Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-12T00:58:53.626310Z
Paper Citation Record · LEDGER
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria
As of 3 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2605.08354.
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 2026-08-03T06:30:56.289259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-01T08:28:06.378236Z
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
57 of 57 outbound references displayed
- verified exact27
- verified fuzzy27
- unresolved2
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 3950b2ef-8a37-4881-a12a-ed5e522102e3 · outbound
This paper cites Critique-out-Loud Reward Models.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Critique-out-Loud Reward Models
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T08:36:24.356872Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 3c1de410-f99e-4810-9016-3affbb0af498 · outbound
This paper cites Qwen3-VL Technical Report.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Qwen3-VL Technical Report
Reference 2
- Resolution
- verified exact
- local_arxiv, observed 2026-05-12T08:36:24.331270Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 67e4e27a-9bf3-4a2e-bd66-08d3fb699d2d · outbound
This paper cites Moderate Deviations for the Capacity of the Random Walk range in dimension four.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Moderate Deviations for the Capacity of the Random Walk range in dimension four
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T08:36:24.292545Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 751c03d4-9d29-4bd9-b0fa-7bd35d7c2d10 · outbound
This paper cites Training Diffusion Models with Reinforcement Learning.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Training Diffusion Models with Reinforcement Learning
Reference 4
- Resolution
- verified exact
- local_arxiv, observed 2026-05-12T08:36:24.298241Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 3028d25a-25a0-4fcb-b689-756345ae9744 · outbound
This paper cites Instructpix2pix: Learning to follow image editing instructions.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Instructpix2pix: Learning to follow image editing instructions
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.718423Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 94ec0c11-2b73-4052-ade5-f0e772219fb8 · outbound
This paper cites BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-05-12T08:36:24.266270Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation bb07873a-e4b5-4054-b07a-f76fb25daa70 · outbound
This paper cites ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image Generation.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image Generation
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T08:36:24.278633Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 14a70efd-31b6-4aef-8aab-62b2bfe6e0db · outbound
This paper cites Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-05-12T08:36:24.236265Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 7e40d11d-1492-47f6-b196-1ce587337ad4 · outbound
This paper cites Emerging properties in unified multimodal pretraining.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Emerging properties in unified multimodal pretraining
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.708913Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 220490a5-c16f-4634-9554-e69ecdd5b569 · outbound
This paper cites Dpok: Reinforcement learning for fine-tuning text-to-image diffusion models.Advances in Neural Information Processing Systems, 36:79858–79885.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Dpok: Reinforcement learning for fine-tuning text-to-image diffusion models.Advances in Neural Information Processing Systems, 36:79858–79885
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.705009Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 94418f0c-a87d-4137-8814-a051a95c7b09 · outbound
This paper cites Geneval: An object-focused framework for evaluating text-to-image alignment.Advances in Neural Information Processing Systems, 36:52132–52152.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Geneval: An object-focused framework for evaluating text-to-image alignment.Advances in Neural Information Processing Systems, 36:52132–52152
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.700699Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation aac6e40c-4f2e-499c-a4ec-3e83fe2e7850 · outbound
This paper cites Gemini 3.1 Pro - Model Card.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Gemini 3.1 Pro - Model Card
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.702418Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 82e0c2ce-0264-4cb4-8b98-f868390fdc05 · outbound
This paper cites Llm- rubric: A multidimensional, calibrated approach to automated evaluation of natural language texts.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Llm- rubric: A multidimensional, calibrated approach to automated evaluation of natural language texts
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.710887Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 611c32cd-5636-45e5-9627-99183a15092b · outbound
