Pith. sign in

Paper Citation Record · LEDGER

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction

As of 4 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 0 inbound Pith citation observations for arXiv:2606.01850.

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

pith.paper-citation-record.v1
2606.01850 v1

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T15:06:31.471481Z

measured 65 of 65 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

65 of 65 outbound references displayed

  • verified exact23
  • verified fuzzy0
  • unresolved40
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 59de48c3-622e-4d99-a464-9c7acdcd3c9a · outbound

This paper cites The Llama 3 Herd of Models.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction The Llama 3 Herd of Models

Reference 1

Resolution
verified exact
local_arxiv, observed 2026-07-01T22:46:18.791229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:c312594a1221845538eba33bd6114b07f706cebacba0aa3f13b7f19155ea52ae

Observation c29c6064-9446-4d5d-8299-0ffe8725852c · outbound

This paper cites GPT-4 Technical Report.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction GPT-4 Technical Report

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-07-01T22:46:18.754894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:74a7215a6e53484d908bb66f2639df5f6a5acd7cd71c710d64a79dd287b90d8d

Observation 05abfdb4-3c27-43da-9e22-5019c2326537 · outbound

This paper cites Hierarchically robust zero-shot vision- language models,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Hierarchically robust zero-shot vision- language models,

Reference 3

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:501282911744ad77ddac45bed5018a5a3c4e825e11fdbf8b2e43ba7ed75c0da3

Observation 9c4c8930-3b5d-440f-9374-6668584d9152 · outbound

This paper cites Tug-of-war no more: Harmonizing accuracy and robustness in vision-language models via stability-aware task vector merging,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Tug-of-war no more: Harmonizing accuracy and robustness in vision-language models via stability-aware task vector merging,

Reference 4

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:7735e32ebcdf7ad8d386662cbfd724d9a486ae72f291e3805f91127523883a81

Observation d0f614b9-c972-4a1e-81c1-ff826623a67a · outbound

This paper cites Craft-lora: Content-style personalization via rank-constrained adaptation and training-free fusion,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Craft-lora: Content-style personalization via rank-constrained adaptation and training-free fusion,

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-07-01T22:46:18.752071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:9ade348d02c3b75b2414ae44a107de17d1a2f6564268d7b6bd184c369a94077e

Observation 6241198f-c8ad-4dd2-b082-203b58cf95ec · outbound

This paper cites Reason in Chains, Learn in Trees: Self-Rectification and Grafting for Multi-turn Agent Policy Optimization.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Reason in Chains, Learn in Trees: Self-Rectification and Grafting for Multi-turn Agent Policy Optimization

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-07-01T22:46:18.774163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:37e0c56ba1b6a82333c59a27f97ba283eb045154a26b438840469c039ef075e2

Observation 8cf83743-55ce-43c1-a19d-599462620a26 · outbound

This paper cites Sage: Accelerating vision-language models via entropy-guided adaptive speculative decoding,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Sage: Accelerating vision-language models via entropy-guided adaptive speculative decoding,

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-07-01T22:46:18.843999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:1321733eeaf6a703e549946196ef0904cd662a02f571e3715452a9cd8fbc9b0f

Observation 6dd0053c-a9d9-4b3a-86b0-1b67cc7cc140 · outbound

This paper cites A survey on model compression for large language models,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction A survey on model compression for large language models,

Reference 8

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:aee958c2a5d230e812b86e321db71c882e50879d95b3c46fe4c963d4fc240e96

Observation 6623c3d7-48a5-404b-b252-b05337c3c152 · outbound

This paper cites Optimal brain restoration for joint quantization and sparsification of llms.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Optimal brain restoration for joint quantization and sparsification of llms

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-07-01T22:46:18.828161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:1b8927143de8b6a3bb9022bf28f0531199828f11505e5136a2dad1d4dbd817a4

Observation 7395a3f5-a960-4144-9bad-fe8e8d61e59a · outbound

This paper cites Quarot: Outlier-free 4-bit inference in rotated llms,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Quarot: Outlier-free 4-bit inference in rotated llms,

