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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
2512.10403 | https://paperswithcode.co/api/v1/papers/arxiv/2512.10403?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2508.11644 | https://paperswithcode.co/api/v1/papers/arxiv/2508.11644?include_resources=true | 200 | true | 87021 | HetSyn: Versatile Timescale Integration in Spiking Neural Networks via Heterogeneous Synapses | https://arxiv.org/abs/2508.11644 | [] | [] | [] | [] | [] |
2601.08198 | https://paperswithcode.co/api/v1/papers/arxiv/2601.08198?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2505.16311 | https://paperswithcode.co/api/v1/papers/arxiv/2505.16311?include_resources=true | 200 | true | 86212 | Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventions | https://arxiv.org/abs/2505.16311 | [] | [] | [] | [] | [] |
2506.09350 | https://paperswithcode.co/api/v1/papers/arxiv/2506.09350?include_resources=true | 200 | true | 50920 | Autoregressive Adversarial Post-Training for Real-Time Interactive Video Generation | https://arxiv.org/abs/2506.09350 | [] | [
{
"url": "https://seaweed-apt.com/2",
"is_official": true
}
] | [] | [] | [] |
2502.01025 | https://paperswithcode.co/api/v1/papers/arxiv/2502.01025?include_resources=true | 200 | true | 43537 | Knowing When to Stop: Dynamic Context Cutoff for Large Language Models | https://arxiv.org/abs/2502.01025 | [
{
"url": "https://github.com/ruoyuxie/when-to-stop",
"owner": "ruoyuxie",
"name": "when-to-stop",
"stars": 2,
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"source": "ai_extraction"
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] | [
{
"url": "https://royxie.com/when-to-stop-project",
"is_official": true
}
] | [] | [] | [] |
2502.05171 | https://paperswithcode.co/api/v1/papers/arxiv/2502.05171?include_resources=true | 200 | true | 43844 | Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach | https://arxiv.org/abs/2502.05171 | [
{
"url": "https://github.com/seal-rg/recurrent-pretraining",
"owner": "seal-rg",
"name": "recurrent-pretraining",
"stars": 856,
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{
"url": "https://github.com/gair-nlp/prox",
"owner": "GAIR-NLP",
"name": "ProX",
"stars": 263,
... | [] | [] | [] | [] |
2510.19950 | https://paperswithcode.co/api/v1/papers/arxiv/2510.19950?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2510.08632 | https://paperswithcode.co/api/v1/papers/arxiv/2510.08632?include_resources=true | 200 | true | 86364 | Next Semantic Scale Prediction via Hierarchical Diffusion Language Models | https://arxiv.org/abs/2510.08632 | [] | [] | [] | [] | [] |
2511.21584 | https://paperswithcode.co/api/v1/papers/arxiv/2511.21584?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2510.06954 | https://paperswithcode.co/api/v1/papers/arxiv/2510.06954?include_resources=true | 200 | true | 86470 | From Condensation to Rank Collapse: A Two-Stage Analysis of Transformer Training Dynamics | https://arxiv.org/abs/2510.06954 | [] | [] | [] | [] | [] |
2506.12115 | https://paperswithcode.co/api/v1/papers/arxiv/2506.12115?include_resources=true | 200 | true | 87320 | Eliciting Reasoning in Language Models with Cognitive Tools | https://arxiv.org/abs/2506.12115 | [] | [] | [] | [] | [] |
2505.05758 | https://paperswithcode.co/api/v1/papers/arxiv/2505.05758?include_resources=true | 200 | true | 48560 | APOLLO: Automated LLM and Lean Collaboration for Advanced Formal Reasoning | https://arxiv.org/abs/2505.05758v2 | [
{
"url": "https://github.com/aziksh-ospanov/apollo",
"owner": "aziksh-ospanov",
"name": "APOLLO",
"stars": 11,
"is_official": true,
"source": "ai_extraction"
}
] | [] | [] | [] | [] |
2506.04411 | https://paperswithcode.co/api/v1/papers/arxiv/2506.04411?include_resources=true | 200 | true | 86357 | Self-Supervised Contrastive Learning is Approximately Supervised Contrastive Learning | https://arxiv.org/abs/2506.04411 | [] | [] | [] | [] | [] |
