Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2407.07093.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T15:08:46.193864Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T23:44:26.563485Z
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 ff40a103-66df-49a8-b964-dbba660c3632 · inbound
Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study cites this paper.
Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T15:08:46.193864Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9e716d84-766f-4dc6-aed0-b8be8d68a267 · 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 FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T14:07:20.500384Z
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 228f981d-3990-4ee7-9114-36f5423c8cd1 · inbound
Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models cites this paper.
Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T23:44:26.565633Z
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 eff43df2-b394-4e4e-a658-a82b267efead · 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 FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation
Reference 34
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T12:46:04.794501Z
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 c74d2b19-e863-46c6-89a3-37c4cf22ceef · 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 FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T01:35:43.528232Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.