{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/softmax/papers/25","list_of":"/method/softmax","method":"Softmax","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":25,"pages_in_order":375,"rows_per_page":100,"rows":[2401,2500],"of":37443,"counts":{"archive_papers_tagged":37443,"with_a_code_link":15869,"where_syntology_ran_a_sample":4578,"not_listed_spam_title":0,"listed":37443,"listed_where_code_ran":4578,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3835,"every_run_a_failure_of_syntologys_instrument":743,"listed_with_a_run_with_no_instrument_failure":3835,"listed_every_run_a_failure_of_syntologys_instrument":743,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/softmax","prev":"/method/softmax/papers/24","next":"/method/softmax/papers/26","papers":[{"paper":null,"slug":"detecting-credit-card-fraud-via-heterogeneous","title":"Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attention","date":"2025-04-11","arxiv_id":"2504.08183","n_code_links":0,"syntology":null},{"paper":null,"slug":"distributed-kalman-filter-with-ultimately","title":"Distributed Kalman Filter with Ultimately Accurate Fused Measurement Covariance","date":"2025-04-11","arxiv_id":"2504.08302","n_code_links":0,"syntology":null},{"paper":null,"slug":"dreamfuse-adaptive-image-fusion-with","title":"DreamFuse: Adaptive Image Fusion with Diffusion Transformer","date":"2025-04-11","arxiv_id":"2504.08291","n_code_links":0,"syntology":null},{"paper":null,"slug":"drivaer-transformer-a-high-precision-and-fast","title":"DrivAer Transformer: A high-precision and fast prediction method for vehicle aerodynamic drag coefficient based on the DrivAerNet++ dataset","date":"2025-04-11","arxiv_id":"2504.08217","n_code_links":0,"syntology":null},{"paper":null,"slug":"examining-gpt-s-capability-to-generate-and","title":"Examining GPT's Capability to Generate and Map Course Concepts and Their Relationship","date":"2025-04-11","arxiv_id":"2504.08856","n_code_links":0,"syntology":null},{"paper":"/paper/hypercore-the-core-framework-for-building","slug":"hypercore-the-core-framework-for-building","title":"HyperCore: The Core Framework for Building Hyperbolic Foundation Models with Comprehensive Modules","date":"2025-04-11","arxiv_id":"2504.08912","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["graph-and-geometric-learning/hypercore"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"hypergraph-vision-transformers-images-are","title":"Hypergraph Vision Transformers: Images are More than Nodes, More than Edges","date":"2025-04-11","arxiv_id":"2504.08710","n_code_links":0,"syntology":null},{"paper":null,"slug":"integrated-ensemble-of-bert-and-features","title":"Integrated ensemble of BERT- and features-based models for authorship attribution in Japanese literary works","date":"2025-04-11","arxiv_id":"2504.08527","n_code_links":0,"syntology":null},{"paper":null,"slug":"jupiter-fast-and-resource-efficient","title":"Jupiter: Fast and Resource-Efficient Collaborative Inference of Generative LLMs on Edge Devices","date":"2025-04-11","arxiv_id":"2504.08242","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-from-elders-making-an-llm-powered","title":"Learning from Elders: Making an LLM-powered Chatbot for Retirement Communities more Accessible through User-centered Design","date":"2025-04-11","arxiv_id":"2504.08985","n_code_links":0,"syntology":null},{"paper":null,"slug":"llm-for-comparative-narrative-analysis","title":"LLM for Comparative Narrative Analysis","date":"2025-04-11","arxiv_id":"2504.08211","n_code_links":0,"syntology":null},{"paper":null,"slug":"llmtaxo-leveraging-large-language-models-for","title":"LLMTaxo: Leveraging Large Language Models for Constructing Taxonomy of Factual Claims from Social