✨OpenNovelty: An LLM-powered Agentic System for Verifiable Scholarly Novelty Assessment

📝 Summary:
OpenNovelty is an LLM-powered agentic system for verifiable scholarly novelty assessment in peer review. It retrieves and analyzes prior work via semantic search and taxonomy construction, generating evidence-backed reports grounded in real papers. This tool aims to promote fair, consistent, and ...

🔹 Publication Date: Published on Jan 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.01576
• PDF: https://arxiv.org/pdf/2601.01576
• Project Page: https://www.opennovelty.org/
• Github: https://github.com/january-blue/OpenNovelty

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✨COMPASS: A Framework for Evaluating Organization-Specific Policy Alignment in LLMs

📝 Summary:
COMPASS evaluates large language models' compliance with organizational policies, revealing significant gaps in enforcing prohibitions despite strong performance on legitimate requests. AI-generated s...

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.01836
• PDF: https://arxiv.org/pdf/2601.01836
• Github: https://github.com/AIM-Intelligence/COMPASS

🔹 Models citing this paper:
• https://huggingface.co/AIM-Intelligence/COMPASS_Qwen2.5-7B-Instruct_LoRA
• https://huggingface.co/AIM-Intelligence/COMPASS_gemma-3-4b-it_LoRA

✨ Datasets citing this paper:
• https://huggingface.co/datasets/AIM-Intelligence/COMPASS-Policy-Alignment-Testbed-Dataset
• https://huggingface.co/datasets/AIM-Intelligence/COMPASS-Policy-aware-SFT-Dataset

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✨Project Ariadne: A Structural Causal Framework for Auditing Faithfulness in LLM Agents

📝 Summary:
Project Ariadne uses structural causal models and counterfactual logic to evaluate the causal integrity of LLM reasoning, revealing a faithfulness gap where reasoning traces are not reliable drivers o...

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02314
• PDF: https://arxiv.org/pdf/2601.02314
• Github: https://github.com/skhanzad/AridadneXAI

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✨GARDO: Reinforcing Diffusion Models without Reward Hacking

📝 Summary:
Online reinforcement learning for diffusion model fine-tuning suffers from reward hacking due to proxy reward mismatches, which GARDO addresses through selective regularization, adaptive reference upd...

🔹 Publication Date: Published on Dec 30, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24138
• PDF: https://arxiv.org/pdf/2512.24138
• Project Page: https://tinnerhrhe.github.io/gardo_project/
• Github: https://github.com/tinnerhrhe/gardo

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✨IMA++: ISIC Archive Multi-Annotator Dermoscopic Skin Lesion Segmentation Dataset

📝 Summary:
A large-scale public multi-annotator skin lesion segmentation dataset is introduced with extensive metadata for annotator analysis and consensus modeling. AI-generated summary Multi-annotator medical ...

🔹 Publication Date: Published on Dec 25, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21472
• PDF: https://arxiv.org/pdf/2512.21472
• Github: https://github.com/sfu-mial/IMAplusplus

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✨Toward Stable Semi-Supervised Remote Sensing Segmentation via Co-Guidance and Co-Fusion

📝 Summary:
A semi-supervised remote sensing image segmentation framework combines vision-language and self-supervised models to reduce pseudo-label drift through dual-student architecture and semantic co-guidanc...

🔹 Publication Date: Published on Dec 28, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23035
• PDF: https://arxiv.org/pdf/2512.23035
• Project Page: https://xavierjiezou.github.io/Co2S/
• Github: https://github.com/XavierJiezou/Co2S

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✨SWE-Lego: Pushing the Limits of Supervised Fine-tuning for Software Issue Resolving

📝 Summary:
SWE-Lego achieves state-of-the-art software issue resolution through a lightweight supervised fine-tuning approach. It uses a high-quality dataset and refined training procedures like error masking and a difficulty-based curriculum, outperforming complex methods. Performance is further boosted by...

🔹 Publication Date: Published on Jan 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.01426
• PDF: https://arxiv.org/pdf/2601.01426
• Project Page: https://github.com/SWE-Lego/SWE-Lego
• Github: https://github.com/SWE-Lego/SWE-Lego

🔹 Models citing this paper:
• https://huggingface.co/SWE-Lego/SWE-Lego-Qwen3-8B
• https://huggingface.co/SWE-Lego/SWE-Lego-Qwen3-32B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/SWE-Lego/SWE-Lego-Real-Data
• https://huggingface.co/datasets/SWE-Lego/SWE-Lego-Synthetic-Data

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#SoftwareEngineering #MachineLearning #LLM #FineTuning #AIforCode

✨M-ErasureBench: A Comprehensive Multimodal Evaluation Benchmark for Concept Erasure in Diffusion Models

📝 Summary:
Existing concept erasure methods in diffusion models are vulnerable to non-text inputs. M-ErasureBench is a new multimodal evaluation framework, and IRECE is a module to restore robustness against these attacks, reducing concept reproduction.

🔹 Publication Date: Published on Dec 28, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22877
• PDF: https://arxiv.org/pdf/2512.22877

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#DiffusionModels #ConceptErasure #MultimodalAI #AISafety #MachineLearning

✨InfiniDepth: Arbitrary-Resolution and Fine-Grained Depth Estimation with Neural Implicit Fields

📝 Summary:
InfiniDepth represents depth as neural implicit fields using a local implicit decoder, enabling continuous 2D coordinate querying for arbitrary-resolution depth estimation and superior performance in ...

🔹 Publication Date: Published on Jan 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03252
• PDF: https://arxiv.org/pdf/2601.03252
• Github: https://zju3dv.github.io/InfiniDepth

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✨FFP-300K: Scaling First-Frame Propagation for Generalizable Video Editing

📝 Summary:
A new large-scale video dataset and framework are presented that enable effective first-frame propagation without runtime guidance through adaptive spatio-temporal positional encoding and self-distill...

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.01720
• PDF: https://arxiv.org/pdf/2601.01720
• Project Page: https://ffp-300k.github.io/

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