2412.05723 | https://paperswithcode.co/api/v1/papers/arxiv/2412.05723?include_resources=true | 200 | true | 41181 | Training-Free Bayesianization for Low-Rank Adapters of Large Language Models | https://arxiv.org/abs/2412.05723v2 | [
{
"url": "https://github.com/wang-ml-lab/bayesian-peft",
"owner": "Wang-ML-Lab",
"name": "bayesian-peft",
"stars": 30,
"is_official": true,
"source": "links_json"
}
] | [] | [] | [] | [] |
2412.16482 | https://paperswithcode.co/api/v1/papers/arxiv/2412.16482?include_resources=true | 200 | true | 86746 | Learn2Mix: Training Neural Networks Using Adaptive Data Integration | https://arxiv.org/abs/2412.16482 | [] | [] | [] | [] | [] |
2603.28824 | https://paperswithcode.co/api/v1/papers/arxiv/2603.28824?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2411.03859 | https://paperswithcode.co/api/v1/papers/arxiv/2411.03859?include_resources=true | 200 | true | 87207 | UniTraj: Learning a Universal Trajectory Foundation Model from Billion-Scale Worldwide Traces | https://arxiv.org/abs/2411.03859 | [] | [] | [] | [] | [] |
2509.20890 | https://paperswithcode.co/api/v1/papers/arxiv/2509.20890?include_resources=true | 200 | true | 67825 | FerretNet: Efficient Synthetic Image Detection via Local Pixel Dependencies | https://arxiv.org/abs/2509.20890 | [
{
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"owner": "xigua7105",
"name": "FerretNet",
"stars": 0,
"is_official": true,
"source": "hf_api"
}
] | [] | [] | [] | [] |
2511.04343 | https://paperswithcode.co/api/v1/papers/arxiv/2511.04343?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2502.11525 | https://paperswithcode.co/api/v1/papers/arxiv/2502.11525?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2504.12083 | https://paperswithcode.co/api/v1/papers/arxiv/2504.12083?include_resources=true | 200 | true | 47784 | Self-alignment of Large Video Language Models with Refined Regularized Preference Optimization | https://arxiv.org/abs/2504.12083 | [
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"url": "https://github.com/pritamqu/rrpo",
"owner": "pritamqu",
"name": "RRPO",
"stars": 7,
"is_official": true,
"source": "ai_extraction"
}
] | [
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"url": "https://pritamsarkar.com/RRPO/",
"is_official": true
},
{
"url": "https://pritamqu.github.io/RRPO/",
"is_official": true
}
] | [] | [] | [] |
2506.10899 | https://paperswithcode.co/api/v1/papers/arxiv/2506.10899?include_resources=true | 200 | true | 87234 | Demystifying Spectral Feature Learning for Instrumental Variable Regression | https://arxiv.org/abs/2506.10899 | [] | [] | [] | [] | [] |
2510.05552 | https://paperswithcode.co/api/v1/papers/arxiv/2510.05552?include_resources=true | 200 | true | 85964 | Channel Simulation and Distributed Compression with Ensemble Rejection Sampling | https://arxiv.org/abs/2510.05552 | [] | [] | [] | [] | [] |
2506.07570 | https://paperswithcode.co/api/v1/papers/arxiv/2506.07570?include_resources=true | 200 | true | 50773 | LLM-driven Indoor Scene Layout Generation via Scaled Human-aligned Data Synthesis and Multi-Stage Preference Optimization | https://arxiv.org/abs/2506.07570 | [
{
"url": "https://github.com/PolySummit/OptiScene",
"owner": "PolySummit",
"name": "OptiScene",
"stars": 0,
"is_official": true,
"source": "hf_api"
}
] | [] | [] | [] | [] |
2601.14778 | https://paperswithcode.co/api/v1/papers/arxiv/2601.14778?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2506.08015 | https://paperswithcode.co/api/v1/papers/arxiv/2506.08015?include_resources=true | 200 | true | 50821 | 4DGT: Learning a 4D Gaussian Transformer Using Real-World Monocular Videos | https://arxiv.org/abs/2506.08015 | [
{
"url": "https://github.com/facebookresearch/4dgt",
"owner": "facebookresearch",
"name": "4dgt",
"stars": 0,
"is_official": true,
"source": "hf_api"