Media","date":"2025-04-11","arxiv_id":"2504.12325","n_code_links":0,"syntology":null},{"paper":null,"slug":"long-context-in-context-compression-by","title":"Long Context In-Context Compression by Getting to the Gist of Gisting","date":"2025-04-11","arxiv_id":"2504.08934","n_code_links":0,"syntology":null},{"paper":null,"slug":"millions-of-states-designing-a-scalable-moe","title":"Millions of States: Designing a Scalable MoE Architecture with RWKV-7 Meta-learner","date":"2025-04-11","arxiv_id":"2504.08247","n_code_links":0,"syntology":null},{"paper":"/paper/mimic-in-context-learning-for-multimodal","slug":"mimic-in-context-learning-for-multimodal","title":"Mimic In-Context Learning for Multimodal Tasks","date":"2025-04-11","arxiv_id":"2504.08851","n_code_links":1,"syntology":null},{"paper":null,"slug":"mineworld-a-real-time-and-open-source","title":"MineWorld: a Real-Time and Open-Source Interactive World Model on Minecraft","date":"2025-04-11","arxiv_id":"2504.08388","n_code_links":0,"syntology":null},{"paper":null,"slug":"mixdit-accelerating-image-diffusion","title":"MixDiT: Accelerating Image Diffusion Transformer Inference with Mixed-Precision MX Quantization","date":"2025-04-11","arxiv_id":"2504.08398","n_code_links":0,"syntology":null},{"paper":null,"slug":"modernbert-or-debertav3-examining","title":"ModernBERT or DeBERTaV3? Examining Architecture and Data Influence on Transformer Encoder Models Performance","date":"2025-04-11","arxiv_id":"2504.08716","n_code_links":0,"syntology":null},{"paper":null,"slug":"motiondreamer-one-to-many-motion-synthesis","title":"MotionDreamer: One-to-Many Motion Synthesis with Localized Generative Masked Transformer","date":"2025-04-11","arxiv_id":"2504.08959","n_code_links":0,"syntology":null},{"paper":null,"slug":"muon-accelerated-attention-distillation-for","title":"Muon-Accelerated Attention Distillation for Real-Time Edge Synthesis via Optimized Latent Diffusion","date":"2025-04-11","arxiv_id":"2504.08451","n_code_links":0,"syntology":null},{"paper":"/paper/out-of-style-rag-s-fragility-to-linguistic","slug":"out-of-style-rag-s-fragility-to-linguistic","title":"Out of Style: RAG's Fragility to Linguistic Variation","date":"2025-04-11","arxiv_id":"2504.08231","n_code_links":1,"syntology":{"ran":11,"of":14,"n_ran_checked":11,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["springcty/rag-fragility-to-linguistic-variation"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/pact-pruning-and-clustering-based-token","slug":"pact-pruning-and-clustering-based-token","title":"PACT: Pruning and Clustering-Based Token Reduction for Faster Visual Language Models","date":"2025-04-11","arxiv_id":"2504.08966","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["orailix/pact"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"passive-underwater-acoustic-signal-separation","title":"Passive Underwater Acoustic Signal Separation based on Feature Decoupling Dual-path Network","date":"2025-04-11","arxiv_id":"2504.08371","n_code_links":0,"syntology":null},{"paper":null,"slug":"pca-rag-principal-component-analysis-for","title":"PCA-RAG: Principal Component Analysis for Efficient Retrieval-Augmented Generation","date":"2025-04-11","arxiv_id":"2504.08386","n_code_links":0,"syntology":null},{"paper":null,"slug":"pne-sgan-probabilistic-ndt-enhanced-semantic","title":"PNE-SGAN: Probabilistic NDT-Enhanced Semantic Graph Attention Network for LiDAR Loop Closure Detection","date":"2025-04-11","arxiv_id":"2504.08280","n_code_links":0,"syntology":null},{"paper":null,"slug":"rtlrepocoder-repository-level-rtl-code","title":"RTLRepoCoder: Repository-Level RTL Code Completion through the Combination of Fine-Tuning and Retrieval