Scheduled Commit

data/2509.21789.json

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+{"paper_url": "https://huggingface.co/papers/2509.21789", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Exposing Hallucinations To Suppress Them: VLMs Representation Editing With Generative Anchors](https://huggingface.co/papers/2509.21997) (2025)\n* [D-LEAF: Localizing and Correcting Hallucinations in Multimodal LLMs via Layer-to-head Attention Diagnostics](https://huggingface.co/papers/2509.07864) (2025)\n* [Mitigating Hallucination in Multimodal LLMs with Layer Contrastive Decoding](https://huggingface.co/papers/2509.25177) (2025)\n* [Two Causes, Not One: Rethinking Omission and Fabrication Hallucinations in MLLMs](https://huggingface.co/papers/2509.00371) (2025)\n* [Diving into Mitigating Hallucinations from a Vision Perspective for Large Vision-Language Models](https://huggingface.co/papers/2509.13836) (2025)\n* [MRFD: Multi-Region Fusion Decoding with Self-Consistency for Mitigating Hallucinations in LVLMs](https://huggingface.co/papers/2508.10264) (2025)\n* [Analyzing and Mitigating Object Hallucination: A Training Bias Perspective](https://huggingface.co/papers/2508.04567) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2509.24203.json

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+{"paper_url": "https://huggingface.co/papers/2509.24203", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [It Takes Two: Your GRPO Is Secretly DPO](https://huggingface.co/papers/2510.00977) (2025)\n* [Prosperity before Collapse: How Far Can Off-Policy RL Reach with Stale Data on LLMs?](https://huggingface.co/papers/2510.01161) (2025)\n* [FlowRL: Matching Reward Distributions for LLM Reasoning](https://huggingface.co/papers/2509.15207) (2025)\n* [HAEPO: History-Aggregated Exploratory Policy Optimization](https://huggingface.co/papers/2508.18884) (2025)\n* [Towards a Unified View of Large Language Model Post-Training](https://huggingface.co/papers/2509.04419) (2025)\n* [CE-GPPO: Coordinating Entropy via Gradient-Preserving Clipping Policy Optimization in Reinforcement Learning](https://huggingface.co/papers/2509.20712) (2025)\n* [One-Token Rollout: Guiding Supervised Fine-Tuning of LLMs with Policy Gradient](https://huggingface.co/papers/2509.26313) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2509.24304.json

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+{"paper_url": "https://huggingface.co/papers/2509.24304", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Video-MTR: Reinforced Multi-Turn Reasoning for Long Video Understanding](https://huggingface.co/papers/2508.20478) (2025)\n* [FameMind: Frame-Interleaved Video Reasoning via Reinforcement Learning](https://huggingface.co/papers/2509.24008) (2025)\n* [TAR-TVG: Enhancing VLMs with Timestamp Anchor-Constrained Reasoning for Temporal Video Grounding](https://huggingface.co/papers/2508.07683) (2025)\n* [Reinforcing Video Reasoning Segmentation to Think Before It Segments](https://huggingface.co/papers/2508.11538) (2025)\n* [LOVE-R1: Advancing Long Video Understanding with an Adaptive Zoom-in Mechanism via Multi-Step Reasoning](https://huggingface.co/papers/2509.24786) (2025)\n* [ReWatch-R1: Boosting Complex Video Reasoning in Large Vision-Language Models through Agentic Data Synthesis](https://huggingface.co/papers/2509.23652) (2025)\n* [MOSS-ChatV: Reinforcement Learning with Process Reasoning Reward for Video Temporal Reasoning](https://huggingface.co/papers/2509.21113) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2509.25729.json

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+{"paper_url": "https://huggingface.co/papers/2509.25729", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Privacy-Aware In-Context Learning for Large Language Models](https://huggingface.co/papers/2509.13625) (2025)\n* [SynBench: A Benchmark for Differentially Private Text Generation](https://huggingface.co/papers/2509.14594) (2025)\n* [Zero-Shot Privacy-Aware Text Rewriting via Iterative Tree Search](https://huggingface.co/papers/2509.20838) (2025)\n* [Privacy-Aware Decoding: Mitigating Privacy Leakage of Large Language Models in Retrieval-Augmented Generation](https://huggingface.co/papers/2508.03098) (2025)\n* [RL-Finetuned LLMs for Privacy-Preserving Synthetic Rewriting](https://huggingface.co/papers/2508.19286) (2025)\n* [Adaptive Backtracking for Privacy Protection in Large Language Models](https://huggingface.co/papers/2508.06087) (2025)\n* [Evaluating Differentially Private Generation of Domain-Specific Text](https://huggingface.co/papers/2508.20452) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2509.26330.json

