arxiv_id string | pwc_url string | status int64 | found bool | pwc_id string | title string | url_abs string | repositories list | project_pages list | hf_models list | hf_datasets list | hf_spaces list |
|---|---|---|---|---|---|---|---|---|---|---|---|
2502.11546 | https://paperswithcode.co/api/v1/papers/arxiv/2502.11546?include_resources=true | 200 | true | 44345 | DCAD-2000: A Multilingual Dataset across 2000+ Languages with Data Cleaning as Anomaly Detection | https://arxiv.org/abs/2502.11546v2 | [
{
"url": "https://github.com/yl-shen/dcad-2000",
"owner": "yl-shen",
"name": "DCAD-2000",
"stars": 5,
"is_official": true,
"source": "links_json"
}
] | [] | [] | [] | [] |
2510.17245 | https://paperswithcode.co/api/v1/papers/arxiv/2510.17245?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2505.15138 | https://paperswithcode.co/api/v1/papers/arxiv/2505.15138?include_resources=true | 200 | true | 86705 | Global Convergence for Average Reward Constrained MDPs with Primal-Dual Actor Critic Algorithm | https://arxiv.org/abs/2505.15138 | [] | [] | [] | [] | [] |
2508.15084 | https://paperswithcode.co/api/v1/papers/arxiv/2508.15084?include_resources=true | 200 | true | 86836 | Kernel-based Equalized Odds: A Quantification of Accuracy-Fairness Trade-off in Fair Representation Learning | https://arxiv.org/abs/2508.15084 | [] | [] | [] | [] | [] |
2510.21250 | https://paperswithcode.co/api/v1/papers/arxiv/2510.21250?include_resources=true | 200 | true | 65923 | Improved Training Technique for Shortcut Models | https://arxiv.org/abs/2510.21250 | [] | [] | [] | [] | [] |
2507.04194 | https://paperswithcode.co/api/v1/papers/arxiv/2507.04194?include_resources=true | 200 | true | 86600 | Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning | https://arxiv.org/abs/2507.04194 | [] | [] | [] | [] | [] |
2410.04525 | https://paperswithcode.co/api/v1/papers/arxiv/2410.04525?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2506.19212 | https://paperswithcode.co/api/v1/papers/arxiv/2506.19212?include_resources=true | 200 | true | 86455 | Scaffolding Dexterous Manipulation with Vision-Language Models | https://arxiv.org/abs/2506.19212 | [] | [] | [] | [] | [] |
2505.24003 | https://paperswithcode.co/api/v1/papers/arxiv/2505.24003?include_resources=true | 200 | true | 85846 | Multi-Modal View Enhanced Large Vision Models for Long-Term Time Series Forecasting | https://arxiv.org/abs/2505.24003 | [] | [] | [] | [] | [] |
2506.05735 | https://paperswithcode.co/api/v1/papers/arxiv/2506.05735?include_resources=true | 200 | true | 86448 | Do LLMs Really Forget? Evaluating Unlearning with Knowledge Correlation and Confidence Awareness | https://arxiv.org/abs/2506.05735 | [
{
"url": "https://github.com/graph-com/knowledge_unlearning",
"owner": "graph-com",
"name": "knowledge_unlearning",
"stars": 14,
"is_official": true,
"source": "neurips2025_import"
}
] | [] | [] | [] | [] |
2505.20997 | https://paperswithcode.co/api/v1/papers/arxiv/2505.20997?include_resources=true | 200 | true | 86233 | BIPNN: Learning to Solve Binary Integer Programming via Hypergraph Neural Networks | https://arxiv.org/abs/2505.20997 | [] | [] | [] | [] | [] |
2511.01065 | https://paperswithcode.co/api/v1/papers/arxiv/2511.01065?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2505.12553 | https://paperswithcode.co/api/v1/papers/arxiv/2505.12553?include_resources=true | 200 | true | 86547 | Hamiltonian Descent Algorithms for Optimization: Accelerated Rates via Randomized Integration Time | https://arxiv.org/abs/2505.12553 | [] | [] | [] | [] | [] |
