Paper Citation Record · LEDGER

Provable Low-Frequency Bias of In-Context Learning of Representations

As of 15 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 1 inbound Pith citation observation for arXiv:2507.13540.

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

pith.paper-citation-record.v1
2507.13540 v2

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:37:03.643683Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-08T17:00:37.250246Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T17:51:09.334284Z

Reference resolution

19 of 19 outbound references displayed

verified exact1
verified fuzzy4
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7e260cc1-35e0-42b0-8615-44b098b2e65d · outbound

This paper cites A.1 E VENTS IN A RANDOM WALK SEQUENCE Theorem 3 (Theorem 1 in (Fan et al., 2021)).

Provable Low-Frequency Bias of In-Context Learning of Representations A.1 E VENTS IN A RANDOM WALK SEQUENCE Theorem 3 (Theorem 1 in (Fan et al., 2021))

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:37:04.524602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation deca8069-da0c-437e-813b-8acbc46c6ab6 · outbound

This paper cites Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers.

Provable Low-Frequency Bias of In-Context Learning of Representations Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T16:37:02.712441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c881234e-b7ab-4a2f-b6d5-038eaa931e9e · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

Provable Low-Frequency Bias of In-Context Learning of Representations Measuring Mathematical Problem Solving With the MATH Dataset

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T16:37:02.864036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0af5446-026e-47f2-8d34-93a54cd36b13 · outbound

This paper cites In-Context Convergence of Transformers.

Provable Low-Frequency Bias of In-Context Learning of Representations In-Context Convergence of Transformers

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T16:37:02.984306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f1c8bea-505c-460e-bcb0-050be785130b · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Provable Low-Frequency Bias of In-Context Learning of Representations Semi-Supervised Classification with Graph Convolutional Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T16:37:03.059773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbb59033-0b0e-47da-a17c-3453b7beacaf · outbound

This paper cites ICLR: In-Context Learning of Representations.

Provable Low-Frequency Bias of In-Context Learning of Representations ICLR: In-Context Learning of Representations

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T16:37:03.260977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9a24332-1d2f-4ed5-8a10-8797d88d55d0 · outbound

This paper cites Hopfield Networks is All You Need.

Provable Low-Frequency Bias of In-Context Learning of Representations Hopfield Networks is All You Need

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T16:37:03.328081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af382494-60fd-4af5-bd66-f65b4a65f50c · outbound

This paper cites Spectral and algebraic graph theory, incomplete draft, dated december 4, 2019,.

Provable Low-Frequency Bias of In-Context Learning of Representations Spectral and algebraic graph theory, incomplete draft, dated december 4, 2019,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:37:04.814915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e86347a4-52b2-4b0b-b888-38fbdd3e9914 · outbound

This paper cites How Transformers Get Rich: Approximation and Dynamics Analysis.

Provable Low-Frequency Bias of In-Context Learning of Representations How Transformers Get Rich: Approximation and Dynamics Analysis

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T16:37:03.512234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4f92c17-1f35-4b49-a87d-f33224c049d1 · outbound

This paper cites (γ1, γ2, Z , U ) and σ′ : Rd → Rd be a great mapping w.r.t.(γ′ 1, γ′ 2, Z , U ), then σ1 ◦ σ2 is a great mapping w.r.t.(γ1γ′ 1, γ2γ′ 2, Z , U ).

Provable Low-Frequency Bias of In-Context Learning of Representations (γ1, γ2, Z , U ) and σ′ : Rd → Rd be a great mapping w.r.t.(γ′ 1, γ′ 2, Z , U ), then σ1 ◦ σ2 is a great mapping w.r.t.(γ1γ′ 1, γ2γ′ 2, Z , U )

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:37:04.308245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 60e3553d-05d9-4500-b701-c28612b2ca6c · outbound

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

Provable Low-Frequency Bias of In-Context Learning of Representations Transformers Meet In-Context Learning: A Universal Approximation Theory

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-06T16:37:03.120800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 485b4f5b-17a1-461a-9d15-5c3c0df911df · outbound

This paper cites Asymptotic theory of in-context learning by linear attention.

Provable Low-Frequency Bias of In-Context Learning of Representations Asymptotic theory of in-context learning by linear attention

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-06T16:37:03.175444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37875f45-884d-404b-8888-193318e70b76 · outbound

This paper cites Recurrent self-attention dynamics: An energy-agnostic perspective from jacobians.

Provable Low-Frequency Bias of In-Context Learning of Representations Recurrent self-attention dynamics: An energy-agnostic perspective from jacobians

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-06T16:37:03.442170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d7268b3-ffa3-4de4-b18e-e101cd042960 · outbound

This paper cites Exploring the robustness of in-context learning with noisy labels.

Provable Low-Frequency Bias of In-Context Learning of Representations Exploring the robustness of in-context learning with noisy labels

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:37:04.901804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0c25e03d-0d9a-4a2b-83a1-9c53fa21a201 · outbound

This paper cites Hyper-SET: Designing Transformers via Hyperspherical Energy Minimization.

Provable Low-Frequency Bias of In-Context Learning of Representations Hyper-SET: Designing Transformers via Hyperspherical Energy Minimization

Reference 2021

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:37:04.045002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9c0ed3e4-7792-4070-80ae-b784f7715728 · outbound

This paper cites A mathematical perspective on Transformers.

Provable Low-Frequency Bias of In-Context Learning of Representations A mathematical perspective on Transformers

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T16:37:02.796390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd57a1c7-9e2e-4e4f-8c4c-f6967efd72af · outbound

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

Provable Low-Frequency Bias of In-Context Learning of Representations What learning algorithm is in-context learning? Investigations with linear models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T16:37:02.536151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25d3e5df-f91d-472d-9a9d-bef5904c0dbd · outbound

This paper cites Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?.

Provable Low-Frequency Bias of In-Context Learning of Representations Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T16:37:03.216653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 200f9cda-2031-48cd-8e3e-5712925c66a9 · outbound

This paper cites Language models are few-shot learners.

Provable Low-Frequency Bias of In-Context Learning of Representations Language models are few-shot learners

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T16:37:02.587659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 7dec9c26-646a-4dcf-a1b1-03ab48c189ef · inbound

Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learning cites this paper.

Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learning Provable Low-Frequency Bias of In-Context Learning of Representations

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:51:09.336932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.