Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-23T06:56:51.829741Z
Paper Citation Record · LEDGER
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design
As of 18 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 4 inbound Pith citation observations for arXiv:2412.14590.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T14:45:38.647231Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-04T00:49:18.139563Z
Reference resolution
51 of 51 outbound references displayed
- verified exact14
- verified fuzzy28
- unresolved7
- parse uncertain0
- malformed identifier0
- metadata mismatch2
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 37e62c2a-8bcb-451e-ae7e-285763dcdce8 · outbound
This paper cites an unresolved cited work.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Unresolved cited work
Reference 1
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T06:57:41.068343Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 80233a95-9765-431e-8da9-c8aec65df4f4 · outbound
This paper cites Gulavani, Alexey Tumanov, and Ramachandran Ramjee.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Gulavani, Alexey Tumanov, and Ramachandran Ramjee
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.060729Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3d05d206-a179-4663-894f-efe5f138528e · outbound
This paper cites QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T06:57:40.303236Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a79ce376-f19e-4d98-b145-ba71dbc37eb5 · outbound
This paper cites an unresolved cited work.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Unresolved cited work
Reference 4
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T06:57:41.066006Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9e2c2e61-c1ba-4ead-b6a2-fa5ad332ef80 · outbound
This paper cites Autogptq.
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.063348Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b435e830-b05f-4aed-a269-f03613b4b5c5 · outbound
This paper cites A systematic classification of knowledge, reasoning, and context within the ARC dataset.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design A systematic classification of knowledge, reasoning, and context within the ARC dataset
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.076269Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 328dbda5-aab3-4f19-9012-b69289a0ed10 · outbound
This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Sparks of Artificial General Intelligence: Early experiments with GPT-4
Reference 7
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-23T06:57:40.294413Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation cf5f2a50-3df0-4687-92a0-c22a5f281592 · outbound
This paper cites Quip: 2-bit quantization of large language models with guarantees.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Quip: 2-bit quantization of large language models with guarantees
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.073634Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 44d0ce27-b02e-4ede-9552-4d15ae1f48d9 · outbound
This paper cites an unresolved cited work.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Unresolved cited work
Reference 9
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T06:57:41.071072Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b567af91-f51d-4045-b786-727f64249083 · outbound
This paper cites LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-05-23T06:57:40.298346Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 36e87cfe-e0b8-4882-80f6-23765d942231 · outbound
This paper cites Spqr: A sparse-quantized representation for near-lossless LLM weight compression.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Spqr: A sparse-quantized representation for near-lossless LLM weight compression
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.133770Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 5dd118ca-c11b-4669-9c0a-5f7cfc8f4d10 · outbound
This paper cites Mahoney, and Kurt Keutzer.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Mahoney, and Kurt Keutzer
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.136046Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 625e4c1c-2724-4088-9700-81c4b2dfb807 · outbound
This paper cites Optimal brain compression: A framework for accurate post-training quantization and pruning.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Optimal brain compression: A framework for accurate post-training quantization and pruning
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.131468Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7d4685eb-7449-43f7-8289-f7c56f092f09 · outbound
This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
Reference 14
- Resolution
- verified exact
- local_arxiv, observed 2026-05-23T06:57:40.248123Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c7e285a5-b683-40a4-abc5-450fa0e3c48c · outbound
This paper cites A framework for few-shot language model evaluation, 07 2024.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design A framework for few-shot language model evaluation, 07 2024
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.145836Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 15924654-a6e9-4bb0-a154-96407715c746 · outbound
This paper cites The Unreasonable Ineffectiveness of the Deeper Layers.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design The Unreasonable Ineffectiveness of the Deeper Layers
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T06:57:40.285477Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3c2d872c-6dc6-4506-bb6f-d8a2b6a34909 · outbound
This paper cites Qwen2.5: A party of foundation models.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Qwen2.5: A party of foundation models
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.127122Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2b793956-7161-48b8-8df3-de9b75fb91c2 · outbound
This paper cites Stork, and Gregory J.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Stork, and Gregory J
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.129322Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 0c8721d8-0d08-4ff2-ad35-51587c21c02f · outbound
This paper cites DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T06:57:40.262382Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation dd189893-7f68-4131-ae81-4c7c4203e583 · outbound
