Paper Citation Record · LEDGER

Distilling Large Language Models for Network Active Queue Management

As of 13 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2501.16734.

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

pith.paper-citation-record.v1
2501.16734 v3

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T11:09:43.633367Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

36 of 36 outbound references displayed

verified exact1
verified fuzzy27
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6d17c06c-bef4-4c3a-9c3c-f92f7dff1f61 · outbound

This paper cites Random early detection gateways for congestion avoidance,.

Distilling Large Language Models for Network Active Queue Management Random early detection gateways for congestion avoidance,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:44.177098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5ad5e2b8-75f9-4376-ac30-962c29b2cf6c · outbound

This paper cites Controlled Delay Active Queue Management,.

Distilling Large Language Models for Network Active Queue Management Controlled Delay Active Queue Management,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T11:09:43.512966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d02c0c6-80fb-4e44-8e67-1455fb405adf · outbound

This paper cites Proportional Integral Controller Enhanced (PIE): A Lightweight Control Scheme to Address the Bufferbloat Problem,.

Distilling Large Language Models for Network Active Queue Management Proportional Integral Controller Enhanced (PIE): A Lightweight Control Scheme to Address the Bufferbloat Problem,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:44.161573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 202eefae-b038-41b3-b1a0-c3063e10a16f · outbound

This paper cites Low Latency, Low Loss, and Scalable Throughput (L4S) Internet Service: Architecture,.

Distilling Large Language Models for Network Active Queue Management Low Latency, Low Loss, and Scalable Throughput (L4S) Internet Service: Architecture,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:44.152224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1e1c721d-80b8-4580-80de-146723e25de3 · outbound

This paper cites Active queue management in L4S with asynchronous advantage actor-critic: A FreeBSD networking stack perspective,.

Distilling Large Language Models for Network Active Queue Management Active queue management in L4S with asynchronous advantage actor-critic: A FreeBSD networking stack perspective,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:44.141867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 93a001f5-94d2-4c01-831a-c50f0195cae2 · outbound

This paper cites Learning to harness bandwidth with multipath congestion control and scheduling,.

Distilling Large Language Models for Network Active Queue Management Learning to harness bandwidth with multipath congestion control and scheduling,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:44.133148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8a2c2c51-4151-44c9-a5d5-64c487fd813c · outbound

This paper cites Fair and efficient distributed edge learning with hybrid multipath tcp,.

Distilling Large Language Models for Network Active Queue Management Fair and efficient distributed edge learning with hybrid multipath tcp,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:44.123758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation df6aaadb-9c04-45ce-bcf4-49776fc6abbc · outbound

This paper cites Intelligent active queue man- agement using explicit congestion notification,.

Distilling Large Language Models for Network Active Queue Management Intelligent active queue man- agement using explicit congestion notification,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:44.113244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ec36fd72-65eb-4bdc-bf7f-ebbabba30c8b · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Distilling Large Language Models for Network Active Queue Management Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T11:09:43.538395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ed95cf8-7d1b-4473-9d1f-1082f946f1bd · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Distilling Large Language Models for Network Active Queue Management DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T11:09:43.541877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47241210-f404-4f1b-9ded-e758fe25a176 · outbound

This paper cites Netllm: Adapting large language models for networking,.

Distilling Large Language Models for Network Active Queue Management Netllm: Adapting large language models for networking,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:44.102875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f19b5d3d-2736-4e86-97e5-68367d174dc7 · outbound

This paper cites On large language model based joint source channel coding for semantic communication,.

Distilling Large Language Models for Network Active Queue Management On large language model based joint source channel coding for semantic communication,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:44.093966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3a24581e-d87a-4595-bdbc-c6b08c97e06d · outbound

This paper cites Deakin RF-sensing: Experiments on correlated knowledge distillation for monitoring human postures with radios,.

Distilling Large Language Models for Network Active Queue Management Deakin RF-sensing: Experiments on correlated knowledge distillation for monitoring human postures with radios,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:44.083354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7898cafd-d65d-4c18-ae71-5e94cca0c527 · outbound

This paper cites Combating bufferbloat in multi- bottleneck networks: Theory and algorithms,.

Distilling Large Language Models for Network Active Queue Management Combating bufferbloat in multi- bottleneck networks: Theory and algorithms,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:44.072656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e1224d76-0ef6-4e39-aedd-6c1250984c33 · outbound

This paper cites Bufferbloat: Dark buffers in the internet: Networks without effective aqm may again be vulnerable to congestion collapse.

Distilling Large Language Models for Network Active Queue Management Bufferbloat: Dark buffers in the internet: Networks without effective aqm may again be vulnerable to congestion collapse

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:44.061197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7ae7ee49-f6c1-4df5-93b3-6a7d9d81a718 · outbound

This paper cites Controlling queue delay,.

Distilling Large Language Models for Network Active Queue Management Controlling queue delay,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:44.050262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 46b23ba1-9819-414a-b143-e2ac6f7c1caf · outbound

This paper cites Pfed: A prediction-based fair active queue management algorithm,.

Distilling Large Language Models for Network Active Queue Management Pfed: A prediction-based fair active queue management algorithm,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:44.039389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2b332b7c-03e0-4fd4-80cb-3dc085d5e038 · outbound

This paper cites Active queue management based on q-learning traffic predictor,.

