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data/2403.09193.json

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+{"paper_url": "https://huggingface.co/papers/2403.09193", "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* [Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion](https://huggingface.co/papers/2412.04424) (2024)\n* [Dynamic-VLM: Simple Dynamic Visual Token Compression for VideoLLM](https://huggingface.co/papers/2412.09530) (2024)\n* [Discriminative Fine-tuning of LVLMs](https://huggingface.co/papers/2412.04378) (2024)\n* [SilVar: Speech Driven Multimodal Model for Reasoning Visual Question Answering and Object Localization](https://huggingface.co/papers/2412.16771) (2024)\n* [Optimizing Vision-Language Interactions Through Decoder-Only Models](https://huggingface.co/papers/2412.10758) (2024)\n* [MASS: Overcoming Language Bias in Image-Text Matching](https://huggingface.co/papers/2501.11469) (2025)\n* [Real Classification by Description: Extending CLIP's Limits of Part Attributes Recognition](https://huggingface.co/papers/2412.13947) (2024)\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/2501.12370.json

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+{"paper_url": "https://huggingface.co/papers/2501.12370", "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* [Mixture of Hidden-Dimensions Transformer](https://huggingface.co/papers/2412.05644) (2024)\n* [The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws](https://huggingface.co/papers/2501.12486) (2025)\n* [The interplay between domain specialization and model size: a case study in the legal domain](https://huggingface.co/papers/2501.02068) (2025)\n* [Scaling Law for Language Models Training Considering Batch Size](https://huggingface.co/papers/2412.01505) (2024)\n* [Memory Layers at Scale](https://huggingface.co/papers/2412.09764) (2024)\n* [Sloth: scaling laws for LLM skills to predict multi-benchmark performance across families](https://huggingface.co/papers/2412.06540) (2024)\n* [Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs](https://huggingface.co/papers/2412.13337) (2024)\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/2501.14723.json

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+{"paper_url": "https://huggingface.co/papers/2501.14723", "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* [Repository Structure-Aware Training Makes SLMs Better Issue Resolver](https://huggingface.co/papers/2412.19031) (2024)\n* [CoRNStack: High-Quality Contrastive Data for Better Code Ranking](https://huggingface.co/papers/2412.01007) (2024)\n* [Improving FIM Code Completions via Context & Curriculum Based Learning](https://huggingface.co/papers/2412.16589) (2024)\n* [Design choices made by LLM-based test generators prevent them from finding bugs](https://huggingface.co/papers/2412.14137) (2024)\n* [Efficiently Serving LLM Reasoning Programs with Certaindex](https://huggingface.co/papers/2412.20993) (2024)\n* [TDD-Bench Verified: Can LLMs Generate Tests for Issues Before They Get Resolved?](https://huggingface.co/papers/2412.02883) (2024)\n* [GenX: Mastering Code and Test Generation with Execution Feedback](https://huggingface.co/papers/2412.13464) (2024)\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/2501.14912.json

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+{"paper_url": "https://huggingface.co/papers/2501.14912", "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* [An Efficient Unsupervised Framework for Convex Quadratic Programs via Deep Unrolling](https://huggingface.co/papers/2412.01051) (2024)\n* [Differentiable Convex Optimization Layers in Neural Architectures: Foundations and Perspectives](https://huggingface.co/papers/2412.20679) (2024)\n* [A Hessian-informed hyperparameter optimization for differential learning rate](https://huggingface.co/papers/2501.06954) (2025)\n* [Marvel: Accelerating Safe Online Reinforcement Learning with Finetuned Offline Policy](https://huggingface.co/papers/2412.04426) (2024)\n* [Representation and Regression Problems in Neural Networks: Relaxation, Generalization, and Numerics](https://huggingface.co/papers/2412.01619) (2024)\n* [EDoRA: Efficient Weight-Decomposed Low-Rank Adaptation via Singular Value Decomposition](https://huggingface.co/papers/2501.12067) (2025)\n* [Towards Simple and Provable Parameter-Free Adaptive Gradient Methods](https://huggingface.co/papers/2412.19444) (2024)\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/2501.15368.json