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+{"paper_url": "https://huggingface.co/papers/2509.26330", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [SETR: A Two-Stage Semantic-Enhanced Framework for Zero-Shot Composed Image Retrieval](https://huggingface.co/papers/2509.26012) (2025)\n* [Chain-of-Thought Re-ranking for Image Retrieval Tasks](https://huggingface.co/papers/2509.14746) (2025)\n* [UniFGVC: Universal Training-Free Few-Shot Fine-Grained Vision Classification via Attribute-Aware Multimodal Retrieval](https://huggingface.co/papers/2508.04136) (2025)\n* [EVENT-Retriever: Event-Aware Multimodal Image Retrieval for Realistic Captions](https://huggingface.co/papers/2509.00751) (2025)\n* [Enhancing Supervised Composed Image Retrieval via Reasoning-Augmented Representation Engineering](https://huggingface.co/papers/2508.11272) (2025)\n* [Beyond Simple Edits: Composed Video Retrieval with Dense Modifications](https://huggingface.co/papers/2508.14039) (2025)\n* [Dual Prompt Learning for Adapting Vision-Language Models to Downstream Image-Text Retrieval](https://huggingface.co/papers/2508.04028) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.00137.json

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+{"paper_url": "https://huggingface.co/papers/2510.00137", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation](https://huggingface.co/papers/2508.06781) (2025)\n* [RRRA: Resampling and Reranking through a Retriever Adapter](https://huggingface.co/papers/2508.11670) (2025)\n* [CoDiEmb: A Collaborative yet Distinct Framework for Unified Representation Learning in Information Retrieval and Semantic Textual Similarity](https://huggingface.co/papers/2508.11442) (2025)\n* [Zero-shot Multimodal Document Retrieval via Cross-modal Question Generation](https://huggingface.co/papers/2508.17079) (2025)\n* [Does Generative Retrieval Overcome the Limitations of Dense Retrieval?](https://huggingface.co/papers/2509.22116) (2025)\n* [Granite Embedding R2 Models](https://huggingface.co/papers/2508.21085) (2025)\n* [Improving Dense Passage Retrieval with Multiple Positive Passages](https://huggingface.co/papers/2508.09534) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.00428.json

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+{"paper_url": "https://huggingface.co/papers/2510.00428", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [PriorRG: Prior-Guided Contrastive Pre-training and Coarse-to-Fine Decoding for Chest X-ray Report Generation](https://huggingface.co/papers/2508.05353) (2025)\n* [CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decoding](https://huggingface.co/papers/2509.23379) (2025)\n* [AMRG: Extend Vision Language Models for Automatic Mammography Report Generation](https://huggingface.co/papers/2508.09225) (2025)\n* [EditGRPO: Reinforcement Learning with Post -Rollout Edits for Clinically Accurate Chest X-Ray Report Generation](https://huggingface.co/papers/2509.22812) (2025)\n* [RegionMed-CLIP: A Region-Aware Multimodal Contrastive Learning Pre-trained Model for Medical Image Understanding](https://huggingface.co/papers/2508.05244) (2025)\n* [RadEval: A framework for radiology text evaluation](https://huggingface.co/papers/2509.18030) (2025)\n* [RadAgents: Multimodal Agentic Reasoning for Chest X-ray Interpretation with Radiologist-like Workflows](https://huggingface.co/papers/2509.20490) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.01123.json

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+{"paper_url": "https://huggingface.co/papers/2510.01123", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Train Long, Think Short: Curriculum Learning for Efficient Reasoning](https://huggingface.co/papers/2508.08940) (2025)\n* [Metacognitive Reuse: Turning Recurring LLM Reasoning Into Concise Behaviors](https://huggingface.co/papers/2509.13237) (2025)\n* [Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models](https://huggingface.co/papers/2509.26626) (2025)\n* [Less is More Tokens: Efficient Math Reasoning via Difficulty-Aware Chain-of-Thought Distillation](https://huggingface.co/papers/2509.05226) (2025)\n* [BudgetThinker: Empowering Budget-aware LLM Reasoning with Control Tokens](https://huggingface.co/papers/2508.17196) (2025)\n* [RLAD: Training LLMs to Discover Abstractions for Solving Reasoning Problems](https://huggingface.co/papers/2510.02263) (2025)\n* [Explore-Execute Chain: Towards an Efficient Structured Reasoning Paradigm](https://huggingface.co/papers/2509.23946) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.01143.json

