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Paper Citation Record · LEDGER

Extreme Compression of Large Language Models via Additive Quantization

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 41 inbound Pith citation observations for arXiv:2401.06118.

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

pith.paper-citation-record.v1
2401.06118 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 41 of 41 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T23:03:44.588832Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T14:39:57.850108Z

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0 of 0 outbound references displayed

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3dd06b7b-8bce-49cb-a565-a81aefc033e8 · inbound

LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models cites this paper.

LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models Extreme Compression of Large Language Models via Additive Quantization

Reference 2

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arxiv_id, observed 2026-05-11T12:40:02.208737Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-11T12:40:02.129674Z digest=sha256:79e345d6a7051320c59900ea64757ec36a062dd57b7c3a4a3575adafb28a290c

Observation d88f3d30-7423-4d92-b7f0-ba9d9a364a0d · inbound

SpinQuant: LLM quantization with learned rotations cites this paper.

SpinQuant: LLM quantization with learned rotations Extreme Compression of Large Language Models via Additive Quantization

Reference 4

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arxiv_id, observed 2026-05-15T15:52:34.665680Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-15T15:52:34.606853Z digest=sha256:cee9e7f693bac45960888b3546da63e26265a7d1675c04362031549ed72391ef

Observation 658cba5f-2a5f-4553-a33f-3deabaab3908 · inbound

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models cites this paper.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Extreme Compression of Large Language Models via Additive Quantization

Reference 11

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T23:03:44.588832Z digest=sha256:12251bb1936fd00794a4b93190a29d2f7a857e8ede898c2dd9a8e25303833ff7

Observation daa42476-cae2-4cc7-a772-afad805be48b · inbound

NSNQuant: A Double Normalization Approach for Calibration-Free Low-Bit Vector Quantization of KV Cache cites this paper.

NSNQuant: A Double Normalization Approach for Calibration-Free Low-Bit Vector Quantization of KV Cache Extreme Compression of Large Language Models via Additive Quantization

Reference 11

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source=pdf_text observed=2026-08-07T14:42:20.242101Z digest=sha256:b32e1bc70861d353fee446c660f2f8a58ab2ac7ca3ed6a05058adc923cafc51e

Observation 9f794de9-c001-4ba4-8925-f3957faa6e3a · inbound

FPTQuant: Function-Preserving Transforms for LLM Quantization cites this paper.

FPTQuant: Function-Preserving Transforms for LLM Quantization Extreme Compression of Large Language Models via Additive Quantization

Reference 38

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source=pdf_text observed=2026-08-07T10:43:46.566561Z digest=sha256:16d54ecb3f0e1f31db1dc964650911e5f35bd3cc5403884c4cbc62768bc1deda

Observation bb9922a0-e0f7-4fd0-a282-f63fb37a2d3d · inbound

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling cites this paper.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling Extreme Compression of Large Language Models via Additive Quantization

Reference 9

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no resolver link, observed 2026-08-07T10:42:40.742493Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:42:40.742493Z digest=sha256:5e42e080d09ac67edd8998f0aeed71c8e6716ea9d9144cb4f6acde2342140dbd

Observation 58eaad20-086d-4325-9ae9-7323145700c7 · inbound

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models cites this paper.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Extreme Compression of Large Language Models via Additive Quantization

Reference 16

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source=pdf_text observed=2026-08-06T23:00:19.532049Z digest=sha256:f8a911555810512a8e194f9ffebaad18d8ab9eaea10272437d50f9c27912b591

Observation e2441ab2-3498-49a2-b5ac-13ea8d13180f · inbound

CCQ: Convolutional Code for Extreme Low-bit Quantization in LLMs cites this paper.

CCQ: Convolutional Code for Extreme Low-bit Quantization in LLMs Extreme Compression of Large Language Models via Additive Quantization

Reference 7

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source=arxiv_source observed=2026-08-06T19:09:13.539493Z digest=sha256:cd6f234b014b96c9b11b4f15f934b4c43e2614669161aeb26d812ff8402273b3

Observation 3a7063dd-279e-4af3-b72b-569d925c50c6 · inbound

Provable Post-Training Quantization: Theoretical Analysis of OPTQ and Qronos cites this paper.

