Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models | Paper Citation Record

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This paper cites Analogs of Linguistic Structure in Deep Representations.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Analogs of Linguistic Structure in Deep Representations

Reference 1

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This paper cites Improving image generation with better captions.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Improving image generation with better captions

Reference 2

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This paper cites Language development: Form and function in emerging grammars.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Language development: Form and function in emerging grammars

Reference 3

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This paper cites brms: Bayesian Regression Models using Stan, 2024.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models brms: Bayesian Regression Models using Stan, 2024

Reference 4

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This paper cites Bootstrapping & the origin of concepts.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Bootstrapping & the origin of concepts

Reference 5

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Observation 13f76ca9-bd94-49c4-a78f-26c6dc938374 · outbound

This paper cites Ontogenetic origins of human integer representations.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Ontogenetic origins of human integer representations

Reference 6

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Observation e6ae8a47-a4e6-441a-bc3b-66091c62abf8 · outbound

This paper cites Topological structure in visual perception.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Topological structure in visual perception

Reference 7

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This paper cites A unified account of numerosity perception.Nature human behaviour, 4(12):1265–1272, 2020.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models A unified account of numerosity perception.Nature human behaviour, 4(12):1265–1272, 2020

Reference 8

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Observation 96315fe4-2b13-4545-b3af-f8fb1a5f7218 · outbound

This paper cites Aspects of the Theory of Syntax.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Aspects of the Theory of Syntax

Reference 9

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Observation 559b2412-7433-457e-95aa-ba4d582be6d4 · outbound

This paper cites Alternative representations of time, number, and rate.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Alternative representations of time, number, and rate

Reference 10

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Observation 934e4f23-96d2-4aa8-8874-ef9f744c6898 · outbound

This paper cites Testing Relational Understanding in Text-Guided Image Generation.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Testing Relational Understanding in Text-Guided Image Generation

Reference 11

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This paper cites VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance

Reference 12

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This paper cites de Saint-Exup ´ery and I.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models de Saint-Exup ´ery and I

Reference 13

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Observation 03248c18-9c6d-4bbe-bf98-5ea155e3e2d5 · outbound

This paper cites The number sense: How the mind creates mathematics.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models The number sense: How the mind creates mathematics

Reference 14

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Observation 8569d1b7-ed32-43e4-8848-099a6033e716 · outbound

This paper cites Development of elementary numerical abilities: A neuronal model.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Development of elementary numerical abilities: A neuronal model

Reference 15

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This paper cites Three parietal circuits for number processing.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Three parietal circuits for number processing

Reference 16

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This paper cites Describing scenes hardly seen.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Describing scenes hardly seen

Reference 17

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This paper cites Relate: Physically plausible multi-object scene synthesis using structured latent spaces.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Relate: Physically plausible multi-object scene synthesis using structured latent spaces

Reference 18

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This paper cites Cultural constraints on grammar and cognition in pirah ˜a: Another look at the design features of human language.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Cultural constraints on grammar and cognition in pirah ˜a: Another look at the design features of human language

Reference 19

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Observation 5da876e1-c1b5-479d-9959-25b7ed6e6c51 · outbound

This paper cites Perspective (In)consistency of Paint by Text.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Perspective (In)consistency of Paint by Text

Reference 20

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This paper cites no” and “not.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models no” and “not

Reference 21

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This paper cites Seeing physics in the blink of an eye.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Seeing physics in the blink of an eye

Reference 22

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This paper cites Bridging the data gap between children and large language models.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Bridging the data gap between children and large language models

Reference 23

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This paper cites The psychophysics of chasing: A case study in the perception of animacy.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models The psychophysics of chasing: A case study in the perception of animacy

Reference 24

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This paper cites Mapping the early language environment using all-day recordings and automated analysis.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Mapping the early language environment using all-day recordings and automated analysis

Reference 25

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This paper cites Rapid apprehension of the coherence of action scenes.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Rapid apprehension of the coherence of action scenes

Reference 26

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Observation 790d7b49-c8c2-4853-9207-a2fef9b0c6b8 · outbound

This paper cites It’s not just what we don’t know: The mapping problem in the acquisition of negation.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models It’s not just what we don’t know: The mapping problem in the acquisition of negation

