LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents | Paper Citation Record

Observation a71f740f-d3d9-4677-9062-9a859c121ed2 · outbound

This paper cites Ntire 2017 challenge on single image super-resolution: Dataset and study.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Ntire 2017 challenge on single image super-resolution: Dataset and study

Reference 1

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Observation 885fa7f9-ef37-4fad-a6a1-1878598b762e · outbound

This paper cites Contour detection and hierarchical image segmen- tation.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Contour detection and hierarchical image segmen- tation

Reference 2

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Observation 647f7e6b-f76b-4d62-a6ce-4126286beb2b · outbound

This paper cites Low-complexity single-image super-resolution based on nonnegative neighbor embedding.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Low-complexity single-image super-resolution based on nonnegative neighbor embedding

Reference 3

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Observation ad1a8b47-b0c9-42dc-ba6e-0d51214c2e9e · outbound

This paper cites Language models are few-shot learners.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Language models are few-shot learners

Reference 4

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Observation e3fc2440-bee9-4372-9eea-5bed1456082c · outbound

This paper cites IQA-PyTorch: Pytorch toolbox for image quality assessment.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents IQA-PyTorch: Pytorch toolbox for image quality assessment

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:12.555525Z

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Observation 715007ef-f2bc-4eec-b400-470e43f7cddb · outbound

This paper cites Activating more pixels in image super-resolution transformer.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Activating more pixels in image super-resolution transformer

Reference 6

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Observation cc661cb6-599d-4e5b-9d1d-f0d560d69e54 · outbound

This paper cites Dual aggregation transformer for image super-resolution.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Dual aggregation transformer for image super-resolution

Reference 7

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Observation 036d102c-ea6b-4b67-b1e3-d9eef877f6a0 · outbound

This paper cites InstructIR: High-Quality Image Restoration Following Human Instructions.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents InstructIR: High-Quality Image Restoration Following Human Instructions

Reference 8

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Observation 815b772b-1c56-4b94-8b26-4db979aa9af2 · outbound

This paper cites Image super-resolution using deep convolutional net- works.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Image super-resolution using deep convolutional net- works

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:12.509741Z

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Observation c3a63179-f830-4c6b-999a-5e806ba716f4 · outbound

This paper cites Large Language Model for Lossless Image Compression with Visual Prompts.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Large Language Model for Lossless Image Compression with Visual Prompts

Reference 10

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Observation 8a1eb445-b991-443d-8e4b-883cf377db05 · outbound

This paper cites Generative diffusion prior for unified image restoration and enhancement.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Generative diffusion prior for unified image restoration and enhancement

Reference 11

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Observation 434dadc3-9af8-4810-b233-3cbb75c2f509 · outbound

This paper cites Openagi: When llm meets domain experts.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Openagi: When llm meets domain experts

Reference 12

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Observation d9e76664-a755-4259-925c-99ff9e6ec574 · outbound

This paper cites MambaIRv2: Attentive State Space Restoration.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents MambaIRv2: Attentive State Space Restoration

Reference 13

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Observation 8a021c38-29e2-4d48-9a61-f9eb448b9a55 · outbound

This paper cites Mambair: A simple baseline for image restoration with state-space model.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Mambair: A simple baseline for image restoration with state-space model

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:12.456588Z

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Observation a6c3ef35-155f-47d9-8ba8-f279f8939f58 · outbound

This paper cites Visual program- ming: Compositional visual reasoning without training.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Visual program- ming: Compositional visual reasoning without training

Reference 15

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Observation d41417ed-d0df-43eb-ac94-5e68ac107254 · outbound

This paper cites Single image super-resolution from transformed self-exemplars.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Single image super-resolution from transformed self-exemplars

Reference 16

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Observation a256351d-b53d-456a-9c66-a36135a5602e · outbound

This paper cites Photo- realistic single image super-resolution using a generative ad- versarial network.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Photo- realistic single image super-resolution using a generative ad- versarial network

Reference 17

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Observation cb82c3b2-4801-4788-b972-61b6bcf29deb · outbound

