Paper Citation Record · LEDGER

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation

As of 20 August 2026, this Paper Citation Record lists 85 of 85 outbound references and 2 inbound Pith citation observations for arXiv:2506.10395.

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

pith.paper-citation-record.v1
2506.10395 v2

Coverage vector

measured 85 of 85 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:34:12.154340Z

measured 87 of 87 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T19:17:19.634242Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T18:51:15.880853Z

Reference resolution

85 of 85 outbound references displayed

verified exact2
verified fuzzy18
unresolved65
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation eebf5b90-1bdb-45b1-a9e5-af521b65bd51 · outbound

This paper cites write newline.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T04:33:59.234925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a020a70d-a6a7-4c98-9182-37b551e521db · outbound

This paper cites The Llama 3 Herd of Models.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation The Llama 3 Herd of Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T04:33:59.321702Z

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Observation abc4ea03-17c9-4738-936f-47ed169a939d · outbound

This paper cites CM3: A Causal Masked Multimodal Model of the Internet.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation CM3: A Causal Masked Multimodal Model of the Internet

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T04:33:59.440871Z

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Observation 28a22b10-b9cf-4c6f-9bdd-35e81f4db077 · outbound

This paper cites Menick, Sebastian Borgeaud, Andy Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, and Kar \' e n Simonyan.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Menick, Sebastian Borgeaud, Andy Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, and Kar \' e n Simonyan

Reference 4

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Observation 9c6ba7fd-8ebb-4380-a453-fdc378c12828 · outbound

This paper cites Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond, 2023.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond, 2023

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T04:33:59.735365Z

Source-reported events for the cited work

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Observation 85ca09f3-7f82-4bec-868e-88e0543cf97a · outbound

This paper cites Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:00.049480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f708b75-a8e2-4530-91a4-596384414343 · outbound

This paper cites Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:00.161811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2af2ac08-d9d3-4a7e-823b-559c41fbd359 · outbound

This paper cites ShareGPT4V: Improving Large Multi-Modal Models with Better Captions.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation ShareGPT4V: Improving Large Multi-Modal Models with Better Captions

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:00.326020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 810f32d4-c9b2-4610-8376-0015a635568e · outbound

This paper cites Are We on the Right Way for Evaluating Large Vision-Language Models?.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Are We on the Right Way for Evaluating Large Vision-Language Models?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:00.478226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdd57776-9a51-47f4-9f03-ec46aa1e85e7 · outbound

This paper cites InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:00.637220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 282b3c3f-0bf0-434e-a7b2-4d2c58bc8d31 · outbound

This paper cites Dream LLM : Synergistic multimodal comprehension and creation.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Dream LLM : Synergistic multimodal comprehension and creation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:00.829274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31c365d3-95ab-449f-a0bc-836c440bcaa2 · outbound

This paper cites Taming transformers for high-resolution image synthesis.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Taming transformers for high-resolution image synthesis

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:00.945047Z

Source-reported events for the cited work

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Observation 0a84b836-d5e9-4f8a-b917-d0214a4a70ce · outbound

This paper cites Scaling rectified flow transformers for high-resolution image synthesis.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Scaling rectified flow transformers for high-resolution image synthesis

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:18.805912Z

Source-reported events for the cited work

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Observation 005b0f3d-381c-4faa-8df6-bd1e16f6c3e6 · outbound

This paper cites Mme: A comprehensive evaluation benchmark for multimodal large language models, 2024.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Mme: A comprehensive evaluation benchmark for multimodal large language models, 2024

Reference 15

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Observation dd691fa1-fb58-40bf-b061-aef520ac4b56 · outbound

This paper cites SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:01.423591Z

Source-reported events for the cited work

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Observation 176c3921-7018-4cdd-8b03-1bf1c652af5b · outbound

This paper cites Geneval: An object-focused framework for evaluating text-to-image alignment.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Geneval: An object-focused framework for evaluating text-to-image alignment

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:18.610344Z

Source-reported events for the cited work

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Observation 8d0a0171-406d-4d70-be15-7df991db1f82 · outbound

This paper cites Making the v in vqa matter: Elevating the role of image understanding in visual question answering.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Making the v in vqa matter: Elevating the role of image understanding in visual question answering

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:18.408391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4cc5fe1e-282d-46d5-9df9-8d11ba897449 · outbound

This paper cites Vizwiz grand challenge: Answering visual questions from blind people.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Vizwiz grand challenge: Answering visual questions from blind people

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:18.190173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3ecddb5c-dceb-48f4-9232-86434787a8e4 · outbound

