Paper Citation Record · LEDGER

LLMs Can Teach Themselves to Better Predict the Future

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2502.05253.

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

pith.paper-citation-record.v1
2502.05253 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:42:38.315838Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e80675f6-8f4c-4e8a-bd3f-5b246f79b6d7 · inbound

Prompt Engineering Large Language Models' Forecasting Capabilities cites this paper.

Prompt Engineering Large Language Models' Forecasting Capabilities LLMs Can Teach Themselves to Better Predict the Future

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T11:42:38.315838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b890b0b9-bdee-45fe-8b8f-a21e56b3cacc · inbound

Advancing Event Forecasting through Massive Training of Large Language Models: Challenges, Solutions, and Broader Impacts cites this paper.

Advancing Event Forecasting through Massive Training of Large Language Models: Challenges, Solutions, and Broader Impacts LLMs Can Teach Themselves to Better Predict the Future

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T14:22:31.315943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c7ddbb0-5645-4eea-a73c-510a6c9fb2bc · inbound

ClinQueryAgent: A Conversational Agent for Population Health Management cites this paper.

ClinQueryAgent: A Conversational Agent for Population Health Management LLMs Can Teach Themselves to Better Predict the Future

Reference 177

Resolution: verified exact; arxiv_id, observed 2026-05-21T01:33:55.595457Z

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.

Observation 0323c331-cb35-4e73-a3a4-caffdb86b8ec · inbound

StakeBench: Evaluating Language Understanding Grounded in Market Commitment cites this paper.

StakeBench: Evaluating Language Understanding Grounded in Market Commitment LLMs Can Teach Themselves to Better Predict the Future

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T21:43:58.617527Z

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.

Observation 2feff8c3-9c75-4290-829a-6eab0d18db06 · inbound

Verifiable Rewards for Calibrated Probabilistic Forecasting cites this paper.

Verifiable Rewards for Calibrated Probabilistic Forecasting LLMs Can Teach Themselves to Better Predict the Future

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T19:47:18.739986Z

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.

Observation e73e150f-594b-4f83-ad2e-ac9d6f02ec42 · inbound

Diverse Evidence, Better Forecasts: Multi-Agent Deliberation Under Information Asymmetry cites this paper.

Diverse Evidence, Better Forecasts: Multi-Agent Deliberation Under Information Asymmetry LLMs Can Teach Themselves to Better Predict the Future

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:48:32.592091Z

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.