Paper Citation Record · LEDGER

BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2402.04291.

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

pith.paper-citation-record.v1
2402.04291 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T01:35:43.424174Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T17:29:59.598269Z

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 1a65a08d-29e5-4f7d-828b-3f25473f029d · inbound

BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook cites this paper.

BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-19T14:07:20.532683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 11bbacbb-8d30-4d7a-ba15-e6e76bdae4f2 · inbound

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

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:44:26.527117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 484e2527-2485-4734-b792-761a3c65e4f0 · inbound

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba cites this paper.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:46:12.386959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d212f42f-ff90-49a5-b2bb-7f5777c66fa7 · inbound

Rethinking Output Alignment For 1-bit Post-Training Quantization of Large Language Models cites this paper.

Rethinking Output Alignment For 1-bit Post-Training Quantization of Large Language Models BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-16T19:33:20.045575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ff87af45-80f1-478e-b383-526c913f3621 · inbound

Rethinking Output Alignment For 1-bit Post-Training Quantization of Large Language Models cites this paper.

Rethinking Output Alignment For 1-bit Post-Training Quantization of Large Language Models BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-21T16:44:16.051208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a8dbb3ec-b5cb-4742-a280-a340499c1734 · inbound

BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models cites this paper.

BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-21T14:14:12.429098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 14983f5b-f3ce-478e-8351-9f8e55a627e6 · inbound

DeFakeQ: Enabling Real-Time Deepfake Detection on Edge Devices via Adaptive Bidirectional Quantization cites this paper.

DeFakeQ: Enabling Real-Time Deepfake Detection on Edge Devices via Adaptive Bidirectional Quantization BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:25:58.454938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 01a2b157-3771-47ac-9d24-53ab3f1105c3 · 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 BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:11:20.901825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b25976f0-233e-4e9c-b058-6f1085a9c777 · 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 BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-19T18:02:42.252920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 62e182d1-d659-4bf0-b781-2d94e0dc591f · inbound

Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond cites this paper.

Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 151

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:26:08.267403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f8184116-a3ff-45f7-823b-0d92410c0264 · inbound

Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond cites this paper.

Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 151

Resolution: verified exact; arxiv_id, observed 2026-07-04T17:29:59.600173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e8b3629a-5b40-4cf3-8760-ea834bed7624 · inbound

Different Prompts, Different Ranks: Prompt-aware Dynamic Rank Selection for SVD-based LLM Compression cites this paper.

Different Prompts, Different Ranks: Prompt-aware Dynamic Rank Selection for SVD-based LLM Compression BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:41:33.931244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 198e1cda-1994-40b0-8805-c112939a89ed · inbound

A Composite Activation Function for Learning Stable Binary Representations cites this paper.

A Composite Activation Function for Learning Stable Binary Representations BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:07:07.889038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c6d3918c-db8d-4859-9499-d1f268594e5c · inbound

LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection cites this paper.

LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 51

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T02:06:27.021816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 04611a71-ae80-4987-907f-f98a45a393df · inbound

LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection cites this paper.

LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 51

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T11:24:38.268077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation aaa755f9-e6a9-4844-a821-f6f040bd8598 · inbound

MorphoQuant: Modality-Aware Quantization for Omni-modal Large Language Models cites this paper.

MorphoQuant: Modality-Aware Quantization for Omni-modal Large Language Models BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 31

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T06:56:44.636154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cf02dd73-87d5-4c53-a505-2d2c622e2adc · 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 BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-02T08:16:48.283866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 347e9c7b-18b6-4ab6-859a-48634cbf0a36 · inbound

OffQ: Taming Structured Outliers in LLM Quantization by Offsetting cites this paper.

OffQ: Taming Structured Outliers in LLM Quantization by Offsetting BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:27:09.699784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d39fd99e-8550-427d-bc25-842c1b824fa9 · inbound

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models cites this paper.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:43.424174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.