This paper cites Benchmarking single- image dehazing and beyond.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Benchmarking single- image dehazing and beyond

Reference 18

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Observation b8e8fc0e-7af4-407e-8d54-383b2b49adfb · outbound

This paper cites All-in-one image restoration for unknown corruption.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents All-in-one image restoration for unknown corruption

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:12.368823Z

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Observation 1acb091a-3681-4f4b-85f8-e8eb0b071115 · outbound

This paper cites Blip- 2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Blip- 2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T21:50:10.950340Z

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Observation 21d57936-eb0c-4fb8-a966-3d205485987b · outbound

This paper cites Efficient and explicit modelling of image hierarchies for image restora- tion.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Efficient and explicit modelling of image hierarchies for image restora- tion

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T21:50:10.956707Z

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Observation f1147482-5ac2-4337-baf5-7ab8d8d6ddf2 · outbound

This paper cites Swinir: Image restoration using swin transformer.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Swinir: Image restoration using swin transformer

Reference 22

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Observation 29a0590e-6c39-44f3-9648-65b84c2c83fd · outbound

This paper cites Enhanced deep residual networks for single image super-resolution.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Enhanced deep residual networks for single image super-resolution

Reference 23

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Observation aee752a6-09be-42f7-8dbc-3e47d1ad6c88 · outbound

This paper cites Chameleon: Plug-and-play compositional reasoning with large language models.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Chameleon: Plug-and-play compositional reasoning with large language models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:12.288220Z

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Observation 91358287-4537-43d1-91e6-89d1bd7a81bd · outbound

This paper cites ProRes: Exploring Degradation-aware Visual Prompt for Universal Image Restoration.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents ProRes: Exploring Degradation-aware Visual Prompt for Universal Image Restoration

Reference 25

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Observation 0ba493d4-8696-42b3-98f1-3ddff080b1d5 · outbound

This paper cites Waterloo exploration database: New challenges for image quality as- sessment models.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Waterloo exploration database: New challenges for image quality as- sessment models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:12.268274Z

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Observation cf310897-0a62-41bf-b30e-d178753ab6d7 · outbound

This paper cites A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:12.250273Z

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Observation cd985205-e321-42d3-a5bb-73d2084d55b1 · outbound

This paper cites Sketch-based manga retrieval using manga109 dataset.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Sketch-based manga retrieval using manga109 dataset

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:12.230177Z

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Observation 3127c43c-9a6f-4105-80a8-1a8fa872ea22 · outbound

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LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Unresolved cited work

Reference 29

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Observation 5e8f42e0-4784-4a8c-85b4-fc41bf390932 · outbound

This paper cites Augmented Language Models: a Survey.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Augmented Language Models: a Survey

Reference 30

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Observation 4e59ba38-25ed-4e99-be01-4ec5fa60ef3e · outbound

This paper cites completely blind.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents completely blind

Reference 31

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Observation b8cf2b81-c1b7-4d09-b6ca-32818976e621 · outbound

This paper cites Embodiedgpt: Vision-language pre-training via embodied chain of thought.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Embodiedgpt: Vision-language pre-training via embodied chain of thought

Reference 32

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Observation efc3b614-8187-4cc2-9ef9-92b519b67dd6 · outbound

This paper cites Gpt-4 technical report, 2023.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Gpt-4 technical report, 2023

Reference 33

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Observation 92125719-83b0-44d0-ae0c-f21a4db567b0 · outbound

This paper cites PromptIR: Prompting for All-in-One Blind Image Restoration.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents PromptIR: Prompting for All-in-One Blind Image Restoration

Reference 34

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

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Code Llama: Open Foundation Models for Code

Reference 35

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Observation 4fa438ff-f3a5-48ff-96b4-a145bdd881a2 · outbound

This paper cites Toolformer: Lan- guage models can teach themselves to use tools.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Toolformer: Lan- guage models can teach themselves to use tools

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:12.147284Z

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Observation 6c967387-e0ce-4a10-bfc2-af65594f4ced · outbound

