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Paper Citation Record · LEDGER

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation

As of 8 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2506.11105.

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

pith.paper-citation-record.v1
2506.11105 v3

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:57:41.323817Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

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

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T15:40:25.838442Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T10:06:00.741881Z

Reference resolution

25 of 25 outbound references displayed

  • verified exact0
  • verified fuzzy13
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 84e0255c-4ca2-4a3d-8e7c-0e9ad05e850e · outbound

This paper cites an unresolved cited work.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation Unresolved cited work

Reference 1

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Source-reported events for the cited work

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

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Observation 05f03a05-2f44-448e-9809-6cfccdd8b2ae · outbound

This paper cites A question-entailment approach to question answering.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation A question-entailment approach to question answering

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f09ff05b-569d-46e5-a537-b8bd914e0fef · outbound

This paper cites Deploying quantized llms with onnx runtime.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation Deploying quantized llms with onnx runtime

Reference 3

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verified fuzzy
raw_fallback, observed 2026-08-07T05:57:41.657852Z

Source-reported events for the cited work

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

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Observation 2e734c49-5944-4f40-a131-d8f25f35064f · outbound

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

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 62fecb93-8efb-410f-9a81-c521590072e5 · outbound

This paper cites Natural language processing models reveal neural dynamics of human conversation.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation Natural language processing models reveal neural dynamics of human conversation

Reference 5

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verified fuzzy
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Source-reported events for the cited work

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

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Observation ee585287-0bc1-4913-bc7f-611596487e92 · outbound

This paper cites Survey of the state of the art in natural language generation: Core tasks, appli- cations and evaluation.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation Survey of the state of the art in natural language generation: Core tasks, appli- cations and evaluation

Reference 6

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verified fuzzy
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Source-reported events for the cited work

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

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Observation b00d5b82-70a8-4223-a7ca-09e58fe0f521 · outbound

This paper cites The Llama 3 Herd of Models.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation The Llama 3 Herd of Models

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ccdee7e0-425a-4450-bed7-d00ea8d62d8b · outbound

This paper cites EfficientLLM: Scalable Pruning-Aware Pretraining for Architecture-Agnostic Edge Language Models.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation EfficientLLM: Scalable Pruning-Aware Pretraining for Architecture-Agnostic Edge Language Models

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 25dc4475-ac81-4c93-abb3-2c1447392e6a · outbound

This paper cites Lighteval: A lightweight framework for llm evaluation, 2023.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation Lighteval: A lightweight framework for llm evaluation, 2023

Reference 9

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Source-reported events for the cited work

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

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Observation 424ffe57-0339-4011-ac16-af33a36aecc2 · outbound

This paper cites MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 3ca68548-dd02-471a-9791-9d1b3f5fb26c · outbound

This paper cites Principles of internal medicine.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation Principles of internal medicine

Reference 11

Resolution
verified fuzzy
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Source-reported events for the cited work

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

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Observation c13e541f-9be3-4ab5-b397-dae0dae71767 · outbound

This paper cites What disease does this patient have? a large-scale open domain question answering dataset from medical exams.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation What disease does this patient have? a large-scale open domain question answering dataset from medical exams

Reference 12

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no resolver link, observed 2026-08-07T05:57:41.285784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 92c75fcc-e8ed-429c-b157-dd9e7f08bcf8 · outbound

This paper cites Pubmedqa: A dataset for biomedical research question answering.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation Pubmedqa: A dataset for biomedical research question answering

Reference 13

Resolution
verified fuzzy
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Source-reported events for the cited work

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

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Observation 936f8e10-2d70-48d1-9e1a-e46d0b7a9e31 · outbound

This paper cites A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges

Reference 14

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no resolver link, observed 2026-08-07T05:57:41.291471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 9effa606-12ff-43e2-b147-7508822512d3 · outbound

This paper cites All-in-one tuning and struc- tural pruning for domain-specific llms, 2024.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation All-in-one tuning and struc- tural pruning for domain-specific llms, 2024