2506.21683 | https://paperswithcode.co/api/v1/papers/arxiv/2506.21683?include_resources=true | 200 | true | 87319 | Risk-Averse Total-Reward Reinforcement Learning | https://arxiv.org/abs/2506.21683 | [] | [] | [] | [] | [] |
2510.03012 | https://paperswithcode.co/api/v1/papers/arxiv/2510.03012?include_resources=true | 200 | true | 86509 | PocketSR: The Super-Resolution Expert in Your Pocket Mobiles | https://arxiv.org/abs/2510.03012 | [] | [] | [] | [] | [] |
2505.22159 | https://paperswithcode.co/api/v1/papers/arxiv/2505.22159?include_resources=true | 200 | true | 50001 | ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation | https://arxiv.org/abs/2505.22159 | [] | [
{
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"is_official": true
},
{
"url": "https://sites.google.com/view/forcevla2025/",
"is_official": true
}
] | [] | [] | [] |
2509.19662 | https://paperswithcode.co/api/v1/papers/arxiv/2509.19662?include_resources=true | 200 | true | 87379 | Non-Clairvoyant Scheduling with Progress Bars | https://arxiv.org/abs/2509.19662 | [] | [] | [] | [] | [] |
2505.15293 | https://paperswithcode.co/api/v1/papers/arxiv/2505.15293?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2507.05101 | https://paperswithcode.co/api/v1/papers/arxiv/2507.05101?include_resources=true | 200 | true | 51716 | PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs | https://arxiv.org/abs/2507.05101 | [
{
"url": "https://github.com/SophieSarceau/PRING",
"owner": "SophieSarceau",
"name": "PRING",
"stars": 14,
"is_official": true,
"source": "ai_extraction"
}
] | [] | [] | [] | [] |
2503.02453 | https://paperswithcode.co/api/v1/papers/arxiv/2503.02453?include_resources=true | 200 | true | 86787 | Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations | https://arxiv.org/abs/2503.02453 | [] | [] | [] | [] | [] |
2507.12465 | https://paperswithcode.co/api/v1/papers/arxiv/2507.12465?include_resources=true | 200 | true | 51931 | PhysX: Physical-Grounded 3D Asset Generation | https://arxiv.org/abs/2507.12465 | [
{
"url": "https://github.com/ziangcao0312/PhysX",
"owner": "ziangcao0312",
"name": "PhysX",
"stars": 338,
"is_official": true,
"source": "links_json"
}
] | [
{
"url": "https://physx-3d.github.io/",
"is_official": true
}
] | [] | [] | [] |
2506.12421 | https://paperswithcode.co/api/v1/papers/arxiv/2506.12421?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2510.16321 | https://paperswithcode.co/api/v1/papers/arxiv/2510.16321?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2502.00384 | https://paperswithcode.co/api/v1/papers/arxiv/2502.00384?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2410.15555 | https://paperswithcode.co/api/v1/papers/arxiv/2410.15555?include_resources=true | 200 | true | 85809 | Bayesian Concept Bottleneck Models with LLM Priors | https://arxiv.org/abs/2410.15555 | [] | [] | [] | [] | [] |
2507.10069 | https://paperswithcode.co/api/v1/papers/arxiv/2507.10069?include_resources=true | 200 | true | 87125 | ElasticMM: Efficient Multimodal LLMs Serving with Elastic Multimodal Parallelism | https://arxiv.org/abs/2507.10069 | [] | [] | [] | [] | [] |
2506.12811 | https://paperswithcode.co/api/v1/papers/arxiv/2506.12811?include_resources=true | 200 | true | 85775 | Flow-Based Policy for Online Reinforcement Learning | https://arxiv.org/abs/2506.12811 | [] | [] | [] | [] | [] |
2506.14761 | https://paperswithcode.co/api/v1/papers/arxiv/2506.14761?include_resources=true | 200 | true | 51238 | From Bytes to Ideas: Language Modeling with Autoregressive U-Nets | https://arxiv.org/abs/2506.14761 | [
{
"url": "https://github.com/facebookresearch/lingua/tree/main/apps/aunet",
"owner": "facebookresearch",
"name": "lingua",
"stars": 4749,
"is_official": true,