This paper cites Masked Autoencoders Are Scalable Vision Learners.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Masked Autoencoders Are Scalable Vision Learners

Reference 20

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Source-reported events for the cited work

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Observation 29f63196-c174-40ca-bc2c-c11ce4af7382 · outbound

This paper cites Gqa: A new dataset for real-world visual reasoning and compositional question answering.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Gqa: A new dataset for real-world visual reasoning and compositional question answering

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:17.952933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c7a3e172-d7a5-4053-9f1c-a43cb76fc897 · outbound

This paper cites Unified language-vision pretraining in LLM with dynamic discrete visual tokenization.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Unified language-vision pretraining in LLM with dynamic discrete visual tokenization

Reference 22

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Observation 61a80a70-4b79-4239-9a52-ac12d2f74da1 · outbound

This paper cites A diagram is worth a dozen images.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation A diagram is worth a dozen images

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:17.721711Z

Source-reported events for the cited work

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Observation a52519a8-677c-4275-bdfe-737ef7d2a1d5 · outbound

This paper cites Generating images with multimodal language models.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Generating images with multimodal language models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:17.490002Z

Source-reported events for the cited work

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Observation e870ab70-d91a-408c-ad38-3c090f0f4a8a · outbound

This paper cites MIMIC-IT: Multi-Modal In-Context Instruction Tuning.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation MIMIC-IT: Multi-Modal In-Context Instruction Tuning

Reference 25

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Source-reported events for the cited work

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Observation 79fde0c5-1a91-4e10-926f-c9d7b402a850 · outbound

This paper cites SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:03.070205Z

Source-reported events for the cited work

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Observation 12899f82-3563-44a6-9a78-941a20b1afd3 · outbound

This paper cites LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:03.206849Z

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Observation 273f10d9-7205-4d87-8284-ca2e3c2ef32a · outbound

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Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Unresolved cited work

Reference 28

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Source-reported events for the cited work

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Observation 9a25378b-887d-4c8f-b888-4c69caf597a7 · outbound

This paper cites Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:03.476230Z

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Observation e07f2fb3-48c4-46de-91da-d8bf02d63ddb · outbound

This paper cites Evaluating Object Hallucination in Large Vision-Language Models.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Evaluating Object Hallucination in Large Vision-Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:03.678107Z

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Observation c4ad44f6-9d62-4bf8-baf6-b5e6f06e999c · outbound

This paper cites Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll \' a r, and C.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll \' a r, and C

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:03.799566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98e74cdd-e023-4e57-82bb-a755b74a2fad · outbound

This paper cites HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:03.967311Z

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Observation f8a241d6-d815-4b89-b308-3c81e11cf694 · outbound

This paper cites Improved Baselines with Visual Instruction Tuning.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Improved Baselines with Visual Instruction Tuning

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc9d2ee5-14e5-4b01-92b2-1e4f746c6f5e · outbound

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Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Visual Instruction Tuning

Reference 34

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Observation aa3dcf34-721e-4f12-8389-df32b9d9580c · outbound

This paper cites Llava-next: Improved reasoning, ocr, and world knowledge, January 2024 a.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Llava-next: Improved reasoning, ocr, and world knowledge, January 2024 a

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:17.057930Z

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Observation ce5fc821-db12-452d-8655-ac7a47e1b085 · outbound

This paper cites Visual instruction tuning.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Visual instruction tuning

Reference 36

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Observation 396b95b1-89d1-4a06-9d74-74649dc13acf · outbound

This paper cites MMBench: Is Your Multi-modal Model an All-around Player?.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation MMBench: Is Your Multi-modal Model an All-around Player?

Reference 37

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Observation e6ea8826-9138-48fc-b20d-1235829373dc · outbound

This paper cites On the hidden mystery of ocr in large multimodal models, 2024 c.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation On the hidden mystery of ocr in large multimodal models, 2024 c

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:16.931157Z

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Observation 74ab8d04-505b-4eb6-a6e8-66c7fc7cfcfe · outbound

This paper cites Learn to explain: Multimodal reasoning via thought chains for science question answering.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Learn to explain: Multimodal reasoning via thought chains for science question answering

Reference 39

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Observation b37d7701-e2da-4f1e-88e1-4496d035711c · outbound

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Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts

Reference 40

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Observation 87ae0daa-a108-4529-bfcc-022c9b0aa4aa · outbound

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Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:05.315938Z

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Observation f1179f3e-2376-42ea-8792-0760c0b39fc7 · outbound

