Paper Citation Record · LEDGER

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models

As of 7 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 1 inbound Pith citation observation for arXiv:2506.23025.

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

pith.paper-citation-record.v1
2506.23025 v1

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:58:36.879455Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T07:56:00.180707Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T08:03:14.581676Z

Reference resolution

56 of 56 outbound references displayed

verified exact0
verified fuzzy0
unresolved56
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 043f9fd2-3378-42ef-8928-1c0162ce458b · outbound

This paper cites an unresolved cited work.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:58:38.488170Z

Source-reported events for the cited work

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Observation 31e32094-9184-4ef5-b85a-4af7c5319f8b · outbound

This paper cites an unresolved cited work.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:58:38.265945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 18dc2cab-fef3-4f54-859a-d81e85191989 · outbound

This paper cites an unresolved cited work.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Unresolved cited work

Reference 3

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Observation 2853d9a1-345e-4dfa-aa63-05545243bb85 · outbound

This paper cites QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs

Reference 4

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Observation 01a92cbf-2490-48e8-80d5-b488dc582114 · outbound

This paper cites an unresolved cited work.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Unresolved cited work

Reference 5

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Observation d1ee3448-2f97-4f2d-9dc6-bfeaa50cc125 · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 6

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Source-reported events for the cited work

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Observation 5940ab03-f85c-4634-9de9-cce79a301aff · outbound

This paper cites Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling

Reference 7

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Source-reported events for the cited work

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Observation f8a1a219-c077-4728-8b4e-ddfe7109fddd · outbound

This paper cites an unresolved cited work.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Unresolved cited work

Reference 8

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Source-reported events for the cited work

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Observation de534ba9-d725-4922-a149-af32acf7edfb · outbound

This paper cites an unresolved cited work.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Unresolved cited work

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c66afc96-aa07-459f-a274-0d1f24c6dbf8 · outbound

This paper cites an unresolved cited work.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:58:38.038396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7557cc23-80e5-42a1-a5cd-49651c845cac · outbound

This paper cites On the Use of ArXiv as a Dataset.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models On the Use of ArXiv as a Dataset

Reference 11

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Observation 9e22eb0b-b7f9-462d-808d-bc85989b7dae · outbound

This paper cites LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b24b96ee-9cb2-4c1f-a8be-9595a0b2aec9 · outbound

This paper cites The case for 4-bit precision: k-bit Inference Scaling Laws.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models The case for 4-bit precision: k-bit Inference Scaling Laws

Reference 14

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Source-reported events for the cited work

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Observation 1fd2de14-4d4f-44a4-accc-9c142150dc9a · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe734b39-930f-4176-a525-f89adae7241c · outbound

This paper cites MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6a3106f-23bd-4367-8473-6d828b051915 · outbound

This paper cites AI and Memory Wall.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models AI and Memory Wall

Reference 17

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Source-reported events for the cited work

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Observation 395c0f88-5bd1-4427-9476-5ab598ea55f1 · outbound

This paper cites OLMo: Accelerating the Science of Language Models.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models OLMo: Accelerating the Science of Language Models

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b36cff78-d508-44b1-b1b1-12d62d7a8d87 · outbound

This paper cites Inference Performance Optimization for Large Language Models on CPUs.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Inference Performance Optimization for Large Language Models on CPUs

Reference 19

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Source-reported events for the cited work

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Observation eac6f8c2-5709-47ae-8a0a-d9a27c70092a · outbound

This paper cites an unresolved cited work.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Unresolved cited work

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c123ad72-3ab9-41d7-bb34-84dec3aa30c4 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Training Compute-Optimal Large Language Models

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36fa20ab-efa3-44ba-a884-97dd37e055d0 · outbound

This paper cites Mixtral of Experts.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Mixtral of Experts

Reference 22

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Source-reported events for the cited work

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Observation ec326f9f-d017-4383-810f-92a610d37ac2 · outbound

This paper cites an unresolved cited work.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Unresolved cited work

Reference 23

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Source-reported events for the cited work

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Observation 07e6f9ba-7983-4f3e-b55f-e2ca0af43b6a · outbound

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models A Study of BFLOAT16 for Deep Learning Training

Reference 24

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Source-reported events for the cited work

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Observation e1620616-bb66-4051-bb89-a466468f14ea · outbound

This paper cites Scaling Laws for Neural Language Models.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Scaling Laws for Neural Language Models

Reference 25

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eecffa83-f6e9-4163-aecf-1d9b0200a1dc · outbound

This paper cites Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at Scale.

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

Reference 26

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Observation b0cd3050-1600-4ae6-8a3e-6399f56a75cc · outbound

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Reference 27

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Observation 3defb7d4-bbef-4f89-a002-db3a59ac1463 · outbound

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Unresolved cited work

Reference 28

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Observation 8baf29ef-435a-4f8c-958d-ec9d4197cd86 · outbound

This paper cites an unresolved cited work.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Unresolved cited work

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Decoupled Weight Decay Regularization

Reference 30

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Unresolved cited work

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 32

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Mixed Precision Training

Reference 33

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Source-reported events for the cited work

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Unresolved cited work

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Unresolved cited work

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Unresolved cited work

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Observation b2aa463b-8e76-4e21-9235-7ae63ca4fb51 · outbound

This paper cites Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws

Reference 38

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models GLU Variants Improve Transformer

Reference 39

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models SlimPajama-DC: Understanding Data Combinations for LLM Training

Reference 40

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU

Reference 41

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

Reference 42

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Unresolved cited work

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 44

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Unresolved cited work

Reference 45

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This paper cites Zyda: A 1.3T Dataset for Open Language Modeling.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Zyda: A 1.3T Dataset for Open Language Modeling

Reference 46

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 47

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Attention Is All You Need

Reference 48

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models BitNet: Scaling 1-bit Transformers for Large Language Models

Reference 49

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 50

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Crowdsourcing Multiple Choice Science Questions

Reference 51

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Reference 52

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Unresolved cited work

Reference 53

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models Automatic Chain of Thought Prompting in Large Language Models

Reference 54

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This paper cites A Survey on Efficient Inference for Large Language Models.

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models A Survey on Efficient Inference for Large Language Models

Reference 55

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models online" 'onlinestring :=

Reference 56

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Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models write newline

Reference 57

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Pith citing papers

Observation 4e30dd8c-6d9e-4981-965e-8f1f400d7a6d · inbound

BitTP: The Lightweight Trajectory Prediction Model with BitLLM for Edge-Devices cites this paper.

BitTP: The Lightweight Trajectory Prediction Model with BitLLM for Edge-Devices Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:03:14.583306Z

Source-reported events for the cited work

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