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Paper Citation Record · LEDGER

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation

As of 7 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2603.23867.

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

pith.paper-citation-record.v1
2603.23867 v2

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-13T19:17:12.798847Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

19 of 19 outbound references displayed

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  • unresolved19
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 34882ee6-037f-4d91-a7c6-a4c2ba38623b · outbound

This paper cites GPT-4 Technical Report.

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation GPT-4 Technical Report

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T19:17:12.798847Z digest=sha256:d16470795ef380f24e33426efd8e5c1611a40557ee37d357eba6ec264d49cda9

Observation 0ff792e7-8b9c-4353-8c69-51924c0e1adc · outbound

This paper cites Neural Probabilistic Circuits: Enabling Compositional and Interpretable Predictions through Logical Reasoning.

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation Neural Probabilistic Circuits: Enabling Compositional and Interpretable Predictions through Logical Reasoning

Reference 2

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source=pdf_text observed=2026-07-13T19:17:12.798847Z digest=sha256:b345f21d03d27e682eedcbcd20f63e1dbb844c442ffd71c94ad73b9b0ea5c664

Observation b3af5b0e-e7c6-45bf-a35a-5e7f71ed2df7 · outbound

This paper cites On the Measure of Intelligence.

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation On the Measure of Intelligence

Reference 3

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source=pdf_text observed=2026-07-13T19:17:12.798847Z digest=sha256:2f099da9fdc9f10f42147d7b32204630fcd8de2fa054da68ffc974bba4067c6e

Observation d96aa147-f64a-4db7-93a0-b162fad3a5c8 · outbound

This paper cites If Concept Bottlenecks are the Question, are Foundation Models the Answer?.

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation If Concept Bottlenecks are the Question, are Foundation Models the Answer?

Reference 4

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source=pdf_text observed=2026-07-13T19:17:12.798847Z digest=sha256:5977af92d69ddd34fb14d9d50acb88961371f63a19a608435a877978926f8499

Observation 11e205aa-9116-43fe-8b95-1e0f17a62f83 · outbound

This paper cites Towards Learning to Reason: Comparing LLMs with Neuro-Symbolic on Arithmetic Relations in Abstract Reasoning.

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation Towards Learning to Reason: Comparing LLMs with Neuro-Symbolic on Arithmetic Relations in Abstract Reasoning

Reference 5

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source=pdf_text observed=2026-07-13T19:17:12.798847Z digest=sha256:12ca50d71c4854e9e4964b07eee05ba5666e22a4b991d2662dee09b911c90022

Observation c4e747bc-ae85-4918-8b53-efc6a95ff856 · outbound

This paper cites Neptune: A neuro-pythonic framework for tunable compositional reasoning on vision-language.

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation Neptune: A neuro-pythonic framework for tunable compositional reasoning on vision-language

Reference 6

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source=pdf_text observed=2026-07-13T19:17:12.798847Z digest=sha256:433b620f45fe6306c45094286368cb7e5bb1682008fb2e7abd0d876e3f52d23d

Observation 5fab66ec-f01e-4d1c-8d14-648d505a1781 · outbound

This paper cites To neuro-symbolic classification and beyond by compiling description logic ontologies to probabilistic circuits.arXiv preprint arXiv:2601.14894,.

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation To neuro-symbolic classification and beyond by compiling description logic ontologies to probabilistic circuits.arXiv preprint arXiv:2601.14894,

Reference 7

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source=pdf_text observed=2026-07-13T19:17:12.798847Z digest=sha256:a141af578940adf552c209d27aad320314c3eb13e1e431316c76855b0286cf28

Observation 216a2eec-d293-4cb5-9d44-acd2543db6d7 · outbound

This paper cites Boosting Deductive Reasoning with Step Signals In RLHF.

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation Boosting Deductive Reasoning with Step Signals In RLHF

Reference 8

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source=pdf_text observed=2026-07-13T19:17:12.798847Z digest=sha256:cce687038741545dbff71b229b55c2b608cbcaf2e12f2a434e53a721d58e7cde

Observation f1219849-e60b-43f8-b27a-5ee0939fb65f · outbound

This paper cites Symbol grounding in neuro-symbolic ai: A gentle introduction to reasoning shortcuts.arXiv preprint arXiv:2510.14538,.

