Paper Citation Record · LEDGER

Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2402.19442.

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

pith.paper-citation-record.v1
2402.19442 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 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 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:13:25.061703Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T23:42:49.980406Z

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 b66905c9-9f50-4e79-91d7-c9bb567967f5 · inbound

Transformers versus the EM Algorithm in Multi-class Clustering cites this paper.

Transformers versus the EM Algorithm in Multi-class Clustering Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-08T17:13:25.061703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a597f3a7-8913-43cb-bfa4-167279020fab · inbound

Learning Compositional Functions with Transformers from Easy-to-Hard Data cites this paper.

Learning Compositional Functions with Transformers from Easy-to-Hard Data Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:34.110434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0a96b15-0d08-4d63-bc77-d4a28a323726 · inbound

Transformers Meet In-Context Learning: A Universal Approximation Theory cites this paper.

Transformers Meet In-Context Learning: A Universal Approximation Theory Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T10:33:35.674370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c02b589-bf51-4c11-a0ad-75d7b76b6c0f · inbound

A Theoretical Study of (Hyper) Self-Attention through the Lens of Interactions: Representation, Training, Generalization cites this paper.

A Theoretical Study of (Hyper) Self-Attention through the Lens of Interactions: Representation, Training, Generalization Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T06:10:17.068890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9de3bf8a-670e-4ffd-8d13-05f02c5f9db0 · inbound

Federated In-Context Learning: Iterative Refinement for Improved Answer Quality cites this paper.

Federated In-Context Learning: Iterative Refinement for Improved Answer Quality Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:38.662534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b67edb81-f98b-49eb-beb4-e52eea0919f0 · inbound

Towards Theoretical Understanding of Transformer Test-Time Computing: Investigation on In-Context Linear Regression cites this paper.

Towards Theoretical Understanding of Transformer Test-Time Computing: Investigation on In-Context Linear Regression Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T22:10:17.436794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c25d9971-bf1e-4d93-a035-a836d7cc835e · inbound

Train Once, Reuse Everywhere: Generalizable Implicit In-Context Learning by Routing Attention cites this paper.

Train Once, Reuse Everywhere: Generalizable Implicit In-Context Learning by Routing Attention Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:22.033438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18964baf-208c-49d5-a14c-5e94b62590f0 · inbound

Unveiling the Mechanisms of Multi-Hop Reasoning in Transformers via Identity Bridge cites this paper.

Unveiling the Mechanisms of Multi-Hop Reasoning in Transformers via Identity Bridge Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:13.762801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99309c63-be3c-48db-9006-f03ff2e86828 · inbound

How Can Mamba Learn In Context with Outliers and Generalize Provably? cites this paper.

How Can Mamba Learn In Context with Outliers and Generalize Provably? Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-04T13:29:29.816756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4e110b9-28e3-49d1-876c-6439086ea6e7 · inbound

Transformers with RL or SFT Provably Learn Sparse Boolean Functions, But Differently cites this paper.

Transformers with RL or SFT Provably Learn Sparse Boolean Functions, But Differently Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T20:57:09.961879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b06d115-a9c7-4bdd-a19c-cb2f78b48da0 · inbound

Specialization of softmax attention heads: insights from the high-dimensional single-location model cites this paper.

Specialization of softmax attention heads: insights from the high-dimensional single-location model Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T19:04:54.999021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83271fa1-e835-4869-b7bb-96e36e77611f · inbound

Learning to Adapt: In-Context Learning Beyond Stationarity cites this paper.

Learning to Adapt: In-Context Learning Beyond Stationarity Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:45:59.438462Z

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 a096d3db-07f8-4329-b978-d42eb840abcb · inbound

Agentic Transformers Provably Learn to Search via Reinforcement Learning cites this paper.

Agentic Transformers Provably Learn to Search via Reinforcement Learning Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality

Reference 45

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T23:42:49.981802Z

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.