Reference 10

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:e9bf75f5e62f60702c1b8e6c5610c444ef6ad88c6d1280c7a1948c422023d195

Observation 4383c464-0448-47cf-8564-05127baa65a3 · outbound

This paper cites Awq: Activation-aware weight quantization for on-device llm compression and acceleration,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Awq: Activation-aware weight quantization for on-device llm compression and acceleration,

Reference 11

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:1b9a5546dcd31645400a294b5a252e400b136cc197a8f5ca6bd1855a5d7d1955

Observation 3aebab37-eceb-4c60-9c5d-1b5a1b6d9200 · outbound

This paper cites Smoothquant: Accurate and efficient post-training quantization for large language models,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Smoothquant: Accurate and efficient post-training quantization for large language models,

Reference 12

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:833f752abf9a2f2b04b93573911b60355ed0b3fe5d901ca961a4a82f954c6c15

Observation d756b9da-03ad-49a9-8cdc-6d18df1f4c7e · outbound

This paper cites Gpt3. int8 (): 8-bit matrix multiplica- tion for transformers at scale,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Gpt3. int8 (): 8-bit matrix multiplica- tion for transformers at scale,

Reference 13

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:c4a96ba32051d66ae81347fc29ff2ebe7ae3611586ed3a558e48f9d000098aa2

Observation 4b35e794-1220-4de6-a555-49985cbe81f3 · outbound

This paper cites Omniquant: Omnidirectionally calibrated quantization for large language models,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Omniquant: Omnidirectionally calibrated quantization for large language models,

Reference 14

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:dc0c972b9484542763f1468cf6efaaca4b878cf075a24eb2e4de41007394179e

Observation 24649066-9457-4dce-a146-6bd87d91ee89 · outbound

This paper cites SliceGPT: Compress Large Language Models by Deleting Rows and Columns.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-07-01T22:46:18.778128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:b7b5fd5c3a1c443e62f53131c7ef0d42685a653131f8b02c1a1a7605b368a188

Observation 26d3e0ef-c46a-4617-b020-7b6dd8fc5f80 · outbound

This paper cites Sparsegpt: Massive language models can be accurately pruned in one-shot,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Sparsegpt: Massive language models can be accurately pruned in one-shot,

Reference 16

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:800d65445a086e7485f0c489f60b809acb965d4e23c8016dbb7f33f79f763fc6

Observation a968e66f-ab4c-47d9-a399-ecb2248a3720 · outbound

This paper cites Fluctuation-based adaptive structured pruning for large language models,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Fluctuation-based adaptive structured pruning for large language models,

Reference 17

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:5a3bb489b5fbe3e7c155c787306242d1e7975af10257d87a0fd5f3b10c993707

Observation 692ef21a-c487-4af7-bdcf-9080ef22333d · outbound

This paper cites Language Models (Mostly) Know What They Know.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Language Models (Mostly) Know What They Know

Reference 18

Resolution
verified exact
local_arxiv, observed 2026-07-01T22:46:18.781342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:399fafdd5e076708c397578b29ef1e52767808c5f28f833d6c6365ab7c335f77

Observation 4a2b974c-485c-42cb-a9a9-3c5e1b7c81f1 · outbound

This paper cites A tutorial on conformal prediction.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction A tutorial on conformal prediction

Reference 19

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:e6ee98c8b0975d988b9e8ff80b9b484f7b0d53637b0725f6b0baa95fc4c27e8d

Observation 983d20bb-3941-4726-9bf3-c7d2c795e04e · outbound

This paper cites Conformal prediction: A gentle introduction,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Conformal prediction: A gentle introduction,

Reference 20

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:446af28900762bb4d48952e4bb7b97679751e9532a6b53ef8189bf0ed4e50396

Observation d85497b9-ea5c-4474-900d-134035fe0df1 · outbound

This paper cites Benchmarking llms via uncertainty quantification,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Benchmarking llms via uncertainty quantification,

Reference 21

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:d8f4dc06b229a581fa9364154a39cda8e9dfc72c0d373ed1fb47683b0dda9e02