Distilling Large Language Models for Network Active Queue Management Active queue management based on q-learning traffic predictor,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:44.028952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d228ab49-5715-4845-88df-98754bd35829 · outbound

This paper cites Deep reinforcement learning based active queue management for iot networks,.

Distilling Large Language Models for Network Active Queue Management Deep reinforcement learning based active queue management for iot networks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:44.017769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6a09e1ec-ba61-4156-8f3f-3c1898572124 · outbound

This paper cites Attention is all you need,.

Distilling Large Language Models for Network Active Queue Management Attention is all you need,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T11:09:43.575554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9997065d-f4a6-4382-9037-e145f2b21b70 · outbound

This paper cites Low- latency and resource-efficient service function chaining orchestration in network function virtualization,.

Distilling Large Language Models for Network Active Queue Management Low- latency and resource-efficient service function chaining orchestration in network function virtualization,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:43.999401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1faf6def-af86-4dd1-a803-5b96761a781a · outbound

This paper cites Cost-efficient service function chain orchestration for low-latency applications in nfv networks,.

Distilling Large Language Models for Network Active Queue Management Cost-efficient service function chain orchestration for low-latency applications in nfv networks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:43.987465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2ad5da89-34d3-4c5c-8871-6c1df44029d2 · outbound

This paper cites Large language model simulator for cold-start recommendation,.

Distilling Large Language Models for Network Active Queue Management Large language model simulator for cold-start recommendation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:43.976194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a91bd8d2-0d43-46c7-a466-1f70e38a179c · outbound

This paper cites Artificial general intelligence (agi)-native wireless systems: A journey beyond 6g,.

Distilling Large Language Models for Network Active Queue Management Artificial general intelligence (agi)-native wireless systems: A journey beyond 6g,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T11:09:43.590123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2516ba58-29f0-4da3-bfdc-754bc4dcc015 · outbound

This paper cites GenAINet: Enabling Wireless Collective Intelligence via Knowledge Transfer and Reasoning.

Distilling Large Language Models for Network Active Queue Management GenAINet: Enabling Wireless Collective Intelligence via Knowledge Transfer and Reasoning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T11:09:43.593429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5536c1c6-1c21-4fbf-ad8a-4db6884601d3 · outbound

This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90% chatgpt quality,.

Distilling Large Language Models for Network Active Queue Management Vicuna: An open-source chatbot impressing gpt-4 with 90% chatgpt quality,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:43.961174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9e69e09f-eeef-4d9b-8424-861930017843 · outbound

This paper cites SafeCOMM: A Study on Safety Degradation in Fine-Tuned Telecom Large Language Models.

Distilling Large Language Models for Network Active Queue Management SafeCOMM: A Study on Safety Degradation in Fine-Tuned Telecom Large Language Models

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-08-11T03:24:29.182979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 786345bd-d768-42d4-8e8e-d6b96093f340 · outbound

This paper cites Tele-LLMs: A Series of Specialized Large Language Models for Telecommunications.

Distilling Large Language Models for Network Active Queue Management Tele-LLMs: A Series of Specialized Large Language Models for Telecommunications

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T11:09:43.602749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc107cd4-341a-42ca-b9fc-bea6548d0309 · outbound

This paper cites TSpec-LLM: An Open-source Dataset for LLM Understanding of 3GPP Specifications.

Distilling Large Language Models for Network Active Queue Management TSpec-LLM: An Open-source Dataset for LLM Understanding of 3GPP Specifications

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T11:09:43.605949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb101a27-6421-4da6-a301-57260241e2b1 · outbound

This paper cites Safety-tuned llamas: Lessons from improving the safety of large language models that follow instructions,.

Distilling Large Language Models for Network Active Queue Management Safety-tuned llamas: Lessons from improving the safety of large language models that follow instructions,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:43.951852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9d46da8a-1dbc-4e16-a72b-5851fc389a90 · outbound

This paper cites Safe lora: the silver lining of reducing safety risks when fine-tuning large language models,.

Distilling Large Language Models for Network Active Queue Management Safe lora: the silver lining of reducing safety risks when fine-tuning large language models,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:43.941606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8946731b-9029-489c-82bd-14e2b3246b9e · outbound

This paper cites Safe- merge: Preserving safety alignment in fine-tuned large language models via selective layer-wise model merging,.

Distilling Large Language Models for Network Active Queue Management Safe- merge: Preserving safety alignment in fine-tuned large language models via selective layer-wise model merging,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:43.931354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 35da7fe8-30e3-4e19-b66c-f34a268c02a8 · outbound

This paper cites The Addition of Explicit Congestion Notification (ECN) to IP,.

Distilling Large Language Models for Network Active Queue Management The Addition of Explicit Congestion Notification (ECN) to IP,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:43.920370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bfc71b6f-9f9e-4f78-b76e-59f6c4c34ff8 · outbound

This paper cites The Benefits of Using Explicit Congestion Notification (ECN),.

Distilling Large Language Models for Network Active Queue Management The Benefits of Using Explicit Congestion Notification (ECN),

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:43.909732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4cc375d1-9dfe-43a1-80e1-6f480180f5fd · outbound

This paper cites Chatgpt,.

Distilling Large Language Models for Network Active Queue Management Chatgpt,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:43.898515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8d12f58b-44de-4a83-98e8-ebdeddbb898e · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer,.

Distilling Large Language Models for Network Active Queue Management Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:09:43.888254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.