Provable Post-Training Quantization: Theoretical Analysis of OPTQ and Qronos Extreme Compression of Large Language Models via Additive Quantization

Reference 6

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arxiv_id, observed 2026-05-18T23:52:53.045199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-18T23:52:06.036879Z digest=sha256:833b0493209a15408fae3ba885fb38244de6208348ef490798ad21fb212a5a94

Observation dfc0ec88-b4a0-4c5d-9b0e-e792557c7531 · 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 Extreme Compression of Large Language Models via Additive Quantization

Reference 14

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source=arxiv_source observed=2026-08-05T15:52:44.907408Z digest=sha256:19ecddbe4f9da50797e59e1c56bd669bbc0252673643e90a62803fe076acb444

Observation 333bcaa5-066f-428e-9eee-ded1225fba65 · inbound

Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches cites this paper.

Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches Extreme Compression of Large Language Models via Additive Quantization

Reference 5

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arxiv_id, observed 2026-05-18T13:41:25.968867Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-18T13:36:55.938673Z digest=sha256:25016c41ab4a112cacea34a0c6c4a4d6ade9be49a3cfc180cce503bc04661a13

Observation cf5c1dce-4a29-48eb-9245-3601303fbc57 · inbound

SignRoundV2: Toward Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs cites this paper.

SignRoundV2: Toward Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs Extreme Compression of Large Language Models via Additive Quantization

Reference 2

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arxiv_id, observed 2026-05-21T17:10:24.846706Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-21T17:08:05.945757Z digest=sha256:b56ae9b5409b44705ee15a3b2580c3b6fc66ba7fc41f04f4cd2d4036d61442fd

Observation cd9276b6-6e56-42e4-b282-ef7f11db4a9d · inbound

From Segments to Scenes: Temporal Understanding for Agentic Autonomous Driving via Vision-Language Models cites this paper.

From Segments to Scenes: Temporal Understanding for Agentic Autonomous Driving via Vision-Language Models Extreme Compression of Large Language Models via Additive Quantization

Reference 15

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Unavailable: canonical work link unavailable.

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Observation 489a29cb-7857-4be1-bf98-15d8e6810e65 · inbound

SALAAD: Sparse And Low-Rank Adaptation via ADMM for Large Language Model Inference cites this paper.

SALAAD: Sparse And Low-Rank Adaptation via ADMM for Large Language Model Inference Extreme Compression of Large Language Models via Additive Quantization

Reference 2024

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T06:01:14.100670Z digest=sha256:ea34543f6eced229378cf4151d56521c97f78c2f5ba12f346ea2ae827cffe134

Observation 45b46223-496e-4c88-ba72-22c31e4d0991 · inbound

BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models cites this paper.

BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models Extreme Compression of Large Language Models via Additive Quantization

Reference 5

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arxiv_id, observed 2026-05-21T14:14:12.382706Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation e5ef73dd-2ab6-4029-9439-b0f719b04bf2 · inbound

CoreQ: Learning-Free Mismatch Correction and Successive Rounding for Quantization cites this paper.

CoreQ: Learning-Free Mismatch Correction and Successive Rounding for Quantization Extreme Compression of Large Language Models via Additive Quantization

Reference 6

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arxiv_id, observed 2026-05-16T06:52:28.430581Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation f334d98c-2e02-47ea-ab38-a8fb93907250 · inbound

S2O: Early Stopping for Sparse Attention via Online Permutation cites this paper.

S2O: Early Stopping for Sparse Attention via Online Permutation Extreme Compression of Large Language Models via Additive Quantization

Reference 4

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arxiv_id, observed 2026-05-15T19:36:32.866811Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 6ba3859a-434d-4e99-95de-461bee102348 · inbound

ECG Foundation Models and Medical LLMs for Agentic Cardiovascular Intelligence at the Edge: A Review and Outlook cites this paper.