Reference 27

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This paper cites Seeing what’s possible: Disconnected visual parts are confused for their potential wholes.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Seeing what’s possible: Disconnected visual parts are confused for their potential wholes

Reference 28

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This paper cites The perception of relations.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models The perception of relations

Reference 29

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This paper cites A phone in a basket looks like a knife in a cup: The perception of abstract relations.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models A phone in a basket looks like a knife in a cup: The perception of abstract relations

Reference 30

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This paper cites Number sense across the lifespan as revealed by a massive internet-based sample.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Number sense across the lifespan as revealed by a massive internet-based sample

Reference 31

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This paper cites Social evaluation by preverbal infants.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Social evaluation by preverbal infants

Reference 32

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This paper cites Conceptual precursors to language.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Conceptual precursors to language

Reference 33

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Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Structural ambiguity and lexical relations

Reference 34

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This paper cites A natural history of negation.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models A natural history of negation

Reference 35

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This paper cites T2i-compbench: A compre- hensive benchmark for open-world compositional text-to-image generation.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models T2i-compbench: A compre- hensive benchmark for open-world compositional text-to-image generation

Reference 36

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This paper cites On the origins of denial negation.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models On the origins of denial negation

Reference 37

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Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Clevr: A diagnostic dataset for compositional language and elementary visual reasoning

Reference 38

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Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Understanding negation: Issues in the processing of negation

Reference 39

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This paper cites The experiential view of language comprehen- sion: How is negation represented.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models The experiential view of language comprehen- sion: How is negation represented

Reference 40

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Observation 67a16fed-a47b-44c6-a8ec-b6c5c667c8ea · outbound

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Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Perception of partly occluded objects in infancy

Reference 41

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Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Visual number sense in untrained deep neural networks

Reference 42

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This paper cites Building machines that learn and think like people.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Building machines that learn and think like people

Reference 43

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This paper cites Holistic evaluation of text-to-image models.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Holistic evaluation of text-to-image models

Reference 44

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Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Gligen: Open-set grounded text-to-image generation

Reference 45

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This paper cites LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models

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Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Microsoft coco: Common objects in context

Reference 47

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This paper cites Compositional Visual Generation with Composable Diffusion Models.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Compositional Visual Generation with Composable Diffusion Models

Reference 48

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This paper cites Training Priors Predict Text-To-Image Model Performance.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Training Priors Predict Text-To-Image Model Performance

Reference 49

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This paper cites Topological relations between objects are categorically coded.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Topological relations between objects are categorically coded

Reference 50

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This paper cites sjPlot: Data Visualization for Statistics in Social Science , 2024.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models sjPlot: Data Visualization for Statistics in Social Science , 2024

Reference 51

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Observation 0e0dfa05-19dd-46b6-bfb1-0e3eeab940e5 · outbound

This paper cites Dissociating language and thought in large language models.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Dissociating language and thought in large language models

Reference 52

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This paper cites A very preliminary analysis of DALL-E 2.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models A very preliminary analysis of DALL-E 2

Reference 53

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This paper cites A mode control model of counting and timing processes.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models A mode control model of counting and timing processes

Reference 54

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Observation 9929c1a7-088a-4e9e-b417-370c9f4a2d27 · outbound

This paper cites The Illusion of State in State-Space Models.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models The Illusion of State in State-Space Models

Reference 55

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Observation 79cc6957-069e-4584-b154-e54745273429 · outbound

This paper cites GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-12T12:36:46.557514Z

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Observation 6884b0a0-50d3-4511-b80e-8e7f2265bdf9 · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T12:36:46.562860Z

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Observation 0bfa69f2-8efb-491c-bd71-e01806e24e52 · outbound

This paper cites Compo- sitional text-to-image generation with dense blob representations.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Compo- sitional text-to-image generation with dense blob representations

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.277286Z

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Observation 71da9b23-935b-4069-8ad6-e6b5f7c03920 · outbound

This paper cites An introduction to the approximate number system.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models An introduction to the approximate number system

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.265489Z

Source-reported events for the cited work

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Observation e2b467c4-3397-472d-83e2-6555bf281f60 · outbound