"source": "ai_extraction"
},
{
"url": "https://github.com/facebookresearch/lingua",
"owner": "facebookresearch",
"nam... | [] | [] | [] | [] |
2502.00657 | https://paperswithcode.co/api/v1/papers/arxiv/2502.00657?include_resources=true | 200 | true | 43512 | LLM Safety Alignment is Divergence Estimation in Disguise | https://arxiv.org/abs/2502.00657v2 | [
{
"url": "https://github.com/rhaldarpurdue/kldo",
"owner": "rhaldarpurdue",
"name": "kldo",
"stars": 1,
"is_official": true,
"source": "links_json"
}
] | [] | [] | [] | [] |
2506.12025 | https://paperswithcode.co/api/v1/papers/arxiv/2506.12025?include_resources=true | 200 | true | 87426 | Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs | https://arxiv.org/abs/2506.12025 | [] | [] | [] | [] | [] |
2505.13431 | https://paperswithcode.co/api/v1/papers/arxiv/2505.13431?include_resources=true | 200 | true | 87321 | A Practical Guide for Incorporating Symmetry in Diffusion Policy | https://arxiv.org/abs/2505.13431 | [] | [] | [] | [] | [] |
2602.03066 | https://paperswithcode.co/api/v1/papers/arxiv/2602.03066?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2605.29097 | https://paperswithcode.co/api/v1/papers/arxiv/2605.29097?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2511.06597 | https://paperswithcode.co/api/v1/papers/arxiv/2511.06597?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2502.04677 | https://paperswithcode.co/api/v1/papers/arxiv/2502.04677?include_resources=true | 200 | true | 87024 | LLM Query Scheduling with Prefix Reuse and Latency Constraints | https://arxiv.org/abs/2502.04677 | [] | [] | [] | [] | [] |
2412.03409 | https://paperswithcode.co/api/v1/papers/arxiv/2412.03409?include_resources=true | 200 | true | 40977 | PrefixKV: Adaptive Prefix KV Cache is What Vision Instruction-Following Models Need for Efficient Generation | https://arxiv.org/abs/2412.03409v2 | [
{
"url": "https://github.com/thu-mig/prefixkv",
"owner": "THU-MIG",
"name": "PrefixKV",
"stars": 15,
"is_official": true,
"source": "ai_extraction"
}
] | [] | [] | [] | [] |
2507.08956 | https://paperswithcode.co/api/v1/papers/arxiv/2507.08956?include_resources=true | 200 | true | 85843 | Beyond Scores: Proximal Diffusion Models | https://arxiv.org/abs/2507.08956 | [] | [] | [] | [] | [] |
2505.23316 | https://paperswithcode.co/api/v1/papers/arxiv/2505.23316?include_resources=true | 200 | true | 87229 | Proximalized Preference Optimization for Diverse Feedback Types: A Decomposed Perspective on DPO | https://arxiv.org/abs/2505.23316 | [] | [] | [] | [] | [] |
2504.12908 | https://paperswithcode.co/api/v1/papers/arxiv/2504.12908?include_resources=true | 200 | true | 87022 | Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation | https://arxiv.org/abs/2504.12908 | [] | [] | [] | [] | [] |
2504.20039 | https://paperswithcode.co/api/v1/papers/arxiv/2504.20039?include_resources=true | 200 | true | 48203 | AutoJudge: Judge Decoding Without Manual Annotation | https://arxiv.org/abs/2504.20039 | [
{
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"owner": "garipovroma",
"name": "autojudge",
"stars": 17,
"is_official": true,
"source": "ai_extraction"
}
] | [] | [] | [] | [] |
2601.16447 | https://paperswithcode.co/api/v1/papers/arxiv/2601.16447?include_resources=true | 200 | true | 62160 | Mixing Expert Knowledge: Bring Human Thoughts Back To the Game of Go | https://arxiv.org/abs/2601.16447 | [] | [] | [] | [] | [] |
2510.20162 | https://paperswithcode.co/api/v1/papers/arxiv/2510.20162?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2502.05435 | https://paperswithcode.co/api/v1/papers/arxiv/2502.05435?include_resources=true | 200 | true | 87012 | Unbiased Sliced Wasserstein Kernels for High-Quality Audio Captioning | https://arxiv.org/abs/2502.05435 | [] | [] | [] | [] | [] |
