Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-22T10:07:35.063038Z
Paper Citation Record · LEDGER
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation
As of 4 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 1 inbound Pith citation observation for arXiv:2605.04062.
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 2026-08-03T06:30:56.289259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-10T04:03:37.649301Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-10T04:06:44.643562Z
Reference resolution
63 of 63 outbound references displayed
- verified exact17
- verified fuzzy44
- unresolved1
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation f961b3c9-df77-4c1e-ad16-9d02f24a7aef · outbound
This paper cites QuaRot: Outlier-free 4-bit inference in rotated LLMs.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation QuaRot: Outlier-free 4-bit inference in rotated LLMs
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.395093Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 2cd76207-a1ca-4625-9e11-5c52ba118040 · outbound
This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
Reference 2
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T10:11:23.344625Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation dc70212e-e80a-4e46-a3d8-77ca0c48d393 · outbound
This paper cites PIQA: Reasoning about physical commonsense in natural language.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation PIQA: Reasoning about physical commonsense in natural language
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.402581Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation a5c1d3fb-669c-4e2b-b13f-c17514790916 · outbound
This paper cites Evaluating Large Language Models Trained on Code.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation Evaluating Large Language Models Trained on Code
Reference 4
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T10:11:23.350500Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 42279ed3-7ae8-47e3-90dd-e7314a927488 · outbound
This paper cites EfficientQAT: Efficient quantization-aware training for large language models.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation EfficientQAT: Efficient quantization-aware training for large language models
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.387826Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 1f0ec0dc-2034-4cac-b634-1270240103fe · outbound
This paper cites Optimize weight rounding via signed gradient descent for the quantization of LLMs.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation Optimize weight rounding via signed gradient descent for the quantization of LLMs
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.375678Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 7f306b5d-20c3-4e9a-bd3a-48f489c6e267 · outbound
This paper cites BoolQ: Exploring the surprising difficulty of natural yes/no questions.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation BoolQ: Exploring the surprising difficulty of natural yes/no questions
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.379982Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 125d4ba5-e555-451e-9e28-157cd1f31ace · outbound
This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T10:11:23.392490Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 8bff71ef-9420-479b-a8ae-974a5b83bb0b · outbound
This paper cites Training Verifiers to Solve Math Word Problems.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation Training Verifiers to Solve Math Word Problems
Reference 9
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T10:11:23.403689Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation e52770b9-8a2b-4ed4-9e06-5fa327afa278 · outbound
This paper cites The case for 4-bit precision: K-bit inference scaling laws.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation The case for 4-bit precision: K-bit inference scaling laws
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.383791Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 2e291874-1f4a-48b9-98e1-c4b17a19cb15 · outbound
This paper cites BitDistiller: Unleashing the potential of sub-4-bit LLMs via self-distillation.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation BitDistiller: Unleashing the potential of sub-4-bit LLMs via self-distillation
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.391452Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation a5ff85df-7574-4850-b439-4a4884658b5b · outbound
This paper cites Extreme compression of large language models via additive quantization.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation Extreme compression of large language models via additive quantization
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.355138Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 24e0a399-61a7-4a44-9562-ff6857e1072d · outbound
This paper cites How contextual are contextualized word representations? Comparing the geometry of BERT, ELMo, and GPT-2 embeddings.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation How contextual are contextualized word representations? Comparing the geometry of BERT, ELMo, and GPT-2 embeddings
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.364011Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 1e83dd21-b734-4dc2-b2a1-9e176d8a7ce3 · outbound
This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
Reference 14
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T10:11:23.398644Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 543fecd1-07ae-427f-bd77-074065051469 · outbound
This paper cites Video-MME: The first-ever comprehensive evaluation benchmark of multi-modal LLMs in video analysis.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation Video-MME: The first-ever comprehensive evaluation benchmark of multi-modal LLMs in video analysis
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.371861Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 8587b9ab-a66d-4359-b398-b671c248dad7 · outbound
This paper cites APTQ: Attention-aware post- training mixed-precision quantization for large language models.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation APTQ: Attention-aware post- training mixed-precision quantization for large language models
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.351411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation d6dfa4e6-a751-41bc-82cf-b21c37cf8780 · outbound
This paper cites Aligning AI With Shared Human Values.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation Aligning AI With Shared Human Values
Reference 17
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T10:11:23.408621Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 2546a4f5-db2c-413d-bb46-75a8d712738a · outbound
This paper cites Measuring Massive Multitask Language Understanding.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation Measuring Massive Multitask Language Understanding
Reference 18
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T10:11:23.378403Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T01:08:06.256034+00:00.
