Paper Citation Record · LEDGER

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

As of 11 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 81 inbound Pith citation observations for arXiv:2402.17764.

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

pith.paper-citation-record.v1
2402.17764 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-17T20:11:43.559035Z

measured 96 of 96 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 81 of 81 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T04:39:36.651851Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

15 of 15 outbound references displayed

verified exact11
verified fuzzy4
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

4
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 6ed7758e-8f28-40bc-b6e5-3ef79e985736 · outbound

This paper cites PIQA: Reasoning about Physical Commonsense in Natural Language.

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits PIQA: Reasoning about Physical Commonsense in Natural Language

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-17T20:11:43.579176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ebe2dd47-76ea-4ae0-8eab-1ef0591b80c7 · outbound

This paper cites QuIP: 2-Bit Quantization of Large Language Models With Guarantees.

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits QuIP: 2-Bit Quantization of Large Language Models With Guarantees

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:11:43.587320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 748af0af-8307-4d6c-8294-999daa0e1f98 · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-17T20:11:43.592820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d95a5740-8aac-4239-8d4c-9c5b0dc8b239 · outbound

This paper cites 1.1 computing’s energy problem (and what we can do about it).

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits 1.1 computing’s energy problem (and what we can do about it)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:11:43.628840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a4b37a48-0e5e-488d-b708-90e236006bbd · outbound

This paper cites AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration.

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-17T20:11:43.613495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1c419be6-b123-48e5-9496-e9a9e191bd9d · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-17T20:11:43.617671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bcc91c43-f4f7-4bb4-b316-08043777029a · outbound

This paper cites The LAMBADA dataset: Word prediction requiring a broad discourse context.

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits The LAMBADA dataset: Word prediction requiring a broad discourse context

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:11:43.638060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 673bde67-6f19-40a8-b307-831406d2df90 · outbound

This paper cites Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-17T20:11:43.621702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f7ce7652-0189-4e66-b47f-c0ccaf36028f · outbound

This paper cites GLU Variants Improve Transformer.

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits GLU Variants Improve Transformer

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-17T20:11:43.625496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 28701e4b-a19a-4ee3-8cbf-e3743c6ad444 · outbound

This paper cites QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks.

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:11:43.597896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7c51eeda-e3b8-4611-8952-cd8fe81bbc3d · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits LLaMA: Open and Efficient Foundation Language Models

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-17T20:11:43.601830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 10e746fe-ad1f-4327-a580-3bf189ee2d7c · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-17T20:11:43.605815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 52093775-c8de-44d3-9c9d-79444cc69ebc · outbound

This paper cites Liu, and Matt Gardner.

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits Liu, and Matt Gardner

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:11:43.635116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 21ee2189-913e-44dd-a16b-bdbb9b3e81bd · outbound

This paper cites BitNet: Scaling 1-bit Transformers for Large Language Models.

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits BitNet: Scaling 1-bit Transformers for Large Language Models

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-17T20:11:43.609612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3e29fde8-2272-401c-8852-8af0206b19f3 · outbound

This paper cites SmoothQuant: accurate and efficient post-training quantization for large language models.

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits SmoothQuant: accurate and efficient post-training quantization for large language models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:11:43.632013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

Observation 1c3c529f-d120-49a8-82bc-a475ec652e09 · inbound

1.58-bit FLUX cites this paper.

1.58-bit FLUX The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T04:39:36.651851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04dc7294-ab78-4fd3-9355-d82b13b6312d · inbound

Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones cites this paper.

Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T00:46:17.793499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9132cc3c-5d72-4be5-9d55-e4a14c08dcfd · inbound

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator cites this paper.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T19:08:07.937338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b50f20f-bbe8-4aef-b7bf-46858c41276f · inbound

iServe: An Intent-based Serving System for LLMs cites this paper.

iServe: An Intent-based Serving System for LLMs The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-10T21:37:14.342665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a09b6e50-0616-4d55-8c6d-c82b853a45a5 · inbound

Tool Unlearning for Tool-Augmented LLMs cites this paper.

