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Paper Citation Record · LEDGER

Iterate to Accelerate: A Unified Framework for Iterative Reasoning and Feedback Convergence

As of 17 August 2026, this Paper Citation Record lists 6 of 6 outbound references and 0 inbound Pith citation observations for arXiv:2502.03787.

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

pith.paper-citation-record.v1
2502.03787 v1

Coverage vector

measured 6 of 6 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T00:52:14.197771Z

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

6 of 6 outbound references displayed

  • verified exact2
  • verified fuzzy1
  • unresolved3
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bf869ad0-a2b3-4d08-a970-3cb6cabe026d · outbound

This paper cites Mirror descent and nonlinear projected subgradient methods for convex optimization.

Iterate to Accelerate: A Unified Framework for Iterative Reasoning and Feedback Convergence Mirror descent and nonlinear projected subgradient methods for convex optimization

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-09T00:52:14.172417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T00:52:14.172417Z digest=sha256:4f1e9e7055d73f2621351b9a05716ec81e935181bcdfeba68d41f532cfdb7d53

Observation 4854e5db-2fda-4b73-8e08-f32e01157fa1 · outbound

This paper cites The Power of Depth for Feedforward Neural Networks.

Iterate to Accelerate: A Unified Framework for Iterative Reasoning and Feedback Convergence The Power of Depth for Feedforward Neural Networks

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-09T00:52:14.177600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T00:52:14.177600Z digest=sha256:f23c0a862b81360925ddbc3082fd9ddc2ceafc11631dead18c4d415b352410d7

Observation ddd7fc60-926a-4024-ae36-a7e25be86da6 · outbound

This paper cites Contextual Feedback Loops: Amplifying Deep Reasoning with Iterative Top-Down Feedback.

Iterate to Accelerate: A Unified Framework for Iterative Reasoning and Feedback Convergence Contextual Feedback Loops: Amplifying Deep Reasoning with Iterative Top-Down Feedback

Reference 3

Resolution
verified exact
local_arxiv, observed 2026-08-09T00:52:14.280057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-09T00:52:14.182478Z digest=sha256:19f8b6396d0cbd292aced44395d1418cd7c0d19a0b7145fa4df852bcfce59628

Observation 2f04596f-9058-4e05-9a1b-0d3f84d28d57 · outbound

This paper cites A method for solving the convex programming problem with convergence rate o(1/k2).

Iterate to Accelerate: A Unified Framework for Iterative Reasoning and Feedback Convergence A method for solving the convex programming problem with convergence rate o(1/k2)

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T00:52:14.302822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-09T00:52:14.187649Z digest=sha256:0376a7ada09455d87388137d0a1ed700dbbc2509dee3df3107a6162d7957e4d2

Observation 4b2127da-97ec-4295-bbfe-d755e5174519 · outbound

This paper cites Benefits of depth in neural networks.

Iterate to Accelerate: A Unified Framework for Iterative Reasoning and Feedback Convergence Benefits of depth in neural networks

Reference 5

Resolution
verified exact
local_arxiv, observed 2026-08-09T00:52:14.260841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-09T00:52:14.192888Z digest=sha256:a86d4a3d91af63f12c04401a54ecaa720512bf726c79c5d13ebdad632a817987

Observation cf56fcad-8814-43af-9c4e-f6c285cc7354 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

Iterate to Accelerate: A Unified Framework for Iterative Reasoning and Feedback Convergence Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-09T00:52:14.197771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T00:52:14.197771Z digest=sha256:e65cb276265299caf1da290d65798801a2cc2f655e492049fe8f48df227c32ae

Pith citing papers

No inbound Pith citation observations are available.