This paper cites Velma: Verbalization embodiment of llm agents for vision and language navigation in street view.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Velma: Verbalization embodiment of llm agents for vision and language navigation in street view

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:12.128915Z

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Observation 75482cab-a53d-436c-8a2f-9ebd629b376a · outbound

This paper cites Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face

Reference 38

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Observation 35270167-7a97-4928-af06-74a134b273a2 · outbound

This paper cites Reflexion: Language agents with verbal reinforcement learning.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Reflexion: Language agents with verbal reinforcement learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:12.093644Z

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Observation 1c6ad935-63b0-4bcc-b609-ea051fe35889 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 40

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This paper cites Vipergpt: Vi- sual inference via python execution for reasoning.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Vipergpt: Vi- sual inference via python execution for reasoning

Reference 41

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Observation 40985add-d3d6-48cd-9aaf-d93dea9bab6e · outbound

This paper cites Ntire 2017 challenge on single image super-resolution: Methods and results.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Ntire 2017 challenge on single image super-resolution: Methods and results

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:12.051738Z

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Observation d557b078-754d-4ed3-b0d0-d9c7a3689b07 · outbound

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

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 43

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Observation 57676b9e-d4b7-4908-a418-5df361f0fdfa · outbound

This paper cites Ex- ploring clip for assessing the look and feel of images.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Ex- ploring clip for assessing the look and feel of images

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:12.034169Z

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Observation 7ca601da-b8ab-4d85-82f7-4605768c9324 · outbound

This paper cites Ultra-high-definition low-light image enhancement: A benchmark and transformer-based method.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Ultra-high-definition low-light image enhancement: A benchmark and transformer-based method

Reference 45

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Observation 56a235a7-bc44-4f2b-98da-ad83696fc9db · outbound

This paper cites Re- covering realistic texture in image super-resolution by deep spatial feature transform.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Re- covering realistic texture in image super-resolution by deep spatial feature transform

Reference 46

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Observation 8d1fbda6-7aa9-4f4f-b9e7-c0daaf8822bc · outbound

This paper cites Esrgan: En- hanced super-resolution generative adversarial networks.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Esrgan: En- hanced super-resolution generative adversarial networks

Reference 47

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Observation e5681ea9-1100-49a9-a59b-90cabada8c1d · outbound

This paper cites Real-esrgan: Training real-world blind super-resolution with pure synthetic data.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Real-esrgan: Training real-world blind super-resolution with pure synthetic data

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:11.970760Z

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Observation dcfe73b2-b419-4f84-8dd1-01a5fdb92dd5 · outbound

This paper cites Images speak in images: A generalist painter for in-context visual learning.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Images speak in images: A generalist painter for in-context visual learning

Reference 49

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Observation 30666326-53ff-4f48-898b-a03e72498599 · outbound

This paper cites Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels

Reference 50

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Source-reported events for the cited work

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Observation 7b0939a4-25f2-46bf-9423-079ba7e5eafc · outbound

This paper cites Towards open-ended visual quality comparison, 2024.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Towards open-ended visual quality comparison, 2024

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:11.939932Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 673a462f-c004-4176-8628-a264b606f20a · outbound

This paper cites Diffir: Efficient diffusion model for image restoration.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Diffir: Efficient diffusion model for image restoration

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:11.921855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 243e1d4a-a73e-4a27-a85e-d9071652084f · outbound

This paper cites Learning texture transformer network for image super-resolution.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Learning texture transformer network for image super-resolution

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:11.905055Z

Source-reported events for the cited work

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Observation e995cae7-ee60-438c-b2b6-ce275abfd0c3 · outbound

This paper cites Octopus: Embodied Vision-Language Programmer from Environmental Feedback.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Octopus: Embodied Vision-Language Programmer from Environmental Feedback

Reference 54

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Observation 8d3037da-d1bf-4175-ba1d-3a020ab9831c · outbound

This paper cites Maniqa: Multi-dimension attention network for no-reference image quality assessment.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Maniqa: Multi-dimension attention network for no-reference image quality assessment