This paper cites Docvqa: A dataset for vqa on document images.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Docvqa: A dataset for vqa on document images

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:05.474321Z

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Observation f0ddd885-f659-40fb-a918-8f9af08c3c99 · outbound

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Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Infographicvqa

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:05.634106Z

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Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:05.794775Z

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Observation c74be96a-30ec-4c99-aca8-4fa8592c5a13 · outbound

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

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Learning transferable visual models from natural language supervision

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:16.730462Z

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Observation 20385547-d54e-4e6f-ad8d-2dfec6e24874 · outbound

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Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Unresolved cited work

Reference 46

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Observation 04974964-d317-4823-be2f-d05af2983131 · outbound

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

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:06.226877Z

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Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation High-resolution image synthesis with latent diffusion models

Reference 48

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

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation High-resolution image synthesis with latent diffusion models

Reference 49

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This paper cites Multimodal Instruction Tuning with Conditional Mixture of LoRA.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Multimodal Instruction Tuning with Conditional Mixture of LoRA

Reference 50

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Observation ec43e49d-4005-475c-9c85-60df80513c47 · outbound

This paper cites Towards vqa models that can read.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Towards vqa models that can read

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:16.178169Z

Source-reported events for the cited work

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Observation b455ec78-c8a9-44bc-9ca5-25d79365c58f · outbound

This paper cites Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:06.956907Z

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Observation b6fb765c-2f9b-4a37-8260-721aab517faa · outbound

This paper cites EVA-CLIP: Improved Training Techniques for CLIP at Scale.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation EVA-CLIP: Improved Training Techniques for CLIP at Scale

Reference 53

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Observation b107e098-ca99-4fa3-9c50-ba1aa94d0951 · outbound

This paper cites Emu: Generative Pretraining in Multimodality.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Emu: Generative Pretraining in Multimodality

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:07.236721Z

Source-reported events for the cited work

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Observation 9157b874-4a5e-4133-bc4e-ebce19cfbb08 · outbound

This paper cites Generative multimodal models are in-context learners.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Generative multimodal models are in-context learners

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:15.826498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b6b0a231-7596-49d1-8b53-45a7d0f0e3ad · outbound

This paper cites CoDi-2: In-Context, Interleaved, and Interactive Any-to-Any Generation.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation CoDi-2: In-Context, Interleaved, and Interactive Any-to-Any Generation

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:07.537541Z

Source-reported events for the cited work

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Observation ff032ef9-9d8c-4eac-9f1c-0536508ec074 · outbound

This paper cites Any-to-any generation via composable diffusion.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Any-to-any generation via composable diffusion

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:15.538723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f3aefd29-efa1-4137-8cf6-000253965e3c · outbound

This paper cites Chameleon: Mixed-modal early-fusion foundation models, 2024.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Chameleon: Mixed-modal early-fusion foundation models, 2024

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:07.871098Z

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Observation ff2de168-136a-4a9c-a1e1-8385b4d6a2fe · outbound

This paper cites MM-Interleaved: Interleaved Image-Text Generative Modeling via Multi-modal Feature Synchronizer.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation MM-Interleaved: Interleaved Image-Text Generative Modeling via Multi-modal Feature Synchronizer

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:08.000903Z

Source-reported events for the cited work

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Observation 0129f496-dcae-4c81-bc71-311c6cc4aecf · outbound

This paper cites Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:08.124461Z

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Observation 0b5b9bf6-2cc9-4da9-b2db-d3b84535404f · outbound

This paper cites Eyes wide shut? exploring the visual shortcomings of multimodal llms.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Eyes wide shut? exploring the visual shortcomings of multimodal llms

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:15.221743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 969ed406-5d8c-4ea7-9be1-56d92ad587cb · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation LLaMA: Open and Efficient Foundation Language Models

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:08.477979Z

Source-reported events for the cited work

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Observation 0ffe2784-43c0-407f-87e9-88ba41cf170c · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:08.648626Z

Source-reported events for the cited work

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Observation 7d658a2d-493f-4c3d-8b4e-c7ef1a5e2a14 · outbound

This paper cites To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:08.755303Z

Source-reported events for the cited work

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Observation 2219aa29-5f93-4240-afd0-11434893aa1e · outbound

This paper cites OFA: unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation OFA: unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:14.903752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7c8c064b-8e4f-45de-9a24-3a57bbc9bddb · outbound

This paper cites Image as a foreign language: BEIT pretraining for vision and vision-language tasks.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Image as a foreign language: BEIT pretraining for vision and vision-language tasks

