Paper Citation Record · LEDGER

When Search Engine Services meet Large Language Models: Visions and Challenges

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2407.00128.

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

pith.paper-citation-record.v1
2407.00128 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:28:32.493532Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T16:58:11.958552Z

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 02f17f69-2774-4a77-a0e2-bb2b4a120c92 · inbound

BatchLLM: Optimizing Large Batched LLM Inference with Global Prefix Sharing and Throughput-oriented Token Batching cites this paper.

BatchLLM: Optimizing Large Batched LLM Inference with Global Prefix Sharing and Throughput-oriented Token Batching When Search Engine Services meet Large Language Models: Visions and Challenges

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-23T16:58:11.961735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cc342e36-f668-4509-99bf-8cafcda1b9c3 · inbound

PaSa: An LLM Agent for Comprehensive Academic Paper Search cites this paper.

PaSa: An LLM Agent for Comprehensive Academic Paper Search When Search Engine Services meet Large Language Models: Visions and Challenges

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T19:28:32.493532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e473578f-6892-4132-860e-fda9dfc47eca · inbound

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning cites this paper.

Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning When Search Engine Services meet Large Language Models: Visions and Challenges

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T14:27:50.005082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93df9836-5edd-4514-b5d5-c6b9338f4aa6 · inbound

Reinforcement Fine-Tuning for Reasoning towards Multi-Step Multi-Source Search in Large Language Models cites this paper.

Reinforcement Fine-Tuning for Reasoning towards Multi-Step Multi-Source Search in Large Language Models When Search Engine Services meet Large Language Models: Visions and Challenges

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T05:20:04.658459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9686e40c-03cc-4790-8961-f2f059f63c0b · inbound

SwiftSpec: Ultra-Low Latency LLM Decoding by Scaling Asynchronous Speculative Decoding cites this paper.

SwiftSpec: Ultra-Low Latency LLM Decoding by Scaling Asynchronous Speculative Decoding When Search Engine Services meet Large Language Models: Visions and Challenges

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T04:15:44.977729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5ca7c4f-3319-459b-b0ad-2f5dae35a1e0 · inbound

Taming the Chaos: Coordinated Autoscaling for Heterogeneous and Disaggregated LLM Inference cites this paper.

Taming the Chaos: Coordinated Autoscaling for Heterogeneous and Disaggregated LLM Inference When Search Engine Services meet Large Language Models: Visions and Challenges

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T15:45:40.814751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.