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation Symbol grounding in neuro-symbolic ai: A gentle introduction to reasoning shortcuts.arXiv preprint arXiv:2510.14538,

Reference 9

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source=pdf_text observed=2026-07-13T19:17:12.798847Z digest=sha256:ec06900ff2df9ff45088fbd54d527609b8a9923c2163da92aec9043128b26137

Observation 21e0d37d-0ce5-4f2a-9ce3-c9aa2b680209 · outbound

This paper cites an unresolved cited work.

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation Unresolved cited work

Reference 10

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source=pdf_text observed=2026-07-13T19:17:12.798847Z digest=sha256:0aa36ef4e59c0457c0cea200d85b5f33d62dcf3be32f810331e5f8c69a78be49

Observation 1b93ca87-8f6d-494f-837d-6f6a06bdd313 · outbound

This paper cites Code Llama: Open Foundation Models for Code.

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation Code Llama: Open Foundation Models for Code

Reference 11

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source=pdf_text observed=2026-07-13T19:17:12.798847Z digest=sha256:f4b1700e098afe4d97ef16994b704fcbd4bd559470596af37e1d90236726185e

Observation ece1d806-9ddd-45c4-994a-c436591e4200 · outbound

This paper cites Proofwriter: Generating implications, proofs, and abduc- tive statements over natural language.

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation Proofwriter: Generating implications, proofs, and abduc- tive statements over natural language

Reference 12

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T19:17:12.798847Z digest=sha256:58d6a75cd5dcdf41c25e35c804b9d0e58729ef7157aaf2c0b02d358f66f74922

Observation b493db6e-1d31-4667-ad95-08a9c5687ea6 · outbound

This paper cites EasyARC: Evaluating Vision Language Models on True Visual Reasoning.

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation EasyARC: Evaluating Vision Language Models on True Visual Reasoning

Reference 13

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source=pdf_text observed=2026-07-13T19:17:12.798847Z digest=sha256:2c2b82ad8cad7221ce048de9b214cc26367c004143015f53492e5b809e5e2982

Observation 7e34bfb1-97f8-47b6-8b71-a7e4e15c0f47 · outbound

This paper cites The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision).

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T19:17:12.798847Z digest=sha256:9d014144f2d284c23a3d1203003409c80f324a578d64a98e62508a79b8d2e4f2

Observation 6460b670-f6d7-44a2-9ea5-af64b6d2838c · outbound

This paper cites VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relations.

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relations

Reference 15

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T19:17:12.798847Z digest=sha256:04eea041539fa8d55d32d79cf6a7ecdda73093a3cfbae5061a752f88474e0dbe

Observation aa1af09b-22bd-4dd6-8ca5-5e1990417b8d · outbound

This paper cites MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understanding.

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understanding

Reference 16

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source=pdf_text observed=2026-07-13T19:17:12.798847Z digest=sha256:aaac56e08e1c75c85edb2adc73b07e9687de0b0f10b53c8be68e8d55c36d96d9

Observation 4a0d7af2-3066-4282-8fdb-e0926c72dcc7 · outbound

This paper cites Extracting Symbolic Rules from Natural Language.In this paper, we assume access to symbolic rules for visual deductive reasoning tasks.

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation Extracting Symbolic Rules from Natural Language.In this paper, we assume access to symbolic rules for visual deductive reasoning tasks

Reference 17

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source=pdf_text observed=2026-07-13T19:17:12.798847Z digest=sha256:de18019d4863e7640d8bf6e6f9202fbd6885fc1b1f27754956117dfb313f2ea8

Observation 7694cc80-b4ec-4eb7-ab09-4d1785978d56 · outbound

This paper cites an unresolved cited work.

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation Unresolved cited work

Reference 18

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source=pdf_text observed=2026-07-13T19:17:12.798847Z digest=sha256:8106a1aba17809b4c51a32c601763c4d14bd6dcbf884b7105c52f799427744ac

Observation e5e36281-2c1e-4d89-8cef-b3aaac75faff · outbound

This paper cites Specifically, we use the�������benchmark to create datasets for various visual deductive reasoning tasks.

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation Specifically, we use the�������benchmark to create datasets for various visual deductive reasoning tasks

Reference 19

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source=pdf_text observed=2026-07-13T19:17:12.798847Z digest=sha256:e37fde19bf9f4d36416184c58d4200fe8746e96f9eadac9fe2e5bcf86720087d

Pith citing papers

No inbound Pith citation observations are available.