Augmentation","date":"2025-04-11","arxiv_id":"2504.08862","n_code_links":0,"syntology":null},{"paper":null,"slug":"sarformer-an-acquisition-parameter-aware","title":"SARFormer -- An Acquisition Parameter Aware Vision Transformer for Synthetic Aperture Radar Data","date":"2025-04-11","arxiv_id":"2504.08441","n_code_links":0,"syntology":null},{"paper":"/paper/seebias-a-comprehensive-tool-for-assessing","slug":"seebias-a-comprehensive-tool-for-assessing","title":"seeBias: A Comprehensive Tool for Assessing and Visualizing AI Fairness","date":"2025-04-11","arxiv_id":"2504.08418","n_code_links":1,"syntology":null},{"paper":null,"slug":"steering-clip-s-vision-transformer-with","title":"Steering CLIP's vision transformer with sparse autoencoders","date":"2025-04-11","arxiv_id":"2504.08729","n_code_links":0,"syntology":null},{"paper":null,"slug":"swan-gpt-an-efficient-and-scalable-approach","title":"SWAN-GPT: An Efficient and Scalable Approach for Long-Context Language Modeling","date":"2025-04-11","arxiv_id":"2504.08719","n_code_links":0,"syntology":null},{"paper":"/paper/the-other-side-of-the-coin-exploring-fairness","slug":"the-other-side-of-the-coin-exploring-fairness","title":"The Other Side of the Coin: Exploring Fairness in Retrieval-Augmented Generation","date":"2025-04-11","arxiv_id":"2504.12323","n_code_links":1,"syntology":null},{"paper":null,"slug":"training-free-guidance-in-text-to-video","title":"Training-free Guidance in Text-to-Video Generation via Multimodal Planning and Structured Noise Initialization","date":"2025-04-11","arxiv_id":"2504.08641","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-learns-optimal-variable-selection","title":"Transformer Learns Optimal Variable Selection in Group-Sparse Classification","date":"2025-04-11","arxiv_id":"2504.08638","n_code_links":0,"syntology":null},{"paper":null,"slug":"vlmt-vision-language-multimodal-transformer","title":"VLMT: Vision-Language Multimodal Transformer for Multimodal Multi-hop Question Answering","date":"2025-04-11","arxiv_id":"2504.08269","n_code_links":0,"syntology":null},{"paper":null,"slug":"zipir-latent-pyramid-diffusion-transformer","title":"ZipIR: Latent Pyramid Diffusion Transformer for High-Resolution Image Restoration","date":"2025-04-11","arxiv_id":"2504.08591","n_code_links":0,"syntology":null},{"paper":"/paper/a-system-for-comprehensive-assessment-of-rag","slug":"a-system-for-comprehensive-assessment-of-rag","title":"A System for Comprehensive Assessment of RAG Frameworks","date":"2025-04-10","arxiv_id":"2504.07803","n_code_links":1,"syntology":null},{"paper":"/paper/agentada-skill-adaptive-data-analytics-for","slug":"agentada-skill-adaptive-data-analytics-for","title":"AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery","date":"2025-04-10","arxiv_id":"2504.07421","n_code_links":1,"syntology":null},{"paper":null,"slug":"ai-coding-with-few-shot-prompting-for","title":"AI Coding with Few-Shot Prompting for Thematic Analysis","date":"2025-04-10","arxiv_id":"2504.07408","n_code_links":0,"syntology":null},{"paper":"/paper/ai-slop-to-ai-polish-aligning-language-models","slug":"ai-slop-to-ai-polish-aligning-language-models","title":"AI-Slop to AI-Polish? Aligning Language Models through Edit-Based Writing Rewards and Test-time Computation","date":"2025-04-10","arxiv_id":"2504.07532","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["salesforce/creativity_eval"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"apsq-additive-partial-sum-quantization-with","title":"APSQ: Additive Partial Sum Quantization with Algorithm-Hardware Co-Design","date":"2025-04-10","arxiv_id":"2505.03748","n_code_links":0,"syntology":null},{"paper":null,"slug":"attentiondefense-leveraging-system-prompt","title":"AttentionDefense: Leveraging System Prompt Attention for