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Paper Citation Record · LEDGER

A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 21 inbound Pith citation observations for arXiv:2411.03350.

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

pith.paper-citation-record.v1
2411.03350 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 21 of 21 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:25:10.306623Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

17
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Reference 186

Resolution
verified exact
arxiv_id, observed 2026-05-19T11:08:27.712589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-19T11:08:27.472508Z digest=sha256:8d4d195b873e6ee516765a52d2e6657d5f1895b5510af242114f7d13f32f7f52

Observation a3008e07-0a3c-4090-bb95-9321f52ad1f8 · inbound

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices cites this paper.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness

Reference 58

Resolution
verified exact
arxiv_id, observed 2026-05-23T01:05:16.135665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-23T01:03:26.037233Z digest=sha256:c6e8e74b0b957f45e9e6a502708e6fbb5779d3bc49e5f69790c15bf123cff63f

Reference 76

Resolution
verified exact
arxiv_id, observed 2026-05-16T11:55:51.034841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-16T11:55:50.897500Z digest=sha256:e7cd3c22d9e7e40d0db99bcfde583043ba5b1665f1aefb7ebcc34d155839efdb

Observation 5fc159c6-a4f4-462a-8797-6b4e79b3f834 · inbound

Enhancing Reasoning Capabilities in SLMs with Reward Guided Dataset Distillation cites this paper.

Enhancing Reasoning Capabilities in SLMs with Reward Guided Dataset Distillation A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-06T22:45:03.646532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T22:45:03.646532Z digest=sha256:9bb5fcf953b5f71a5bea45c8e9e44f5e6c88a69c49eb281a4576f45149ea848e

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T18:51:26.988505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:51:26.988505Z digest=sha256:0afd11a0d6a9920551cacec8ff487b72a11df09d709dc463d0c4abc05819cec7

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T10:32:40.842264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T10:32:40.842264Z digest=sha256:29b2ea631a196af26bdb414ea5bfc761c9c4c00ccaeaeecb3df36327b5269a5a

Observation 55ab1f11-415e-48c9-b4f1-643748c2ce20 · inbound

Unifying Mixture of Experts and Multi-Head Latent Attention for Efficient Language Models cites this paper.

Unifying Mixture of Experts and Multi-Head Latent Attention for Efficient Language Models A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-06T05:51:39.974567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T05:51:39.974567Z digest=sha256:5ee5348b1eb5c913e9be3b298cb5d824871fd4015fa4606ff9771eab964835ba

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-05T15:21:32.640611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:21:32.640611Z digest=sha256:e4384046813a3e76b49ceabd7acde37d31f889a4a0315c533870b90a6677cb18

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-05T11:10:01.491388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T11:10:01.491388Z digest=sha256:bb702386cbc9029617432c141930729c87b68270aa7ef37cfd5b3164ae60163e

Observation 323f0912-bb5f-4454-b7ae-7d7337b098e0 · inbound

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial cites this paper.

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness

Reference 243

Resolution
unresolved
no resolver link, observed 2026-08-05T04:50:32.434461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T04:50:32.434461Z digest=sha256:67251c2ec1aa37f95365e8cf44facb87da9ae212abc5da264ab278b1a3082d44

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-18T18:42:48.164971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=arxiv_source observed=2026-05-18T18:42:29.744124Z digest=sha256:19c2efa667f7ced3cd4fbb46a41e7a5516f3f4efeacba13950e0070ce90dce8d

Observation d9b1f5f0-7467-46eb-9432-acd14e36dde9 · inbound

Mitigating Attention Localization in Small Scale: Self-Attention Refinement via One-step Belief Propagation cites this paper.

Mitigating Attention Localization in Small Scale: Self-Attention Refinement via One-step Belief Propagation A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-04T22:30:12.162721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T22:30:12.162721Z digest=sha256:29451ba48d69b118a300acb1e0964c945e27ed20af7aed91504698ecda223273

Observation 1f2962c7-ab84-4b05-b9ed-d2241fc44d40 · inbound

Automatic Failure Attribution and Critical Step Prediction Method for Multi-Agent Systems Based on Causal Inference cites this paper.

