Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2408.01803.
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 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:17:55.235854Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T16:18:37.366967Z
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 90417c47-7f34-4e1a-bcb9-5743b96231d0 · inbound
Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression cites this paper.
Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:17:55.235854Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0ea9d9c9-e5d8-4fa1-9e07-1bbab04c40a3 · 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 STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T19:33:20.054629Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 7d10aee3-7698-48ae-b528-f2e18b401515 · 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 STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T16:44:16.063202Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 06c25bc9-2e26-40a3-b579-e9053ef890ee · inbound
QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models cites this paper.
QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs
Reference 8
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-15T20:20:17.411005Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 1f94459c-563d-4e56-9da8-76b8067679ed · 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 STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs
Reference 8
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-10T05:36:02.313684Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 5ce8f240-7ee0-4f61-9da0-1e93097a59fe · 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 STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs
Reference 8
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-19T18:02:42.265638Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 5e8ce95e-ede3-46ec-bb2c-be04ea0cdcba · inbound
SAB-LVLM: Significance-Aware Binarization for Large Vision-Language Models cites this paper.
SAB-LVLM: Significance-Aware Binarization for Large Vision-Language Models STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs
Reference 5
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-03T16:18:37.368415Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.