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:09.022182Z

Source-reported events for the cited work

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Observation 1d99262d-5b9d-4c38-be12-0575e788c3eb · outbound

This paper cites Emu3: Next-Token Prediction is All You Need.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Emu3: Next-Token Prediction is All You Need

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:09.192768Z

Source-reported events for the cited work

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Observation 974ce6fa-37c5-4c72-883f-1f57d968f342 · outbound

This paper cites Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:09.374451Z

Source-reported events for the cited work

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Observation d18c754e-cc81-41f7-95a0-bf11ed74dfb9 · outbound

This paper cites NExT-GPT: Any-to-Any Multimodal LLM.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation NExT-GPT: Any-to-Any Multimodal LLM

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:09.517877Z

Source-reported events for the cited work

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Observation aa67e46c-d63e-481c-8fef-55eceba2b082 · outbound

This paper cites Grok 1.5v: The next generation of ai.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Grok 1.5v: The next generation of ai

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:14.727112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ff7c1f67-5593-4f19-ae75-b98806a9f200 · outbound

This paper cites Show-o: One Single Transformer to Unify Multimodal Understanding and Generation.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:09.844460Z

Source-reported events for the cited work

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Observation d30a2810-ca8f-4506-841d-cab57a499d09 · outbound

This paper cites LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:09.966497Z

Source-reported events for the cited work

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Observation 877fdd3f-effc-4341-8f7d-aee0f9d1ff3a · outbound

This paper cites Multiinstruct: Improving multi-modal zero-shot learning via instruction tuning.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Multiinstruct: Improving multi-modal zero-shot learning via instruction tuning

Reference 73

Resolution: verified exact; doi, observed 2026-08-07T04:34:12.568692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 60240428-c3d7-42de-b785-8280a413f474 · outbound

This paper cites Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:10.263657Z

Source-reported events for the cited work

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Observation 1cd2c1f0-cfcb-4753-a007-e055acbfb868 · outbound

This paper cites Modality-specialized synergizers for interleaved vision-language generalists.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Modality-specialized synergizers for interleaved vision-language generalists

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:14.442097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8f22ff07-7612-4a2a-b947-666c44328754 · outbound

This paper cites Retrieval-augmented multimodal language modeling.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Retrieval-augmented multimodal language modeling

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:14.215441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e7c6d5cf-9f27-42b2-ad58-643a88ed7b46 · outbound

This paper cites mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:10.749553Z

Source-reported events for the cited work

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Observation eb2dfaed-8279-4d42-9cac-c2fa98baea49 · outbound

This paper cites LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmark.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmark

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:10.895265Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9b4edd9-af5c-4184-8228-db135bf16a19 · outbound

This paper cites Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:11.061508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 825db274-4934-4ccb-9e22-98aa3e98a425 · outbound

This paper cites MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:11.250107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12e13317-6f53-4ab6-ad1b-3267e3526906 · outbound

This paper cites Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:11.405831Z

Source-reported events for the cited work

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Observation 1f35042e-3a44-45ce-86a2-9093cc06b9a2 · outbound

This paper cites Sigmoid loss for language image pre-training.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Sigmoid loss for language image pre-training

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:11.533516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c35cc7c8-52e8-4e1f-897b-b484e3188537 · outbound

This paper cites AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:11.720293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1137c489-997b-411f-82db-76314dc056c9 · outbound

This paper cites Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:11.878440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df24cd45-8a6c-4865-9926-9dcdf3affe65 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:12.006826Z

Source-reported events for the cited work

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Observation 9c091c59-76f3-4992-bda8-50607a9d87c7 · outbound

This paper cites VL-GPT: A Generative Pre-trained Transformer for Vision and Language Understanding and Generation.

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation VL-GPT: A Generative Pre-trained Transformer for Vision and Language Understanding and Generation

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-07T04:34:12.154340Z

Source-reported events for the cited work

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Pith citing papers

Observation 00f45ee6-cf2c-4edd-b665-fcf19c93116a · inbound

Show-o2: Improved Native Unified Multimodal Models cites this paper.

Show-o2: Improved Native Unified Multimodal Models Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation

Reference 132

Resolution: verified exact; arxiv_id, observed 2026-05-12T18:51:15.884365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 98aa2f0d-0924-40d1-a253-9fb7348f8004 · inbound

Transferability Between Understanding and Generation in Unified Multimodal Models cites this paper.

Transferability Between Understanding and Generation in Unified Multimodal Models Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation

Reference 97

Resolution: unresolved; no resolver link, observed 2026-07-11T19:17:19.634242Z

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Unavailable: canonical work link unavailable.