2510.20250 | https://paperswithcode.co/api/v1/papers/arxiv/2510.20250?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2505.23433 | https://paperswithcode.co/api/v1/papers/arxiv/2505.23433?include_resources=true | 200 | true | 86927 | Diversity-Aware Policy Optimization for Large Language Model Reasoning | https://arxiv.org/abs/2505.23433 | [] | [] | [] | [] | [] |
2507.13348 | https://paperswithcode.co/api/v1/papers/arxiv/2507.13348?include_resources=true | 200 | true | 51966 | VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning | https://arxiv.org/abs/2507.13348 | [
{
"url": "https://github.com/dvlab-research/visionthink",
"owner": "dvlab-research",
"name": "VisionThink",
"stars": 440,
"is_official": true,
"source": "ai_extraction"
}
] | [] | [] | [] | [] |
2510.12013 | https://paperswithcode.co/api/v1/papers/arxiv/2510.12013?include_resources=true | 200 | true | 85929 | Statistical Guarantees for High-Dimensional Stochastic Gradient Descent | https://arxiv.org/abs/2510.12013 | [] | [] | [] | [] | [] |
2509.19300 | https://paperswithcode.co/api/v1/papers/arxiv/2509.19300?include_resources=true | 200 | true | 52943 | CAR-Flow: Condition-Aware Reparameterization Aligns Source and Target
for Better Flow Matching | https://arxiv.org/abs/2509.19300 | [] | [] | [] | [] | [] |
2505.19536 | https://paperswithcode.co/api/v1/papers/arxiv/2505.19536?include_resources=true | 200 | true | 49747 | FlowCut: Rethinking Redundancy via Information Flow for Efficient Vision-Language Models | https://arxiv.org/abs/2505.19536 | [
{
"url": "https://github.com/tungchintao/flowcut",
"owner": "TungChintao",
"name": "FlowCut",
"stars": 26,
"is_official": true,
"source": "links_json"
}
] | [] | [] | [] | [] |
2511.00940 | https://paperswithcode.co/api/v1/papers/arxiv/2511.00940?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2511.06961 | https://paperswithcode.co/api/v1/papers/arxiv/2511.06961?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2510.21078 | https://paperswithcode.co/api/v1/papers/arxiv/2510.21078?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2509.16748 | https://paperswithcode.co/api/v1/papers/arxiv/2509.16748?include_resources=true | 200 | true | 86247 | HyPlaneHead: Rethinking Tri-plane-like Representations in Full-Head Image Synthesis | https://arxiv.org/abs/2509.16748 | [] | [] | [] | [] | [] |
2503.03961 | https://paperswithcode.co/api/v1/papers/arxiv/2503.03961?include_resources=true | 200 | true | 87046 | A Little Depth Goes a Long Way: The Expressive Power of Log-Depth Transformers | https://arxiv.org/abs/2503.03961 | [] | [] | [] | [] | [] |
2505.24181 | https://paperswithcode.co/api/v1/papers/arxiv/2505.24181?include_resources=true | 200 | true | 72523 | SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought | https://arxiv.org/abs/2505.24181 | [] | [] | [] | [] | [] |
2506.01031 | https://paperswithcode.co/api/v1/papers/arxiv/2506.01031?include_resources=true | 200 | true | 72440 | NavBench: Probing Multimodal Large Language Models for Embodied
Navigation | https://arxiv.org/abs/2506.01031 | [] | [] | [] | [] | [] |
2510.17960 | https://paperswithcode.co/api/v1/papers/arxiv/2510.17960?include_resources=true | 200 | true | 53682 | AION-1: Omnimodal Foundation Model for Astronomical Sciences | https://arxiv.org/abs/2510.17960 | [
{
"url": "https://github.com/PolymathicAI/AION",
"owner": "PolymathicAI",
"name": "AION",
"stars": 98,
"is_official": true,
"source": "hf_api"
}
] | [] | [] | [] | [] |
