Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
When Search Engine Services meet Large Language Models: Visions and Challenges
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2407.00128.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:27:50.005082Z
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 2026-08-08T06:32:00.761636+00:00.
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.