Observation f21e8e7e-2700-4aa0-a9f4-28bddf7f46f4 · outbound

This paper cites Conformal language modeling,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Conformal language modeling,

Reference 22

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:e160e6945e2f47e6190ef3e644dee28df4ffcfd7a9ad39867fa09013fec22d4d

Observation 47fc6dca-75ea-42d9-9302-6866237046b5 · outbound

This paper cites Large language model validity via enhanced conformal prediction methods,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Large language model validity via enhanced conformal prediction methods,

Reference 23

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:2b71e74a80c978b26b5fd685b8c3c48049d1e5539a19f432b3a845080f570cdc

Observation a5981ecf-42fc-4c0d-8137-3b6c5ca1bfda · outbound

This paper cites Quantifying deep learning model uncertainty in conformal predic- tion,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Quantifying deep learning model uncertainty in conformal predic- tion,

Reference 24

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:0266da20bd5a271a93cfe69e04b1a9304cd83288b35dbf5f39168e1e148e7227

Observation f7b32b2b-399b-4e55-aaed-1d5600c4977b · outbound

This paper cites Robust machine unlearning for quantized neural networks via adaptive gradient reweighting with similar labels,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Robust machine unlearning for quantized neural networks via adaptive gradient reweighting with similar labels,

Reference 25

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:ea954201993ea4dd0b3662ab65393ff1e88ea52a74e7c1ea115e399b22219fd2

Observation fdd612f8-e508-44a8-a7d8-d8d0cb8c9972 · outbound

This paper cites Enhancing quantization-aware training on edge devices via relative entropy coreset selection and cascaded layer correction,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Enhancing quantization-aware training on edge devices via relative entropy coreset selection and cascaded layer correction,

Reference 26

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:c83e13b460350abcd6c3d2a4bd74cc81aa69b3a29773584c08c76b50bfdc7078

Observation e041f625-ec04-470a-86e2-7fe856026361 · outbound

This paper cites Forget by Uncertainty: Orthogonal Entropy Unlearning for Quantized Neural Networks.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Forget by Uncertainty: Orthogonal Entropy Unlearning for Quantized Neural Networks

Reference 27

Resolution
verified exact
local_arxiv, observed 2026-07-01T22:46:18.794326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:33922a9d675d98266e514774bfab6d6867f0c6313e81258c2bafd3db99da49d3

Observation 981826a0-36cf-407a-8dac-77ec06d3a941 · outbound

This paper cites Data-free quantization of vision transformers via easy-to-hard synthesis and activation correction,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Data-free quantization of vision transformers via easy-to-hard synthesis and activation correction,

Reference 28

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:2f3ea0edbdf85129d27ad03a369b705d7b549c07b145065c5198b99dab44be5a

Observation 921eacc7-d485-49f1-bd32-fa9e85f21d96 · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction A Simple and Effective Pruning Approach for Large Language Models

Reference 29

Resolution
verified exact
local_arxiv, observed 2026-07-01T22:46:18.770209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:501c1287e9ab81c4d5ad5319ce23874ca38a466b50089a915d12b2b065e7612f

Observation 7f4d30ba-906e-4deb-9c5f-45c2bc81bd00 · outbound

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

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 30

Resolution
verified exact
local_arxiv, observed 2026-07-01T22:46:18.823494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:6fa880be986861713090aadfbdddd474f9a996f52e9eb6f487f2b8a7404a7dfb

Observation 6d120a39-e591-498a-9d40-f4e5d8383207 · outbound

This paper cites SpinQuant: LLM quantization with learned rotations.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction SpinQuant: LLM quantization with learned rotations

Reference 31

Resolution
verified exact
local_arxiv, observed 2026-07-01T22:46:18.819737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:eb5f2f479254705cc686ce1b5f95220bf736464e219d698e2c00c6d64e62a352

Observation 98e884a1-213b-43b2-b173-82f5fba7a664 · outbound

This paper cites On the Convergence of Muon and Beyond.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction On the Convergence of Muon and Beyond