Tool Unlearning for Tool-Augmented LLMs The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-09T16:44:01.282391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d768a23c-ee22-4ea7-96f3-1d4167d6fee5 · inbound

Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers cites this paper.

Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 298

Resolution: unresolved; no resolver link, observed 2026-08-09T16:30:40.483475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a59fb065-89e2-437c-af6e-5e24e6dc7a94 · inbound

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations cites this paper.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-08T20:44:48.451632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2261239-fbc5-4be4-bb27-14fe8c678270 · 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 The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-08T15:08:46.199820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b9858a8-0484-4a0d-9041-bcaa8dd21d10 · inbound

DarwinLM: Evolutionary Structured Pruning of Large Language Models cites this paper.

DarwinLM: Evolutionary Structured Pruning of Large Language Models The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-08T11:39:09.518281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 779fd41e-4701-4cc1-aa53-ab21a07dc204 · inbound

Low-Resolution Neural Networks cites this paper.

Low-Resolution Neural Networks The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T23:43:16.740170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44b64a10-e955-461f-9b51-5e8de27128da · inbound

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices cites this paper.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 172

Resolution: verified exact; local_arxiv, observed 2026-05-23T01:05:16.308383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 124fdf37-b321-4136-8a77-7f46bb17a56f · inbound

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 cites this paper.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T10:59:50.599189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6af10574-3c48-4c09-ba65-7a7e18cf931d · inbound

Scaling Law for Quantization-Aware Training cites this paper.

Scaling Law for Quantization-Aware Training The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T15:41:07.779945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ac0e8c3-8980-4f1a-b4ed-17e7271a73a8 · inbound

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision cites this paper.

Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:20.452480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 014e16a1-b4ea-4ef7-97ab-01a5432cd264 · inbound

Highly Efficient and Effective LLMs with Multi-Boolean Architectures cites this paper.

Highly Efficient and Effective LLMs with Multi-Boolean Architectures The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 7

Resolution: metadata mismatch; local_arxiv, observed 2026-05-19T12:42:18.014148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6122dbcf-038a-4938-ab2c-cba40786105b · inbound

Ultra-Quantisation: Efficient Embedding Search via 1.58-bit Encodings cites this paper.

Ultra-Quantisation: Efficient Embedding Search via 1.58-bit Encodings The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T12:11:01.202720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe34546e-ff26-441d-a538-263c9d7a31ed · inbound

ReTern: Exploiting Natural Redundancy and Sign Transformations for Enhanced Fault Tolerance in Compute-in-Memory based Ternary LLMs cites this paper.

ReTern: Exploiting Natural Redundancy and Sign Transformations for Enhanced Fault Tolerance in Compute-in-Memory based Ternary LLMs The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T11:57:45.599535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84545911-dfa3-4c53-8391-f89bb49083ec · inbound

BitTTS: Highly Compact Text-to-Speech Using 1.58-bit Quantization and Weight Indexing cites this paper.

BitTTS: Highly Compact Text-to-Speech Using 1.58-bit Quantization and Weight Indexing The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T11:07:15.936246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4a5bee6-7270-406f-96b0-cdef85c9aeae · inbound

SiLIF: Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks cites this paper.

SiLIF: Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-19T11:07:15.289121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7d34dc5c-336d-4bd7-a94e-4299a4482785 · inbound

MiniCPM4: Ultra-Efficient LLMs on End Devices cites this paper.

MiniCPM4: Ultra-Efficient LLMs on End Devices The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T05:31:20.875138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1b3ffef-71cc-4b2a-b09b-6a9bbeeb543e · inbound

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities cites this paper.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:16.365607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6a8cc48-9708-4edd-af1d-e0d5f8486afb · 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 The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T21:58:34.684907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b38e8279-7910-48a2-8332-c385286c1364 · inbound

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead cites this paper.

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 233

Resolution: unresolved; no resolver link, observed 2026-08-06T21:36:40.767790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ef31ab3-d958-4cce-b0bb-8418c5a03bcb · inbound

QS4D: Quantization-aware training for efficient hardware deployment of structured state-space sequential models cites this paper.

