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Paper Citation Record · LEDGER

FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2310.03214.

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

pith.paper-citation-record.v1
2310.03214 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:30:49.176813Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T23:08:35.630964Z

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 dbe81b54-a6bf-4101-b6cd-58dbd2021756 · inbound

TrustLLM: Trustworthiness in Large Language Models cites this paper.

TrustLLM: Trustworthiness in Large Language Models FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Reference 223

Resolution
metadata mismatch
arxiv_id, observed 2026-05-18T11:17:08.635117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-18T11:17:08.108565Z digest=sha256:47765b05a2d6c4d653c3c16b2bc87f04d268bc4dd10e2950532d29fa8982ce9f

Observation 7aa91253-c41c-47e9-92fb-065d31917b89 · inbound

Hallucination is Inevitable: An Innate Limitation of Large Language Models cites this paper.

Hallucination is Inevitable: An Innate Limitation of Large Language Models FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Reference 72

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T20:38:43.499435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-15T20:38:43.411206Z digest=sha256:af85f1d205294f491e0ccc07ef318f357faf05fd52716c88bbaca512cf7d5761

Observation 412a9de1-07f2-4107-824c-10a164340c2d · inbound

Retrieval-Augmented Generation for Natural Language Processing: A Survey cites this paper.

Retrieval-Augmented Generation for Natural Language Processing: A Survey FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Reference 166

Resolution
verified exact
arxiv_id, observed 2026-05-23T23:08:35.633074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-23T23:06:41.081461Z digest=sha256:f6f3739b5b01c5d8c3d6ca402889e21591f5aba7a4fb597e9267ea1ef9c63105

Observation 43273d06-de7a-4164-bbcc-43fe555e2475 · inbound

VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents cites this paper.

VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Reference 23

Resolution
metadata mismatch
arxiv_id, observed 2026-05-16T15:37:25.835641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-16T15:37:25.781240Z digest=sha256:1b2f2d0b96d6b0509aa125650dc241b9d2c6c16964887ee2858b5359daca7281

Observation 1437c2c2-9997-49b4-80fa-c6512fe92af5 · inbound

Measuring short-form factuality in large language models cites this paper.

Measuring short-form factuality in large language models FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-15T06:45:50.275719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-15T06:45:50.219157Z digest=sha256:622fa1d149eb692323407f14250276ce2d430b16fd055e3b1265b80eaa67d686

Observation 7bfb5957-8f4e-486c-abb1-0d0b8ada7775 · inbound

ToolRL: Reward is All Tool Learning Needs cites this paper.

ToolRL: Reward is All Tool Learning Needs FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Reference 35

Resolution
metadata mismatch
arxiv_id, observed 2026-05-14T00:26:48.547728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-14T00:26:48.291431Z digest=sha256:64bf4beb5d8931e083206e0b0158df4260fc80728d99b20809c870a773ed1c14

Observation b3c868cd-e093-4271-b5e2-06a9605037a7 · inbound

BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese cites this paper.

BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Reference 16

Resolution
metadata mismatch
arxiv_id, observed 2026-05-17T22:04:49.962763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-17T22:04:49.915916Z digest=sha256:04b00f14bad5c31c68a58c232703186f70b4723ee4381944c38843603b71556c

Observation e5579007-51a0-4ab3-a5cb-7df265016de8 · inbound

MedBrowseComp: Benchmarking Medical Deep Research and Computer Use cites this paper.

MedBrowseComp: Benchmarking Medical Deep Research and Computer Use FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T15:30:49.176813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:30:49.176813Z digest=sha256:f326820547e1f38dd8decb7c5ee36f0d160651c459bb2349b240a473596f19c9

Observation bbbbe329-a461-41ea-a917-3a64ed83bf5a · inbound

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation cites this paper.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T15:19:18.807656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:19:18.807656Z digest=sha256:4b5b689567eed67fb0f75cf0cd02fc81897ca48b1aebe0703375fa9ec241be88

Observation 09ae33ef-8dc0-4363-8dba-2c58248638cc · inbound

MaskSearch: A Universal Pre-Training Framework to Enhance Agentic Search Capability cites this paper.

