Pith. sign in

Paper Citation Record · LEDGER

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

As of 15 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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:ccbdefd5ff13ba9388251b86760de4a65f3947a997791f0bf104ca5b0afda6e2

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:ff0c2d9a82e95a1ceaccdf1287e799a5fd082e8ad775d2c953dff679a0aa58a3

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=sha256:4544e44c4b8a9763bf57b056bd4757a7094e3a254cae59aefadbd41078da481e

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:ff6532d4c75d935acde3dcce2f48cb421f1944d040699b6768c2754c76f94b40

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:dd535fcc3ecc41c19587e4a256d3b7d6a7917e46d18c466262fd3d99d661b1bc

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:7018018a8210a460276ba7ed0bf60a5bc4666ea929a582dcb50fbcf91c25853b

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:49241c0d561cf303764775923c9df69ff1258b95ef2be5472e5424b0c51b2bd3

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:a661e006908e1d3fcebedd462d548c5a87746b6482f77476f52ae6f93f9c279e

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:392942849f5d9ee3889b3efbfaf87a189290adbccbf22b1301b2e20fbbc54b4b

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=sha256:33a2434ee43a95d5ba16d1de15f7493f870c99fa99e0c8fbb9d3b515e75026c7

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=sha256:71962d50e5d5d9e2b114e71607f98baec73ebd05884c735807e187f94e05cac1

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:add348536a439909068e8f6d2d1ec57640da6bba3b2a063c9c76294a65883981

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=sha256:5ef1f0452bfe384d30e20fff82a71aacb5906d79987f327a1dee4237aa59c42d

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=sha256:4f8f5795d0e51857c54c9692786764ab8101da4a656bad83a194675f08fef1cd

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:35c1c9fe78294cda1b7e4b36b746dffa03af2e098f99ce8097854c3996519c23

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:e7270c82a6c61c8eb4338499f233d4238eb7fcd1a7b4001c4c507bf46c15edcb

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=sha256:2b4a619c57d78fe963e4b5b15d943f93674282a11f880c285632a3f9226bd5be

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:ea79c7aead7a00fbb2100ab890bdef8389174635af9dcd5b6b49f136748f9134

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=sha256:8073fcd1b086a8e86a2ab2fe9ae9b774ec953c490aaa6a7582207b567bfb0e5e

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:e0c9a552d15d6fe65815cdff5a8e83294fab2eff3c48fbe97cc1a6e77f229f99

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=sha256:5b438a765d011b9fbf160240c7d1f93746a97b77f5b6eef7350fc754b5847f8d

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=sha256:423a1a40d5575c88dc7999a1f9d391999cbd5b8532978079ddc5fc9e42138cba

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=sha256:1be9e6ef65c44e1c43bd110db0ce73f178057aa82b89498339e39f0c83a4eb80

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=sha256:80930130e95f2948513870171586a1553217b5b67f4306b301716fd0ac484b8b

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=sha256:4de960964f6192b2d6234fbfa09ef35deb609b2c384b14f47cc9b37a8ad1bc9a

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=sha256:01436f8646a48e1f8e76371e2f34489c8cd5eff298b2ebdde13849a72026a3c4

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:eb22e75aa1a9076c360940a392fe42212afd58b812eb946cfe82232ddae1180a

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:dfe62ca735953b38b1c33539eb12a3404f9f78f3eb671e4ae068aec2b27244de

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:e869feae935d217eea5e9179696e4bc2a1f6f647787403051f29551312afb29d

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:ec2650ffc8baefb3c8053e85bbe3fcf175d172a50d96b85f16226a21c56cfa54

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:07c1c1a40ccd4f895bcd6542b4dd738fb36b9fd025318b14202dee5c8d7dc950

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:25bca5644ac0216e3b977cdba58852d2f0dd4805976387c1acd8dc42392f9958

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:4c22fdbe775fedb077819167d2356de10fd4852e33a2e33e2cf035fea53d55ec

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=sha256:646821227a11de5efc307de37a57f189ad5f445496886e8e3f8a8020f15f2ef3

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=sha256:06ea6d7863ec27495c9d76bad74e47e53019b334eff433a7b9a49e6a948488f9

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:d6b5b8f6697cc7eae073b6c96e48161b17dbda7d0cae55f4c2160c582f42bca7

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=sha256:9541122b9c15fa1fcf16a51da82d53b12271cf8193b16ec493870ffef79c0670

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=sha256:5fe4fff3c1a5fde970c63a0343a26c356185d36dbe2e4eae37458d7e54974356

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:29575b5ddd42f48da2900cdf2ed79b80707ddb95b9f1a88a9d1269ac31e46471

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=sha256:624b122ee22683b6d7c68373ae18e4e611227d109a687447bfad38d076a1adcc

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:d86a1b950aa8c81cb58a199e01b08e2af095d40b38f9e67814c76061f2435a9d

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:cd2409e81874407bee5bd1d98b6f6225aa43c732fb84346a22645c4e4de0effd

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=sha256:219ac456e643b281bea5f7975bdc70079423c7b1dd6c57d23cd90484c4d29c06

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:fc3c5f9b9f013dd194aec273c37eb9a8bcf32e0366a499a106003fb3db2ca91f

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:e1eef767e31bf3543f26772f52e2b5839f50c5c2e6ba0a34dd99be70479c2699

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:6352394318b320a86bd2d2ae357e10b54652b09c7e63ad0fa258401cdb93a7ee

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=sha256:7ddd89db20062755c59a60b59217c041a1c76a31742200a932852023f05c0e38

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=sha256:6670a36a87e1af598ef95cf183e14caff21ce042450481794b117ae99d82297c

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:46389fa7382a7f8690ce673e721af9ec86a896be8ee34444fdffa4fc40691d22

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:11f90974d8bb84d6f2016cac6de1fe34cc1da8cc6848b42a9b125454b1bc12d4

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:a5b3c72309fb73c1c17534c4a4617fbaad0a56c3de79ae07350522a3caa25862

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=sha256:2781757b4d7ff9ba5f717e39d2a66ccc2e4930066b19f4c7e61797011f7b9acd

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=sha256:83d95055f1977abd5204020023f9f2e7e4650ec7939d80ee69a652398d9f2062

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:de8557c2e3b3322e8db2ca84b5ae847e8a729f2ac97ff8e11ca2e235bce74237

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=sha256:7a13031e956a66ded2182ed8d5534aadefd586af81a5bf105c253ef101d780e2

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:11997291561ef4940f6945f1536402f7f5e7cc805c6198ff3b90aa70fc42e3f0

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=sha256:22cbd345a956be3bce3499c78d6a256618f954d68dfbc896bb63e10df5717cca

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=sha256:163b005bd22d45689f0a95afd8e66f8bdcdf862750d5a0f9647272343d2fbea0

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=sha256:1f1a24f171fca4bd32c331bc9b023479d1def8194379f177705a7fcff00674cc

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:cf09c2f033835c09d642bb8301a43df06e664e4be24830a948fe8c630267afa8

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:2f0cd99a2c468667a0897e353e0041055df8a305f1dcd051b983ca7d8812b29f

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:202304b2ef45016e6e26574003caab5da9fb1bf333f7b47840d8cb73ee22af10

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:04d51c325325d5b41c4787c6bb6a03bda35e12ec3feff87ee8083a25ae279159

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T15:06:31.471481Z digest=sha256:9ce18a3def12c9ece2b4565eaf20a747c289db6f6d9c906311c14286fcdbea2d

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=sha256:9c507e794a757bfe47f5af69e359fccd1f68f63831e553521b580f12e8511185

Pith citing papers

No inbound Pith citation observations are available.