Explainable Defense Against Novel Jailbreaks","date":"2025-04-10","arxiv_id":"2504.12321","n_code_links":0,"syntology":null},{"paper":null,"slug":"beating-transformers-using-synthetic","title":"Beating Transformers using Synthetic Cognition","date":"2025-04-10","arxiv_id":"2504.07619","n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-feature-importance-feature","title":"Beyond Feature Importance: Feature Interactions in Predicting Post-Stroke Rigidity with Graph Explainable AI","date":"2025-04-10","arxiv_id":"2504.08150","n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-llms-a-linguistic-approach-to-causal","title":"Beyond LLMs: A Linguistic Approach to Causal Graph Generation from Narrative Texts","date":"2025-04-10","arxiv_id":"2504.07459","n_code_links":0,"syntology":null},{"paper":null,"slug":"breaking-the-barriers-video-vision","title":"Breaking the Barriers: Video Vision Transformers for Word-Level Sign Language Recognition","date":"2025-04-10","arxiv_id":"2504.07792","n_code_links":0,"syntology":null},{"paper":"/paper/can-reasoning-llms-enhance-clinical-document","slug":"can-reasoning-llms-enhance-clinical-document","title":"Can Reasoning LLMs Enhance Clinical Document Classification?","date":"2025-04-10","arxiv_id":"2504.08040","n_code_links":1,"syntology":null},{"paper":null,"slug":"categorical-unsupervised-variational-acoustic","title":"Categorical Unsupervised Variational Acoustic Clustering","date":"2025-04-10","arxiv_id":"2504.07652","n_code_links":0,"syntology":null},{"paper":null,"slug":"chronoformer-time-aware-transformer","title":"ChronoFormer: Time-Aware Transformer Architectures for Structured Clinical Event Modeling","date":"2025-04-10","arxiv_id":"2504.07373","n_code_links":0,"syntology":null},{"paper":"/paper/climatebench-m-a-multi-modal-climate-data","slug":"climatebench-m-a-multi-modal-climate-data","title":"ClimateBench-M: A Multi-Modal Climate Data Benchmark with a Simple Generative Method","date":"2025-04-10","arxiv_id":"2504.07394","n_code_links":1,"syntology":null},{"paper":null,"slug":"conceptformer-towards-efficient-use-of","title":"ConceptFormer: Towards Efficient Use of Knowledge-Graph Embeddings in Large Language Models","date":"2025-04-10","arxiv_id":"2504.07624","n_code_links":0,"syntology":null},{"paper":"/paper/contrastivegaussian-high-fidelity-3d","slug":"contrastivegaussian-high-fidelity-3d","title":"ContrastiveGaussian: High-Fidelity 3D Generation with Contrastive Learning and Gaussian Splatting","date":"2025-04-10","arxiv_id":"2504.08100","n_code_links":1,"syntology":null},{"paper":"/paper/deep-learning-meets-teleconnections-improving","slug":"deep-learning-meets-teleconnections-improving","title":"Deep Learning Meets Teleconnections: Improving S2S Predictions for European Winter Weather","date":"2025-04-10","arxiv_id":"2504.07625","n_code_links":1,"syntology":null},{"paper":null,"slug":"dgocc-depth-aware-global-query-based-network","title":"DGOcc: Depth-aware Global Query-based Network for Monocular 3D Occupancy Prediction","date":"2025-04-10","arxiv_id":"2504.07524","n_code_links":0,"syntology":null},{"paper":null,"slug":"distilling-knowledge-from-heterogeneous","title":"Distilling Knowledge from Heterogeneous Architectures for Semantic Segmentation","date":"2025-04-10","arxiv_id":"2504.07691","n_code_links":0,"syntology":null},{"paper":null,"slug":"end-to-end-facial-expression-detection-in","title":"End-to-End Facial Expression Detection in Long Videos","date":"2025-04-10","arxiv_id":"2504.07660","n_code_links":0,"syntology":null},{"paper":null,"slug":"fmnv-a-dataset-of-media-published-news-videos","title":"FMNV: A Dataset of Media-Published News Videos for Fake News