Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T21:50:11.273397Z
Paper Citation Record · LEDGER
LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents
As of 22 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 1 inbound Pith citation observation for arXiv:2412.04090.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T11:38:34.520355Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-16T11:38:35.974881Z
Reference resolution
73 of 73 outbound references displayed
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- unresolved28
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External citation measurements
No source-named external measurement is stored.
Outbound references
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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
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
- Resolution
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Source-reported events for the cited work
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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
- Resolution
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Source-reported events for the cited work
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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
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Source-reported events for the cited work
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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
- Resolution
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Source-reported events for the cited work
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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
- Resolution
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Source-reported events for the cited work
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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
- Resolution
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Source-reported events for the cited work
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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
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Source-reported events for the cited work
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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
- Resolution
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Source-reported events for the cited work
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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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
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Source-reported events for the cited work
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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
- Resolution
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Source-reported events for the cited work
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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
- Resolution
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Source-reported events for the cited work
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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
- Resolution
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Source-reported events for the cited work
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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
- Resolution
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Source-reported events for the cited work
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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
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Source-reported events for the cited work
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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
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
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Source-reported events for the cited work
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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
- Resolution
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Source-reported events for the cited work
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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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
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
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
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
- Resolution
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Source-reported events for the cited work
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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
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Source-reported events for the cited work
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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
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Source-reported events for the cited work
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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
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Source-reported events for the cited work
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Observation 3127c43c-9a6f-4105-80a8-1a8fa872ea22 · outbound
This paper cites an unresolved cited work.
LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Unresolved cited work
Reference 29
- Resolution
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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
- Resolution
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Source-reported events for the cited work
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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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LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Code Llama: Open Foundation Models for Code
Reference 35
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Source-reported events for the cited work
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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
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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
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Observation 75482cab-a53d-436c-8a2f-9ebd629b376a · outbound
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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
- Resolution
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Observation 35270167-7a97-4928-af06-74a134b273a2 · outbound
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LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Reflexion: Language agents with verbal reinforcement learning
Reference 39
- Resolution
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Source-reported events for the cited work
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Observation 1c6ad935-63b0-4bcc-b609-ea051fe35889 · outbound
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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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LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents Vipergpt: Vi- sual inference via python execution for reasoning
Reference 41
- Resolution
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Source-reported events for the cited work
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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
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Observation d557b078-754d-4ed3-b0d0-d9c7a3689b07 · outbound
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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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Source-reported events for the cited work
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Observation 57676b9e-d4b7-4908-a418-5df361f0fdfa · outbound
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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
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Source-reported events for the cited work
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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
- Resolution
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- raw_fallback, observed 2026-08-11T21:50:12.016070Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T21:50:11.999247Z
Source-reported events for the cited work
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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
- Resolution
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Source-reported events for the cited work
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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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+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 2026-08-22T06:32:14.747728+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
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T21:50:11.157794Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T21:50:11.169700Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T21:50:11.180430Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T21:50:11.192543Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
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
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-11T21:50:11.680532Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Pith citing papers
Observation ef0d8445-ff8b-4fb6-a0f6-42c18cbc46b8 · inbound
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: KwaiSR Dataset and Study cites this paper.
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: KwaiSR Dataset and Study LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents
Reference 22
- Resolution
- verified exact
- local_arxiv, observed 2026-08-16T11:38:35.980206Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.