Reference 32

Resolution
verified exact
local_arxiv, observed 2026-07-01T22:46:18.787807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:f84aa8348ad0f568af1c01f6312bb29ee0500ba08fbf688e822fb941283b2659

Observation fd0b5d0b-286f-4f6a-acba-71fc20eba47c · outbound

This paper cites MuonEq: Balancing Before Orthogonalization with Lightweight Equilibration.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction MuonEq: Balancing Before Orthogonalization with Lightweight Equilibration

Reference 33

Resolution
verified exact
local_arxiv, observed 2026-07-01T22:46:18.816414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:b1de7b3063cd733bb4f322edb08169e8b3ab7e96740a1401a55ce56c6ceded75

Observation e33f6acd-9136-4c0c-baba-0478dafd47f5 · outbound

This paper cites Mgup: A momentum-gradient alignment update policy for stochastic optimization,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Mgup: A momentum-gradient alignment update policy for stochastic optimization,

Reference 34

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:7c49193a33c848e1278a1b2282fb16ad2bd569bfde3d564ec3e73ffbfa17275b

Observation de5ab11c-f32d-4107-9505-d88625e45136 · outbound

This paper cites Flatquant: Flatness matters for llm quantization,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Flatquant: Flatness matters for llm quantization,

Reference 35

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:85d729c0c6453afbfbac4c2167e627d353b3dbb3d18fa5acfb85fb4bf0e8cbfe

Observation 9a096357-403a-4042-8909-89f4696db5e3 · outbound

This paper cites Qlora: Efficient finetuning of quantized llms,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Qlora: Efficient finetuning of quantized llms,

Reference 36

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:8cf48fb8c8a0fd483c3d6a897e48d4ae318f63a2de50b9bf57347ed20d65838c

Observation 1ba43593-0de7-462a-864c-f10b67d9a34a · outbound

This paper cites Atom: Low-bit quantization for efficient and accurate llm serving,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Atom: Low-bit quantization for efficient and accurate llm serving,

Reference 37

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:30c67dd6c913ee79204bc2006f7caf08bab48378790ec48242f3b8b47167ca01

Observation f79e1972-de29-4b66-bd84-392c0859c4a5 · outbound

This paper cites Llm-pruner: On the structural pruning of large language models,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Llm-pruner: On the structural pruning of large language models,

Reference 38

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:16b681b4c4d8f847d28c389a7ba560899430e98f76b96f97ac461de436886d11

Observation 1b890620-5df4-4a21-837e-53664f9130f0 · outbound

This paper cites On Verbalized Confidence Scores for LLMs.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction On Verbalized Confidence Scores for LLMs

Reference 39

Resolution
verified exact
local_arxiv, observed 2026-07-01T22:46:18.784471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:a1f4e9b38b371d9692080bce63752fb5e5159d2df36c122d8bdae5b64684c60b

Observation 45b944c9-3582-4ed7-b24e-fbf3fe630d1f · outbound

This paper cites Ensemble based systems in decision making,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Ensemble based systems in decision making,

Reference 40

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:a26cbf3905dcf43d2e934b1de8649b9a1ce18f50da9ee19fd1d787be8a8db0fc

Observation c292060a-945d-46c3-abcc-06874f859fd5 · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Simple and scalable predictive uncertainty estimation using deep ensembles,

Reference 41

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:a630d5d20a978d98f7ecc92260fee4ebe90589429b7267e2e11c98c219c848f8

Observation 67f7469e-91d5-44c3-8bcf-36fa8d781b19 · outbound

This paper cites Probabilistic forecasting using monte carlo dropout neural networks,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Probabilistic forecasting using monte carlo dropout neural networks,

Reference 42

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:ca8c996208331ec6d2aedc26c85337c7d44655bfbcadea3f6b1490e38d18e129

Observation 50ada927-4515-45aa-8d0a-e63ca5e1b8c1 · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncer- tainty in deep learning,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Dropout as a bayesian approximation: Representing model uncer- tainty in deep learning,