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+{"paper_url": "https://huggingface.co/papers/2501.15368", "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* [VITA-1.5: Towards GPT-4o Level Real-Time Vision and Speech Interaction](https://huggingface.co/papers/2501.01957) (2025)\n* [Lyra: An Efficient and Speech-Centric Framework for Omni-Cognition](https://huggingface.co/papers/2412.09501) (2024)\n* [Valley2: Exploring Multimodal Models with Scalable Vision-Language Design](https://huggingface.co/papers/2501.05901) (2025)\n* [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://huggingface.co/papers/2412.05271) (2024)\n* [OpenOmni: Large Language Models Pivot Zero-shot Omnimodal Alignment across Language with Real-time Self-Aware Emotional Speech Synthesis](https://huggingface.co/papers/2501.04561) (2025)\n* [From Specific-MLLM to Omni-MLLM: A Survey about the MLLMs alligned with Multi-Modality](https://huggingface.co/papers/2412.11694) (2024)\n* [Optimizing Vision-Language Interactions Through Decoder-Only Models](https://huggingface.co/papers/2412.10758) (2024)\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/2501.15369.json

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+{"paper_url": "https://huggingface.co/papers/2501.15369", "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* [RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone](https://huggingface.co/papers/2412.10995) (2024)\n* [Cascaded Multi-Scale Attention for Enhanced Multi-Scale Feature Extraction and Interaction with Low-Resolution Images](https://huggingface.co/papers/2412.02197) (2024)\n* [RecConv: Efficient Recursive Convolutions for Multi-Frequency Representations](https://huggingface.co/papers/2412.19628) (2024)\n* [CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices](https://huggingface.co/papers/2412.13273) (2024)\n* [DiC: Rethinking Conv3x3 Designs in Diffusion Models](https://huggingface.co/papers/2501.00603) (2024)\n* [Unity is Strength: Unifying Convolutional and Transformeral Features for Better Person Re-Identification](https://huggingface.co/papers/2412.17239) (2024)\n* [Token Cropr: Faster ViTs for Quite a Few Tasks](https://huggingface.co/papers/2412.00965) (2024)\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/2501.15383.json

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+{"paper_url": "https://huggingface.co/papers/2501.15383", "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* [Qwen2.5 Technical Report](https://huggingface.co/papers/2412.15115) (2024)\n* [Adjoint sharding for very long context training of state space models](https://huggingface.co/papers/2501.00692) (2025)\n* [Bootstrap Your Own Context Length](https://huggingface.co/papers/2412.18860) (2024)\n* [YuLan-Mini: An Open Data-efficient Language Model](https://huggingface.co/papers/2412.17743) (2024)\n* [LeMo: Enabling LEss Token Involvement for MOre Context Fine-tuning](https://huggingface.co/papers/2501.09767) (2025)\n* [DeepSeek-V3 Technical Report](https://huggingface.co/papers/2412.19437) (2024)\n* [Compressing KV Cache for Long-Context LLM Inference with Inter-Layer Attention Similarity](https://huggingface.co/papers/2412.02252) (2024)\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/2501.15420.json

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+{"paper_url": "https://huggingface.co/papers/2501.15420", "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* [Next Patch Prediction for Autoregressive Visual Generation](https://huggingface.co/papers/2412.15321) (2024)\n* [[MASK] is All You Need](https://huggingface.co/papers/2412.06787) (2024)\n* [Vision-Driven Prompt Optimization for Large Language Models in Multimodal Generative Tasks](https://huggingface.co/papers/2501.02527) (2025)\n* [E-CAR: Efficient Continuous Autoregressive Image Generation via Multistage Modeling](https://huggingface.co/papers/2412.14170) (2024)\n* [Coordinate In and Value Out: Training Flow Transformers in Ambient Space](https://huggingface.co/papers/2412.03791) (2024)\n* [RandAR: Decoder-only Autoregressive Visual Generation in Random Orders](https://huggingface.co/papers/2412.01827) (2024)\n* [Enhancing Diffusion Models for High-Quality Image Generation](https://huggingface.co/papers/2412.14422) (2024)\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/2501.15427.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2501.15427", "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* [Aligning Instruction Tuning with Pre-training](https://huggingface.co/papers/2501.09368) (2025)\n* [CharacterBox: Evaluating the Role-Playing Capabilities of LLMs in Text-Based Virtual Worlds](https://huggingface.co/papers/2412.05631) (2024)\n* [OmniChat: Enhancing Spoken Dialogue Systems with Scalable Synthetic Data for Diverse Scenarios](https://huggingface.co/papers/2501.01384) (2025)\n* [HammerBench: Fine-Grained Function-Calling Evaluation in Real Mobile Device Scenarios](https://huggingface.co/papers/2412.16516) (2024)\n* [SweetieChat: A Strategy-Enhanced Role-playing Framework for Diverse Scenarios Handling Emotional Support Agent](https://huggingface.co/papers/2412.08389) (2024)\n* [Curriculum-style Data Augmentation for LLM-based Metaphor Detection](https://huggingface.co/papers/2412.02956) (2024)\n* [Reasoning-Enhanced Self-Training for Long-Form Personalized Text Generation](https://huggingface.co/papers/2501.04167) (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/2501.15570.json