ECG Foundation Models and Medical LLMs for Agentic Cardiovascular Intelligence at the Edge: A Review and Outlook Extreme Compression of Large Language Models via Additive Quantization

Reference 99

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arxiv_id, observed 2026-05-13T20:28:14.274229Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 32fc55e5-b72b-42a9-a150-90d68dcd6960 · inbound

FP4 Explore, BF16 Train: Diffusion Reinforcement Learning via Efficient Rollout Scaling cites this paper.

FP4 Explore, BF16 Train: Diffusion Reinforcement Learning via Efficient Rollout Scaling Extreme Compression of Large Language Models via Additive Quantization

Reference 54

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arxiv_id, observed 2026-05-11T05:25:56.614042Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 4b9d5074-6984-40e8-a8c0-1370479d3c14 · inbound

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling cites this paper.

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling Extreme Compression of Large Language Models via Additive Quantization

Reference 9

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arxiv_id, observed 2026-05-10T05:36:02.303475Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T05:29:51.182114Z digest=sha256:b1c5d4e2bde635e0da6285b68be80d58dd6e2c577f1c6481f409b485cf3be7ca

Observation 3a17d4df-ac86-439a-9dfd-bebaa3d296e9 · inbound

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling cites this paper.

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling Extreme Compression of Large Language Models via Additive Quantization

Reference 9

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arxiv_id, observed 2026-05-19T18:02:42.259097Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T18:01:08.514022Z digest=sha256:5e552d4dc871db1a3606a7c36b43ec863ac4a0d8b7fe73f6bc34985740383356

Observation 0dbeb0ac-ca5b-4fbc-8c29-b34318db1bef · inbound

SAW-INT4: System-Aware 4-Bit KV-Cache Quantization for Real-World LLM Serving cites this paper.

SAW-INT4: System-Aware 4-Bit KV-Cache Quantization for Real-World LLM Serving Extreme Compression of Large Language Models via Additive Quantization

Reference 5

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arxiv_id, observed 2026-05-10T03:14:08.292383Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 6a650f0d-7fa8-4f00-b4a7-398ca6af3654 · inbound

LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation cites this paper.

LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation Extreme Compression of Large Language Models via Additive Quantization

Reference 39

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arxiv_id, observed 2026-05-11T12:46:04.531356Z

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Observation 48269ae2-16fa-4a43-ab45-baf1c23ae9cf · inbound

GAMMA: Global Bit Allocation for Mixed-Precision Models under Arbitrary Budgets cites this paper.

GAMMA: Global Bit Allocation for Mixed-Precision Models under Arbitrary Budgets Extreme Compression of Large Language Models via Additive Quantization

Reference 45

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arxiv_id, observed 2026-05-20T12:28:16.821701Z

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source=arxiv_source observed=2026-05-20T12:25:39.417436Z digest=sha256:fba099b080f631125bc2cf0c60be2da8fde62acbdd2b4cc20f7929fe286c26f6

Observation 2a2a897d-b308-4140-bfad-424919528f20 · inbound

ActQuant: Sub-4-bit Action-Guided Quantization for Vision-Language-Action Models cites this paper.

ActQuant: Sub-4-bit Action-Guided Quantization for Vision-Language-Action Models Extreme Compression of Large Language Models via Additive Quantization

Reference 7

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arxiv_id, observed 2026-07-01T15:05:47.976295Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-30T17:54:56.386488Z digest=sha256:7c918f3abbf4086343f00ff95e264363301810ff97101be1211d7bc25775a621

Observation 3cda3c24-9941-44c3-89d3-d58c3e24e172 · inbound

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

Influence-Inspired Spectral Rotations for Extreme Low-Bit LLM Quantization Extreme Compression of Large Language Models via Additive Quantization

Reference 13

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arxiv_id, observed 2026-06-30T12:14:39.062248Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-30T12:13:18.805668Z digest=sha256:4f2091afec17dabc6185ad9485ec44b1864e1b1364091027158e45e2a9ca3b32

Observation d72d6847-4844-4887-8057-75c4c71efd3f · inbound

WINDQuant: Weight-Informed Neural Decision-Making for Global Mixed-Precision LLM Quantization cites this paper.

