Paper Citation Record · LEDGER

Dynamical Behaviors of the Gradient Flows for In-Context Learning

As of 21 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2412.16683.

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

pith.paper-citation-record.v1
2412.16683 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T10:29:08.945338Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

24 of 24 outbound references displayed

verified exact0
verified fuzzy19
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 15e674d8-9f5c-4f80-a03c-88670f30f1cb · outbound

This paper cites Trans- formers learn to implement preconditioned gradient descent for in-context learning.

Dynamical Behaviors of the Gradient Flows for In-Context Learning Trans- formers learn to implement preconditioned gradient descent for in-context learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:29:09.213504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f530158b-c7ee-47ba-aceb-de293cec1201 · outbound

This paper cites What learning algorithm is in-context learning? investigations with linear models.

Dynamical Behaviors of the Gradient Flows for In-Context Learning What learning algorithm is in-context learning? investigations with linear models

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:29:09.203012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f1e003a8-90f9-4d9e-b087-77484a780fac · outbound

This paper cites Max-margin token selection in attention mechanism.

Dynamical Behaviors of the Gradient Flows for In-Context Learning Max-margin token selection in attention mechanism

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:29:09.191650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a33852e2-9dc4-459b-a7c2-c21601778cb1 · outbound

This paper cites Training dynamics of multi-head softmax attention for in-context learning: Emer- gence, convergence, and optimality.

Dynamical Behaviors of the Gradient Flows for In-Context Learning Training dynamics of multi-head softmax attention for in-context learning: Emer- gence, convergence, and optimality

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:29:09.180248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a1c52861-b25a-4c69-83a4-be706ae38d60 · outbound

This paper cites What can transformers learn in-context? a case study of simple function classes.

Dynamical Behaviors of the Gradient Flows for In-Context Learning What can transformers learn in-context? a case study of simple function classes

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:29:09.169865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d23fe40f-3442-4c01-8e43-3d629b6a561e · outbound

This paper cites Can looped transformers learn to implement multi- step gradient descent for in-context learning? InProceedings of Interna- tional Conference on Machine Learning (ICML), 2024.

Dynamical Behaviors of the Gradient Flows for In-Context Learning Can looped transformers learn to implement multi- step gradient descent for in-context learning? InProceedings of Interna- tional Conference on Machine Learning (ICML), 2024

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:29:09.159276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 12d6e7c0-92fa-44d6-a809-fb768a57900b · outbound

This paper cites Can a Transformer Represent a Kalman Filter?.

Dynamical Behaviors of the Gradient Flows for In-Context Learning Can a Transformer Represent a Kalman Filter?

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T10:29:08.889049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0b4e135-4e95-4121-a6b3-617901f31900 · outbound

This paper cites In-context convergence of transformers.

Dynamical Behaviors of the Gradient Flows for In-Context Learning In-context convergence of transformers

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:29:09.148137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c0c77310-202c-49c0-a402-5ba810ee0be9 · outbound

This paper cites On a formula for the product-moment coefficient of any order of a normal frequency distribution in any number of variables.

Dynamical Behaviors of the Gradient Flows for In-Context Learning On a formula for the product-moment coefficient of any order of a normal frequency distribution in any number of variables

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T10:29:08.896463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8c51a1e-141d-4efe-b1fd-6ccac766a62a · outbound

This paper cites Transformers learn nonlinear features in context: Nonconvex mean-field dynamics on the attention landscape.

Dynamical Behaviors of the Gradient Flows for In-Context Learning Transformers learn nonlinear features in context: Nonconvex mean-field dynamics on the attention landscape

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:29:09.131879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bd91ce87-353f-49e3-be28-11b2834cc85a · outbound

This paper cites Can language models learn from explanations in context?.

Dynamical Behaviors of the Gradient Flows for In-Context Learning Can language models learn from explanations in context?

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T10:29:08.903433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25215766-b489-48c2-9b4f-cce340d03c58 · outbound

This paper cites Fine-grained analysis of in-context linear estimation: Data, architecture, and beyond.