Detection","date":"2025-04-10","arxiv_id":"2504.07687","n_code_links":0,"syntology":null},{"paper":null,"slug":"from-token-to-line-enhancing-code-generation","title":"LSR-MCTS: Alleviating Long Range Dependency in Code Generation","date":"2025-04-10","arxiv_id":"2504.07433","n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-artificial-intelligence-for-3","title":"Generative Artificial Intelligence for Internet of Things Computing: A Systematic Survey","date":"2025-04-10","arxiv_id":"2504.07635","n_code_links":0,"syntology":null},{"paper":null,"slug":"genetic-programming-with-reinforcement","title":"Genetic Programming with Reinforcement Learning Trained Transformer for Real-World Dynamic Scheduling Problems","date":"2025-04-10","arxiv_id":"2504.07779","n_code_links":0,"syntology":null},{"paper":null,"slug":"has-the-creativity-of-large-language-models","title":"Has the Creativity of Large-Language Models peaked? An analysis of inter- and intra-LLM variability","date":"2025-04-10","arxiv_id":"2504.12320","n_code_links":0,"syntology":null},{"paper":"/paper/heart-failure-prediction-using-modal","slug":"heart-failure-prediction-using-modal","title":"Heart Failure Prediction using Modal Decomposition and Masked Autoencoders for Scarce Echocardiography Databases","date":"2025-04-10","arxiv_id":"2504.07606","n_code_links":1,"syntology":null},{"paper":null,"slug":"holopart-generative-3d-part-amodal","title":"HoloPart: Generative 3D Part Amodal Segmentation","date":"2025-04-10","arxiv_id":"2504.07943","n_code_links":0,"syntology":null},{"paper":"/paper/how-do-large-language-models-understand","slug":"how-do-large-language-models-understand","title":"How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective","date":"2025-04-10","arxiv_id":"2504.07898","n_code_links":1,"syntology":null},{"paper":null,"slug":"independence-is-not-an-issue-in-neurosymbolic","title":"Independence Is Not an Issue in Neurosymbolic AI","date":"2025-04-10","arxiv_id":"2504.07851","n_code_links":0,"syntology":null},{"paper":null,"slug":"intelligent-dos-and-ddos-detection-a-hybrid","title":"Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to Network Security","date":"2025-04-10","arxiv_id":"2504.07478","n_code_links":0,"syntology":null},{"paper":null,"slug":"jepa4rec-learning-effective-language","title":"JEPA4Rec: Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecture","date":"2025-04-10","arxiv_id":"2504.10512","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-object-focused-attention","title":"Learning Object Focused Attention","date":"2025-04-10","arxiv_id":"2504.08166","n_code_links":0,"syntology":null},{"paper":null,"slug":"malware-analysis-assisted-by-ai-with-r2ai","title":"Malware analysis assisted by AI with R2AI","date":"2025-04-10","arxiv_id":"2504.07574","n_code_links":0,"syntology":null},{"paper":"/paper/mrd-rag-enhancing-medical-diagnosis-with","slug":"mrd-rag-enhancing-medical-diagnosis-with","title":"MRD-RAG: Enhancing Medical Diagnosis with Multi-Round Retrieval-Augmented Generation","date":"2025-04-10","arxiv_id":"2504.07724","n_code_links":1,"syntology":null},{"paper":null,"slug":"novel-pooling-based-vgg-lite-for-pneumonia","title":"Novel Pooling-based VGG-Lite for Pneumonia and Covid-19 Detection from Imbalanced Chest X-Ray Datasets","date":"2025-04-10","arxiv_id":"2504.07468","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-practice-of-deep-hierarchical-ensemble","title":"On the Practice of Deep Hierarchical Ensemble Network for Ad Conversion Rate Prediction","date":"2025-04-10","arxiv_id":"2504.08169","n_code_links":0,"syntology":null},{"paper":"/paper/p2object-single-point-supervised-object","slug":"p2object-single-point-supervised-object","title":"P2Object: Single Point Supervised Object Detection and Instance