Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2310.15746.
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 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T11:23:35.180569Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T18:48:20.497474Z
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 e15215bb-2977-40be-9adc-111a73415858 · inbound
AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning cites this paper.
AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T18:48:20.500200Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 53543478-6a96-4a27-bf01-ae481adfb4ab · inbound
Error-driven Data-efficient Large Multimodal Model Tuning cites this paper.
Error-driven Data-efficient Large Multimodal Model Tuning Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation
Reference 78
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T11:18:35.830611Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 408d6e04-f94f-4611-96b2-40f5fc579868 · inbound
WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents cites this paper.
WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:23:35.180569Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.