Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Scaling Laws for Predicting Downstream Performance in LLMs
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2410.08527.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:58:30.992061Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T22:17:25.629075Z
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 c1797943-bf77-4b43-bc41-999019518ea6 · inbound
Next Token Perception Score: Analytical Assessment of your LLM Perception Skills cites this paper.
Next Token Perception Score: Analytical Assessment of your LLM Perception Skills Scaling Laws for Predicting Downstream Performance in LLMs
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:58:30.992061Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8542d1e3-e4cc-49f8-814b-9781fd41ba4f · inbound
AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs cites this paper.
AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs Scaling Laws for Predicting Downstream Performance in LLMs
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:08:27.623180Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 06d61465-6c18-4418-877b-64bf5ba8edb1 · inbound
Predictable Scale: Part II, Farseer: A Refined Scaling Law in Large Language Models cites this paper.
Predictable Scale: Part II, Farseer: A Refined Scaling Law in Large Language Models Scaling Laws for Predicting Downstream Performance in LLMs
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:21:29.286677Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ae4811b5-062f-4137-887c-5ec844d12f87 · inbound
Energy-Based Transformers are Scalable Learners and Thinkers cites this paper.
Energy-Based Transformers are Scalable Learners and Thinkers Scaling Laws for Predicting Downstream Performance in LLMs
Reference 105
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:42:37.034045Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 440f1416-05d1-4078-92af-ff3d249b8c13 · inbound
Rethinking Expert Trajectory Utilization in LLM Post-training for Mathematical Reasoning cites this paper.
Rethinking Expert Trajectory Utilization in LLM Post-training for Mathematical Reasoning Scaling Laws for Predicting Downstream Performance in LLMs
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T22:43:37.969742Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 28818bde-616b-4aaf-8dab-a741557291f2 · inbound
Prescriptive Scaling Reveals the Evolution of Language Model Capabilities cites this paper.
Prescriptive Scaling Reveals the Evolution of Language Model Capabilities Scaling Laws for Predicting Downstream Performance in LLMs
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T22:58:12.973879Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 828ccb55-f230-4fac-830c-6e578779135f · inbound
Scaling Laws for Cross-Encoder Reranking cites this paper.
Scaling Laws for Cross-Encoder Reranking Scaling Laws for Predicting Downstream Performance in LLMs
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T16:10:09.158186Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 391719ac-5a10-4d7e-9b99-10e27ed69511 · inbound
Early Data Exposure Improves Robustness to Subsequent Fine-Tuning cites this paper.
Early Data Exposure Improves Robustness to Subsequent Fine-Tuning Scaling Laws for Predicting Downstream Performance in LLMs
Reference 5
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-14T20:47:58.630302Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 91c3a74d-58a6-4860-9948-cf57ed64d58e · inbound
Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling cites this paper.
Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling Scaling Laws for Predicting Downstream Performance in LLMs
Reference 245
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-15T03:08:59.480331Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a7a374ed-0c14-446c-8d42-76a0b062dbe8 · inbound
Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale cites this paper.
Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale Scaling Laws for Predicting Downstream Performance in LLMs
Reference 66
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T22:17:25.630641Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.