}
] | [
{
"url": "https://4dgt.github.io/",
"is_official": true
},
{
"url": "https://4dgt.github.io",
"is_official": true
}
] | [] | [] | [] |
2512.13534 | https://paperswithcode.co/api/v1/papers/arxiv/2512.13534?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2509.01200 | https://paperswithcode.co/api/v1/papers/arxiv/2509.01200?include_resources=true | 200 | true | 85824 | SimulMEGA: MoE Routers are Advanced Policy Makers for Simultaneous Speech Translation | https://arxiv.org/abs/2509.01200 | [] | [] | [] | [] | [] |
2505.18522 | https://paperswithcode.co/api/v1/papers/arxiv/2505.18522?include_resources=true | 200 | true | 86770 | How Does Sequence Modeling Architecture Influence Base Capabilities of Pre-trained Language Models? Exploring Key Architecture Design Principles to Avoid Base Capabilities Degradation | https://arxiv.org/abs/2505.18522 | [] | [] | [] | [] | [] |
2506.15721 | https://paperswithcode.co/api/v1/papers/arxiv/2506.15721?include_resources=true | 200 | true | 72272 | Bohdi: Heterogeneous LLM Fusion with Automatic Data Exploration | https://arxiv.org/abs/2506.15721 | [
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"owner": "gjq100",
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"stars": 0,
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}
] | [] | [] | [] | [] |
2506.01281 | https://paperswithcode.co/api/v1/papers/arxiv/2506.01281?include_resources=true | 200 | true | 85930 | On the Hardness of Approximating Distributions with Probabilistic Circuits | https://arxiv.org/abs/2506.01281 | [] | [] | [] | [] | [] |
2502.08021 | https://paperswithcode.co/api/v1/papers/arxiv/2502.08021?include_resources=true | 200 | true | 85822 | Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol | https://arxiv.org/abs/2502.08021 | [] | [] | [] | [] | [] |
2504.18428 | https://paperswithcode.co/api/v1/papers/arxiv/2504.18428?include_resources=true | 200 | true | 48144 | PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts | https://arxiv.org/abs/2504.18428v2 | [
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"owner": "QwenLM",
"name": "PolyMath",
"stars": 37,
"is_official": true,
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] | [
{
"url": "https://Qwen-PolyMath.github.io/",
"is_official": true
}
] | [] | [] | [] |
2505.21908 | https://paperswithcode.co/api/v1/papers/arxiv/2505.21908?include_resources=true | 200 | true | 86661 | Reinforcement Learning for Out-of-Distribution Reasoning in LLMs: An Empirical Study on Diagnosis-Related Group Coding | https://arxiv.org/abs/2505.21908 | [] | [] | [] | [] | [] |
2505.18231 | https://paperswithcode.co/api/v1/papers/arxiv/2505.18231?include_resources=true | 200 | true | 49595 | NSNQuant: A Double Normalization Approach for Calibration-Free Low-Bit Vector Quantization of KV Cache | https://arxiv.org/abs/2505.18231 | [] | [] | [] | [] | [] |
2504.02298 | https://paperswithcode.co/api/v1/papers/arxiv/2504.02298?include_resources=true | 200 | true | 87011 | SPACE: SPike-Aware Consistency Enhancement for Test-Time Adaptation in Spiking Neural Networks | https://arxiv.org/abs/2504.02298 | [
{
"url": "https://github.com/ethanxyluo/space",
"owner": "ethanxyluo",
"name": "space",
"stars": 5,
"is_official": true,
"source": "neurips2025_import"
}
] | [] | [] | [] | [] |
2505.14669 | https://paperswithcode.co/api/v1/papers/arxiv/2505.14669?include_resources=true | 200 | true | 49214 | Quartet: Native FP4 Training Can Be Optimal for Large Language Models | https://arxiv.org/abs/2505.14669 | [
{
"url": "https://github.com/ist-daslab/quartet",
"owner": "IST-DASLab",
"name": "Quartet",
"stars": 113,
"is_official": true,
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] | [
{
"url": "https://neurips.cc/virtual/2025/loc/san-diego/poster/117521",
"is_official": true
}
] | [] | [] | [] |