QS4D: Quantization-aware training for efficient hardware deployment of structured state-space sequential models The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T19:16:25.228170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d84c986-129c-4fd4-b835-8f89345a68ea · inbound

GeLaCo: An Evolutionary Approach to Layer Compression cites this paper.

GeLaCo: An Evolutionary Approach to Layer Compression The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T17:45:20.837218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6be5ff3b-e4ce-4b64-95a0-70415c2308f4 · inbound

IDFace: Face Template Protection for Efficient and Secure Identification cites this paper.

IDFace: Face Template Protection for Efficient and Secure Identification The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:45.053733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0954d307-b23a-4dc3-bc57-344105274223 · inbound

A Lower Bound for the Number of Linear Regions of Ternary ReLU Regression Neural Networks cites this paper.

A Lower Bound for the Number of Linear Regions of Ternary ReLU Regression Neural Networks The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-19T03:32:01.762696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 960f05ba-c912-4153-9768-d97c5c8f7c9f · inbound

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models cites this paper.

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-21T23:44:26.580598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fbe3254e-fe04-42e9-a748-c7b3257d7641 · inbound

ShadowNPU: System and Algorithm Co-design for NPU-Centric On-Device LLM Inference cites this paper.

ShadowNPU: System and Algorithm Co-design for NPU-Centric On-Device LLM Inference The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 37

Resolution: metadata mismatch; local_arxiv, observed 2026-05-18T22:06:52.222484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c36f15e2-7215-42dc-9825-61c32a2ec28b · inbound

Quantized but Deceptive? A Multi-Dimensional Truthfulness Evaluation of Quantized LLMs cites this paper.

Quantized but Deceptive? A Multi-Dimensional Truthfulness Evaluation of Quantized LLMs The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-05T15:52:45.002146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a5adeb0-ae94-4451-b8b8-197f23d13716 · inbound

ENSI: Efficient Non-Interactive Secure Inference for Large Language Models cites this paper.

ENSI: Efficient Non-Interactive Secure Inference for Large Language Models The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T19:10:41.622492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf9fd146-3feb-4195-8599-f66ebfd672e6 · inbound

RobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization cites this paper.

RobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-05-22T13:14:53.404360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d3a83a0d-8caf-4976-a42d-d531587c0b4c · inbound

Key and Value Weights Are Probably All You Need: On the Necessity of the Query, Key, Value weight Triplet in Self-Attention Transformers cites this paper.

Key and Value Weights Are Probably All You Need: On the Necessity of the Query, Key, Value weight Triplet in Self-Attention Transformers The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-18T03:40:50.552066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f1cef842-222b-4098-947c-da559c04ce88 · 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 The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T20:11:43.638959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d5b55fd8-4410-4da5-9220-8ee585d33059 · inbound

ButterflyMoE: Compression-Scalable Ternary Experts via Structured Butterfly Orbits cites this paper.

ButterflyMoE: Compression-Scalable Ternary Experts via Structured Butterfly Orbits The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T09:32:55.265006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0a5fbfe-cd90-43c3-b680-9c2addb12af4 · inbound

FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deployment cites this paper.

FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deployment The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T05:22:22.448661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25be9bda-b504-4dd6-9bb6-c6a746924fda · inbound

Na\"ive PAINE: Lightweight Text-to-Image Generation Improvement with Prompt Evaluation cites this paper.

Na\"ive PAINE: Lightweight Text-to-Image Generation Improvement with Prompt Evaluation The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 49

Resolution: unresolved; no resolver link, observed 2026-07-14T22:28:35.981853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ad78fbc-1d9b-4da5-be8b-71aa46d181d8 · inbound

NativeTernary: A Self-Delimiting Binary Encoding with Unary Run-Length Hierarchy Markers for Ternary Neural Network Weights, Structured Data, and General Computing Infrastructure cites this paper.

NativeTernary: A Self-Delimiting Binary Encoding with Unary Run-Length Hierarchy Markers for Ternary Neural Network Weights, Structured Data, and General Computing Infrastructure The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:11:43.638959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1d293173-3f92-480b-bddd-e6d3bcc2850b · inbound

STQuant: Spatio-Temporal Adaptive Framework for Optimizer Quantization in Large Multimodal Model Training cites this paper.