Reference 43

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:0880e898a40dc8536834a414c704ae8af393305e600464f590fec477352aa511

Observation d5db20cd-b328-4f2d-99d5-2abe7762f2e8 · outbound

This paper cites Ares: Multimodal adaptive reasoning via difficulty-aware token-level entropy shaping.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Ares: Multimodal adaptive reasoning via difficulty-aware token-level entropy shaping

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-07-01T22:46:18.758413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:4c30b08e25a223a96e93dee34d8be3dfd9a71bfa6c3219435a2198b07df89ed9

Observation 5d31fcc2-09b0-4647-8245-745d29322d8c · outbound

This paper cites Evidential deep learning to quantify classification uncertainty,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Evidential deep learning to quantify classification uncertainty,

Reference 45

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:a0ae179470d430d9bfaffc3a5892ea30667268327289b351a3e41fc8087bbf54

Observation 09fc3086-d4a3-4f68-b97c-671f6532a062 · outbound

This paper cites Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation

Reference 46

Resolution
verified exact
local_arxiv, observed 2026-07-01T22:46:18.801024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:db147423631392b5e189ed4f979781de40eb52e9720701683f8954e614aff519

Observation b31f8f6c-f05a-4924-8418-0ed0eb8584a0 · outbound

This paper cites On calibration of modern neural networks,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction On calibration of modern neural networks,

Reference 47

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:c14e274d2ae8f9ffa80e03df7a9f9d997736411a9abb4dc26b9b3b70feed76a8

Observation 6304519f-0b8b-4617-b98a-81579f7d1042 · outbound

This paper cites Revisiting the calibration of modern neural networks,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Revisiting the calibration of modern neural networks,

Reference 48

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:df4cb0b6e5236046c9c005341cf4a4f3436e4453fc3e16c3a0a412ef862a2e8e

Observation 54c73e54-e4a5-4b4d-8322-9b49493dc69b · outbound

This paper cites COPU: Conformal Prediction for Uncertainty Quantification in Natural Language Generation.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction COPU: Conformal Prediction for Uncertainty Quantification in Natural Language Generation

Reference 49

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T22:46:18.812890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:b13f478f66cfcfaebe8c45e2b75c66f66cd730dc6d1d1c769e2ea0d7473ce9d6

Observation 9e00818c-01ea-4440-aff0-eecb5df4489c · outbound

This paper cites Conformal Prediction with Large Language Models for Multi-Choice Question Answering.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Conformal Prediction with Large Language Models for Multi-Choice Question Answering

Reference 50

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T22:46:18.766333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:a2564d7fae30639f96ff8dbde2bd96c6068473691a89943130752a81262dd708

Observation bc3362e3-3fcf-4eb5-89eb-66a64c34b32d · outbound

This paper cites Non-exchangeable conformal language generation with nearest neighbors,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Non-exchangeable conformal language generation with nearest neighbors,

Reference 51

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:cb23dcd284c3b00a9ee29a746fef93a87a106a349acc8b70198e32a82fe25256

Observation 7a4e18fd-2518-4086-a179-3bdd727e009b · outbound

This paper cites Uncertainty quantification and confidence calibration in large language models: A survey,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Uncertainty quantification and confidence calibration in large language models: A survey,

Reference 52

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:cfa4ce9c4b91462da052c21989b7730a8a691b052f85f6ff5f2c0bf91315fde1

Observation 7abc3a4e-cac6-4f24-9bdf-b2908886a0ea · outbound

This paper cites Quantized can still be calibrated: A unified framework to calibration in quantized large language models,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Quantized can still be calibrated: A unified framework to calibration in quantized large language models,

Reference 53

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:626b1f54eb692cd546f835cd769779d541ff6152dbc2f20573a9063a27d751ec

Observation c90abc4a-195e-4f1d-adc7-763b3b22d4a7 · outbound

This paper cites Least ambiguous set-valued classifiers with bounded error levels,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Least ambiguous set-valued classifiers with bounded error levels,

Reference 54

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:d925cfa012ea4932e14ec58489abdd9788117913122a5135aa347520abea7e32

