Paper Citation Record · LEDGER

RESOLVE: Relational Reasoning with Symbolic and Object-Level Features Using Vector Symbolic Processing

As of 14 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 1 inbound Pith citation observation for arXiv:2411.08290.

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

pith.paper-citation-record.v1
2411.08290 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:52:17.512237Z

measured 15 of 15 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 2026-08-04T08:12:18.277158Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

14 of 14 outbound references displayed

verified exact3
verified fuzzy6
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 87754812-2d33-4d23-af34-ade556487308 · outbound

This paper cites Relational Concept Bottleneck Models.

RESOLVE: Relational Reasoning with Symbolic and Object-Level Features Using Vector Symbolic Processing Relational Concept Bottleneck Models

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-12T21:52:17.651140Z

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 64eedf76-e234-4f2e-9593-2cbc1dc2ad91 · outbound

This paper cites On Neural Architecture Inductive Biases for Relational Tasks.

RESOLVE: Relational Reasoning with Symbolic and Object-Level Features Using Vector Symbolic Processing On Neural Architecture Inductive Biases for Relational Tasks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T21:52:17.471398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd824e3d-5738-4218-8c08-8bfc251d0713 · outbound

This paper cites Emergent Symbols through Binding in External Memory.

RESOLVE: Relational Reasoning with Symbolic and Object-Level Features Using Vector Symbolic Processing Emergent Symbols through Binding in External Memory

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T21:52:17.493633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30af392f-9c7c-471c-8e13-a4a94fe33a85 · outbound

This paper cites We use a batch size of 128 and train for 500 epochs.

RESOLVE: Relational Reasoning with Symbolic and Object-Level Features Using Vector Symbolic Processing We use a batch size of 128 and train for 500 epochs

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:52:17.663926Z

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 7400a4a0-c0c1-4084-b43f-6288261046d2 · outbound

This paper cites Positional symbols are used as the symbol assignment mechanism, which are learned parameters of the model.

RESOLVE: Relational Reasoning with Symbolic and Object-Level Features Using Vector Symbolic Processing Positional symbols are used as the symbol assignment mechanism, which are learned parameters of the model

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:52:17.677427Z

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 080de195-9b86-4434-a49c-769669cdd648 · outbound

This paper cites set" with probability 1/2 and a non-.

RESOLVE: Relational Reasoning with Symbolic and Object-Level Features Using Vector Symbolic Processing set" with probability 1/2 and a non-

Reference 1024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:52:17.706371Z

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 b6fdd57a-be39-4908-8458-eb6a544e2813 · outbound

This paper cites However, inthistask, theinputfeatures used as a sequence of objects are derived from the first convolutional layer of the pre-trainedCNN.

RESOLVE: Relational Reasoning with Symbolic and Object-Level Features Using Vector Symbolic Processing However, inthistask, theinputfeatures used as a sequence of objects are derived from the first convolutional layer of the pre-trainedCNN

Reference 1700

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:52:17.691993Z

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 6364591d-f18f-425b-ba64-3e4509f2e311 · outbound

This paper cites Same-different problems strain convolutional neural networks.

RESOLVE: Relational Reasoning with Symbolic and Object-Level Features Using Vector Symbolic Processing Same-different problems strain convolutional neural networks

Reference 1938

Resolution: verified exact; local_arxiv, observed 2026-08-12T21:52:17.578493Z

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 f6ba9f17-9fda-4d40-92f3-e63ef81f9fa8 · outbound

This paper cites Analysing Mathematical Reasoning Abilities of Neural Models.

RESOLVE: Relational Reasoning with Symbolic and Object-Level Features Using Vector Symbolic Processing Analysing Mathematical Reasoning Abilities of Neural Models

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-12T21:52:17.489307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02c96156-e950-4c4d-ac19-132d866b81d3 · outbound

This paper cites A Novel Hyperdimensional Computing Framework for Online Time Series Forecasting on the Edge.

RESOLVE: Relational Reasoning with Symbolic and Object-Level Features Using Vector Symbolic Processing A Novel Hyperdimensional Computing Framework for Online Time Series Forecasting on the Edge

Reference 2018

Resolution: verified exact; local_arxiv, observed 2026-08-12T21:52:17.608416Z

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 34aff145-286e-4171-a423-fab5b3737082 · outbound

This paper cites Single Output Tasks In this section, we provide comprehensive information on the architectures, hyperparameters, and implementation details of our experiments.

RESOLVE: Relational Reasoning with Symbolic and Object-Level Features Using Vector Symbolic Processing Single Output Tasks In this section, we provide comprehensive information on the architectures, hyperparameters, and implementation details of our experiments

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:52:17.720056Z

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 d020435b-6081-4c96-9d94-5ad7be73ea4c · outbound

This paper cites Logic tensor networks.

RESOLVE: Relational Reasoning with Symbolic and Object-Level Features Using Vector Symbolic Processing Logic tensor networks

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:52:17.734296Z

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 c2a6aa94-dd3e-48e0-a184-fb49d63df5ad · outbound

This paper cites Neural Turing Machines.

RESOLVE: Relational Reasoning with Symbolic and Object-Level Features Using Vector Symbolic Processing Neural Turing Machines

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-12T21:52:17.466832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation caa24a4d-b54b-4be1-bc61-7add03b09fda · outbound

This paper cites Slot Abstractors: Toward Scalable Abstract Visual Reasoning.

RESOLVE: Relational Reasoning with Symbolic and Object-Level Features Using Vector Symbolic Processing Slot Abstractors: Toward Scalable Abstract Visual Reasoning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-12T21:52:17.480202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 81020540-1b95-4a42-acb1-d24ff67cd64f · inbound

A Vision Toward Energy-Efficient Domain-Specific Artificial Intelligence Models and Agents cites this paper.

A Vision Toward Energy-Efficient Domain-Specific Artificial Intelligence Models and Agents RESOLVE: Relational Reasoning with Symbolic and Object-Level Features Using Vector Symbolic Processing

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-04T08:12:18.277158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.