This paper cites Compositional abilities emerge multiplicatively: Exploring diffusion models on a synthetic task.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Compositional abilities emerge multiplicatively: Exploring diffusion models on a synthetic task

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.252884Z

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Observation a2da7f6b-64d5-4a2d-bc5f-10dff647c4d0 · outbound

This paper cites Numerical cognition in bees and other insects.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Numerical cognition in bees and other insects

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.241065Z

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Observation cb62398f-26c5-42a8-bedc-4f5f5a1e20b9 · outbound

This paper cites The human imagination: the cognitive neuroscience of visual mental imagery.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models The human imagination: the cognitive neuroscience of visual mental imagery

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.228825Z

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Observation bb535975-4a20-426e-b2a8-5bf16202f596 · outbound

This paper cites Beyond the turk: Alternative platforms for crowdsourcing behavioral research.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Beyond the turk: Alternative platforms for crowdsourcing behavioral research

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.217023Z

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Observation ba613a16-d1b1-4fc7-a359-4f099d51e9c1 · outbound

This paper cites Human Evaluation of Text-to-Image Models on a Multi-Task Benchmark.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Human Evaluation of Text-to-Image Models on a Multi-Task Benchmark

Reference 64

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Observation 3cdba7a0-64f9-4186-8a60-d28f97cc9ea2 · outbound

This paper cites Exact number concepts are limited to the verbal count range.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Exact number concepts are limited to the verbal count range

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.204999Z

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Observation e1f530cd-4993-4457-a24a-91bfb1b69c96 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Learning transferable visual models from natural language supervision

Reference 66

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Observation 6966dcb4-64ad-4fe9-bdad-5bda05dfa588 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T12:36:46.601609Z

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Observation f7334498-8951-401e-9c4c-50b7aa8d5f94 · outbound

This paper cites Can generative multimodal models count to ten? In Proceedings of the Annual Meeting of the Cognitive Science Society, volume 46, 2024.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Can generative multimodal models count to ten? In Proceedings of the Annual Meeting of the Cognitive Science Society, volume 46, 2024

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.186182Z

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Observation 1d321195-a136-4fd2-bead-abb4fb8b9c83 · outbound

This paper cites Linguistic binding in diffusion models: Enhancing attribute correspondence through attention map alignment.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Linguistic binding in diffusion models: Enhancing attribute correspondence through attention map alignment

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.172919Z

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Observation 2e9f0fb6-1b52-4fdc-9ff0-0721fe297db0 · outbound

This paper cites Impact of Pretraining Term Frequencies on Few-Shot Reasoning.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Impact of Pretraining Term Frequencies on Few-Shot Reasoning

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-12T12:36:46.613065Z

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Observation 473ad483-004d-44d5-84cb-3b94d082f059 · outbound

This paper cites High- resolution image synthesis with latent diffusion models.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models High- resolution image synthesis with latent diffusion models

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.159835Z

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Observation 6f97b17c-dc60-40d7-b6d6-e2622dd94ae6 · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 72

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Observation f08da05e-799b-438c-b7f7-cf88bfc1643a · outbound

This paper cites Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-12T12:36:46.625115Z

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Observation ba5f0dc1-37b0-4c0f-963b-97dcfb1ed73d · outbound

This paper cites Verification of text ideas during reading.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Verification of text ideas during reading

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.137109Z

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Observation 4e78648b-8b4d-46ed-a375-7744321dfa47 · outbound

This paper cites Objectstitch: Object compositing with diffusion model.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Objectstitch: Object compositing with diffusion model

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.124402Z

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Observation 69f4e52e-29d7-4398-bd90-215922613ea0 · outbound

This paper cites Initial knowledge: Six suggestions.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Initial knowledge: Six suggestions

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.111617Z

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Observation 9215d3c6-f0c7-4b53-8434-b5e5657dc2d1 · outbound

This paper cites What babies know: Core knowledge and composition volume 1, volume 1.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models What babies know: Core knowledge and composition volume 1, volume 1

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.098074Z

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Observation db2693e6-c10e-499c-b2fe-4edb35332b7d · outbound

This paper cites Core knowledge.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Core knowledge

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.085385Z

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Observation e4436f69-743e-4473-9f68-8a7fc1573075 · outbound

