Fully Sparsely-Activated
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T05:11:11.670107Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 61ede6c2-ba32-4ba1-8217-129d279a9873 · inbound
Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches cites this paper.
Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches Q-Sparse: All Large Language Models can be Fully Sparsely-Activated
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:41:25.857400Z
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.
Observation d5b53a5f-1a26-4f81-b1b2-5e1bfac41b43 · inbound
RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models cites this paper.
RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models Q-Sparse: All Large Language Models can be Fully Sparsely-Activated
Reference 62
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T19:43:54.758454Z
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.