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+{"paper_url": "https://huggingface.co/papers/2510.01143", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [ParaThinker: Native Parallel Thinking as a New Paradigm to Scale LLM Test-time Compute](https://huggingface.co/papers/2509.04475) (2025)\n* [The Majority is not always right: RL training for solution aggregation](https://huggingface.co/papers/2509.06870) (2025)\n* [Jointly Reinforcing Diversity and Quality in Language Model Generations](https://huggingface.co/papers/2509.02534) (2025)\n* [Attention as a Compass: Efficient Exploration for Process-Supervised RL in Reasoning Models](https://huggingface.co/papers/2509.26628) (2025)\n* [Sample More to Think Less: Group Filtered Policy Optimization for Concise Reasoning](https://huggingface.co/papers/2508.09726) (2025)\n* [MEML-GRPO: Heterogeneous Multi-Expert Mutual Learning for RLVR Advancement](https://huggingface.co/papers/2508.09670) (2025)\n* [MoEs Are Stronger than You Think: Hyper-Parallel Inference Scaling with RoE](https://huggingface.co/papers/2509.17238) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.01179.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.01179", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries](https://huggingface.co/papers/2508.15760) (2025)\n* [DualTune: Decoupled Fine-Tuning for On-Device Agentic Systems](https://huggingface.co/papers/2510.00229) (2025)\n* [MCPVerse: An Expansive, Real-World Benchmark for Agentic Tool Use](https://huggingface.co/papers/2508.16260) (2025)\n* [ToolRM: Outcome Reward Models for Tool-Calling Large Language Models](https://huggingface.co/papers/2509.11963) (2025)\n* [ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction](https://huggingface.co/papers/2508.12685) (2025)\n* [MCPMark: A Benchmark for Stress-Testing Realistic and Comprehensive MCP Use](https://huggingface.co/papers/2509.24002) (2025)\n* [Towards General Agentic Intelligence via Environment Scaling](https://huggingface.co/papers/2509.13311) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.01260.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.01260", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [TinyAC: Bringing Autonomic Computing Principles to Resource-Constrained Systems](https://huggingface.co/papers/2509.19350) (2025)\n* [KubeIntellect: A Modular LLM-Orchestrated Agent Framework for End-to-End Kubernetes Management](https://huggingface.co/papers/2509.02449) (2025)\n* [LLMind 2.0: Distributed IoT Automation with Natural Language M2M Communication and Lightweight LLM Agents](https://huggingface.co/papers/2508.13920) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.01265.json

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+{"paper_url": "https://huggingface.co/papers/2510.01265", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Reinforcement Mid-Training](https://huggingface.co/papers/2509.24375) (2025)\n* [Reinforcement Learning on Pre-Training Data](https://huggingface.co/papers/2509.19249) (2025)\n* [VCRL: Variance-based Curriculum Reinforcement Learning for Large Language Models](https://huggingface.co/papers/2509.19803) (2025)\n* [Learning to Reason as Action Abstractions with Scalable Mid-Training RL](https://huggingface.co/papers/2509.25810) (2025)\n* [Proximal Supervised Fine-Tuning](https://huggingface.co/papers/2508.17784) (2025)\n* [One-Token Rollout: Guiding Supervised Fine-Tuning of LLMs with Policy Gradient](https://huggingface.co/papers/2509.26313) (2025)\n* [On the Generalization of SFT: A Reinforcement Learning Perspective with Reward Rectification](https://huggingface.co/papers/2508.05629) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.01284.