MaskSearch: A Universal Pre-Training Framework to Enhance Agentic Search Capability FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-07T13:59:19.592921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:59:19.592921Z digest=sha256:bfaac6627b5628afa47e3a28678296b5a7ddd640447dfd5c5a2719137fbfc450

Observation 08972d1d-e610-4d58-b77d-cb567c6db29b · inbound

Measuring Faithfulness and Abstention: An Automated Pipeline for Evaluating LLM-Generated 3-ply Case-Based Legal Arguments cites this paper.

Measuring Faithfulness and Abstention: An Automated Pipeline for Evaluating LLM-Generated 3-ply Case-Based Legal Arguments FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:20.932076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:03:20.932076Z digest=sha256:05df2fd590be443ea3b68d877669f7a3a5dbaad18bdc2a6dc2af674c776e6484

Observation eaffdb4d-1101-41ab-997a-768283925386 · inbound

AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions cites this paper.

AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-07T05:01:08.242929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:01:08.242929Z digest=sha256:139be1b40697c8c4354e6af7c87b29c27a259a762f6122768db7f924c7876ae4

Observation d8c20d1b-95af-4037-8062-b16bf24b2286 · inbound

The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMs cites this paper.

The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMs FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Reference 163

Resolution
unresolved
no resolver link, observed 2026-08-07T10:17:27.093581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:17:27.093581Z digest=sha256:ec8395395d26fb5782bea13b964757646735c6c1d2ac0c65ba68172303541008

Observation 08b7a7ea-8170-4a21-9949-99ec7938a3a1 · inbound

A Survey on Proactive Defense Strategies Against Misinformation in Large Language Models cites this paper.

A Survey on Proactive Defense Strategies Against Misinformation in Large Language Models FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-06T20:03:51.269716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:03:51.269716Z digest=sha256:96664bfafa293bc7a43abaaed35c6a5530b5ccae5cd4816f61cc7711d285de34

Observation e0acf173-bc65-4a14-b507-924764220ed0 · inbound

Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook cites this paper.

Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Reference 51

Resolution
unresolved
no resolver link, observed 2026-07-14T20:48:19.312615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T20:48:19.312615Z digest=sha256:23e0b7b3337dcee689c0e82333ac878ff3940e7c674fbd1da88d2942110621ad

Observation b76d7bb1-a4de-4601-a895-788053473b84 · inbound

Illocutionary Explanation Planning for Source-Faithful Explanations in Retrieval-Augmented Language Models cites this paper.

Illocutionary Explanation Planning for Source-Faithful Explanations in Retrieval-Augmented Language Models FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Reference 51

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T10:39:56.811393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-15T10:39:11.866574Z digest=sha256:3cfe41b83d1b8cb38712d1d4dd8a35120b33f3d4c8a0362990925d1f88feb3dc

Observation 02f2d08a-56d3-4610-a009-2bc0aef997f7 · inbound

Teaching AI Through Benchmark Construction: QuestBench as a Course-Based Practice for Accountable Knowledge Work cites this paper.

Teaching AI Through Benchmark Construction: QuestBench as a Course-Based Practice for Accountable Knowledge Work FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Reference 33

Resolution
metadata mismatch
arxiv_id, observed 2026-05-21T04:13:56.745949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-21T04:10:37.172329Z digest=sha256:933601188125c8331f1cbf105c725dda8a82eaaeb87e4dcb70661cbda563970b

Observation 25d91cd0-1526-44ee-a172-89520d665e65 · inbound

Teaching AI Through Benchmark Construction: QuestBench as a Course-Based Practice for Accountable Knowledge Work cites this paper.

Teaching AI Through Benchmark Construction: QuestBench as a Course-Based Practice for Accountable Knowledge Work FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Reference 33

Resolution
metadata mismatch
arxiv_id, observed 2026-05-22T09:44:46.007950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-22T09:42:30.305592Z digest=sha256:7b0dd114230d88cf50a63467ed8ed9a2f96bf8dc17c4dd73b8d7d4a4e8899cc0

Observation a67c6d19-9723-4e83-81bb-0e70107bbc44 · inbound

When Memory Lies: An Empirical Study of Spatial Memory Staleness in VLM Agents cites this paper.

When Memory Lies: An Empirical Study of Spatial Memory Staleness in VLM Agents FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-06T21:49:20.538232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:49:20.538232Z digest=sha256:dcc222020ef9f6693e2e39d5e08c06be20eeccac6b6f0b34db927adb8672fbae