Observation e458e1ad-0a71-4fd2-a60b-cd8ba0c6d002 · outbound
This paper cites Rethinking channel dimensions to isolate outliers for low-bit weight quantization of large language models.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation Rethinking channel dimensions to isolate outliers for low-bit weight quantization of large language models
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.340391Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 0f7523c3-b73a-4ed3-a2f7-d7ccee8bfc62 · outbound
This paper cites Distilling the Knowledge in a Neural Network.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation Distilling the Knowledge in a Neural Network
Reference 21
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T10:11:23.372678Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 37b6000e-0665-460f-a48a-7aa921c62f46 · outbound
This paper cites BiLLM: Pushing the limit of post-training quantization for LLMs.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation BiLLM: Pushing the limit of post-training quantization for LLMs
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.344117Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation f19aace1-b7e1-4304-b652-0fce979b318a · outbound
This paper cites SliM-LLM: Salience-driven mixed-precision quantization for large language models.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation SliM-LLM: Salience-driven mixed-precision quantization for large language models
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.347766Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation f72b67e5-f8c7-4aa1-9ea4-5d0d3a931661 · outbound
This paper cites Q-Palette: Fractional-bit quantizers toward optimal bit allocation for efficient LLM deployment.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation Q-Palette: Fractional-bit quantizers toward optimal bit allocation for efficient LLM deployment
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T10:11:23.361467Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation c960889b-af23-4732-984f-ba40751322fe · outbound
This paper cites Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Models.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Models
Reference 25
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T10:11:23.366758Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 55ebc97e-5b74-440a-acf2-56ae90bc073f · outbound
This paper cites GPTAQ: Efficient finetuning-free quantization for asymmetric calibration.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation GPTAQ: Efficient finetuning-free quantization for asymmetric calibration
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.367967Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 7c419a65-3793-4128-ab62-1968bc5aecbf · outbound
This paper cites TGIF: A new dataset and benchmark on animated gif description.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation TGIF: A new dataset and benchmark on animated gif description
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.398820Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 8fd72e5b-85bc-4576-8e60-04ef11418d5e · outbound
This paper cites ARB-LLM: Alternating refined binarizations for large language models.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation ARB-LLM: Alternating refined binarizations for large language models
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.406302Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 891e9a01-59b5-488f-8974-455fd16104a7 · outbound
This paper cites AWQ: Activation-aware weight quantization for on-device LLM compression and acceleration.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation AWQ: Activation-aware weight quantization for on-device LLM compression and acceleration
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.410266Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation e102ee20-fbd5-41a3-82ba-28e176987e02 · outbound
This paper cites TruthfulQA: Measuring how models mimic human falsehoods.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation TruthfulQA: Measuring how models mimic human falsehoods
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.302735Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 2584fc4f-fd92-451e-90db-c990aa2f10c1 · outbound
This paper cites QServe: W4A8KV4 quantization and system co-design for efficient LLM serving.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation QServe: W4A8KV4 quantization and system co-design for efficient LLM serving
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.284151Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 0a9cdf48-9ce6-46c3-b472-f8f8e3709e78 · outbound
This paper cites VPTQ: Extreme low-bit vector post-training quantization for large language models.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation VPTQ: Extreme low-bit vector post-training quantization for large language models
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.288864Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 266fad7a-5546-4724-bb32-a6709a8833ea · outbound
This paper cites LLM-QAT: Data-Free Quantization Aware Training for Large Language Models.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
Reference 33
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T10:11:23.356051Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 73f76727-90bf-47cd-8606-6c3c3e052658 · outbound
This paper cites ParetoQ: Scaling laws in extremely low-bit LLM quantization.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation ParetoQ: Scaling laws in extremely low-bit LLM quantization
Reference 34
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T10:11:23.385125Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation b0ed9a4b-e85b-48fb-ab44-b35dc02deeb7 · outbound
This paper cites SpinQuant: LLM quantization with learned rotations.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation SpinQuant: LLM quantization with learned rotations