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.01284", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [UniVerse-1: Unified Audio-Video Generation via Stitching of Experts](https://huggingface.co/papers/2509.06155) (2025)\n* [X-Streamer: Unified Human World Modeling with Audiovisual Interaction](https://huggingface.co/papers/2509.21574) (2025)\n* [HunyuanVideo-Foley: Multimodal Diffusion with Representation Alignment for High-Fidelity Foley Audio Generation](https://huggingface.co/papers/2508.16930) (2025)\n* [MIDAS: Multimodal Interactive Digital-humAn Synthesis via Real-time Autoregressive Video Generation](https://huggingface.co/papers/2508.19320) (2025)\n* [UniFlow-Audio: Unified Flow Matching for Audio Generation from Omni-Modalities](https://huggingface.co/papers/2509.24391) (2025)\n* [WAVE: Learning Unified&Versatile Audio-Visual Embeddings with Multimodal LLM](https://huggingface.co/papers/2509.21990) (2025)\n* [RAP: Real-time Audio-driven Portrait Animation with Video Diffusion Transformer](https://huggingface.co/papers/2508.05115) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.01304.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.01304", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Perception Before Reasoning: Two-Stage Reinforcement Learning for Visual Reasoning in Vision-Language Models](https://huggingface.co/papers/2509.13031) (2025)\n* [Learning Active Perception via Self-Evolving Preference Optimization for GUI Grounding](https://huggingface.co/papers/2509.04243) (2025)\n* [Simple o3: Towards Interleaved Vision-Language Reasoning](https://huggingface.co/papers/2508.12109) (2025)\n* [RewardMap: Tackling Sparse Rewards in Fine-grained Visual Reasoning via Multi-Stage Reinforcement Learning](https://huggingface.co/papers/2510.02240) (2025)\n* [More Thought, Less Accuracy? On the Dual Nature of Reasoning in Vision-Language Models](https://huggingface.co/papers/2509.25848) (2025)\n* [Look Less, Reason More: Rollout-Guided Adaptive Pixel-Space Reasoning](https://huggingface.co/papers/2510.01681) (2025)\n* [VideoChat-R1.5: Visual Test-Time Scaling to Reinforce Multimodal Reasoning by Iterative Perception](https://huggingface.co/papers/2509.21100) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.01346.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.01346", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Hilbert: Recursively Building Formal Proofs with Informal Reasoning](https://huggingface.co/papers/2509.22819) (2025)\n* [Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction](https://huggingface.co/papers/2508.03613) (2025)\n* [Lean Meets Theoretical Computer Science: Scalable Synthesis of Theorem Proving Challenges in Formal-Informal Pairs](https://huggingface.co/papers/2508.15878) (2025)\n* [GenesisGeo: Technical Report](https://huggingface.co/papers/2509.21896) (2025)\n* [LeanGeo: Formalizing Competitional Geometry problems in Lean](https://huggingface.co/papers/2508.14644) (2025)\n* [EconProver: Towards More Economical Test-Time Scaling for Automated Theorem Proving](https://huggingface.co/papers/2509.12603) (2025)\n* [EvolProver: Advancing Automated Theorem Proving by Evolving Formalized Problems via Symmetry and Difficulty](https://huggingface.co/papers/2510.00732) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.01444.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.01444", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Self-Rewarding Vision-Language Model via Reasoning Decomposition](https://huggingface.co/papers/2508.19652) (2025)\n* [Perception Before Reasoning: Two-Stage Reinforcement Learning for Visual Reasoning in Vision-Language Models](https://huggingface.co/papers/2509.13031) (2025)\n* [AVATAR: Reinforcement Learning to See, Hear, and Reason Over Video](https://huggingface.co/papers/2508.03100) (2025)\n* [CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models](https://huggingface.co/papers/2509.09675) (2025)\n* [MMR1: Enhancing Multimodal Reasoning with Variance-Aware Sampling and Open Resources](https://huggingface.co/papers/2509.21268) (2025)\n* [Latent Visual Reasoning](https://huggingface.co/papers/2509.24251) (2025)\n* [Shuffle-R1: Efficient RL framework for Multimodal Large Language Models via Data-centric Dynamic Shuffle](https://huggingface.co/papers/2508.05612) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.01538.json