This paper cites Event completion: Event based inferences distort memory in a matter of seconds.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Event completion: Event based inferences distort memory in a matter of seconds

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.073339Z

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Observation f864b2d6-c1c7-4fd7-9c29-f733634c7745 · outbound

This paper cites What formal lan- guages can transformers express? a survey.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models What formal lan- guages can transformers express? a survey

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.061167Z

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Observation b7c4ea9b-d936-431c-8810-07bc3e31c0c7 · outbound

This paper cites Lexicalization patterns: Semantic structure in lexical forms.Language typology and syntactic description, 3(99):36–149, 1985.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Lexicalization patterns: Semantic structure in lexical forms.Language typology and syntactic description, 3(99):36–149, 1985

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.048668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6ce38d9f-f64e-4930-9995-9b5fb42ba917 · outbound

This paper cites Winoground: Probing vision and language models for visio-linguistic compositionality.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Winoground: Probing vision and language models for visio-linguistic compositionality

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-12T12:36:46.660940Z

Source-reported events for the cited work

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Observation 151cfa7f-1d1a-4da3-8fc0-8eeef98d67dd · outbound

This paper cites Computing machinery and intelligence.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Computing machinery and intelligence

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.028253Z

Source-reported events for the cited work

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Observation 8507eaa3-cbfe-42da-8ffc-e8c8a45e066d · outbound

This paper cites No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-12T12:36:46.669021Z

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Observation d7f40483-69b2-4946-9b2f-55bcd76ee828 · outbound

This paper cites Help or hinder: Bayesian models of social goal inference.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Help or hinder: Bayesian models of social goal inference

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.014055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 582aa67b-8319-4d12-9e29-a9d57c7fa110 · outbound

This paper cites The automaticity of perceiving animacy: Goal-directed motion in simple shapes influences visuomotor behavior even when task-irrelevant.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models The automaticity of perceiving animacy: Goal-directed motion in simple shapes influences visuomotor behavior even when task-irrelevant

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:47.002105Z

Source-reported events for the cited work

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Observation 19f5b718-f222-4a40-81e6-e2bf5835a53c · outbound

This paper cites Response to affirmative and negative binary statements.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Response to affirmative and negative binary statements

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:46.990126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1cf4b1ae-0c20-42ef-9c66-85507eadd1b5 · outbound

This paper cites Paradoxical effects of thought suppression.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Paradoxical effects of thought suppression

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:46.977745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 548e40fd-e6c8-4d52-ac74-eab25be1b461 · outbound

This paper cites ConceptMix: A Compositional Image Generation Benchmark with Controllable Difficulty.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models ConceptMix: A Compositional Image Generation Benchmark with Controllable Difficulty

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-12T12:36:46.688719Z

Source-reported events for the cited work

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Observation 1c544171-bc50-4147-a155-fbf1d27b7d72 · outbound

This paper cites Boxdiff: Text-to-image synthesis with training-free box-constrained diffu- sion.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Boxdiff: Text-to-image synthesis with training-free box-constrained diffu- sion

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:46.966828Z

Source-reported events for the cited work

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Observation 7a4d810d-a430-4627-8c76-c186d1d61365 · outbound

This paper cites Diffusion models: A comprehensive survey of methods and applications.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Diffusion models: A comprehensive survey of methods and applications

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-12T12:36:46.696397Z

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Observation afb7d961-d17d-406a-a5f1-c74fd5e34666 · outbound

This paper cites Perceiving fully occluded objects via physical simulation.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models Perceiving fully occluded objects via physical simulation

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:46.948079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 393b07c4-5bd3-4922-9cf5-2df755f6c6d3 · outbound

This paper cites When and why vision-language models behave like bags-of-words, and what to do about it? In The Eleventh International Conference on Learning Representations, 2022.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models When and why vision-language models behave like bags-of-words, and what to do about it? In The Eleventh International Conference on Learning Representations, 2022

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:46.937280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 409822be-a2cd-425f-ab26-904f2182c7d6 · outbound

This paper cites I NEED to test how the tool works with extremely simple prompts. DO NOT add any detail, just use it AS-IS:.

Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models I NEED to test how the tool works with extremely simple prompts. DO NOT add any detail, just use it AS-IS:

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:36:46.925933Z

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