STQuant: Spatio-Temporal Adaptive Framework for Optimizer Quantization in Large Multimodal Model Training The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:11:43.638959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 464d50a8-5f39-47cd-b092-af7f63aaf210 · inbound

The Phase Is the Gradient: Equilibrium Propagation for Frequency Learning in Kuramoto Networks cites this paper.

The Phase Is the Gradient: Equilibrium Propagation for Frequency Learning in Kuramoto Networks The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:11:43.638959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ed515efb-bd3a-4323-b22c-ec2218b81731 · inbound

Robust Ultra Low-Bit Post-Training Quantization via Stable Diagonal Curvature Estimate cites this paper.

Robust Ultra Low-Bit Post-Training Quantization via Stable Diagonal Curvature Estimate The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:11:43.638959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5ee7dbde-4379-404c-acb1-5c835287095f · inbound

STAR-Teaming: A Strategy-Response Multiplex Network Approach to Automated LLM Red Teaming cites this paper.

STAR-Teaming: A Strategy-Response Multiplex Network Approach to Automated LLM Red Teaming The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:11:43.638959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bd600ad9-cf6e-4eff-9acc-706636361747 · inbound

Quantization robustness from dense representations of sparse functions in high-capacity kernel associative memory cites this paper.

Quantization robustness from dense representations of sparse functions in high-capacity kernel associative memory The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T20:11:43.638959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5a87311b-1080-4ca5-8234-a06ae805f0ae · inbound

Quantization robustness from dense representations of sparse functions in high-capacity kernel associative memory cites this paper.

Quantization robustness from dense representations of sparse functions in high-capacity kernel associative memory The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T20:11:43.638959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e309450d-092c-483b-bc8f-15208be06ecb · inbound

FairyFuse: Multiplication-Free LLM Inference on CPUs via Fused Ternary Kernels cites this paper.

FairyFuse: Multiplication-Free LLM Inference on CPUs via Fused Ternary Kernels The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:11:43.638959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8490c713-c6ec-4942-bdee-b0df3ccf109c · inbound

MCAP: Deployment-Time Layer Profiling for Memory-Constrained LLM Inference cites this paper.

MCAP: Deployment-Time Layer Profiling for Memory-Constrained LLM Inference The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:11:43.638959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d3a7923e-39fc-4388-87e5-453db2fa3094 · inbound

VitaLLM: A Versatile, Ultra-Compact Ternary LLM Accelerator with Dependency-Aware Scheduling cites this paper.

VitaLLM: A Versatile, Ultra-Compact Ternary LLM Accelerator with Dependency-Aware Scheduling The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:11:43.638959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 72609fdf-3313-4fae-8385-acce39de6da7 · inbound

VitaLLM: A Versatile and Tiny Accelerator for Mixed-Precision LLM Inference on Edge Devices cites this paper.

VitaLLM: A Versatile and Tiny Accelerator for Mixed-Precision LLM Inference on Edge Devices The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:11:43.638959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 10381feb-bc6b-47b0-9a1a-e23e74fc505e · inbound

Trust, but Verify: Peeling Low-Bit Transformer Networks for Training Monitoring cites this paper.

Trust, but Verify: Peeling Low-Bit Transformer Networks for Training Monitoring The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:11:43.638959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 836d318d-128e-44a9-9438-cd9ae7913c18 · 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 The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T20:11:43.638959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9cc71374-f0b1-4eb4-a083-96ebcbeaeb1f · 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 The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 7

Resolution: metadata mismatch; local_arxiv, observed 2026-07-01T13:25:46.097593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f9fe3a1f-dbce-4205-b62e-9c5dd36d1600 · inbound

Fitting Is Not Enough: Smoothness in Extremely Quantized LLMs cites this paper.

Fitting Is Not Enough: Smoothness in Extremely Quantized LLMs The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T20:11:43.638959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 166da4d3-49f2-44c3-98e2-4522fbe98242 · inbound

Fitting Is Not Enough: Smoothness in Extremely Quantized LLMs cites this paper.