Segmentation","date":"2025-04-10","arxiv_id":"2504.07813","n_code_links":1,"syntology":null},{"paper":null,"slug":"pangu-ultra-pushing-the-limits-of-dense-large","title":"Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs","date":"2025-04-10","arxiv_id":"2504.07866","n_code_links":0,"syntology":null},{"paper":null,"slug":"patchtrad-a-patch-based-transformer-focusing","title":"PatchTrAD: A Patch-Based Transformer focusing on Patch-Wise Reconstruction Error for Time Series Anomaly Detection","date":"2025-04-10","arxiv_id":"2504.08827","n_code_links":0,"syntology":null},{"paper":null,"slug":"pogo-a-scalable-proof-of-useful-work-via","title":"PoGO: A Scalable Proof of Useful Work via Quantized Gradient Descent and Merkle Proofs","date":"2025-04-10","arxiv_id":"2504.07540","n_code_links":0,"syntology":null},{"paper":"/paper/radzero-similarity-based-cross-attention-for","slug":"radzero-similarity-based-cross-attention-for","title":"RadZero: Similarity-Based Cross-Attention for Explainable Vision-Language Alignment in Radiology with Zero-Shot Multi-Task Capability","date":"2025-04-10","arxiv_id":"2504.07416","n_code_links":0,"syntology":{"ran":5,"of":6,"n_ran_checked":2,"n_instrument":3,"unverified":1,"pointer_only":6,"phrase":"5 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":null,"slug":"representation-meets-optimization-training","title":"Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology","date":"2025-04-10","arxiv_id":"2504.07379","n_code_links":0,"syntology":null},{"paper":null,"slug":"revisiting-prompt-optimization-with-large","title":"Revisiting Prompt Optimization with Large Reasoning Models-A Case Study on Event Extraction","date":"2025-04-10","arxiv_id":"2504.07357","n_code_links":0,"syntology":null},{"paper":"/paper/srvp-strong-recollection-video-prediction","slug":"srvp-strong-recollection-video-prediction","title":"SRVP: Strong Recollection Video Prediction Model Using Attention-Based Spatiotemporal Correlation Fusion","date":"2025-04-10","arxiv_id":"2504.08012","n_code_links":1,"syntology":null},{"paper":null,"slug":"synthetic-fluency-hallucinations","title":"Synthetic Fluency: Hallucinations, Confabulations, and the Creation of Irish Words in LLM-Generated Translations","date":"2025-04-10","arxiv_id":"2504.07680","n_code_links":0,"syntology":null},{"paper":"/paper/the-urban-impact-of-ai-modeling-feedback","slug":"the-urban-impact-of-ai-modeling-feedback","title":"The Urban Impact of AI: Modeling Feedback Loops in Next-Venue Recommendation","date":"2025-04-10","arxiv_id":"2504.07911","n_code_links":1,"syntology":null},{"paper":null,"slug":"thermostereort-thermal-stereo-matching-in","title":"ThermoStereoRT: Thermal Stereo Matching in Real Time via Knowledge Distillation and Attention-based Refinement","date":"2025-04-10","arxiv_id":"2504.07418","n_code_links":0,"syntology":null},{"paper":null,"slug":"v2v3d-view-to-view-denoised-3d-reconstruction","title":"V2V3D: View-to-View Denoised 3D Reconstruction for Light-Field Microscopy","date":"2025-04-10","arxiv_id":"2504.07853","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-deep-single-image-rectification-approach","title":"A Deep Single Image Rectification Approach for Pan-Tilt-Zoom Cameras","date":"2025-04-09","arxiv_id":"2504.06965","n_code_links":0,"syntology":null},{"paper":"/paper/a-new-training-approach-for-text","slug":"a-new-training-approach-for-text","title":"A new training approach for text classification in Mental Health: LatentGLoss","date":"2025-04-09","arxiv_id":"2504.07245","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-survey-of-new-mid-band-fr3-for-6g-channel","title":"A Survey of New Mid-Band/FR3 for 6G: Channel Measurement, Characterization and Modeling in Outdoor