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+{"paper_url": "https://huggingface.co/papers/2510.01538", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Semantic Alignment](https://huggingface.co/papers/2508.07195) (2025)\n* [Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting](https://huggingface.co/papers/2509.15105) (2025)\n* [TSGym: Design Choices for Deep Multivariate Time-Series Forecasting](https://huggingface.co/papers/2509.17063) (2025)\n* [ARIES: Relation Assessment and Model Recommendation for Deep Time Series Forecasting](https://huggingface.co/papers/2509.06060) (2025)\n* [T3Time: Tri-Modal Time Series Forecasting via Adaptive Multi-Head Alignment and Residual Fusion](https://huggingface.co/papers/2508.04251) (2025)\n* [From Values to Tokens: An LLM-Driven Framework for Context-aware Time Series Forecasting via Symbolic Discretization](https://huggingface.co/papers/2508.09191) (2025)\n* [UniCast: A Unified Multimodal Prompting Framework for Time Series Forecasting](https://huggingface.co/papers/2508.11954) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.01591.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.01591", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Deep Think with Confidence](https://huggingface.co/papers/2508.15260) (2025)\n* [The LLM Already Knows: Estimating LLM-Perceived Question Difficulty via Hidden Representations](https://huggingface.co/papers/2509.12886) (2025)\n* [Latent Self-Consistency for Reliable Majority-Set Selection in Short- and Long-Answer Reasoning](https://huggingface.co/papers/2508.18395) (2025)\n* [Can LLMs Detect Their Confabulations? Estimating Reliability in Uncertainty-Aware Language Models](https://huggingface.co/papers/2508.08139) (2025)\n* [Learning to Refine: Self-Refinement of Parallel Reasoning in LLMs](https://huggingface.co/papers/2509.00084) (2025)\n* [Evolving Language Models without Labels: Majority Drives Selection, Novelty Promotes Variation](https://huggingface.co/papers/2509.15194) (2025)\n* [Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions](https://huggingface.co/papers/2509.23782) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.01691.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.01691", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [MMRQA: Signal-Enhanced Multimodal Large Language Models for MRI Quality Assessment](https://huggingface.co/papers/2509.24888) (2025)\n* [Med-RewardBench: Benchmarking Reward Models and Judges for Medical Multimodal Large Language Models](https://huggingface.co/papers/2508.21430) (2025)\n* [MedAtlas: Evaluating LLMs for Multi-Round, Multi-Task Medical Reasoning Across Diverse Imaging Modalities and Clinical Text](https://huggingface.co/papers/2508.10947) (2025)\n* [RegionMed-CLIP: A Region-Aware Multimodal Contrastive Learning Pre-trained Model for Medical Image Understanding](https://huggingface.co/papers/2508.05244) (2025)\n* [Beyond Classification Accuracy: Neural-MedBench and the Need for Deeper Reasoning Benchmarks](https://huggingface.co/papers/2509.22258) (2025)\n* [Benchmarking GPT-5 for Zero-Shot Multimodal Medical Reasoning in Radiology and Radiation Oncology](https://huggingface.co/papers/2508.13192) (2025)\n* [Med-GLIP: Advancing Medical Language-Image Pre-training with Large-scale Grounded Dataset](https://huggingface.co/papers/2508.10528) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.01796.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.01796", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [IPA: An Information-Preserving Input Projection Framework for Efficient Foundation Model Adaptation](https://huggingface.co/papers/2509.04398) (2025)\n* [Share Your Attention: Transformer Weight Sharing via Matrix-based Dictionary Learning](https://huggingface.co/papers/2508.04581) (2025)\n* [Cross-Model Semantics in Representation Learning](https://huggingface.co/papers/2508.03649) (2025)\n* [SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers](https://huggingface.co/papers/2509.00935) (2025)\n* [Rethinking Transformer Connectivity: TLinFormer, A Path to Exact, Full Context-Aware Linear Attention](https://huggingface.co/papers/2508.20407) (2025)\n* [Cutting the Skip: Training Residual-Free Transformers](https://huggingface.co/papers/2510.00345) (2025)\n* [NIRVANA: Structured pruning reimagined for large language models compression](https://huggingface.co/papers/2509.14230) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.01817.