Pith. sign in

Paper Citation Record · LEDGER

TrafficLLM: Enhancing Large Language Models for Network Traffic Analysis with Generic Traffic Representation

As of 24 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2504.04222.

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

pith.paper-citation-record.v1
2504.04222 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:24:23.983494Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T19:06:03.388572Z

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 6a18cecd-cf04-4f94-9784-b7b047fcc944 · inbound

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving cites this paper.

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving TrafficLLM: Enhancing Large Language Models for Network Traffic Analysis with Generic Traffic Representation

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T04:24:23.983494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:24:23.983494Z digest=sha256:9658ef1db9e1baf3cc1ea33b12eae8e6e1d876f26d615e8a64fa7a7bf8ffb3e9

Observation 02fcd471-13f2-4a59-84ec-187b4914ae78 · inbound

X-PRINT:Platform-Agnostic and Scalable Fine-Grained Encrypted Traffic Fingerprinting cites this paper.

X-PRINT:Platform-Agnostic and Scalable Fine-Grained Encrypted Traffic Fingerprinting TrafficLLM: Enhancing Large Language Models for Network Traffic Analysis with Generic Traffic Representation

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-05T13:25:04.964093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:25:04.964093Z digest=sha256:627303c92040a95476dba932bc0457bb23c087ffb1b51daa88d43a2c6a09fceb

Observation 273d34d4-9916-45c7-865b-bedc4f413319 · inbound

Identifying the Threshold Chain Length for Stress Overshoot in Ring-Linear Polymer Blends under Uniaxial Elongation: The Role of Multiple Threading cites this paper.

Identifying the Threshold Chain Length for Stress Overshoot in Ring-Linear Polymer Blends under Uniaxial Elongation: The Role of Multiple Threading TrafficLLM: Enhancing Large Language Models for Network Traffic Analysis with Generic Traffic Representation

Reference 9

Resolution
unresolved
no resolver link, observed 2026-07-13T18:14:19.481313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T18:14:19.481313Z digest=sha256:60c5d0e77b22734dc85e4c6ad276edc34053d84f1c3c5099f79f9dd558f1a11f

Observation 3d78c717-3522-458a-8d3a-f628d7729f03 · inbound

Identifying the Threshold Chain Length for Stress Overshoot in Ring-Linear Polymer Blends under Uniaxial Elongation: The Role of Multiple Threading cites this paper.

Identifying the Threshold Chain Length for Stress Overshoot in Ring-Linear Polymer Blends under Uniaxial Elongation: The Role of Multiple Threading TrafficLLM: Enhancing Large Language Models for Network Traffic Analysis with Generic Traffic Representation

Reference 9

Resolution
unresolved
no resolver link, observed 2026-07-14T20:09:08.658041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T20:09:08.658041Z digest=sha256:11fdeea28113a977f90dadab2d54f5f0b8d3b8646a6337c0bade3f7b5860c4d9

Observation 14398b33-f9be-4623-8bf6-cfeeaa82d1eb · inbound

Multimodal Reasoning with LLM for Encrypted Traffic Interpretation: A Benchmark cites this paper.

Multimodal Reasoning with LLM for Encrypted Traffic Interpretation: A Benchmark TrafficLLM: Enhancing Large Language Models for Network Traffic Analysis with Generic Traffic Representation

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-11T05:40:57.844453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-10T18:00:59.294760Z digest=sha256:2c4053d7a393c9d45b9741bbbe667b96660262f13ccc50ec501abbdec97a116c

Observation 8a96ce14-ab7c-46c3-8ccc-66a9ba688653 · inbound

ReasonLight: A Multimodal Foundation Model-Enhanced Reinforcement Learning Framework for Zero-Shot Traffic Signal Control cites this paper.

ReasonLight: A Multimodal Foundation Model-Enhanced Reinforcement Learning Framework for Zero-Shot Traffic Signal Control TrafficLLM: Enhancing Large Language Models for Network Traffic Analysis with Generic Traffic Representation

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-06-29T07:33:13.318755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-06-29T07:32:50.210837Z digest=sha256:58bae7cefabce5f131a657be24f16ffdbe0242392d120f9c82dd537804e9ac5c

Observation f445cc56-85ea-4aae-a64f-aed16e4f69dc · inbound

TraceCodec: A Compiler-Backed Neural Codec for Stateful Multi-Flow Network Traffic Traces cites this paper.

TraceCodec: A Compiler-Backed Neural Codec for Stateful Multi-Flow Network Traffic Traces TrafficLLM: Enhancing Large Language Models for Network Traffic Analysis with Generic Traffic Representation

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-07-01T19:06:03.390074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-06-29T00:34:34.662891Z digest=sha256:80438c4f7e3a38bdaafede1a2eca9c6f027056c588859453ed72e3e961624f0b