This paper cites Clipscore: A reference-free evaluation metric for image captioning.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Clipscore: A reference-free evaluation metric for image captioning
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.714590Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation ebd8055c-2e99-4e29-a399-075628cbe76e · outbound
This paper cites ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment
Reference 15
- Resolution
- verified exact
- local_arxiv, observed 2026-05-12T08:36:24.230342Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 0042b714-77f7-44f0-84a3-baadee46c78e · outbound
This paper cites Multimodal rewardbench 2: Evaluating omni reward models for interleaved text and image.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Multimodal rewardbench 2: Evaluating omni reward models for interleaved text and image
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T08:36:24.388067Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 77ff6fad-6979-4da5-a4cc-1e04d4a728b1 · outbound
This paper cites Au- torubric: Rubric-based generative rewards for faithful multimodal reasoning.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Au- torubric: Rubric-based generative rewards for faithful multimodal reasoning
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.712808Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 29a40cbd-35da-484a-97d3-ce1fc4c956a0 · outbound
This paper cites Prometheus: Inducing fine-grained evaluation capability in language models.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Prometheus: Inducing fine-grained evaluation capability in language models
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.722346Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation de4bc6dd-4494-4cfa-a0b4-536200de7216 · outbound
This paper cites Pick-a-pic: An open dataset of user preferences for text-to-image generation.Advances in neural information processing systems, 36:36652–36663.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Pick-a-pic: An open dataset of user preferences for text-to-image generation.Advances in neural information processing systems, 36:36652–36663
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.724310Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 35ad89ad-905c-41bb-bb79-72a63a99e4c9 · outbound
This paper cites FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
Reference 20
- Resolution
- verified exact
- local_arxiv, observed 2026-05-12T08:36:24.272052Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation c9924609-55b8-447e-b3ef-2815b36d444a · outbound
This paper cites Holistic evaluation of text-to-image models.Advances in Neural Information Processing Systems, 36:69981–70011.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Holistic evaluation of text-to-image models.Advances in Neural Information Processing Systems, 36:69981–70011
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.720279Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation c413196e-4f77-489b-afb1-8e4643481b88 · outbound
This paper cites Hp-edit: A human- preference post-training framework for image editing.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Hp-edit: A human- preference post-training framework for image editing
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.716499Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 35f3e1ca-2e85-4bea-8df1-2b605d8329aa · outbound
This paper cites Uniworld-V2: Reinforce Image Editing with Diffusion Negative-aware Finetuning and MLLM Implicit Feedback.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Uniworld-V2: Reinforce Image Editing with Diffusion Negative-aware Finetuning and MLLM Implicit Feedback
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T18:01:19.940440Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation dda05972-e772-46f8-b8de-1766a9614bae · outbound
This paper cites Step1X-Edit: A Practical Framework for General Image Editing.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Step1X-Edit: A Practical Framework for General Image Editing
Reference 24
- Resolution
- verified exact
- local_arxiv, observed 2026-05-12T08:36:24.303623Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation a2450f75-5b43-4524-8d30-c51414d54a7b · outbound
This paper cites Examining reasoning llms-as-judges in non-verifiable llm post-training.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Examining reasoning llms-as-judges in non-verifiable llm post-training
Reference 25
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T08:36:24.309907Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 5fe14fc3-612b-47fa-bcbc-4ef25e8ad837 · outbound
This paper cites Editscore: Unlocking online rl for image editing via high-fidelity reward modeling.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Editscore: Unlocking online rl for image editing via high-fidelity reward modeling
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T08:36:24.248228Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 726d36de-27e6-452d-81be-9bcd8be1e5b9 · outbound