2502.18080 | https://paperswithcode.co/api/v1/papers/arxiv/2502.18080?include_resources=true | 200 | true | 44989 | Towards Thinking-Optimal Scaling of Test-Time Compute for LLM Reasoning | https://arxiv.org/abs/2502.18080 | [
{
"url": "https://github.com/rucbm/tops",
"owner": "RUCBM",
"name": "TOPS",
"stars": 1,
"is_official": true,
"source": "ai_extraction"
}
] | [] | [] | [] | [] |
2506.00641 | https://paperswithcode.co/api/v1/papers/arxiv/2506.00641?include_resources=true | 200 | true | 86645 | AgentAuditor: Human-level Safety and Security Evaluation for LLM Agents | https://arxiv.org/abs/2506.00641 | [] | [] | [] | [] | [] |
2510.20786 | https://paperswithcode.co/api/v1/papers/arxiv/2510.20786?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2509.16974 | https://paperswithcode.co/api/v1/papers/arxiv/2509.16974?include_resources=true | 200 | true | 86651 | Hessian-guided Perturbed Wasserstein Gradient Flows for Escaping Saddle Points | https://arxiv.org/abs/2509.16974 | [] | [] | [] | [] | [] |
2510.03163 | https://paperswithcode.co/api/v1/papers/arxiv/2510.03163?include_resources=true | 200 | true | 85759 | ROGR: Relightable 3D Objects using Generative Relighting | https://arxiv.org/abs/2510.03163 | [] | [] | [] | [] | [] |
2505.17708 | https://paperswithcode.co/api/v1/papers/arxiv/2505.17708?include_resources=true | 200 | true | 86254 | The third pillar of causal analysis? A measurement perspective on causal representations | https://arxiv.org/abs/2505.17708 | [] | [] | [] | [] | [] |
2506.12618 | https://paperswithcode.co/api/v1/papers/arxiv/2506.12618?include_resources=true | 200 | true | 51116 | OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics | https://arxiv.org/abs/2506.12618 | [
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"owner": "locuslab",
"name": "open-unlearning",
"stars": 451,
"is_official": true,
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}
] | [] | [] | [] | [] |
2510.12157 | https://paperswithcode.co/api/v1/papers/arxiv/2510.12157?include_resources=true | 200 | true | 86702 | Self-Verifying Reflection Helps Transformers with CoT Reasoning | https://arxiv.org/abs/2510.12157 | [] | [] | [] | [] | [] |
2505.11475 | https://paperswithcode.co/api/v1/papers/arxiv/2505.11475?include_resources=true | 200 | true | 48870 | HelpSteer3-Preference: Open Human-Annotated Preference Data across Diverse Tasks and Languages | https://arxiv.org/abs/2505.11475 | [] | [
{
"url": "https://huggingface.co/datasets/nvidia/HelpSteer3#preference",
"is_official": true
}
] | [] | [] | [] |
2505.15239 | https://paperswithcode.co/api/v1/papers/arxiv/2505.15239?include_resources=true | 200 | true | 87168 | Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers | https://arxiv.org/abs/2505.15239 | [] | [] | [] | [] | [] |
2410.07961 | https://paperswithcode.co/api/v1/papers/arxiv/2410.07961?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2503.08921 | https://paperswithcode.co/api/v1/papers/arxiv/2503.08921?include_resources=true | 200 | true | 86650 | Revisiting Frank-Wolfe for Structured Nonconvex Optimization | https://arxiv.org/abs/2503.08921 | [] | [] | [] | [] | [] |
2511.07701 | https://paperswithcode.co/api/v1/papers/arxiv/2511.07701?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2506.00799 | https://paperswithcode.co/api/v1/papers/arxiv/2506.00799?include_resources=true | 200 | true | 72456 | Uni-LoRA: One Vector is All You Need | https://arxiv.org/abs/2506.00799 | [] | [] | [] | [] | [] |
2502.07218 | https://paperswithcode.co/api/v1/papers/arxiv/2502.07218?include_resources=true | 200 | true | 44004 | LUNAR: LLM Unlearning via Neural Activation Redirection | https://arxiv.org/abs/2502.07218 | [] | [
{
"url": "https://lil-lab.github.io/respect",
"is_official": true
}
] | [] | [] | [] |
2505.22491 | https://paperswithcode.co/api/v1/papers/arxiv/2505.22491?include_resources=true | 200 | true | 85974 | On the Surprising Effectiveness of Large Learning Rates under Standard Width Scaling | https://arxiv.org/abs/2505.22491 | [] | [] | [] | [] | [] |
