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Paper Citation Record · LEDGER

Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2011.11517.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2011.11517 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:27:14.871555Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-11T00:07:41.814270Z

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 83d5a837-bfa6-42b4-afb4-64e1c7438229 · inbound

Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference cites this paper.

Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-11T12:27:14.871555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:27:14.871555Z digest=sha256:c30313506a10aa2ee58a5f2a2385ea62f010ca969f37a4ec456b6f6759c1d994

Observation d16f0cd9-ff75-4b99-8477-55a4570d5b5c · inbound

Position: Theory of Mind Benchmarks are Broken for Large Language Models cites this paper.

Position: Theory of Mind Benchmarks are Broken for Large Language Models Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games

Reference 61

Resolution
verified exact
local_arxiv, observed 2026-08-11T00:07:41.818567Z

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.

source=arxiv_source observed=2026-08-11T00:07:41.325493Z digest=sha256:c54ea57b3ff3f31cdeab173301bf55aee8872e4a10f7f3e55524bbdcdead94ca