This paper cites Janusflow: Harmonizing autoregression and rectified flow for unified multimodal understanding and generation.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Janusflow: Harmonizing autoregression and rectified flow for unified multimodal understanding and generation
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.756326Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 8a51b68d-fe4a-4d2d-8c29-077d82a2c921 · outbound
This paper cites Hpsv3: Towards wide-spectrum hu- man preference score.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Hpsv3: Towards wide-spectrum hu- man preference score
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.751895Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 17f98ef0-ee7a-41bd-9971-374c7a90c3b5 · outbound
This paper cites Rubriceval: A rubric-level meta-evaluation benchmark for llm judges in instruction following.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Rubriceval: A rubric-level meta-evaluation benchmark for llm judges in instruction following
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T08:36:24.350557Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 86311528-4dd1-4a20-a056-6891e40b38c8 · outbound
This paper cites Rubric is all you need: Improving llm-based code evaluation with question-specific rubrics.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Rubric is all you need: Improving llm-based code evaluation with question-specific rubrics
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.747713Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation a103dbe1-ae2d-40c5-a7c2-a3f824e621ca · outbound
This paper cites Sdxl: Improving latent diffusion models for high-resolution image synthesis.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Sdxl: Improving latent diffusion models for high-resolution image synthesis
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.739283Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation ed96d2fd-1377-400c-b345-3b3f4a14330f · outbound
This paper cites Emu edit: Precise image editing via recognition and generation tasks.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Emu edit: Precise image editing via recognition and generation tasks
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.741093Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 678c22b1-9ed1-47d1-80a1-6820a95bebfd · outbound
This paper cites OpenAI GPT-5 System Card.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria OpenAI GPT-5 System Card
Reference 33
- Resolution
- verified exact
- local_arxiv, observed 2026-05-12T08:36:24.325683Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation f1ef059a-2c0d-4321-9b23-3d1817874303 · outbound
This paper cites Diffusion model alignment using direct preference optimization.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Diffusion model alignment using direct preference optimization
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.743213Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 6cec0736-4e59-48bd-b680-57ae61cf8c34 · outbound
This paper cites Large language models are not fair evaluators.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Large language models are not fair evaluators
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.745480Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation c47fdb40-26ed-4b05-abe8-a256c3429519 · outbound
This paper cites Emu3: Next-Token Prediction is All You Need.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Emu3: Next-Token Prediction is All You Need
Reference 36
- Resolution
- verified exact
- local_arxiv, observed 2026-05-12T08:36:24.285094Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 7b4a540b-ee6c-4d0f-8429-2e42f7c35e6b · outbound
This paper cites Unigenbench++: A unified semantic evaluation benchmark for text-to-image generation.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Unigenbench++: A unified semantic evaluation benchmark for text-to-image generation
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.749780Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 5aa8752d-adbe-44ac-a7ce-fc7e59aee817 · outbound
This paper cites Unified multimodal chain-of-thought reward model through reinforcement fine-tuning.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Unified multimodal chain-of-thought reward model through reinforcement fine-tuning
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.758453Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 7fc6c5ce-6d3f-41cd-b607-275f81146135 · outbound
This paper cites Unified Reward Model for Multimodal Understanding and Generation.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Unified Reward Model for Multimodal Understanding and Generation
Reference 39
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T00:44:31.060111Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation f0eeac27-edf0-4553-bf15-334b90c1b2d2 · outbound
This paper cites Tiif-bench: How does your t2i model follow your instructions?.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Tiif-bench: How does your t2i model follow your instructions?