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:57:41.593262Z

Source-reported events for the cited work

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

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Observation 290ee935-a942-499a-9984-6f2392debecd · outbound

This paper cites BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining

Reference 16

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unresolved
no resolver link, observed 2026-08-07T05:57:41.297335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 4548ad26-746e-40a6-96cf-64f8dec1aacf · outbound

This paper cites Llm- pruner: On the structural pruning of large language mod- els.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation Llm- pruner: On the structural pruning of large language mod- els

Reference 17

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Source-reported events for the cited work

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

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Observation f42e327a-eaf5-4ec6-9c2d-965505f04910 · outbound

This paper cites Multi-rag: A multimodal retrieval- augmented generation system for adaptive video under- standing.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation Multi-rag: A multimodal retrieval- augmented generation system for adaptive video under- standing

Reference 18

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no resolver link, observed 2026-08-07T05:57:41.303258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 6592285e-bc83-4efb-8e4c-11f4f642121c · outbound

This paper cites Post-training quantization of llms with nvidia nemo and nvidia tensorrt model optimizer.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation Post-training quantization of llms with nvidia nemo and nvidia tensorrt model optimizer

Reference 19

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verified fuzzy
raw_fallback, observed 2026-08-07T05:57:41.574027Z

Source-reported events for the cited work

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

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Observation d2a32b51-26db-4980-a9b6-a96ac21083eb · outbound

This paper cites Chatsim: Underwater simulation with natural language prompting.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation Chatsim: Underwater simulation with natural language prompting

Reference 20

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raw_fallback, observed 2026-08-07T05:57:41.564519Z

Source-reported events for the cited work

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

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Observation 2606eb29-3569-482b-8b71-0a0322f7e10f · outbound

This paper cites Large language models encode clinical knowledge.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation Large language models encode clinical knowledge

Reference 21

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raw_fallback, observed 2026-08-07T05:57:41.554361Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T05:57:41.311768Z digest=sha256:c356babd0404a78226f0cc2f9371a580833606f0f06dc643926fb8354ff10501

Observation b2c2255b-aad4-46f9-a341-5fd2022866f7 · outbound

This paper cites Toward expert-level medical question answering with large language models.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation Toward expert-level medical question answering with large language models

Reference 22

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raw_fallback, observed 2026-08-07T05:57:41.544143Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T05:57:41.314763Z digest=sha256:0b4ce7439d93648dfdf2c5dbf07a9ae2a5bebb366067b31c267f9a2fc55dd9ab

Observation ecfea65b-7074-4af8-afe5-bf089cebb983 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation Gemma: Open Models Based on Gemini Research and Technology

Reference 23

Resolution
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no resolver link, observed 2026-08-07T05:57:41.317447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5ed274c0-c79e-4d6a-8a69-55a310c6f575 · outbound

This paper cites ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 24

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no resolver link, observed 2026-08-07T05:57:41.320727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 1aa7ded4-6645-4fe0-bcb5-bcc9d40c039b · outbound

This paper cites Natural language reasoning, a survey.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation Natural language reasoning, a survey

Reference 25

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raw_fallback, observed 2026-08-07T05:57:41.533903Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T05:57:41.323817Z digest=sha256:0fd1f6e641e543a342aa2d01347480200ff679aa7c80b2a9e44621301f35661a

Pith citing papers

Observation 1d07b726-ab40-4815-91fd-b34d5f9f8f4b · inbound

SatReg: Regression-based Neural Architecture Search for Lightweight Satellite Image Segmentation cites this paper.

SatReg: Regression-based Neural Architecture Search for Lightweight Satellite Image Segmentation Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation

Reference 18

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arxiv_id, observed 2026-05-11T10:06:00.757349Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-10T15:40:25.838442Z digest=sha256:2cd942d81bce6d2947fa5ad93ef375c4e8d247859efc9b58601743945282569b