2504.02821 | https://paperswithcode.co/api/v1/papers/arxiv/2504.02821?include_resources=true | 200 | true | 47255 | Sparse Autoencoders Learn Monosemantic Features in Vision-Language Models | https://arxiv.org/abs/2504.02821 | [
{
"url": "https://github.com/explainableml/sae-for-vlm",
"owner": "ExplainableML",
"name": "sae-for-vlm",
"stars": 54,
"is_official": true,
"source": "links_json"
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] | [] | [] | [] | [] |
2505.21785 | https://paperswithcode.co/api/v1/papers/arxiv/2505.21785?include_resources=true | 200 | true | 86878 | Born a Transformer -- Always a Transformer? | https://arxiv.org/abs/2505.21785 | [] | [] | [] | [] | [] |
2408.08252 | https://paperswithcode.co/api/v1/papers/arxiv/2408.08252?include_resources=true | 200 | true | 35872 | Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding | https://arxiv.org/abs/2408.08252v5 | [
{
"url": "https://github.com/masa-ue/svdd",
"owner": "masa-ue",
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"url": "https://github.com/masa-ue/svdd}{https:",
"owner": "masa-ue",
"name": "SVDD}{https:",
"stars": 0,
"is_official": true,... | [] | [] | [] | [] |
2506.02935 | https://paperswithcode.co/api/v1/papers/arxiv/2506.02935?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2505.20355 | https://paperswithcode.co/api/v1/papers/arxiv/2505.20355?include_resources=true | 200 | true | 49875 | GraLoRA: Granular Low-Rank Adaptation for Parameter-Efficient Fine-Tuning | https://arxiv.org/abs/2505.20355 | [
{
"url": "https://github.com/squeezebits/gralora",
"owner": "SqueezeBits",
"name": "GraLoRA",
"stars": 27,
"is_official": true,
"source": "ai_extraction"
}
] | [] | [] | [] | [] |
2512.06963 | https://paperswithcode.co/api/v1/papers/arxiv/2512.06963?include_resources=true | 200 | true | 54647 | VideoVLA: Video Generators Can Be Generalizable Robot Manipulators | https://arxiv.org/abs/2512.06963 | [] | [
{
"url": "https://videovla-nips2025.github.io/",
"is_official": true
}
] | [] | [] | [] |
2510.16582 | https://paperswithcode.co/api/v1/papers/arxiv/2510.16582?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2505.15810 | https://paperswithcode.co/api/v1/papers/arxiv/2505.15810?include_resources=true | 200 | true | 49334 | GUI-G1: Understanding R1-Zero-Like Training for Visual Grounding in GUI Agents | https://arxiv.org/abs/2505.15810 | [
{
"url": "https://github.com/yuqi-zhou/gui-g1",
"owner": "Yuqi-Zhou",
"name": "GUI-G1",
"stars": 21,
"is_official": true,
"source": "links_json"
}
] | [] | [] | [] | [] |
2505.21391 | https://paperswithcode.co/api/v1/papers/arxiv/2505.21391?include_resources=true | 200 | true | 86534 | Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features | https://arxiv.org/abs/2505.21391 | [] | [] | [] | [] | [] |
2502.13257 | https://paperswithcode.co/api/v1/papers/arxiv/2502.13257?include_resources=true | 200 | true | 86798 | Random Forest Autoencoders for Guided Representation Learning | https://arxiv.org/abs/2502.13257 | [] | [] | [] | [] | [] |
2603.07614 | https://paperswithcode.co/api/v1/papers/arxiv/2603.07614?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2506.11743 | https://paperswithcode.co/api/v1/papers/arxiv/2506.11743?include_resources=true | 200 | true | 87171 | Taxonomy of reduction matrices for Graph Coarsening | https://arxiv.org/abs/2506.11743 | [] | [] | [] | [] | [] |
2505.15510 | https://paperswithcode.co/api/v1/papers/arxiv/2505.15510?include_resources=true | 200 | true | 72892 | Visual Thoughts: A Unified Perspective of Understanding Multimodal Chain-of-Thought | https://arxiv.org/abs/2505.15510 | [] | [] | [] | [] | [] |