2505.18976 | https://paperswithcode.co/api/v1/papers/arxiv/2505.18976?include_resources=true | 200 | true | 86143 | GraSS: Scalable Influence Function with Sparse Gradient Compression | https://arxiv.org/abs/2505.18976 | [
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"name": "grass",
"stars": 8,
"is_official": true,
"source": "neurips2025_import"
}
] | [] | [] | [] | [] |
2502.14819 | https://paperswithcode.co/api/v1/papers/arxiv/2502.14819?include_resources=true | 200 | true | 44691 | Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models | https://arxiv.org/abs/2502.14819 | [] | [
{
"url": "https://latent-planning.github.io",
"is_official": true
}
] | [] | [] | [] |
2502.08227 | https://paperswithcode.co/api/v1/papers/arxiv/2502.08227?include_resources=true | 200 | true | 85701 | Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples | https://arxiv.org/abs/2502.08227 | [] | [] | [] | [] | [] |
2504.16980 | https://paperswithcode.co/api/v1/papers/arxiv/2504.16980?include_resources=true | 200 | true | 73366 | Safety Pretraining: Toward the Next Generation of Safe AI | https://arxiv.org/abs/2504.16980 | [] | [] | [] | [] | [] |
2510.11824 | https://paperswithcode.co/api/v1/papers/arxiv/2510.11824?include_resources=true | 200 | true | 87218 | Empirical Study on Robustness and Resilience in Cooperative Multi-Agent Reinforcement Learning | https://arxiv.org/abs/2510.11824 | [
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] | [] | [] | [] | [] |
2505.18266 | https://paperswithcode.co/api/v1/papers/arxiv/2505.18266?include_resources=true | 200 | true | 87198 | Uncovering a Universal Abstract Algorithm for Modular Addition in Neural Networks | https://arxiv.org/abs/2505.18266 | [] | [] | [] | [] | [] |
2506.13922 | https://paperswithcode.co/api/v1/papers/arxiv/2506.13922?include_resources=true | 200 | true | 51185 | DynaGuide: Steering Diffusion Polices with Active Dynamic Guidance | https://arxiv.org/abs/2506.13922 | [
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"stars": 37,
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"url": "https://dynaguide.github.io",
"is_official": true
}
] | [] | [] | [] |
2509.01254 | https://paperswithcode.co/api/v1/papers/arxiv/2509.01254?include_resources=true | 200 | true | 87438 | What Expressivity Theory Misses: Message Passing Complexity for GNNs | https://arxiv.org/abs/2509.01254 | [] | [] | [] | [] | [] |
2506.23881 | https://paperswithcode.co/api/v1/papers/arxiv/2506.23881?include_resources=true | 200 | true | 86056 | Spurious-Aware Prototype Refinement for Reliable Out-of-Distribution Detection | https://arxiv.org/abs/2506.23881 | [] | [] | [] | [] | [] |
2505.10838 | https://paperswithcode.co/api/v1/papers/arxiv/2505.10838?include_resources=true | 200 | true | 48806 | LARGO: Latent Adversarial Reflection through Gradient Optimization for Jailbreaking LLMs | https://arxiv.org/abs/2505.10838 | [] | [] | [] | [] | [] |
2410.20749 | https://paperswithcode.co/api/v1/papers/arxiv/2410.20749?include_resources=true | 200 | true | 39253 | Matryoshka: Learning to Drive Black-Box LLMs with LLMs | https://arxiv.org/abs/2410.20749 | [
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"owner": "lichangh20",
"name": "Matryoshka",
"stars": 8,
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] | [] | [] | [] | [] |
2510.03339 | https://paperswithcode.co/api/v1/papers/arxiv/2510.03339?include_resources=true | 200 | true | 86516 | Pool Me Wisely: On the Effect of Pooling in Transformer-Based Models | https://arxiv.org/abs/2510.03339 | [] | [] | [] | [] | [] |
2511.01334 | https://paperswithcode.co/api/v1/papers/arxiv/2511.01334?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2509.26427 | https://paperswithcode.co/api/v1/papers/arxiv/2509.26427?include_resources=true | 200 | true | 86944 | Ascent Fails to Forget | https://arxiv.org/abs/2509.26427 | [] | [] | [] | [] | [] |