This paper cites Mistral 7B.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Mistral 7B
Reference 20
- Resolution
- verified exact
- local_arxiv, observed 2026-05-23T06:57:40.289405Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation fccbe28f-94f4-442d-b8c0-fcc1435bc337 · outbound
This paper cites Mahoney, and Kurt Keutzer.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Mahoney, and Kurt Keutzer
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.140885Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7ebcf684-9784-4b84-b16c-b8a1dc19ec57 · outbound
This paper cites Scaling Laws for Precision.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Scaling Laws for Precision
Reference 22
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-23T06:57:40.270961Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 5d490239-93d0-46b6-87d2-8ccca3958243 · outbound
This paper cites Mahoney, Joseph Hassoun, Kurt Keutzer, and Amir Gholami.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Mahoney, Joseph Hassoun, Kurt Keutzer, and Amir Gholami
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.107546Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 77a1fb6c-d6b1-455a-9e77-e80c3881cf1a · outbound
This paper cites Denker, and Sara A.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Denker, and Sara A
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.124803Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 46af8c01-ddc2-46c9-8737-68ebd542122a · outbound
This paper cites OWQ: outlier-aware weight quantization for efficient fine-tuning and inference of large language models.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design OWQ: outlier-aware weight quantization for efficient fine-tuning and inference of large language models
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.117685Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 96ff1376-e8d0-45d4-bd22-8ccefc5e0f74 · outbound
This paper cites AWQ: activation-aware weight quantization for on-device LLM compression and acceleration.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design AWQ: activation-aware weight quantization for on-device LLM compression and acceleration
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.119902Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 25cf0132-d301-4547-b25e-42fbae3358b2 · outbound
This paper cites QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T06:57:40.275531Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 5cc3f7dc-0408-44ef-8b6a-a465057f4807 · outbound
This paper cites SpinQuant: LLM quantization with learned rotations.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design SpinQuant: LLM quantization with learned rotations
Reference 28
- Resolution
- verified exact
- local_arxiv, observed 2026-05-23T06:57:40.266681Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 082821a9-6969-4256-a07e-71703f945bdf · outbound
This paper cites Affinequant: Affine transformation quantization for large language models.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Affinequant: Affine transformation quantization for large language models
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.122100Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c5fa5e41-febb-4a3d-acb1-bc99b492e866 · outbound
This paper cites ShortGPT: Layers in Large Language Models are More Redundant Than You Expect.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design ShortGPT: Layers in Large Language Models are More Redundant Than You Expect
Reference 30
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T06:57:40.238529Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 27706596-3357-4875-9fb2-efb2ad40f6f8 · outbound
This paper cites Pointer sentinel mixture models.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Pointer sentinel mixture models
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.109864Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 5925bd4b-8abd-4459-8de0-e2626acedd48 · outbound
This paper cites an unresolved cited work.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Unresolved cited work
Reference 32
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T06:57:41.138711Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7ee67852-e4db-4291-b213-f86024dfce4a · outbound
This paper cites an unresolved cited work.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Unresolved cited work
Reference 33
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T06:57:41.112699Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 24699ae9-ba64-4717-8032-0503701a09f8 · outbound
This paper cites an unresolved cited work.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Unresolved cited work
Reference 34
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T06:57:41.115047Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 70aa6d0d-76cc-418b-9d3e-b091e345afaf · outbound
This paper cites an unresolved cited work.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Unresolved cited work
Reference 35
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T06:57:41.105230Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation df2d04e5-ba55-4ded-9db7-d4e2e52ad99f · outbound
This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design GPQA: A Graduate-Level Google-Proof Q&A Benchmark
Reference 36
- Resolution
- verified exact
- local_arxiv, observed 2026-05-23T06:57:40.233060Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 150ed859-5ba6-42ea-b95d-902aab744314 · outbound
This paper cites Omniquant: Omnidirectionally calibrated quantization for large language models.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Omniquant: Omnidirectionally calibrated quantization for large language models
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.100063Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9a916006-04de-4242-a580-cce8adaaf96f · outbound
This paper cites Musr: Testing the limits of chain-of-thought with multistep soft reasoning.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Musr: Testing the limits of chain-of-thought with multistep soft reasoning
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.097254Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9ab5c030-5907-41f6-9520-ca5bbb367aad · outbound
This paper cites Le, Ed H.