2505.13567 | https://paperswithcode.co/api/v1/papers/arxiv/2505.13567?include_resources=true | 200 | true | 85772 | Learning Dynamics of RNNs in Closed-Loop Environments | https://arxiv.org/abs/2505.13567 | [] | [] | [] | [] | [] |
2511.03168 | https://paperswithcode.co/api/v1/papers/arxiv/2511.03168?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2502.08202 | https://paperswithcode.co/api/v1/papers/arxiv/2502.08202?include_resources=true | 200 | true | 86173 | Privacy amplification by random allocation | https://arxiv.org/abs/2502.08202 | [] | [] | [] | [] | [] |
2405.13375 | https://paperswithcode.co/api/v1/papers/arxiv/2405.13375?include_resources=true | 200 | true | 86838 | Adaptive Data Analysis for Growing Data | https://arxiv.org/abs/2405.13375 | [] | [] | [] | [] | [] |
2505.20033 | https://paperswithcode.co/api/v1/papers/arxiv/2505.20033?include_resources=true | 200 | true | 49800 | EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition | https://arxiv.org/abs/2505.20033v2 | [] | [] | [] | [] | [] |
2507.11690 | https://paperswithcode.co/api/v1/papers/arxiv/2507.11690?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2506.19741 | https://paperswithcode.co/api/v1/papers/arxiv/2506.19741?include_resources=true | 200 | true | 51426 | Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls | https://arxiv.org/abs/2506.19741 | [
{
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"owner": "Luo-Yihong",
"name": "NCT",
"stars": 7,
"is_official": true,
"source": "ai_extraction"
}
] | [] | [] | [] | [] |
2506.21710 | https://paperswithcode.co/api/v1/papers/arxiv/2506.21710?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2408.10609 | https://paperswithcode.co/api/v1/papers/arxiv/2408.10609?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2506.13911 | https://paperswithcode.co/api/v1/papers/arxiv/2506.13911?include_resources=true | 200 | true | 86464 | Logical Expressiveness of Graph Neural Networks with Hierarchical Node Individualization | https://arxiv.org/abs/2506.13911 | [] | [] | [] | [] | [] |
2510.06540 | https://paperswithcode.co/api/v1/papers/arxiv/2510.06540?include_resources=true | 200 | true | 87480 | Scalable Policy-Based RL Algorithms for POMDPs | https://arxiv.org/abs/2510.06540 | [] | [] | [] | [] | [] |
2411.03270 | https://paperswithcode.co/api/v1/papers/arxiv/2411.03270?include_resources=true | 200 | true | 85745 | Stable Matching with Ties: Approximation Ratios and Learning | https://arxiv.org/abs/2411.03270 | [] | [] | [] | [] | [] |
2410.20035 | https://paperswithcode.co/api/v1/papers/arxiv/2410.20035?include_resources=true | 200 | true | 39211 | Training the Untrainable: Introducing Inductive Bias via Representational Alignment | https://arxiv.org/abs/2410.20035 | [] | [
{
"url": "https://untrainable-networks.github.io/",
"is_official": true
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2505.10630 | https://paperswithcode.co/api/v1/papers/arxiv/2505.10630?include_resources=true | 200 | true | 87430 | How many measurements are enough? Bayesian recovery in inverse problems with general distributions | https://arxiv.org/abs/2505.10630 | [] | [] | [] | [] | [] |
2506.19291 | https://paperswithcode.co/api/v1/papers/arxiv/2506.19291?include_resources=true | 200 | true | 85810 | HoliGS: Holistic Gaussian Splatting for Embodied View Synthesis | https://arxiv.org/abs/2506.19291 | [] | [] | [] | [] | [] |
2504.21798 | https://paperswithcode.co/api/v1/papers/arxiv/2504.21798?include_resources=true | 200 | true | 48275 | SWE-smith: Scaling Data for Software Engineering Agents | https://arxiv.org/abs/2504.21798v2 | [