2511.03344 | https://paperswithcode.co/api/v1/papers/arxiv/2511.03344?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2506.06630 | https://paperswithcode.co/api/v1/papers/arxiv/2506.06630?include_resources=true | 200 | true | 86105 | Active Test-time Vision-Language Navigation | https://arxiv.org/abs/2506.06630 | [] | [] | [] | [] | [] |
2506.07491 | https://paperswithcode.co/api/v1/papers/arxiv/2506.07491?include_resources=true | 200 | true | 50765 | SpatialLM: Training Large Language Models for Structured Indoor Modeling | https://arxiv.org/abs/2506.07491 | [
{
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"owner": "manycore-research",
"name": "SpatialLM",
"stars": 4138,
"is_official": true,
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] | [
{
"url": "https://manycore-research.github.io/SpatialLM",
"is_official": true
}
] | [] | [] | [] |
2510.20968 | https://paperswithcode.co/api/v1/papers/arxiv/2510.20968?include_resources=true | 200 | true | 66001 | Neural Mutual Information Estimation with Vector Copulas | https://arxiv.org/abs/2510.20968 | [] | [] | [] | [] | [] |
2403.00957 | https://paperswithcode.co/api/v1/papers/arxiv/2403.00957?include_resources=true | 200 | true | 86107 | Resolution of Simpson's paradox via the common cause principle | https://arxiv.org/abs/2403.00957 | [] | [] | [] | [] | [] |
2503.10799 | https://paperswithcode.co/api/v1/papers/arxiv/2503.10799?include_resources=true | 200 | true | 74102 | Fixed-Point RNNs: Interpolating from Diagonal to Dense | https://arxiv.org/abs/2503.10799 | [] | [] | [] | [] | [] |
2501.18879 | https://paperswithcode.co/api/v1/papers/arxiv/2501.18879?include_resources=true | 200 | true | 86962 | Understanding Generalization in Physics Informed Models through Affine Variety Dimensions | https://arxiv.org/abs/2501.18879 | [] | [] | [] | [] | [] |
2503.24357 | https://paperswithcode.co/api/v1/papers/arxiv/2503.24357?include_resources=true | 200 | true | 73792 | InstructRestore: Region-Customized Image Restoration with Human
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2503.10635 | https://paperswithcode.co/api/v1/papers/arxiv/2503.10635?include_resources=true | 200 | true | 46081 | A Frustratingly Simple Yet Highly Effective Attack Baseline: Over 90% Success Rate Against the Strong Black-box Models of GPT-4.5/4o/o1 | https://arxiv.org/abs/2503.10635 | [
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2506.14866 | https://paperswithcode.co/api/v1/papers/arxiv/2506.14866?include_resources=true | 200 | true | 51247 | OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents | https://arxiv.org/abs/2506.14866 | [
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2509.16915 | https://paperswithcode.co/api/v1/papers/arxiv/2509.16915?include_resources=true | 200 | true | 86290 | Differential Privacy for Euclidean Jordan Algebra with Applications to Private Symmetric Cone Programming | https://arxiv.org/abs/2509.16915 | [] | [] | [] | [] | [] |
2506.00358 | https://paperswithcode.co/api/v1/papers/arxiv/2506.00358?include_resources=true | 200 | true | 72490 | AVROBUSTBENCH: Benchmarking the Robustness of Audio-Visual
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2505.20256 | https://paperswithcode.co/api/v1/papers/arxiv/2505.20256?include_resources=true | 200 | true | 49843 | Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System Collaboration | https://arxiv.org/abs/2505.20256 | [
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2511.00119 | https://paperswithcode.co/api/v1/papers/arxiv/2511.00119?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2507.18537 | https://paperswithcode.co/api/v1/papers/arxiv/2507.18537?include_resources=true | 200 | true | 52059 | TTS-VAR: A Test-Time Scaling Framework for Visual Auto-Regressive Generation | https://arxiv.org/abs/2507.18537 | [