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:11.887612Z

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Observation ce5c498a-bc93-4fbc-bf78-02aa6f4a1ef3 · outbound

This paper cites MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action

Reference 56

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Observation 2f7e0593-5f6b-473d-9b81-d977d1e205d0 · outbound

This paper cites Depicting beyond scores: Advanc- ing image quality assessment through multi-modal language models.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Depicting beyond scores: Advanc- ing image quality assessment through multi-modal language models

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:11.869169Z

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Observation 6001efff-74dd-48bd-93ac-1ae3d5c48f86 · outbound

This paper cites Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild

Reference 58

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Observation c691e23e-2845-4feb-850e-f4c31f0a23c4 · outbound

This paper cites Resshift: Efficient diffusion model for image super-resolution by resid- ual shifting.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Resshift: Efficient diffusion model for image super-resolution by resid- ual shifting

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:11.851485Z

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Observation 82b74cf0-d92b-46a9-abc6-293475057278 · outbound

This paper cites Restormer: Efficient transformer for high-resolution image restoration.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Restormer: Efficient transformer for high-resolution image restoration

Reference 60

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Observation a627171f-3e99-4a8b-8401-e8b7a8cf906e · outbound

This paper cites On single image scale-up using sparse-representations.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents On single image scale-up using sparse-representations

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:11.815910Z

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Observation 0f877b55-6d02-48d0-97be-715702afbf5c · outbound

This paper cites Designing a practical degradation model for deep blind image super-resolution.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Designing a practical degradation model for deep blind image super-resolution

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:11.795836Z

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Observation e8b1bb0d-fab8-49ec-b8d9-8973a574dac5 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents The unreasonable effectiveness of deep features as a perceptual metric

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:11.775950Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e1ad1178-7cc7-4df7-b9da-ed567731189e · outbound

This paper cites Residual dense network for image super-resolution.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Residual dense network for image super-resolution

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:11.756851Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 73bc81da-1ad7-4af5-a239-0a3c7f6fe1de · outbound

This paper cites LM4LV: A Frozen Large Language Model for Low-level Vision Tasks.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents LM4LV: A Frozen Large Language Model for Low-level Vision Tasks

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T21:50:11.222089Z

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Observation 4eab24ad-ec0c-41ad-ac98-ef05a6e0f0d0 · outbound

This paper cites We list the details of training iterations for each stage, the total number of training iterations, and the initial weights of loss functions in Table 2.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents We list the details of training iterations for each stage, the total number of training iterations, and the initial weights of loss functions in Table 2

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:11.738134Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5f65f287-8f27-4ba2-952f-cd376a9b1517 · outbound

This paper cites As demonstrated in the Table, in the all-in-one IR task, LossAgent does not perform as robustly as in the other two tasks.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents As demonstrated in the Table, in the all-in-one IR task, LossAgent does not perform as robustly as in the other two tasks

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:11.719823Z

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Observation 642b11b5-211d-4647-9b35-89be6b2b870c · outbound

This paper cites an unresolved cited work.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-11T21:50:11.700381Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3eaa5f4c-1205-4e34-9195-7ff4e58c2c17 · outbound

This paper cites L1:Perceptual:GAN=0.7:0.3:0.05.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents L1:Perceptual:GAN=0.7:0.3:0.05

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:50:11.660700Z

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Observation fe950fc2-93bd-4843-a5e1-2cbce5f9df4e · outbound

This paper cites an unresolved cited work.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-11T21:50:11.639672Z

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Observation 8d3f7a97-59d7-49d7-8ef6-91e8a5926fd1 · outbound

This paper cites an unresolved cited work.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-11T21:50:11.618997Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 15daa014-4a6c-4e54-b3d0-2ff8cfacd83f · outbound

This paper cites an unresolved cited work.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-08-11T21:50:11.600697Z

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Observation a9125a12-4f08-421f-b95c-68e0d89d8694 · outbound

This paper cites Model Training.

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Model Training

Reference 5000

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