Paper Citation Record · LEDGER

Bitnet.cpp: Efficient Edge Inference for Ternary LLMs

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2502.11880.

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

pith.paper-citation-record.v1
2502.11880 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T01:39:08.003701Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T05:56:40.450862Z

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 dcb65cb6-a69c-473c-b2df-9e5cec4805e4 · inbound

Vec-LUT: Vector Table Lookup for Parallel Ultra-Low-Bit LLM Inference on Edge Devices cites this paper.

Vec-LUT: Vector Table Lookup for Parallel Ultra-Low-Bit LLM Inference on Edge Devices Bitnet.cpp: Efficient Edge Inference for Ternary LLMs

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:48:45.785517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 14d64b60-4bdc-4866-a244-e203f50a02d8 · inbound

BitRL: Reinforcement Learning with 1-bit Quantized Language Models for Resource-Constrained Edge Deployment cites this paper.

BitRL: Reinforcement Learning with 1-bit Quantized Language Models for Resource-Constrained Edge Deployment Bitnet.cpp: Efficient Edge Inference for Ternary LLMs

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:46:42.845825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a9aed4e8-132b-40ab-b01b-7a4135cb7f32 · inbound

Spike-Aware C++ INT8 Inference for Sparse Spiking Language Models on Commodity CPUs cites this paper.

Spike-Aware C++ INT8 Inference for Sparse Spiking Language Models on Commodity CPUs Bitnet.cpp: Efficient Edge Inference for Ternary LLMs

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-02T05:56:40.452885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 444697ed-b0f6-488b-ba50-1d1a30c39f35 · inbound

PolyQ: Codesigning End-to-End Quantization Framework for Scalable Edge CPU LLM Inference cites this paper.

PolyQ: Codesigning End-to-End Quantization Framework for Scalable Edge CPU LLM Inference Bitnet.cpp: Efficient Edge Inference for Ternary LLMs

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T01:39:08.003701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.