Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness
As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2408.04585.
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 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T20:11:46.579271Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-09T05:45:21.132538Z
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 65b51124-8f91-4896-8d7f-7fb4fab057eb · inbound
LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning cites this paper.
LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T20:11:46.579271Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation efba8abc-1835-43b2-98ae-40638c77ee81 · inbound
An Improved Dung Beetle Optimizer for Random Forest Optimization cites this paper.
An Improved Dung Beetle Optimizer for Random Forest Optimization Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T14:00:42.536282Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5e1db72c-b35f-4db1-9f5c-c84c2d198cc2 · inbound
Can Large Language Models Effectively Process and Execute Financial Trading Instructions? cites this paper.
Can Large Language Models Effectively Process and Execute Financial Trading Instructions? Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T21:16:05.063352Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation df377489-4c31-4608-b229-65b32e940be8 · inbound
Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs cites this paper.
Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T12:24:55.240309Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1d28142d-ec45-43ec-b8a5-136c0179501b · inbound
Enhanced Recommendation Combining Collaborative Filtering and Large Language Models cites this paper.
Enhanced Recommendation Combining Collaborative Filtering and Large Language Models Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T04:36:40.677165Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7cfcefac-a811-453a-be83-0ddbb5749e4a · inbound
A Survey: Towards Privacy and Security in Mobile Large Language Models cites this paper.
A Survey: Towards Privacy and Security in Mobile Large Language Models Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness
Reference 65
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T11:39:19.681486Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e57c502c-8f33-4016-ae50-d20f7d7e309e · inbound
Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution cites this paper.
Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-05-09T05:45:21.135565Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.