Automatic Failure Attribution and Critical Step Prediction Method for Multi-Agent Systems Based on Causal Inference A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness

Reference 2836

Resolution
unresolved
no resolver link, observed 2026-08-04T20:21:21.834038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T20:21:21.834038Z digest=sha256:347449ea72b8c5390fac29702d177d124cf60a0b1787a01d8f3c62719dbeadb7

Reference 27

Resolution
metadata mismatch
arxiv_id, observed 2026-05-09T04:15:09.581136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-07T09:47:52.767727Z digest=sha256:055bcb81ba33b1c42f62179a5151fee5b2d4541240338dbd57792831e3957798

Observation 061472c3-4fa2-4650-81ba-e98224fbdf8b · inbound

OrganicHAR: Towards Activity Discovery in Organic Settings for Privacy Preserving Sensors Using Efficient Video Analysis cites this paper.

OrganicHAR: Towards Activity Discovery in Organic Settings for Privacy Preserving Sensors Using Efficient Video Analysis A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness

Reference 70

Resolution
verified exact
arxiv_id, observed 2026-05-20T08:38:10.272618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-20T08:37:44.181899Z digest=sha256:a498fb0518c214ea9f1c22da089d538c25073a06fc67b53c59f0bdd9f79cf346

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-07-03T21:58:59.108975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-06-26T23:56:34.841978Z digest=sha256:ef83b70f070ce2f453bf7da75d794344d50d205abd3470f09c492ebd2dff0ae9

Observation 8516d15b-6f93-4a51-ac5e-26dafe50a3fc · inbound

Does Mixture-of-Experts Actually Help Inference on Consumer and Edge Hardware? An Empirical Study cites this paper.

Does Mixture-of-Experts Actually Help Inference on Consumer and Edge Hardware? An Empirical Study A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-07-04T07:59:39.599016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-06-26T12:30:55.628115Z digest=sha256:be843cdda7860d711bb534ae9430845ea61793b46522205fa4c79db1d87a5399

Observation abdae12e-2d53-4692-8246-628615cbc9fc · inbound

Does Mixture-of-Experts Actually Help Inference on Consumer and Edge Hardware? An Empirical Study cites this paper.

Does Mixture-of-Experts Actually Help Inference on Consumer and Edge Hardware? An Empirical Study A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness

Reference 25

Resolution
unresolved
no resolver link, observed 2026-07-12T13:05:17.273287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T13:05:17.273287Z digest=sha256:aaa391f526caa4c6c8e1187c8f62ec0a2839f6b6e4b1504961ebf57a3bbf0029

Observation 9cb2cf28-77c4-4d2c-a46b-093db0262b0f · inbound

PPE-Bench: A Benchmark for Evaluating MLLM Unlearning under Private-Public Entanglement cites this paper.

PPE-Bench: A Benchmark for Evaluating MLLM Unlearning under Private-Public Entanglement A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness

Reference 45

Resolution
unresolved
no resolver link, observed 2026-07-12T06:18:42.939955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T06:18:42.939955Z digest=sha256:96b4399f9bcee2422f67ca816cc12fbbf1987031532ce68da0f88670b8d517bd

Observation 6cbfb33f-8f8f-4e6e-a7b7-7f38a648ddb1 · inbound

Punching Above Their Weight: Classification-Head Fine-Tuning of Tiny Language Models (TLMs) for Verifiable Multiple-Choice Tasks cites this paper.

Punching Above Their Weight: Classification-Head Fine-Tuning of Tiny Language Models (TLMs) for Verifiable Multiple-Choice Tasks A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness

Reference 12

Resolution
unresolved
no resolver link, observed 2026-07-11T23:52:16.201593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T23:52:16.201593Z digest=sha256:6108417ad12f8051c226322584367001b176c2d237e63b848975d3a3d3e5bd57

Observation cdb6da5a-a3c2-4b90-9f2b-e93652809269 · inbound

Energy- and Memory-Efficient PEFT Methods for Personalized On-Device SLMs on Consumer GPUs cites this paper.

Energy- and Memory-Efficient PEFT Methods for Personalized On-Device SLMs on Consumer GPUs A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-06T23:25:10.306623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:25:10.306623Z digest=sha256:beaf2af557fa5474e57416b46703ec33c53ef287475e903efb4eaa329f5a6ac6