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.01817", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [EG-MLA: Embedding-Gated Multi-head Latent Attention for Scalable and Efficient LLMs](https://huggingface.co/papers/2509.16686) (2025)\n* [SCOUT: Toward Sub-Quadratic Attention via Segment Compression for Optimized Utility in Transformers](https://huggingface.co/papers/2509.00935) (2025)\n* [AQUA: Attention via QUery mAgnitudes for Memory and Compute Efficient Inference in LLMs](https://huggingface.co/papers/2509.11155) (2025)\n* [TPLA: Tensor Parallel Latent Attention for Efficient Disaggregated Prefill and Decode Inference](https://huggingface.co/papers/2508.15881) (2025)\n* [Expected Attention: KV Cache Compression by Estimating Attention from Future Queries Distribution](https://huggingface.co/papers/2510.00636) (2025)\n* [Flash Sparse Attention: An Alternative Efficient Implementation of Native Sparse Attention Kernel](https://huggingface.co/papers/2508.18224) (2025)\n* [Rethinking Transformer Connectivity: TLinFormer, A Path to Exact, Full Context-Aware Linear Attention](https://huggingface.co/papers/2508.20407) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.02173.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.02173", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning](https://huggingface.co/papers/2509.25760) (2025)\n* [Self-Rewarding Vision-Language Model via Reasoning Decomposition](https://huggingface.co/papers/2508.19652) (2025)\n* [HalluGuard: Evidence-Grounded Small Reasoning Models to Mitigate Hallucinations in Retrieval-Augmented Generation](https://huggingface.co/papers/2510.00880) (2025)\n* [FineDialFact: A benchmark for Fine-grained Dialogue Fact Verification](https://huggingface.co/papers/2508.05782) (2025)\n* [DocThinker: Explainable Multimodal Large Language Models with Rule-based Reinforcement Learning for Document Understanding](https://huggingface.co/papers/2508.08589) (2025)\n* [From Faithfulness to Correctness: Generative Reward Models that Think Critically](https://huggingface.co/papers/2509.25409) (2025)\n* [SightSound-R1: Cross-Modal Reasoning Distillation from Vision to Audio Language Models](https://huggingface.co/papers/2509.15661) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.02190.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.02190", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [DeepResearch Arena: The First Exam of LLMs'Research Abilities via Seminar-Grounded Tasks](https://huggingface.co/papers/2509.01396) (2025)\n* [Towards Personalized Deep Research: Benchmarks and Evaluations](https://huggingface.co/papers/2509.25106) (2025)\n* [ReportBench: Evaluating Deep Research Agents via Academic Survey Tasks](https://huggingface.co/papers/2508.15804) (2025)\n* [WideSearch: Benchmarking Agentic Broad Info-Seeking](https://huggingface.co/papers/2508.07999) (2025)\n* [WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent](https://huggingface.co/papers/2508.05748) (2025)\n* [DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesis](https://huggingface.co/papers/2508.20033) (2025)\n* [Do LLM Agents Know How to Ground, Recover, and Assess? A Benchmark for Epistemic Competence in Information-Seeking Agents](https://huggingface.co/papers/2509.22391) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.02209.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.02209", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [QTMRL: An Agent for Quantitative Trading Decision-Making Based on Multi-Indicator Guided Reinforcement Learning](https://huggingface.co/papers/2508.20467) (2025)\n* [AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions](https://huggingface.co/papers/2508.11152) (2025)\n* [Adaptive Alpha Weighting with PPO: Enhancing Prompt-Based LLM-Generated Alphas in Quant Trading](https://huggingface.co/papers/2509.01393) (2025)\n* [QuantAgent: Price-Driven Multi-Agent LLMs for High-Frequency Trading](https://huggingface.co/papers/2509.09995) (2025)\n* [TradingGroup: A Multi-Agent Trading System with Self-Reflection and Data-Synthesis](https://huggingface.co/papers/2508.17565) (2025)\n* [Sentiment-Aware Mean-Variance Portfolio Optimization for Cryptocurrencies](https://huggingface.co/papers/2508.16378) (2025)\n* [MM-DREX: Multimodal-Driven Dynamic Routing of LLM Experts for Financial Trading](https://huggingface.co/papers/2509.05080) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.02245.