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2509.24266 | https://paperswithcode.co/api/v1/papers/arxiv/2509.24266?include_resources=true | 200 | true | 87084 | S$^2$NN: Sub-bit Spiking Neural Networks | https://arxiv.org/abs/2509.24266 | [] | [] | [] | [] | [] |
2506.12110 | https://paperswithcode.co/api/v1/papers/arxiv/2506.12110?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2510.05051 | https://paperswithcode.co/api/v1/papers/arxiv/2510.05051?include_resources=true | 200 | true | 66922 | SegMASt3R: Geometry Grounded Segment Matching | https://arxiv.org/abs/2510.05051 | [
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2512.17094 | https://paperswithcode.co/api/v1/papers/arxiv/2512.17094?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2511.16673 | https://paperswithcode.co/api/v1/papers/arxiv/2511.16673?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2505.12540 | https://paperswithcode.co/api/v1/papers/arxiv/2505.12540?include_resources=true | 200 | true | 48969 | Harnessing the Universal Geometry of Embeddings | https://arxiv.org/abs/2505.12540v2 | [
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2510.03276 | https://paperswithcode.co/api/v1/papers/arxiv/2510.03276?include_resources=true | 200 | true | 85952 | QuadEnhancer: Leveraging Quadratic Transformations to Enhance Deep Neural Networks | https://arxiv.org/abs/2510.03276 | [] | [] | [] | [] | [] |
2502.13692 | https://paperswithcode.co/api/v1/papers/arxiv/2502.13692?include_resources=true | 200 | true | 85815 | Tight Generalization Bounds for Large-Margin Halfspaces | https://arxiv.org/abs/2502.13692 | [] | [] | [] | [] | [] |
2507.11229 | https://paperswithcode.co/api/v1/papers/arxiv/2507.11229?include_resources=true | 200 | true | 86246 | DuetGraph: Coarse-to-Fine Knowledge Graph Reasoning with Dual-Pathway Global-Local Fusion | https://arxiv.org/abs/2507.11229 | [
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2506.10412 | https://paperswithcode.co/api/v1/papers/arxiv/2506.10412?include_resources=true | 200 | true | 72048 | Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate
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2503.17682 | https://paperswithcode.co/api/v1/papers/arxiv/2503.17682?include_resources=true | 200 | true | 46573 | Safe RLHF-V: Safe Reinforcement Learning from Human Feedback in Multimodal Large Language Models | https://arxiv.org/abs/2503.17682 | [
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2405.18754 | https://paperswithcode.co/api/v1/papers/arxiv/2405.18754?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2410.07170 | https://paperswithcode.co/api/v1/papers/arxiv/2410.07170?include_resources=true | 200 | true | 38192 | Parameter Efficient Fine-tuning via Explained Variance Adaptation | https://arxiv.org/abs/2410.07170v4 | [
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2508.15720 | https://paperswithcode.co/api/v1/papers/arxiv/2508.15720?include_resources=true | 200 | true | 69348 | WorldWeaver: Generating Long-Horizon Video Worlds via Rich Perception | https://arxiv.org/abs/2508.15720 | [] | [] | [] | [] | [] |
2412.20984 | https://paperswithcode.co/api/v1/papers/arxiv/2412.20984?include_resources=true | 200 | true | 85742 | AlignAb: Pareto-Optimal Energy Alignment for Designing Nature-Like Antibodies | https://arxiv.org/abs/2412.20984 | [] | [] | [] | [] | [] |
2511.18601 | https://paperswithcode.co/api/v1/papers/arxiv/2511.18601?include_resources=true | 404 | false | null | null | null | null | null | null | null | null |
2505.10978 | https://paperswithcode.co/api/v1/papers/arxiv/2505.10978?include_resources=true | 200 | true | 48819 | Group-in-Group Policy Optimization for LLM Agent Training | https://arxiv.org/abs/2505.10978 | [
{
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