WINDQuant: Weight-Informed Neural Decision-Making for Global Mixed-Precision LLM Quantization Extreme Compression of Large Language Models via Additive Quantization

Reference 1

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arxiv_id, observed 2026-06-29T19:33:53.959601Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation bc8f316e-7897-4f4a-ada1-14ead50af142 · inbound

HARP: Hadamard-Preconditioned Adaptive Rotation Processor for Extreme LLM Quantization cites this paper.

HARP: Hadamard-Preconditioned Adaptive Rotation Processor for Extreme LLM Quantization Extreme Compression of Large Language Models via Additive Quantization

Reference 1

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arxiv_id, observed 2026-06-29T13:53:29.657669Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-29T09:16:37.939055Z digest=sha256:82a17c558c14fbc0511e92c3d368530b7c6f297a0ad1945348f6a83ad735d385

Observation 3a953875-6e80-4582-ac2d-67aab035d42e · inbound

Qift: Shift-Friendly No-Zero W2 Post-Training Quantization for Rotated W2A4/KV4 LLM Inference cites this paper.

Qift: Shift-Friendly No-Zero W2 Post-Training Quantization for Rotated W2A4/KV4 LLM Inference Extreme Compression of Large Language Models via Additive Quantization

Reference 10

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metadata mismatch
arxiv_id, observed 2026-07-01T22:06:15.957269Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-28T15:49:41.836888Z digest=sha256:0866ea193ff8fe1470f7cf2e10547ab87f47413688976fc548a6ee62a65693aa

Observation 13e220e0-5905-4fd6-96e1-1c607db14c73 · inbound

Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression cites this paper.

Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression Extreme Compression of Large Language Models via Additive Quantization

Reference 7

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metadata mismatch
arxiv_id, observed 2026-07-02T01:46:26.898431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-28T11:31:25.851340Z digest=sha256:a14796d6374b058d84de08b32c91842cab07407b96892411b266a5d7e8aa9ca8

Observation f4e9c52e-ef22-423e-affa-381ea50aecf8 · inbound

LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection cites this paper.

LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection Extreme Compression of Large Language Models via Additive Quantization

Reference 48

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arxiv_id, observed 2026-07-02T02:06:27.011046Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-28T11:14:03.535306Z digest=sha256:a6e196cc62d0ba4ce6f9c18e53dcedb0d32dff3378829e1fc02d28b2eea5abb4

Observation 1ea1b4fe-faf0-407b-b888-28787d78b9f9 · inbound

LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection cites this paper.

LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection Extreme Compression of Large Language Models via Additive Quantization

Reference 48

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arxiv_id, observed 2026-06-30T11:24:38.301078Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-30T11:17:53.736872Z digest=sha256:22676fe25d4b4205c6332d11b470f8e2c5bcd206684b0daf5258f342418edd3f

Observation 83aedfc9-6608-44c9-bd81-e3f1d9ee33ca · inbound

Multi-Bitwidth Quantization for LLMs Using Additive Codebooks cites this paper.

Multi-Bitwidth Quantization for LLMs Using Additive Codebooks Extreme Compression of Large Language Models via Additive Quantization

Reference 67

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arxiv_id, observed 2026-07-03T13:48:21.354907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-27T07:29:14.923431Z digest=sha256:0d79a727e83be74c44e5597031a12e73540a8b6055fc7b2ef475652b031ef5e3

Observation c38210ad-15bc-46e6-a32b-2e8b5f66b6ea · inbound

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems cites this paper.