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.02245", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Improving Sampling Efficiency in RLVR through Adaptive Rollout and Response Reuse](https://huggingface.co/papers/2509.25808) (2025)\n* [ETTRL: Balancing Exploration and Exploitation in LLM Test-Time Reinforcement Learning Via Entropy Mechanism](https://huggingface.co/papers/2508.11356) (2025)\n* [More Than One Teacher: Adaptive Multi-Guidance Policy Optimization for Diverse Exploration](https://huggingface.co/papers/2510.02227) (2025)\n* [MMR1: Enhancing Multimodal Reasoning with Variance-Aware Sampling and Open Resources](https://huggingface.co/papers/2509.21268) (2025)\n* [Beyond Pass@1: Self-Play with Variational Problem Synthesis Sustains RLVR](https://huggingface.co/papers/2508.14029) (2025)\n* [CLPO: Curriculum Learning meets Policy Optimization for LLM Reasoning](https://huggingface.co/papers/2509.25004) (2025)\n* [Shuffle-R1: Efficient RL framework for Multimodal Large Language Models via Data-centric Dynamic Shuffle](https://huggingface.co/papers/2508.05612) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.02250.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.02250", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [CoAct-1: Computer-using Agents with Coding as Actions](https://huggingface.co/papers/2508.03923) (2025)\n* [OpenCUA: Open Foundations for Computer-Use Agents](https://huggingface.co/papers/2508.09123) (2025)\n* [ProRe: A Proactive Reward System for GUI Agents via Reasoner-Actor Collaboration](https://huggingface.co/papers/2509.21823) (2025)\n* [Orcust: Stepwise-Feedback Reinforcement Learning for GUI Agent](https://huggingface.co/papers/2509.17917) (2025)\n* [Instruction Agent: Enhancing Agent with Expert Demonstration](https://huggingface.co/papers/2509.07098) (2025)\n* [VeriGUI: Verifiable Long-Chain GUI Dataset](https://huggingface.co/papers/2508.04026) (2025)\n* [ComputerRL: Scaling End-to-End Online Reinforcement Learning for Computer Use Agents](https://huggingface.co/papers/2508.14040) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.02253.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.02253", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [LazyDrag: Enabling Stable Drag-Based Editing on Multi-Modal Diffusion Transformers via Explicit Correspondence](https://huggingface.co/papers/2509.12203) (2025)\n* [LORE: Latent Optimization for Precise Semantic Control in Rectified Flow-based Image Editing](https://huggingface.co/papers/2508.03144) (2025)\n* [Does FLUX Already Know How to Perform Physically Plausible Image Composition?](https://huggingface.co/papers/2509.21278) (2025)\n* [TweezeEdit: Consistent and Efficient Image Editing with Path Regularization](https://huggingface.co/papers/2508.10498) (2025)\n* [Inpaint4Drag: Repurposing Inpainting Models for Drag-Based Image Editing via Bidirectional Warping](https://huggingface.co/papers/2509.04582) (2025)\n* [TDEdit: A Unified Diffusion Framework for Text-Drag Guided Image Manipulation](https://huggingface.co/papers/2509.21905) (2025)\n* [Dragging with Geometry: From Pixels to Geometry-Guided Image Editing](https://huggingface.co/papers/2509.25740) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.02263.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.02263", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Metacognitive Reuse: Turning Recurring LLM Reasoning Into Concise Behaviors](https://huggingface.co/papers/2509.13237) (2025)\n* [SABER: Switchable and Balanced Training for Efficient LLM Reasoning](https://huggingface.co/papers/2508.10026) (2025)\n* [Learning to Refine: Self-Refinement of Parallel Reasoning in LLMs](https://huggingface.co/papers/2509.00084) (2025)\n* [Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning](https://huggingface.co/papers/2508.15507) (2025)\n* [From Harm to Help: Turning Reasoning In-Context Demos into Assets for Reasoning LMs](https://huggingface.co/papers/2509.23196) (2025)\n* [Rethinking Thinking Tokens: LLMs as Improvement Operators](https://huggingface.co/papers/2510.01123) (2025)\n* [The Majority is not always right: RL training for solution aggregation](https://huggingface.co/papers/2509.06870) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.02272.