Dynamical Behaviors of the Gradient Flows for In-Context Learning Fine-grained analysis of in-context linear estimation: Data, architecture, and beyond

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:29:09.122442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f93db7ec-a125-45fb-b5ed-8500513ac5f5 · outbound

This paper cites One-layer transformer provably learns one-nearest neighbor in context.

Dynamical Behaviors of the Gradient Flows for In-Context Learning One-layer transformer provably learns one-nearest neighbor in context

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:29:09.112964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0f9ffda9-c458-40b4-be9d-ba07528ebac1 · outbound

This paper cites Transformers as decision makers: Provable in-context reinforcement learning via supervised pretraining.

Dynamical Behaviors of the Gradient Flows for In-Context Learning Transformers as decision makers: Provable in-context reinforcement learning via supervised pretraining

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:29:09.102441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5b4c4d89-7a8b-4833-a8dc-9139beae2a4c · outbound

This paper cites Almost sure convergence rates analysis and saddle avoidance of stochastic gradient methods.Journal of Machine Learning Research, 25(271):1–40, 2024.

Dynamical Behaviors of the Gradient Flows for In-Context Learning Almost sure convergence rates analysis and saddle avoidance of stochastic gradient methods.Journal of Machine Learning Research, 25(271):1–40, 2024

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:29:09.091858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0bbaae01-eeec-4590-9e2d-01df3308e211 · outbound

This paper cites Local to global: Learning dynamics and effect of initialization for transformers.

Dynamical Behaviors of the Gradient Flows for In-Context Learning Local to global: Learning dynamics and effect of initialization for transformers

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:29:09.081416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6c26b760-74f8-4a00-a074-62f97a24e84c · outbound

This paper cites Geometric theory of dynamical systems: an introduction.

Dynamical Behaviors of the Gradient Flows for In-Context Learning Geometric theory of dynamical systems: an introduction

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:29:09.070718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 344d6d9e-7c66-4063-a0ec-11ea643a59c2 · outbound

This paper cites Scan and snap: Understanding training dynamics and token composition in 1- layer transformer.

Dynamical Behaviors of the Gradient Flows for In-Context Learning Scan and snap: Understanding training dynamics and token composition in 1- layer transformer

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:29:09.059875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8ae944de-929e-46ea-ae2d-5f020c3778a0 · outbound

This paper cites JoMA: Demystifying multilayer transformers via joint dynamics of mlp and attention.

Dynamical Behaviors of the Gradient Flows for In-Context Learning JoMA: Demystifying multilayer transformers via joint dynamics of mlp and attention

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:29:09.049429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 88845b04-6836-472a-8f22-fbe1d785040c · outbound

This paper cites Transformers learn in-context by gradient descents.

Dynamical Behaviors of the Gradient Flows for In-Context Learning Transformers learn in-context by gradient descents

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:29:09.039328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1a960416-dcde-4ec7-81f1-3aa6aa0b7ed0 · outbound

This paper cites A Theoretical Understanding of Self-Correction through In-context Alignment.

Dynamical Behaviors of the Gradient Flows for In-Context Learning A Theoretical Understanding of Self-Correction through In-context Alignment

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T10:29:08.935947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cd25337-396b-4cf0-9f44-35e125a5b3b4 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Dynamical Behaviors of the Gradient Flows for In-Context Learning Chain-of-thought prompting elicits reasoning in large language models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:29:09.029616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c1bbfbcc-c122-4072-8067-61fa72e32e3e · outbound

This paper cites In-Context Learning with Representations: Contextual Generalization of Trained Transformers.

Dynamical Behaviors of the Gradient Flows for In-Context Learning In-Context Learning with Representations: Contextual Generalization of Trained Transformers

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T10:29:08.941633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08c21b49-d240-4781-8599-dd19b353057e · outbound

This paper cites dX m=1 wmx(m) q NX n=1 dX i=1 dX j=1 dX k=1 (wiUjk + Zkwiwj)x(i) n x(j) n x(k) q !# =E.

Dynamical Behaviors of the Gradient Flows for In-Context Learning dX m=1 wmx(m) q NX n=1 dX i=1 dX j=1 dX k=1 (wiUjk + Zkwiwj)x(i) n x(j) n x(k) q !# =E

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:29:09.016909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.