Observation c2aa5a8c-da42-4889-b121-fb2f06863e5d · outbound

This paper cites Classification with valid and adaptive coverage,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Classification with valid and adaptive coverage,

Reference 55

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:eb9e3f98dfb788dc7831ed9b04d50b5d2d2b2a2d6094fb11dd918e559b7e52e0

Observation 4155708a-82f8-4d12-adac-927a8b7bad88 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Measuring Massive Multitask Language Understanding

Reference 56

Resolution
verified exact
local_arxiv, observed 2026-07-01T22:46:18.831983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:5ad38c8c9e62198d2c1149699b07a138321a31b1e10248b3c4d645630095a503

Observation 7b476b7a-e388-4cdf-8172-7d61077fec57 · outbound

This paper cites Cosmos qa: Machine reading compre- hension with contextual commonsense reasoning,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Cosmos qa: Machine reading compre- hension with contextual commonsense reasoning,

Reference 57

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:e5e415b959190c5c25f88f9275788cccbba08f7759094d363c1dd19c38bc505f

Observation e9c51dad-dd4c-49aa-a030-bdd957c864ad · outbound

This paper cites Hellaswag: Can a machine really finish your sentence?.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Hellaswag: Can a machine really finish your sentence?

Reference 58

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:cfa544e46636d3ee54799968d7980b11a9ee62c3950ad60b543b5826b7b60168

Observation 8083f21b-2a91-4e4f-bc8a-3efa1cbc9670 · outbound

This paper cites Halueval: A large-scale hallucination evaluation benchmark for large language models,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Halueval: A large-scale hallucination evaluation benchmark for large language models,

Reference 59

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:ae2565eefec19903f685b584810edd96b2f555dddcc8bfb03e799ec5255c3338

Observation eb06e281-4e4a-4489-887b-19f897e8343d · outbound

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

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 60

Resolution
verified exact
local_arxiv, observed 2026-07-01T22:46:18.797659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:cd8bd0a8af042c71836ab55f0afddccc0a7dd3603f0d4d8a1721223053e30033

Observation 13ced8cd-8377-4b79-b8d8-440842fc4629 · outbound

This paper cites Qwen3 Technical Report.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Qwen3 Technical Report

Reference 61

Resolution
verified exact
local_arxiv, observed 2026-07-01T22:46:18.836163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:82bf97fabe7d7a6350b9b835ea269c9d8600675ab0d1ec8297f308c3c7aa482e

Observation 72d5b0ef-d13b-460f-9109-bcb02398ae6e · outbound

This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 62

Resolution
verified exact
local_arxiv, observed 2026-07-01T22:46:18.761932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:77624007b673c015e0945edbbb1ff7cd6c8054936e9d306def5ca5493a894ac4

Observation 65f294e5-0d66-42ef-8dff-bd9b7e7a6791 · outbound

This paper cites The Falcon Series of Open Language Models.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction The Falcon Series of Open Language Models

Reference 63

Resolution
verified exact
local_arxiv, observed 2026-07-01T22:46:18.807633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:7684a13b72a342a42fc67d49f9c3f77f923e24ebe21fa54fe8a367d8d60be2df

Observation f27e520e-51b7-4d0d-b56e-7298c19d3d0d · outbound

This paper cites Holistic Evaluation of Language Models.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction Holistic Evaluation of Language Models

Reference 64

Resolution
verified exact
local_arxiv, observed 2026-07-01T22:46:18.804484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:31a497f4d250a86650864a6cd3a6f1c2aecb210d90d985d85db315114c4c7e71

Observation 8c75e0a0-b1e0-4c1a-a309-bc50f12b223e · outbound

This paper cites A survey on evaluation of large language models,.

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction A survey on evaluation of large language models,

Reference 65

Resolution
unresolved
no resolver link, observed 2026-06-28T15:06:31.471481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:aefd4e54bb041be80c86215ae5113446acc7a8cf169c534dafdc430fb0ee7397

Pith citing papers

No inbound Pith citation observations are available.