Paper Citation Record · LEDGER

DB-LLM: Accurate Dual-Binarization for Efficient LLMs

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2402.11960.

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

pith.paper-citation-record.v1
2402.11960 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:42:40.497757Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T23:44:26.569012Z

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 65ff2377-1a4c-40bb-821d-6c4448a65f62 · inbound

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling cites this paper.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling DB-LLM: Accurate Dual-Binarization for Efficient LLMs

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.497757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1848e84a-a530-4753-9fdb-3e9f2eba6f7d · inbound

Event-Priori-Based Vision-Language Model for Efficient Visual Understanding cites this paper.

Event-Priori-Based Vision-Language Model for Efficient Visual Understanding DB-LLM: Accurate Dual-Binarization for Efficient LLMs

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:01.276118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43f53ec0-0e2c-48d6-b533-461b8a86d462 · inbound

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models cites this paper.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models DB-LLM: Accurate Dual-Binarization for Efficient LLMs

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:44:26.571115Z

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 41df0c72-a825-45cf-b38e-732d679a99f6 · inbound

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling cites this paper.

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling DB-LLM: Accurate Dual-Binarization for Efficient LLMs

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-10T05:36:02.306104Z

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 0e1a6f99-5f88-41ca-bc6b-e2d0f16cf2bf · inbound

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling cites this paper.

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling DB-LLM: Accurate Dual-Binarization for Efficient LLMs

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-19T18:02:42.235774Z

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 5dba8e9f-72fc-45ef-957d-00cb23144979 · inbound

LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation cites this paper.

LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation DB-LLM: Accurate Dual-Binarization for Efficient LLMs

Reference 46

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:46:04.574800Z

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.