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+{"paper_url": "https://huggingface.co/papers/2501.15570", "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* [BabyHGRN: Exploring RNNs for Sample-Efficient Training of Language Models](https://huggingface.co/papers/2412.15978) (2024)\n* [Does Self-Attention Need Separate Weights in Transformers?](https://huggingface.co/papers/2412.00359) (2024)\n* [SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator](https://huggingface.co/papers/2412.12094) (2024)\n* [LLMs are Also Effective Embedding Models: An In-depth Overview](https://huggingface.co/papers/2412.12591) (2024)\n* [Lillama: Large Language Models Compression via Low-Rank Feature Distillation](https://huggingface.co/papers/2412.16719) (2024)\n* [Align-KD: Distilling Cross-Modal Alignment Knowledge for Mobile Vision-Language Model](https://huggingface.co/papers/2412.01282) (2024)\n* [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://huggingface.co/papers/2412.13663) (2024)\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/2501.15907.json

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+{"paper_url": "https://huggingface.co/papers/2501.15907", "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* [Autoregressive Speech Synthesis with Next-Distribution Prediction](https://huggingface.co/papers/2412.16846) (2024)\n* [Towards Controllable Speech Synthesis in the Era of Large Language Models: A Survey](https://huggingface.co/papers/2412.06602) (2024)\n* [OSUM: Advancing Open Speech Understanding Models with Limited Resources in Academia](https://huggingface.co/papers/2501.13306) (2025)\n* [Interleaved Speech-Text Language Models are Simple Streaming Text to Speech Synthesizers](https://huggingface.co/papers/2412.16102) (2024)\n* [OpenOmni: Large Language Models Pivot Zero-shot Omnimodal Alignment across Language with Real-time Self-Aware Emotional Speech Synthesis](https://huggingface.co/papers/2501.04561) (2025)\n* [TouchTTS: An Embarrassingly Simple TTS Framework that Everyone Can Touch](https://huggingface.co/papers/2412.08237) (2024)\n* [Towards Lightweight and Stable Zero-shot TTS with Self-distilled Representation Disentanglement](https://huggingface.co/papers/2501.08566) (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/2501.16142.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2501.16142", "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* [Enhancing Online Reinforcement Learning with Meta-Learned Objective from Offline Data](https://huggingface.co/papers/2501.07346) (2025)\n* [MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization](https://huggingface.co/papers/2412.12098) (2024)\n* [Deep Reinforcement Learning with Hybrid Intrinsic Reward Model](https://huggingface.co/papers/2501.12627) (2025)\n* [RL + Transformer = A General-Purpose Problem Solver](https://huggingface.co/papers/2501.14176) (2025)\n* [Skill-Enhanced Reinforcement Learning Acceleration from Demonstrations](https://huggingface.co/papers/2412.06207) (2024)\n* [On the Statistical Complexity for Offline and Low-Adaptive Reinforcement Learning with Structures](https://huggingface.co/papers/2501.02089) (2025)\n* [Improving Multi-Step Reasoning Abilities of Large Language Models with Direct Advantage Policy Optimization](https://huggingface.co/papers/2412.18279) (2024)\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/2501.16273.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2501.16273", "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* [Learnings from Scaling Visual Tokenizers for Reconstruction and Generation](https://huggingface.co/papers/2501.09755) (2025)\n* [MoVE-KD: Knowledge Distillation for VLMs with Mixture of Visual Encoders](https://huggingface.co/papers/2501.01709) (2025)\n* [Improving Language Transfer Capability of Decoder-only Architecture in Multilingual Neural Machine Translation](https://huggingface.co/papers/2412.02101) (2024)\n* [End-to-End Long Document Summarization using Gradient Caching](https://huggingface.co/papers/2501.01805) (2025)\n* [SynerGen-VL: Towards Synergistic Image Understanding and Generation with Vision Experts and Token Folding](https://huggingface.co/papers/2412.09604) (2024)\n* [A Multi-Encoder Frozen-Decoder Approach for Fine-Tuning Large Language Models](https://huggingface.co/papers/2501.07818) (2025)\n* [Adaptive Pruning for Large Language Models with Structural Importance Awareness](https://huggingface.co/papers/2412.15127) (2024)\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`"}