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.102699Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 1af3be82-8754-4986-9e91-b642720089cf · outbound
This paper cites Tensorrt-llm.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Tensorrt-llm
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.091846Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 14bf5fc2-f624-4d6f-8bad-89df8cbd4141 · outbound
This paper cites MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
Reference 41
- Resolution
- verified exact
- local_arxiv, observed 2026-05-23T06:57:40.280362Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation eed79741-d8c4-4379-8170-d62df4d12977 · outbound
This paper cites ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks
Reference 42
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T06:57:40.258320Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 76c4fb00-95ce-4142-b113-fc6415d154b3 · outbound
This paper cites Flash-llm: Enabling low-cost and highly-efficient large generative model inference with unstructured sparsity.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Flash-llm: Enabling low-cost and highly-efficient large generative model inference with unstructured sparsity
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.094814Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a272b44b-2ee9-4610-80eb-f1e7cca57fee · outbound
This paper cites Quant-llm: Accelerating the serving of large language models via fp6-centric algorithm-system co-design on modern gpus.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Quant-llm: Accelerating the serving of large language models via fp6-centric algorithm-system co-design on modern gpus
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.143164Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 852e474e-3a66-475d-af0f-0d40db242d5b · outbound
This paper cites Smoothquant: Accurate and efficient post-training quantization for large language models.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Smoothquant: Accurate and efficient post-training quantization for large language models
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.087377Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b98ecc84-49b8-424c-806e-381e79472077 · outbound
This paper cites Zeroquant: Efficient and affordable post-training quantization for large-scale transformers.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Zeroquant: Efficient and affordable post-training quantization for large-scale transformers
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.084338Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 06da1fc8-e47f-4710-8f4f-da7a2c3af0d9 · outbound
This paper cites Orca: A distributed serving system for transformer-based generative models.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Orca: A distributed serving system for transformer-based generative models
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.089673Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f4de0dba-4110-4ca8-9826-474856e95ec3 · outbound
This paper cites RPTQ: Reorder-based Post-training Quantization for Large Language Models.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design RPTQ: Reorder-based Post-training Quantization for Large Language Models
Reference 48
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T06:57:40.243433Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 1c153e90-38a1-464b-9575-64af6d836d91 · outbound
This paper cites Hellaswag: Can a machine really finish your sentence? In Anna Korhonen, David R.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Hellaswag: Can a machine really finish your sentence? In Anna Korhonen, David R
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.079285Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 288a7325-3656-4722-bda6-b4afa6948d63 · outbound
This paper cites Atom: Low-bit quantization for efficient and accurate LLM serving.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Atom: Low-bit quantization for efficient and accurate LLM serving
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:57:41.081983Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3c7fae06-d6b4-4e1a-8f53-58f68161af7f · outbound
This paper cites BatchLLM: Optimizing Large Batched LLM Inference with Global Prefix Sharing and Throughput-oriented Token Batching.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design BatchLLM: Optimizing Large Batched LLM Inference with Global Prefix Sharing and Throughput-oriented Token Batching
Reference 51
- Resolution
- verified exact
- local_arxiv, observed 2026-05-23T06:57:40.253161Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
Observation 2485f3ff-a7bd-485d-a157-0c9e71abb003 · inbound
Squeeze10-LLM: Squeezing LLMs' Weights by 10 Times via a Staged Mixed-Precision Quantization Method cites this paper.
Squeeze10-LLM: Squeezing LLMs' Weights by 10 Times via a Staged Mixed-Precision Quantization Method MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design
Reference 36
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-06T14:45:38.647231Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f7421b5f-a3f4-4fec-90df-40557947d13d · inbound
Breaking Modality Heterogeneity in Low-Bit Quantization for Large Vision-Language Models cites this paper.
Breaking Modality Heterogeneity in Low-Bit Quantization for Large Vision-Language Models MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design
Reference 61
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T05:23:03.651022Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 357fa1c1-a011-493d-b621-bca2b712eb82 · inbound
Displacement Is Not Direction: Evaluating Fidelity Metrics for Quantized LLM Deployment cites this paper.
Displacement Is Not Direction: Evaluating Fidelity Metrics for Quantized LLM Deployment MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design
Reference 11
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-04T00:49:18.141527Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 58e27d8e-bafb-43ad-9522-5e1bd0616350 · 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 MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T06:20:07.112455Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.