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.02272", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Beyond English-Centric Training: How Reinforcement Learning Improves Cross-Lingual Reasoning in LLMs](https://huggingface.co/papers/2509.23657) (2025)\n* [Aligning Multilingual Reasoning with Verifiable Semantics from a High-Resource Expert Model](https://huggingface.co/papers/2509.25543) (2025)\n* [Long Chain-of-Thought Reasoning Across Languages](https://huggingface.co/papers/2508.14828) (2025)\n* [Best-of-L: Cross-Lingual Reward Modeling for Mathematical Reasoning](https://huggingface.co/papers/2509.15811) (2025)\n* [mR3: Multilingual Rubric-Agnostic Reward Reasoning Models](https://huggingface.co/papers/2510.01146) (2025)\n* [Balanced Actor Initialization: Stable RLHF Training of Distillation-Based Reasoning Models](https://huggingface.co/papers/2509.00309) (2025)\n* [Evaluating Multilingual and Code-Switched Alignment in LLMs via Synthetic Natural Language Inference](https://huggingface.co/papers/2508.14735) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.02283.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.02283", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Rolling Forcing: Autoregressive Long Video Diffusion in Real Time](https://huggingface.co/papers/2509.25161) (2025)\n* [LongLive: Real-time Interactive Long Video Generation](https://huggingface.co/papers/2509.22622) (2025)\n* [InfVSR: Breaking Length Limits of Generic Video Super-Resolution](https://huggingface.co/papers/2510.00948) (2025)\n* [WorldWeaver: Generating Long-Horizon Video Worlds via Rich Perception](https://huggingface.co/papers/2508.15720) (2025)\n* [Autoregressive Video Generation beyond Next Frames Prediction](https://huggingface.co/papers/2509.24081) (2025)\n* [LongScape: Advancing Long-Horizon Embodied World Models with Context-Aware MoE](https://huggingface.co/papers/2509.21790) (2025)\n* [LongVie: Multimodal-Guided Controllable Ultra-Long Video Generation](https://huggingface.co/papers/2508.03694) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.02297.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.02297", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Efficient Multi-turn RL for GUI Agents via Decoupled Training and Adaptive Data Curation](https://huggingface.co/papers/2509.23866) (2025)\n* [ComputerRL: Scaling End-to-End Online Reinforcement Learning for Computer Use Agents](https://huggingface.co/papers/2508.14040) (2025)\n* [Agent Lightning: Train ANY AI Agents with Reinforcement Learning](https://huggingface.co/papers/2508.03680) (2025)\n* [RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting](https://huggingface.co/papers/2508.04078) (2025)\n* [AWorld: Orchestrating the Training Recipe for Agentic AI](https://huggingface.co/papers/2508.20404) (2025)\n* [Training Long-Context, Multi-Turn Software Engineering Agents with Reinforcement Learning](https://huggingface.co/papers/2508.03501) (2025)\n* [AutoEP: LLMs-Driven Automation of Hyperparameter Evolution for Metaheuristic Algorithms](https://huggingface.co/papers/2509.23189) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2510.02314.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2510.02314", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [LongSplat: Robust Unposed 3D Gaussian Splatting for Casual Long Videos](https://huggingface.co/papers/2508.14041) (2025)\n* [ComplicitSplat: Downstream Models are Vulnerable to Blackbox Attacks by 3D Gaussian Splat Camouflages](https://huggingface.co/papers/2508.11854) (2025)\n* [Distilled-3DGS:Distilled 3D Gaussian Splatting](https://huggingface.co/papers/2508.14037) (2025)\n* [MarkSplatter: Generalizable Watermarking for 3D Gaussian Splatting Model via Splatter Image Structure](https://huggingface.co/papers/2509.00757) (2025)\n* [HyRF: Hybrid Radiance Fields for Memory-efficient and High-quality Novel View Synthesis](https://huggingface.co/papers/2509.17083) (2025)\n* [FixingGS: Enhancing 3D Gaussian Splatting via Training-Free Score Distillation](https://huggingface.co/papers/2509.18759) (2025)\n* [DET-GS: Depth- and Edge-Aware Regularization for High-Fidelity 3D Gaussian Splatting](https://huggingface.co/papers/2508.04099) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}