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.706839Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation db1432e4-5424-4d15-802e-b1174662c4e7 · outbound
This paper cites Qwen-Image Technical Report.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Qwen-Image Technical Report
Reference 41
- Resolution
- verified exact
- local_arxiv, observed 2026-05-12T08:36:24.375973Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 74ef5eb8-a994-4b05-96d2-987a0ca67840 · outbound
This paper cites OmniGen2: Towards Instruction-Aligned Multimodal Generation.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria OmniGen2: Towards Instruction-Aligned Multimodal Generation
Reference 42
- Resolution
- verified exact
- local_arxiv, observed 2026-05-12T08:36:24.337092Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 0168d091-d002-4654-8ff9-6885d5a3c785 · outbound
This paper cites Editreward: A human- aligned reward model for instruction-guided image editing.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Editreward: A human- aligned reward model for instruction-guided image editing
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T08:36:24.254754Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation b074d729-56c5-4882-8486-bb5b95583ec0 · outbound
This paper cites Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis
Reference 44
- Resolution
- verified exact
- local_arxiv, observed 2026-05-12T08:36:24.260284Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 44cda7a7-86de-4f23-a84f-482ac0943aa9 · outbound
This paper cites Show-o2: Improved Native Unified Multimodal Models.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Show-o2: Improved Native Unified Multimodal Models
Reference 45
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T18:51:16.424084Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 6078169b-dba5-4f1d-a15a-ad1c7a1278aa · outbound
This paper cites Auto-rubric: Learning from implicit weights to explicit rubrics for reward modeling.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Auto-rubric: Learning from implicit weights to explicit rubrics for reward modeling
Reference 46
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T08:36:24.363530Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 755eb721-4eb5-42e5-b070-53f743032229 · outbound
This paper cites Imagereward: Learning and evaluating human preferences for text-to-image generation.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Imagereward: Learning and evaluating human preferences for text-to-image generation
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.731663Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 1a017449-6d68-4f02-ba82-adcdb4578c77 · outbound
This paper cites FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets
Reference 48
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T08:36:24.343551Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation a7c18b8b-711a-4fef-b022-43eee0fbff61 · outbound
This paper cites Imgedit: A unified image editing dataset and benchmark.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Imgedit: A unified image editing dataset and benchmark
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.733363Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation f975d7cb-14a3-4f58-a567-83870abf3486 · outbound
This paper cites Anyedit: Mastering unified high-quality image editing for any idea.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Anyedit: Mastering unified high-quality image editing for any idea
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.735115Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation c2014af7-da42-4db9-9a2b-0df6d4396492 · outbound
This paper cites Magicbrush: A manually annotated dataset for instruction-guided image editing.Advances in Neural Information Processing Systems, 36:31428–31449.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Magicbrush: A manually annotated dataset for instruction-guided image editing.Advances in Neural Information Processing Systems, 36:31428–31449
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.726705Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 44412989-a5e7-4983-9dc1-32f30ac3e326 · outbound
This paper cites Trust your critic: Robust reward modeling and reinforcement learning for faithful image editing and generation.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Trust your critic: Robust reward modeling and reinforcement learning for faithful image editing and generation
Reference 52
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T08:36:24.242285Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 37c8974f-2203-41d6-8cb1-0f07f3b734fb · outbound
This paper cites DiffusionNFT: Online Diffusion Reinforcement with Forward Process.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria DiffusionNFT: Online Diffusion Reinforcement with Forward Process
Reference 53
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T16:54:31.055857Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation ef488f60-6337-473d-b08b-eebaafdf2909 · outbound
This paper cites an unresolved cited work.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Unresolved cited work
Reference 54
- Resolution
- unresolved
- raw_fallback, observed 2026-05-14T09:07:27.729933Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 42fe79d6-ed6f-4fdd-8b64-958bef705378 · outbound
This paper cites This preserves inter- criterion dependencies and avoids inconsistencies introduced by independent scoring and aggregation.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria This preserves inter- criterion dependencies and avoids inconsistencies introduced by independent scoring and aggregation
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T09:07:27.737387Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 2d79d28b-204c-4235-ae57-a10fde60d571 · outbound
This paper cites an unresolved cited work.
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria Unresolved cited work
Reference 56
- Resolution
- unresolved
- raw_fallback, observed 2026-05-14T09:07:27.753811Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 8d987a97-220b-4312-8642-088580d5ca10 · outbound
This paper cites rank": [rank_of_Image1, rank_of_Image2].
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria rank": [rank_of_Image1, rank_of_Image2]
Reference 57
- Resolution
- malformed identifier
- raw_fallback, observed 2026-05-14T09:07:27.760486Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Pith citing papers
Observation 4f787585-04d1-4d5b-8429-ec8508c652b4 · inbound
VideoCoCo: Code-as-CoT for Physically-Consistent Video Generation via an Agentic Dual-Engine System cites this paper.
VideoCoCo: Code-as-CoT for Physically-Consistent Video Generation via an Agentic Dual-Engine System Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T08:28:06.378236Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.