Fitting Is Not Enough: Smoothness in Extremely Quantized LLMs The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 27

Resolution: metadata mismatch; local_arxiv, observed 2026-05-19T15:17:37.527155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6e03f6e3-717a-445a-902d-722081bb3e85 · inbound

A Composite Activation Function for Learning Stable Binary Representations cites this paper.

A Composite Activation Function for Learning Stable Binary Representations The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:11:43.638959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9425f2f0-906e-46bf-af51-41ca8b98e9ef · inbound

Locale-Conditioned Few-Shot Prompting Mitigates Demonstration Regurgitation in On-Device PII Substitution with Small Language Models cites this paper.

Locale-Conditioned Few-Shot Prompting Mitigates Demonstration Regurgitation in On-Device PII Substitution with Small Language Models The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:11:43.638959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 693f5d19-a2a8-4917-a3ff-55522992988a · inbound

FTerViT: Fully Ternary Vision Transformer cites this paper.

FTerViT: Fully Ternary Vision Transformer The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-21T06:03:59.456305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5fa1c8b0-b65e-449a-bc17-a7681966d91a · inbound

GenHAR: Generalizing Cross-domain Human Activity Recognition for Last-mile Delivery cites this paper.

GenHAR: Generalizing Cross-domain Human Activity Recognition for Last-mile Delivery The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:44:42.785698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 55209456-02c5-418b-9fe6-9b69ec285f3c · inbound

Influence-Inspired Spectral Rotations for Extreme Low-Bit LLM Quantization cites this paper.

Influence-Inspired Spectral Rotations for Extreme Low-Bit LLM Quantization The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-06-30T12:14:39.059372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f1eaa6cd-2040-446d-8591-a3d89c6c023b · inbound

Mapping the Schedule x Bit-Width Boundary in Sub-100M Quantisation-Aware Training cites this paper.

Mapping the Schedule x Bit-Width Boundary in Sub-100M Quantisation-Aware Training The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 15

Resolution: metadata mismatch; local_arxiv, observed 2026-06-29T23:14:01.495386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f7a25855-6dc4-4ee3-adff-9ce9641e0a43 · inbound

SPARQLe: Sub-Precision Activation Representation for Quantized LLM Inference cites this paper.

SPARQLe: Sub-Precision Activation Representation for Quantized LLM Inference The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 15

Resolution: metadata mismatch; local_arxiv, observed 2026-06-28T19:32:34.313163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c80f822a-f785-4574-905e-ae327d1f96c9 · inbound

GoldenFloat: A Phi-Derived Static-Split Floating-Point Family from GF4 to GF1024 with a Lucas-Exact Integer Identity cites this paper.

GoldenFloat: A Phi-Derived Static-Split Floating-Point Family from GF4 to GF1024 with a Lucas-Exact Integer Identity The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-07-02T11:26:54.605576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b5057fc8-cfd2-4ef6-9216-2945e7c38973 · inbound

BIDENT: Heterogeneous Operator-level Mapping for Efficient Edge Inference cites this paper.

BIDENT: Heterogeneous Operator-level Mapping for Efficient Edge Inference The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-07-02T11:26:54.831767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4c189ff4-0c09-4710-bccc-3c965af231d7 · inbound

Density Field State Space Models: 1-Bit Distillation, Efficient Inference, and Knowledge Organization in Mamba-2 cites this paper.

Density Field State Space Models: 1-Bit Distillation, Efficient Inference, and Knowledge Organization in Mamba-2 The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-07-01T09:05:37.116532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2dc1e227-54ff-473f-9b83-ca43f32b3fee · inbound

Ternary Mamba: Grouped Quantization-Aware Training of W1.58A16 State Space Models cites this paper.

Ternary Mamba: Grouped Quantization-Aware Training of W1.58A16 State Space Models The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-07-03T21:08:58.131885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9695c930-2ab3-4cd7-98c9-17e9beb325e2 · inbound

BitNet Text Embeddings cites this paper.

