Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
MixPE: Quantization and Hardware Co-design for Efficient LLM Inference
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2411.16158.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T04:37:25.556664Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-15T02:58:34.210349Z
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 acc3f4e0-8a96-4cd3-ad5a-89638b211887 · inbound
Harmonia: Algorithm-Hardware Co-Design for Memory- and Compute-Efficient BFP-based LLM Inference cites this paper.
Harmonia: Algorithm-Hardware Co-Design for Memory- and Compute-Efficient BFP-based LLM Inference MixPE: Quantization and Hardware Co-design for Efficient LLM Inference
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T04:37:25.556664Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ba2d476e-ee61-4bb8-9de5-7a5712dfb5f7 · inbound
Multi-Scale Dequant: Eliminating Dequantization Bottleneck via Activation Decomposition for Efficient LLM Inference cites this paper.
Multi-Scale Dequant: Eliminating Dequantization Bottleneck via Activation Decomposition for Efficient LLM Inference MixPE: Quantization and Hardware Co-design for Efficient LLM Inference
Reference 25
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T02:58:34.211896Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 176971e1-129e-44fe-9653-e42be1804a59 · inbound
StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration cites this paper.
StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration MixPE: Quantization and Hardware Co-design for Efficient LLM Inference
Reference 72
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T01:10:03.032181Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.