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems Extreme Compression of Large Language Models via Additive Quantization

Reference 150

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T11:09:46.230247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T08:09:57.542558Z digest=sha256:67732840874e20fa9e1b86b9fa542aebd47ff655b3bc68566e57ac14b808b007

Observation c86b2b5d-32b6-49d8-833c-44c9cf9ff354 · inbound

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems cites this paper.

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems Extreme Compression of Large Language Models via Additive Quantization

Reference 150

Resolution
unresolved
no resolver link, observed 2026-08-02T10:27:18.293481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T10:27:18.293481Z digest=sha256:d17e58824b9a5c138fb45fd49c55b84b6d3931f61c5b23e2b590575570770706

Observation 07535700-f01f-4327-8453-3e5bdb230fd3 · inbound

Smaller Models, Unexpected Costs: Trade-offs in LLM Quantization for Automated Program Repair cites this paper.

Smaller Models, Unexpected Costs: Trade-offs in LLM Quantization for Automated Program Repair Extreme Compression of Large Language Models via Additive Quantization

Reference 19

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T14:39:57.851690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T03:10:37.068739Z digest=sha256:56566dc1dcf458ab1d731e6d4c3c20de2c61d05a1197e3e21774f9fc6ff12178

Observation 382d3d53-2e97-4f26-8029-f94b47b65942 · inbound

Smaller Models, Unexpected Costs: Trade-offs in LLM Quantization for Automated Program Repair cites this paper.

Smaller Models, Unexpected Costs: Trade-offs in LLM Quantization for Automated Program Repair Extreme Compression of Large Language Models via Additive Quantization

Reference 19

Resolution
unresolved
no resolver link, observed 2026-07-12T11:49:34.865484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T11:49:34.865484Z digest=sha256:dfcc6877836bb6fd871b58abf7abe51e57c65f745aba0960772a53c5e295a96d

Observation b72dd6dc-1cf9-4c3c-b5ed-fd9b823302c7 · inbound

Breaking the Rounding Trap: Securing LLMs against Quantization-Conditioned Backdoors cites this paper.

Breaking the Rounding Trap: Securing LLMs against Quantization-Conditioned Backdoors Extreme Compression of Large Language Models via Additive Quantization

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-06-30T08:04:28.755411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-30T07:47:18.350953Z digest=sha256:81ee12190e07697e13428c25adb059e67fe9e31d44d19875bc86737a1cb27092

Observation a37e7a8f-29ce-45ad-818b-9a0fffe88d7f · inbound

Breaking the Rounding Trap: Securing LLMs against Quantization-Conditioned Backdoors cites this paper.

Breaking the Rounding Trap: Securing LLMs against Quantization-Conditioned Backdoors Extreme Compression of Large Language Models via Additive Quantization

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-04T04:39:06.778635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T04:39:06.778635Z digest=sha256:49222f234f5cfe1f6f8d8566c8d9209c40ed8eee74cf6f28a86a6d84e65d3ac5

Observation 7b6e680d-99cb-4fc7-8bfa-e3b9323788d2 · inbound

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

Reliability Scaling Laws for Quantized Large Language Models Extreme Compression of Large Language Models via Additive Quantization

Reference 87

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T08:45:52.855783Z digest=sha256:0c1ed88b66b0df2ac9f406a9d251906fe484a92f77af38db163674ad295b875a

Observation 2fec4cf6-8036-4a8f-8ff8-cdab958a7770 · inbound

A Motion-Aware Vector Quantization Framework with Centroid Reuse for Efficient VLA Inference cites this paper.

A Motion-Aware Vector Quantization Framework with Centroid Reuse for Efficient VLA Inference Extreme Compression of Large Language Models via Additive Quantization

Reference 8

Resolution
unresolved
no resolver link, observed 2026-07-31T22:36:29.223928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T22:36:29.223928Z digest=sha256:2d8e9d4b0be644f63cb92264a21673c891295a37d3f8abfcc09fca3c02bbcd6d