Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens
As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2411.17691.
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 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-26T11:53:45.787243Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T08:19:44.242007Z
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 7fddf69d-67b1-4f3b-b97f-3c33b387aaf9 · inbound
Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models cites this paper.
Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens
Reference 28
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T23:44:26.595598Z
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 8927b930-967d-4780-bf3a-ff9f647b04da · 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 Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens
Reference 100
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T12:46:04.726341Z
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 634a03fe-9213-4221-ace5-09bea61f201f · inbound
Hyperloop Transformers cites this paper.
Hyperloop Transformers Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T14:16:04.399141Z
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 38216f5d-312e-456c-bafe-110d40f0bde7 · inbound
BitRL: Reinforcement Learning with 1-bit Quantized Language Models for Resource-Constrained Edge Deployment cites this paper.
BitRL: Reinforcement Learning with 1-bit Quantized Language Models for Resource-Constrained Edge Deployment Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T21:46:43.044744Z
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 e24e0882-ef18-4a92-af51-d1b21124b206 · inbound
FTerViT: Fully Ternary Vision Transformer cites this paper.
FTerViT: Fully Ternary Vision Transformer Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens
Reference 47
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T06:03:59.476277Z
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 dbd5c2c7-0cad-45fd-b40b-7b151f9c0f79 · inbound
LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws cites this paper.
LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T04:35:21.786530Z
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 15fb61d1-308b-4cc1-aa01-cebb11a8ea88 · inbound
On the Expressive Power of Weight Quantization in Large Language Models cites this paper.
On the Expressive Power of Weight Quantization in Large Language Models Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens
Reference 34
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T08:19:44.243529Z
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.