Paper Citation Record · LEDGER

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory

As of 16 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2506.15176.

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

pith.paper-citation-record.v1
2506.15176 v2

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:44:50.214867Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

30 of 30 outbound references displayed

verified exact2
verified fuzzy22
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a514cee0-f191-4de1-b69a-9a9612a8f2dd · outbound

This paper cites Modeling interference for the coexistence of 6G networks and passive sensing systems,.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Modeling interference for the coexistence of 6G networks and passive sensing systems,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.635218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 99ab2b98-f61c-4ef6-b719-bb79291016ab · outbound

This paper cites Making cell-free massive MIMO competitive with mmse processing and centralized implementation,.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Making cell-free massive MIMO competitive with mmse processing and centralized implementation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.624502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e32c6e35-35d8-472b-b3ea-38c709d1ad35 · outbound

This paper cites Adaptive coding and channel shaping through reconfigurable intelligent surfaces: An information-theoretic analysis,.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Adaptive coding and channel shaping through reconfigurable intelligent surfaces: An information-theoretic analysis,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.612481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f5efd0cb-ec15-475a-a412-4cc6b4d3e3a3 · outbound

This paper cites Joint Communication and Sensing for 6G -- A Cross-Layer Perspective.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Joint Communication and Sensing for 6G -- A Cross-Layer Perspective

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:44:50.385785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bf166ff8-9b5d-4e4d-b069-639e0d038ef0 · outbound

This paper cites Adaptive and flexible model-based AI for deep receivers in dynamic channels,.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Adaptive and flexible model-based AI for deep receivers in dynamic channels,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.601265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 81a12350-e6cf-4153-b974-69325871a23e · outbound

This paper cites Design of a Standard-Compliant Real-Time Neural Receiver for 5G NR.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Design of a Standard-Compliant Real-Time Neural Receiver for 5G NR

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T19:44:50.133534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b263fe3-9b54-451b-9233-f9b7993f5bf4 · outbound

This paper cites Simeone,Machine Learning for Engineers.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Simeone,Machine Learning for Engineers

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.589737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b8af59c8-bea2-493c-990a-ce3e3850310a · outbound

This paper cites Learning with limited samples: Meta-learning and applications to communication systems,.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Learning with limited samples: Meta-learning and applications to communication systems,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.579078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 68489d3a-02ff-4d57-b75c-7cd59ccf65a9 · outbound

This paper cites Online meta-learning for hybrid model-based deep receivers,.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Online meta-learning for hybrid model-based deep receivers,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.568080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 36e23e28-e534-49cb-bf3e-f8adf5062c38 · outbound

This paper cites Cell-free multi-user MIMO equalization via in-context learning,.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Cell-free multi-user MIMO equalization via in-context learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.555879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c2345ad8-c1fa-45ab-b614-ff8bbffc77e2 · outbound

This paper cites HyperNetworks.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory HyperNetworks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T19:44:50.151863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e6374d6-9376-4579-8d42-7e26b12ec810 · outbound

This paper cites Modular hypernetworks for scalable and adaptive deep MIMO receivers,.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Modular hypernetworks for scalable and adaptive deep MIMO receivers,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.545201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation cf5dfa2d-d4da-4688-b778-d62ed42b48f3 · outbound

This paper cites An Explanation of In-context Learning as Implicit Bayesian Inference.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory An Explanation of In-context Learning as Implicit Bayesian Inference

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T19:44:50.158889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f89b992f-2c10-4284-89c3-702fffaa5131 · outbound

This paper cites Can Mamba learn how to learn? A comparative study on in-context learning tasks,.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Can Mamba learn how to learn? A comparative study on in-context learning tasks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.534054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4e749c1c-2efc-44df-ab3c-07e38de55aa9 · outbound

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

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory What can transformers learn in-context? A case study of simple function classes,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.523814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 43538c69-2063-4f89-9bcc-1c199c0d000c · outbound

This paper cites An introduction to transformers,.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory An introduction to transformers,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T19:44:50.168413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bda0e36-5ce4-4bb3-b5f8-7dcf249ac8d6 · outbound

This paper cites Rethinking invariance in in-context learning,.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Rethinking invariance in in-context learning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.513958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c2d4ef47-5381-4a87-8638-395a774f06c2 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks,.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Model-agnostic meta-learning for fast adaptation of deep networks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.502613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 21b34f2a-458d-4e28-9455-10c43ef2aa58 · outbound

This paper cites Uplink spectral and energy efficiency of cell-free massive MIMO with optimal uniform quantization,.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Uplink spectral and energy efficiency of cell-free massive MIMO with optimal uniform quantization,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.491615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 21c67add-3c5f-48e9-979d-da1a0fca24a9 · outbound

This paper cites Massive MIMO networks: Spectral, energy, and hardware efficiency,.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Massive MIMO networks: Spectral, energy, and hardware efficiency,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.480859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0c07bdab-e370-48b2-a8ea-86f529c5307f · outbound

This paper cites Deep MIMO detection,.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Deep MIMO detection,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.466859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 95c28d59-8f4d-4660-94f2-d772982c3ac7 · outbound

This paper cites Linear transformers are secretly fast weight programmers,.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Linear transformers are secretly fast weight programmers,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.456248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 78350a9b-68d5-479a-8014-4698abc84443 · outbound

This paper cites Understanding Emergent In-Context Learning from a Kernel Regression Perspective.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Understanding Emergent In-Context Learning from a Kernel Regression Perspective

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T19:44:50.190323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86bea593-faab-41f6-8c16-10940d24a44e · outbound

This paper cites Transformers are provably optimal in-context estimators for wireless communications,.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Transformers are provably optimal in-context estimators for wireless communications,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.444342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dbfc4a3b-84db-4ade-a593-6aeea45514c0 · outbound

This paper cites In-context learning for MIMO equalization using transformer-based sequence models,.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory In-context learning for MIMO equalization using transformer-based sequence models,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.431840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f295ed33-f001-4fe9-8032-124e784c6bf6 · outbound

This paper cites In-Context Learned Equalization in Cell-Free Massive MIMO via State-Space Models.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory In-Context Learned Equalization in Cell-Free Massive MIMO via State-Space Models

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:44:50.262150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 00c23729-e0a1-44b1-a928-4ee3cd55bb5d · outbound

This paper cites Further advancements for E-UTRA physical layer aspects (release 9),.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Further advancements for E-UTRA physical layer aspects (release 9),

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.420294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 59ad1906-d8f2-42cf-b103-5d4e1fa11737 · outbound

This paper cites Post- training quantization for vision transformer,.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Post- training quantization for vision transformer,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.408103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6811283c-2f15-4aac-b54d-288c5090860e · outbound

This paper cites Neuromorphic in-context learning for energy-efficient MIMO symbol detection,.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Neuromorphic in-context learning for energy-efficient MIMO symbol detection,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:44:50.397357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b19ad8b3-343b-4480-bfcf-62fc9acabc9d · outbound

This paper cites Turbo-ICL: In-Context Learning-Based Turbo Equalization.

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Turbo-ICL: In-Context Learning-Based Turbo Equalization

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T19:44:50.214867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.