BitNet Text Embeddings The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-07-04T20:00:08.944099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7db72f83-fca9-478a-a924-a376b05b1056 · inbound

BitNet Text Embeddings cites this paper.

BitNet Text Embeddings The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-02T10:15:53.789980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bb1a515-9023-483c-a469-f2032b8a8041 · inbound

CAT-Q: Cost-efficient and Accurate Ternary Quantization for LLMs cites this paper.

CAT-Q: Cost-efficient and Accurate Ternary Quantization for LLMs The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-07-04T13:19:50.564261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e0b92520-541d-4826-998f-2acfe0ed7675 · inbound

Variable Bit-width Quantization: Learning Per-Group Precision for "Bigger-but-Smaller" Language Models cites this paper.

Variable Bit-width Quantization: Learning Per-Group Precision for "Bigger-but-Smaller" Language Models The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-12T06:20:07.112455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d905e8e-9afc-4f10-9072-1676698c4b37 · inbound

ELiTeFormer: An Efficient Transformer for FPGAs cites this paper.

ELiTeFormer: An Efficient Transformer for FPGAs The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 37

Resolution: unresolved; no resolver link, observed 2026-07-12T00:55:09.690245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55c22b52-a722-479b-8ce9-343ed57b45d5 · inbound

Reliability Scaling Laws for Quantized Large Language Models cites this paper.

Reliability Scaling Laws for Quantized Large Language Models The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 156

Resolution: unresolved; no resolver link, observed 2026-07-14T08:45:52.855783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d0836ca-4e65-4ccd-b7a5-d76e4334c752 · inbound

ExTernD: Expanded-Rank Ternary Decomposition Ternary LLM PTQ with Accuracy Approaching Any Quantization Level cites this paper.

ExTernD: Expanded-Rank Ternary Decomposition Ternary LLM PTQ with Accuracy Approaching Any Quantization Level The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T05:02:32.482313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba88d05a-0909-488b-aece-4d58cc9356ac · 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 The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:43.203227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8176a0fe-ea8c-4468-8527-d81641befe1b · inbound

Expressivity of Shallow Neural Networks Over Finite Fields cites this paper.

Expressivity of Shallow Neural Networks Over Finite Fields The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T19:14:24.564799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a60e36d-94c4-45ce-afba-15144cbbb758 · inbound

Lossless-INR: Lossless Volumetric Implicit Neural Representations cites this paper.

Lossless-INR: Lossless Volumetric Implicit Neural Representations The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:53.548518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f910ed4-2455-4295-a3ef-32b77fa34744 · inbound

QuantiBias: Benchmarking Quantization-Induced Bias in LLMs cites this paper.

QuantiBias: Benchmarking Quantization-Induced Bias in LLMs The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:52.748885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fc3c17c-e495-4a2e-a2f7-757e08f1c24a · inbound

VibeVoice-ASR-BitNet Technical Report cites this paper.

VibeVoice-ASR-BitNet Technical Report The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T08:35:25.612348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1f4feff-fa9f-44e5-8f4b-c870502eee3a · inbound

When Can Depth Replace Precision? A Resource Theory of Quantized Neural Computation cites this paper.

When Can Depth Replace Precision? A Resource Theory of Quantized Neural Computation The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 166

Resolution: unresolved; no resolver link, observed 2026-07-30T23:38:38.673762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 403e8a0f-8a21-4b27-9a08-1ce73f75dd6b · inbound

Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts cites this paper.

Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 261

Resolution: unresolved; no resolver link, observed 2026-08-01T16:42:33.201358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e3ad888-4077-4f95-bc9e-c6abf41c0a6b · inbound

Studying quantization trade-offs for efficient inference deployment in machine translation cites this paper.

Studying quantization trade-offs for efficient inference deployment in machine translation The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T07:51:21.978765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fff4e2a5-5f11-4050-af5a-dcd1da594190 · inbound

Studying quantization trade-offs for efficient inference deployment in machine translation cites this paper.

Studying quantization trade-offs for efficient inference deployment in machine translation The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T04:25:21.981921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a13d1009-2d7c-464f-aebc-b95169820873 · inbound

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization cites this paper.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:28.084656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.