Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Improving Multi-Step Reasoning Abilities of Large Language Models with Direct Advantage Policy Optimization
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2412.18279.
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 2026-08-04T19:28:58.286475Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-10T22:20:49.395329Z
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 13723c2e-df2d-4094-92f3-2598b454f8e0 · inbound
Harnessing Uncertainty: Entropy-Modulated Policy Gradients for Long-Horizon LLM Agents cites this paper.
Harnessing Uncertainty: Entropy-Modulated Policy Gradients for Long-Horizon LLM Agents Improving Multi-Step Reasoning Abilities of Large Language Models with Direct Advantage Policy Optimization
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T19:28:58.286475Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e8b1cd13-a158-4d77-8c29-25dfaa2c2a60 · inbound
Application-Driven Pedagogical Knowledge Optimization of Open-Source LLMs via Reinforcement Learning and Supervised Fine-Tuning cites this paper.
Application-Driven Pedagogical Knowledge Optimization of Open-Source LLMs via Reinforcement Learning and Supervised Fine-Tuning Improving Multi-Step Reasoning Abilities of Large Language Models with Direct Advantage Policy Optimization
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T22:20:49.396977Z
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.