Paper Citation Record · LEDGER

Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at Scale

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2407.12327.

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

pith.paper-citation-record.v1
2407.12327 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:58:33.986883Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T13:25:46.087473Z

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 eecffa83-f6e9-4163-aecf-1d9b0200a1dc · inbound

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models cites this paper.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at Scale

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T21:58:33.986883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfec443b-27ca-4e6e-b89b-c9ef32343a69 · inbound

Vec-LUT: Vector Table Lookup for Parallel Ultra-Low-Bit LLM Inference on Edge Devices cites this paper.

Vec-LUT: Vector Table Lookup for Parallel Ultra-Low-Bit LLM Inference on Edge Devices Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at Scale

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:48:45.760623Z

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 850efa2a-1467-4993-817b-82db79d1383b · inbound

Hardware Generation and Exploration of Lookup Table-Based Accelerators for 1.58-bit LLM Inference cites this paper.

Hardware Generation and Exploration of Lookup Table-Based Accelerators for 1.58-bit LLM Inference Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at Scale

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T00:41:16.171134Z

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 6ee22753-2ced-498d-9296-0a1b260ab7c9 · inbound

Litespark Inference For CPUs: Ultra-Fast SIMD Framework for Ternary (1.58-bit) Language Models cites this paper.

Litespark Inference For CPUs: Ultra-Fast SIMD Framework for Ternary (1.58-bit) Language Models Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at Scale

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T20:11:08.748270Z

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 7c2b0742-beac-4baa-bd02-73906f9dc4f3 · inbound

Litespark Inference For CPUs: Ultra-Fast SIMD Framework for Ternary (1.58-bit) Language Models cites this paper.

Litespark Inference For CPUs: Ultra-Fast SIMD Framework for Ternary (1.58-bit) Language Models Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at Scale

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T13:25:46.090153Z

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 5225e769-f08b-48b4-900d-f8525453a07c · inbound

BiSpikCLM: A Spiking Language Model integrating Softmax-Free Spiking Attention and Spike-Aware Alignment Distillation cites this paper.

BiSpikCLM: A Spiking Language Model integrating Softmax-Free Spiking Attention and Spike-Aware Alignment Distillation Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at Scale

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-15T07:15:11.689310Z

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.