Environment","date":"2025-04-09","arxiv_id":"2504.06727","n_code_links":0,"syntology":null},{"paper":"/paper/a-unified-agentic-framework-for-evaluating","slug":"a-unified-agentic-framework-for-evaluating","title":"A Unified Agentic Framework for Evaluating Conditional Image Generation","date":"2025-04-09","arxiv_id":"2504.07046","n_code_links":1,"syntology":null},{"paper":null,"slug":"amad-automasked-attention-for-unsupervised","title":"AMAD: AutoMasked Attention for Unsupervised Multivariate Time Series Anomaly Detection","date":"2025-04-09","arxiv_id":"2504.06643","n_code_links":0,"syntology":null},{"paper":null,"slug":"attributes-aware-visual-emotion","title":"Attributes-aware Visual Emotion Representation Learning","date":"2025-04-09","arxiv_id":"2504.06578","n_code_links":0,"syntology":null},{"paper":"/paper/benchmarking-multimodal-cot-reward-model","slug":"benchmarking-multimodal-cot-reward-model","title":"Benchmarking Multimodal CoT Reward Model Stepwise by Visual Program","date":"2025-04-09","arxiv_id":"2504.06606","n_code_links":1,"syntology":null},{"paper":"/paper/colorizediffusion-v2-enhancing-reference","slug":"colorizediffusion-v2-enhancing-reference","title":"ColorizeDiffusion v2: Enhancing Reference-based Sketch Colorization Through Separating Utilities","date":"2025-04-09","arxiv_id":"2504.06895","n_code_links":2,"syntology":null},{"paper":null,"slug":"diffusioncom-structure-aware-multimodal","title":"DiffusionCom: Structure-Aware Multimodal Diffusion Model for Multimodal Knowledge Graph Completion","date":"2025-04-09","arxiv_id":"2504.06543","n_code_links":0,"syntology":null},{"paper":"/paper/dydit-dynamic-diffusion-transformers-for","slug":"dydit-dynamic-diffusion-transformers-for","title":"DyDiT++: Dynamic Diffusion Transformers for Efficient Visual Generation","date":"2025-04-09","arxiv_id":"2504.06803","n_code_links":1,"syntology":null},{"paper":null,"slug":"endowing-embodied-agents-with-spatial","title":"Endowing Embodied Agents with Spatial Reasoning Capabilities for Vision-and-Language Navigation","date":"2025-04-09","arxiv_id":"2504.08806","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-retrieval-augmented-generative","title":"Evaluating Retrieval Augmented Generative Models for Document Queries in Transportation Safety","date":"2025-04-09","arxiv_id":"2504.07022","n_code_links":0,"syntology":null},{"paper":null,"slug":"face-llava-facial-expression-and-attribute","title":"Face-LLaVA: Facial Expression and Attribute Understanding through Instruction Tuning","date":"2025-04-09","arxiv_id":"2504.07198","n_code_links":0,"syntology":null},{"paper":null,"slug":"fanerv-frequency-separation-and-augmentation","title":"FANeRV: Frequency Separation and Augmentation based Neural Representation for Video","date":"2025-04-09","arxiv_id":"2504.06755","n_code_links":0,"syntology":null},{"paper":null,"slug":"gendop-auto-regressive-camera-trajectory","title":"GenDoP: Auto-regressive Camera Trajectory Generation as a Director of Photography","date":"2025-04-09","arxiv_id":"2504.07083","n_code_links":0,"syntology":null},{"paper":"/paper/kaleidoscope-in-language-exams-for-massively","slug":"kaleidoscope-in-language-exams-for-massively","title":"Kaleidoscope: In-language Exams for Massively Multilingual Vision Evaluation","date":"2025-04-09","arxiv_id":"2504.07072","n_code_links":1,"syntology":null},{"paper":"/paper/linguistic-interpretability-of-transformer","slug":"linguistic-interpretability-of-transformer","title":"Linguistic Interpretability of Transformer-based Language Models: a systematic review","date":"2025-04-09","arxiv_id":"2504.08001","n_code_links":1,"syntology":null}],"record_sha256":"232eaef70328d9c7403f9fc052ca58a9f273f2a322fa05526dec0a38eb1574a1","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}