Paper Citation Record · LEDGER

PB-LLM: Partially Binarized Large Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2310.00034.

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

pith.paper-citation-record.v1
2310.00034 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 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 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:34:28.096136Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T13:38:19.682691Z

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 0996d68a-fd79-417b-a84d-e650e408bd8b · inbound

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models cites this paper.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models PB-LLM: Partially Binarized Large Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T23:00:19.590254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fb6e9d3-a112-4977-ab6f-6936c740d265 · inbound

Squeeze10-LLM: Squeezing LLMs' Weights by 10 Times via a Staged Mixed-Precision Quantization Method cites this paper.

Squeeze10-LLM: Squeezing LLMs' Weights by 10 Times via a Staged Mixed-Precision Quantization Method PB-LLM: Partially Binarized Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:38.610439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 514f4a66-06a9-46f2-bf2a-2466d9e24357 · inbound

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

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models PB-LLM: Partially Binarized Large Language Models

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:44:26.507749Z

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 fbb0cbac-c016-46d5-b43d-27bf9b682114 · inbound

BWTA: Accurate and Efficient Binarized Transformer by Algorithm-Hardware Co-design cites this paper.

BWTA: Accurate and Efficient Binarized Transformer by Algorithm-Hardware Co-design PB-LLM: Partially Binarized Large Language Models

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:28:02.184424Z

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 278c5a4f-aa8c-4b98-afeb-437ef615b932 · 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 PB-LLM: Partially Binarized Large Language Models

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-10T05:41:02.534144Z

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 4d0e72b4-c9a5-4d88-a6f7-4850a32db12e · 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 PB-LLM: Partially Binarized Large Language Models

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-19T18:02:42.246077Z

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 3ee71f21-44b3-4f9b-a573-2c6cd609a253 · 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 PB-LLM: Partially Binarized Large Language Models

Reference 47

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:46:04.743009Z

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 b8b526f6-b6c2-4654-9c8d-9c1a1f011294 · inbound

SAFE-SVD: Sensitivity-Aware Fidelity-Enforcing SVD for Physics Foundation Models cites this paper.

SAFE-SVD: Sensitivity-Aware Fidelity-Enforcing SVD for Physics Foundation Models PB-LLM: Partially Binarized Large Language Models

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:03:17.824218Z

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 b4a8e400-017f-4685-a874-6e5c08c293b8 · inbound

GAMMA: Global Bit Allocation for Mixed-Precision Models under Arbitrary Budgets cites this paper.

GAMMA: Global Bit Allocation for Mixed-Precision Models under Arbitrary Budgets PB-LLM: Partially Binarized Large Language Models

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:28:16.818583Z

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 0be90a99-d55f-4dcc-8755-42ff16b031fb · inbound

Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models cites this paper.

Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models PB-LLM: Partially Binarized Large Language Models

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-07-02T08:16:48.259050Z

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 e637d3b9-7a68-4dce-a83a-6ceb366802c2 · inbound

TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization cites this paper.

TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization PB-LLM: Partially Binarized Large Language Models

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-03T13:38:19.684395Z

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 20305c83-f578-421e-8fdd-eb073bebbf12 · inbound

Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts cites this paper.

Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts PB-LLM: Partially Binarized Large Language Models

Reference 295

Resolution: unresolved; no resolver link, observed 2026-08-01T16:42:38.208119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 475c0f67-624b-4303-8a1f-c5f3e05f8f6d · inbound

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization cites this paper.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization PB-LLM: Partially Binarized Large Language Models

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:28.096136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.