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.298386Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 214f302d-842a-4a48-be86-514406ba5182 · outbound
This paper cites Can a suit of armor conduct electricity? A new dataset for open book question answering.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation Can a suit of armor conduct electricity? A new dataset for open book question answering
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.329191Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation fbb6b3db-d6f5-46c9-a262-a6b31bd3d171 · outbound
This paper cites WinoGrande: An adversarial Winograd schema challenge at scale.Communications of the ACM, 64(9):99–106.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation WinoGrande: An adversarial Winograd schema challenge at scale.Communications of the ACM, 64(9):99–106
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.259509Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 4000a229-bd0b-4ddb-8660-c12706c09d38 · outbound
This paper cites Social IQa: Commonsense reasoning about social interactions.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation Social IQa: Commonsense reasoning about social interactions
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.263123Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 35718577-7606-40bb-88a4-3905d2af106d · outbound
This paper cites OmniQuant: Omnidirectionally calibrated quantization for large language models.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation OmniQuant: Omnidirectionally calibrated quantization for large language models
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.273605Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 6423fe27-80f1-44d9-8a3e-7a3341451373 · outbound
This paper cites FlatQuant: Flatness matters for LLM quantization.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation FlatQuant: Flatness matters for LLM quantization
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.241500Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 02cde144-33ab-48a8-a996-431fcc272a2b · outbound
This paper cites MobileQuant: Mobile-friendly quantization for on-device language models.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation MobileQuant: Mobile-friendly quantization for on-device language models
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.246692Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation f7f4e68e-bb60-4026-8998-6cd5610e9d52 · outbound
This paper cites BERT rediscovers the classical NLP pipeline.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation BERT rediscovers the classical NLP pipeline
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.251457Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 056a4481-c84e-4f8a-87c7-060a250c6fc6 · outbound
This paper cites QuIP#: Even better LLM quantization with hadamard incoherence and lattice codebooks.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation QuIP#: Even better LLM quantization with hadamard incoherence and lattice codebooks
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.335364Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 424c0aec-9df0-49f0-8a14-5fa43f55cab9 · outbound
This paper cites QTIP: Quantization with trellises and incoherence processing.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation QTIP: Quantization with trellises and incoherence processing
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.269743Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation f1e9dd44-ffb8-4759-ab2c-88fac099f431 · outbound
This paper cites BitNet: 1-bit pre-training for large language models.Journal of Machine Learning Research, 26(125):1–29.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation BitNet: 1-bit pre-training for large language models.Journal of Machine Learning Research, 26(125):1–29
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.255773Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation c01de3a4-5157-4469-9b01-cb960ff582a5 · outbound
This paper cites MiniLM: Deep self-attention distillation for task-agnostic compression of pre-trained transformers.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation MiniLM: Deep self-attention distillation for task-agnostic compression of pre-trained transformers
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.227990Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation ccee7f78-1218-4339-8ea6-ca5ed3940f3c · outbound
This paper cites Rethinking kullback-leibler divergence in knowledge distillation for large language models.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation Rethinking kullback-leibler divergence in knowledge distillation for large language models
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.237755Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 61a5580c-c0f1-42df-941d-0160b9f07012 · outbound
This paper cites SmoothQuant: Accurate and efficient post-training quantization for large language models.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation SmoothQuant: Accurate and efficient post-training quantization for large language models
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.223236Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 0af928a6-7a9c-463b-aa02-34a486f50714 · outbound
This paper cites Qwen2.5-Omni Technical Report.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation Qwen2.5-Omni Technical Report
Reference 49
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T10:11:23.328008Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 130ee510-c540-4b5a-aa1b-de9c10d987d1 · outbound
This paper cites OneBit: Towards extremely low-bit large language models.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation OneBit: Towards extremely low-bit large language models
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.306567Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 22b50fb2-f27a-4363-926c-09edde40133a · outbound