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2510.09343 | https://paperswithcode.co/api/v1/papers/arxiv/2510.09343?include_resources=true | 200 | true | 86491 | Enhancing Infrared Vision: Progressive Prompt Fusion Network and Benchmark | https://arxiv.org/abs/2510.09343 | [
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2502.11564 | https://paperswithcode.co/api/v1/papers/arxiv/2502.11564?include_resources=true | 200 | true | 44347 | Continuous Diffusion Model for Language Modeling | https://arxiv.org/abs/2502.11564 | [
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"stars": 0,
"is_official... | [] | [] | [] | [] |
2506.01393 | https://paperswithcode.co/api/v1/papers/arxiv/2506.01393?include_resources=true | 200 | true | 87378 | Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization | https://arxiv.org/abs/2506.01393 | [] | [] | [] | [] | [] |
2506.02528 | https://paperswithcode.co/api/v1/papers/arxiv/2506.02528?include_resources=true | 200 | true | 50459 | RelationAdapter: Learning and Transferring Visual Relation with Diffusion Transformers | https://arxiv.org/abs/2506.02528 | [
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2511.17399 | https://paperswithcode.co/api/v1/papers/arxiv/2511.17399?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2506.18951 | https://paperswithcode.co/api/v1/papers/arxiv/2506.18951?include_resources=true | 200 | true | 51402 | SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applications | https://arxiv.org/abs/2506.18951 | [
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2501.00321 | https://paperswithcode.co/api/v1/papers/arxiv/2501.00321?include_resources=true | 200 | true | 42341 | OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning | https://arxiv.org/abs/2501.00321 | [
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2511.04753 | https://paperswithcode.co/api/v1/papers/arxiv/2511.04753?include_resources=true | 200 | true | 65323 | CPO: Condition Preference Optimization for Controllable Image Generation | https://arxiv.org/abs/2511.04753 | [] | [] | [] | [] | [] |
2505.18882 | https://paperswithcode.co/api/v1/papers/arxiv/2505.18882?include_resources=true | 200 | true | 49662 | Personalized Safety in LLMs: A Benchmark and A Planning-Based Agent Approach | https://arxiv.org/abs/2505.18882v2 | [] | [
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2501.06807 | https://paperswithcode.co/api/v1/papers/arxiv/2501.06807?include_resources=true | 200 | true | 75218 | MPCache: MPC-Friendly KV Cache Eviction for Efficient Private Large
Language Model Inference | https://arxiv.org/abs/2501.06807 | [] | [] | [] | [] | [] |
2504.18530 | https://paperswithcode.co/api/v1/papers/arxiv/2504.18530?include_resources=true | 200 | true | 87076 | Scaling Laws For Scalable Oversight | https://arxiv.org/abs/2504.18530 | [] | [] | [] | [] | [] |
2505.11720 | https://paperswithcode.co/api/v1/papers/arxiv/2505.11720?include_resources=true | 200 | true | 86103 | UGoDIT: Unsupervised Group Deep Image Prior Via Transferable Weights | https://arxiv.org/abs/2505.11720 | [] | [] | [] | [] | [] |
2505.18495 | https://paperswithcode.co/api/v1/papers/arxiv/2505.18495?include_resources=true | 200 | true | 49616 | Beyond Masked and Unmasked: Discrete Diffusion Models via Partial Masking | https://arxiv.org/abs/2505.18495 | [] | [] | [] | [] | [] |
2503.08805 | https://paperswithcode.co/api/v1/papers/arxiv/2503.08805?include_resources=true | 200 | true | 45918 | Filter Like You Test: Data-Driven Data Filtering for CLIP Pretraining | https://arxiv.org/abs/2503.08805 | [
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] | [] | [] | [] | [] |
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