Paper Citation Record · LEDGER

SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2405.14917.

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

pith.paper-citation-record.v1
2405.14917 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 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 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T05:50:24.116703Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T14:47:14.550840Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c8ad4f97-a219-4e50-85c6-26da242e5ca7 · inbound

SpinQuant: LLM quantization with learned rotations cites this paper.

SpinQuant: LLM quantization with learned rotations SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T15:52:34.681870Z

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.

Observation a5b0c445-bbcc-49bd-8740-135a95a0e5ba · inbound

When Attention Sink Emerges in Language Models: An Empirical View cites this paper.

When Attention Sink Emerges in Language Models: An Empirical View SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T17:41:03.864404Z

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.

Observation 44b496b4-7bff-4ffc-b972-096384e6ba83 · inbound

Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse cites this paper.

Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T08:47:37.244434Z

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.

Observation 1be75a2a-b084-498b-b14c-c3677008b99d · inbound

Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse cites this paper.

Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T05:50:24.116703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6b897c3-96d4-4032-9b38-16f18492860a · 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 SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:46:04.349355Z

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.

Observation b02aded9-14cc-4af4-9139-ce33f01cd5d6 · inbound

LoopQ: Quantization for Recursive Transformers cites this paper.

LoopQ: Quantization for Recursive Transformers SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:43:50.878112Z

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.

Observation 23219458-e9af-447e-865a-908508a291b1 · inbound

Prune, Update and Trim: Robust Structured Pruning for Large Language Models cites this paper.

Prune, Update and Trim: Robust Structured Pruning for Large Language Models SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T12:03:15.009495Z

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.

Observation cd31067a-99fd-478b-9687-5dd39e5324e3 · inbound

Prune, Update and Trim: Robust Structured Pruning for Large Language Models cites this paper.

Prune, Update and Trim: Robust Structured Pruning for Large Language Models SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-14T18:53:13.849418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0d4b035-06df-4422-82e6-e6e36d2dc540 · 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 SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T15:05:47.979529Z

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.

Observation bfdcc426-cdfa-46c9-89f2-09671f4c1f80 · inbound

GRINQH: Graded Input-based Quantization Hierarchy for Efficient LLM Generation cites this paper.

GRINQH: Graded Input-based Quantization Hierarchy for Efficient LLM Generation SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:39:45.482385Z

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.

Observation 269cae30-78e8-46b2-963d-9db092a59491 · inbound

Variable Bit-width Quantization: Learning Per-Group Precision for "Bigger-but-Smaller" Language Models cites this paper.

Variable Bit-width Quantization: Learning Per-Group Precision for "Bigger-but-Smaller" Language Models SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-12T06:20:07.112455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b45ab01-72f9-4088-aad4-0a33137a20d7 · inbound

Voltron: Enabling Elastic Multi-Device Execution of LLM Inference for Empowered Edge Intelligence cites this paper.

Voltron: Enabling Elastic Multi-Device Execution of LLM Inference for Empowered Edge Intelligence SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:16:34.329846Z

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.

Observation cba161e3-e671-429f-af05-bfbfa9d31a20 · inbound

KronQ: LLM Quantization via Kronecker-Factored Hessian cites this paper.

KronQ: LLM Quantization via Kronecker-Factored Hessian SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models

Reference 14

Resolution: metadata mismatch; local_arxiv, observed 2026-07-10T14:47:14.551994Z

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.

Observation 86bb6418-b7e6-465e-829d-57b831e2914a · inbound

The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs cites this paper.

The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-07-10T02:26:43.084935Z

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.

Observation 895bdeea-257d-4270-b8bf-ebcaa3480d3e · inbound

The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs cites this paper.

The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models

Reference 84

Resolution: verified exact; local_arxiv, observed 2026-07-10T02:26:42.739286Z

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.

Observation 68738cf3-5029-4e44-8d41-2500a18391f5 · inbound

PolyQ: Codesigning End-to-End Quantization Framework for Scalable Edge CPU LLM Inference cites this paper.

PolyQ: Codesigning End-to-End Quantization Framework for Scalable Edge CPU LLM Inference SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T01:39:05.466106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.