This paper cites Qwen3 Technical Report.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation Qwen3 Technical Report
Reference 51
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T10:11:23.333458Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 34d5ee32-dd65-4476-ad16-a1c22b8920fa · outbound
This paper cites MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
Reference 52
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T10:11:23.338672Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 845e2352-4177-412b-ac55-3a0cee2fe63f · outbound
This paper cites HellaSwag: Can a machine really finish your sentence? InProceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4791–4800.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation HellaSwag: Can a machine really finish your sentence? InProceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4791–4800
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.213545Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 51fdc9ad-fc5a-4151-ab23-9814c3ca14b6 · outbound
This paper cites ABQ-LLM: Arbitrary-bit quantized inference acceleration for large language models.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation ABQ-LLM: Arbitrary-bit quantized inference acceleration for large language models
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.218776Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation fc72bd4a-982a-4d89-aa6d-881f0a2154d4 · outbound
This paper cites LQER: Low-rank quantization error reconstruction for LLMs.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation LQER: Low-rank quantization error reconstruction for LLMs
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.232871Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation f96283b2-251a-4b9d-8b9a-c3624726bb39 · outbound
This paper cites 1.4 Million Open-Source Distilled Reasoning Dataset to Empower Large Language Model Training.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation 1.4 Million Open-Source Distilled Reasoning Dataset to Empower Large Language Model Training
Reference 56
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T10:11:23.322638Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation b1e2146a-7636-48f9-b6ea-ecf2bb61abee · outbound
This paper cites A review on edge large language models: Design, execution, and applications.ACM Computing Surveys, 57(8):1–35.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation A review on edge large language models: Design, execution, and applications.ACM Computing Surveys, 57(8):1–35
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.199071Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 0e836e11-0d25-41a8-80fc-22539a9af58c · outbound
This paper cites Instruction-Following Evaluation for Large Language Models.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation Instruction-Following Evaluation for Large Language Models
Reference 58
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T10:11:23.311393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation b991e72a-c586-4fb0-9ef4-e47fc157c210 · outbound
This paper cites MLVU: Benchmarking multi-task long video understanding.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation MLVU: Benchmarking multi-task long video understanding
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.204218Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 94564f05-3112-47e3-a246-4bfc8ab16281 · outbound
This paper cites an unresolved cited work.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation Unresolved cited work
Reference 60
- Resolution
- unresolved
- raw_fallback, observed 2026-05-22T10:16:24.208831Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 22a84477-c294-492d-bdf4-e182f91b8efc · outbound
This paper cites A survey on model compression for large language models.Transactions of the Association for Computational Linguistics, 12:1556–1577.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation A survey on model compression for large language models.Transactions of the Association for Computational Linguistics, 12:1556–1577
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.193948Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation d269e5dd-79ba-4038-9d96-66e7e69978de · outbound
This paper cites ∼Unif[0,1].
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.359335Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 942348bc-1744-4c97-bedc-87aa4132e2d3 · outbound
This paper cites N−0.5 dout .(17) Since all points lie in a sub-interval of length ρ, taking t=ρ in the definition of D∗ N gives a deviation of1−ρ.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation N−0.5 dout .(17) Since all points lie in a sub-interval of length ρ, taking t=ρ in the definition of D∗ N gives a deviation of1−ρ
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T10:16:24.188428Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 51320670-df27-428b-97b6-f95a52dbcc8f · outbound
This paper cites "" Given a string, find out how many distinct characters (regardless of case) it consists of >>> count_distinct_characters(’xyzXYZ’) 3 >>> count_distinct_characters(’Jerry’) 4.
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation "" Given a string, find out how many distinct characters (regardless of case) it consists of >>> count_distinct_characters(’xyzXYZ’) 3 >>> count_distinct_characters(’Jerry’) 4
Reference 64
- Resolution
- malformed identifier
- arxiv_id, observed 2026-05-22T10:11:23.317146Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Pith citing papers
Observation 4d553ebf-597f-4eb4-8fc0-b1874bbfbf04 · inbound
BiSCo-LLM: Lookup-Free Binary Spherical Coding for Extreme Low-Bit Large Language Model Compression cites this paper.
BiSCo-LLM: Lookup-Free Binary Spherical Coding for Extreme Low-Bit Large Language Model Compression EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation
Reference 65
- Resolution
- verified exact
- local_arxiv, observed 2026-07-10T04:06:44.645477Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.