{"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/13","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":13,"pages_in_order":375,"rows_per_page":100,"rows":[1201,1300],"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/12","next":"/method/softmax/papers/14","papers":[{"paper":null,"slug":"cats-clustering-aggregated-and-time-series","title":"CATS: Clustering-Aggregated and Time Series for Business Customer Purchase Intention Prediction","date":"2025-05-19","arxiv_id":"2505.13558","n_code_links":0,"syntology":null},{"paper":null,"slug":"causal-head-gating-a-framework-for","title":"Causal Head Gating: A Framework for Interpreting Roles of Attention Heads in Transformers","date":"2025-05-19","arxiv_id":"2505.13737","n_code_links":0,"syntology":null},{"paper":"/paper/climate-research-domain-berts-pretraining","slug":"climate-research-domain-berts-pretraining","title":"Climate Research Domain BERTs: Pretraining, Adaptation, and Evaluation","date":"2025-05-19","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"cmlformer-a-dual-decoder-transformer-with","title":"CMLFormer: A Dual Decoder Transformer with Switching Point Learning for Code-Mixed Language Modeling","date":"2025-05-19","arxiv_id":"2505.12587","n_code_links":0,"syntology":null},{"paper":"/paper/competesmoe-statistically-guaranteed-mixture","slug":"competesmoe-statistically-guaranteed-mixture","title":"CompeteSMoE -- Statistically Guaranteed Mixture of Experts Training via Competition","date":"2025-05-19","arxiv_id":"2505.13380","n_code_links":2,"syntology":{"ran":6,"of":8,"n_ran_checked":3,"n_instrument":3,"unverified":2,"pointer_only":3,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","official":{"repos":["fsoft-aic/competesmoe"],"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":["listed","official","unlocated"]}}},{"paper":"/paper/degradation-aware-feature-perturbation-for","slug":"degradation-aware-feature-perturbation-for","title":"Degradation-Aware Feature Perturbation for All-in-One Image Restoration","date":"2025-05-19","arxiv_id":"2505.12630","n_code_links":1,"syntology":{"ran":6,"of":7,"n_ran_checked":6,"n_instrument":0,"unverified":1,"pointer_only":7,"phrase":"6 ran (of which 6 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 6 samples that ran constructed an object rather than computing a result","official":{"repos":["txphome/dfpir"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/dynamic-graph-induced-contour-aware-heat","slug":"dynamic-graph-induced-contour-aware-heat","title":"Dynamic Graph Induced Contour-aware Heat Conduction Network for Event-based Object Detection","date":"2025-05-19","arxiv_id":"2505.12908","n_code_links":1,"syntology":null},{"paper":null,"slug":"eavit-efficient-and-accurate-human-value","title":"EAVIT: Efficient and Accurate Human Value Identification from Text data via LLMs","date":"2025-05-19","arxiv_id":"2505.12792","n_code_links":0,"syntology":null},{"paper":"/paper/effective-and-transparent-rag-adaptive-reward","slug":"effective-and-transparent-rag-adaptive-reward","title":"Effective and Transparent RAG: Adaptive-Reward Reinforcement Learning for Decision Traceability","date":"2025-05-19","arxiv_id":"2505.13258","n_code_links":1,"syntology":null},{"paper":null,"slug":"emergence-of-fixational-and-saccadic","title":"Emergence of Fixational and Saccadic Movements in a Multi-Level Recurrent Attention Model for Vision","date":"2025-05-19","arxiv_id":"2505.13191","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-channel-independent-time-series","title":"Enhancing Channel-Independent Time Series Forecasting via Cross-Variate Patch Embedding","date":"2025-05-19","arxiv_id":"2505.12761","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-latent-computation-in-transformers","title":"Enhancing Latent Computation in Transformers with Latent Tokens","date":"2025-05-19","arxiv_id":"2505.12629","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-transformers-through-conditioned","title":"Enhancing Transformers Through Conditioned Embedded Tokens","date":"2025-05-19","arxiv_id":"2505.12789","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-the-performance-of-rag-methods-for","title":"Evaluating the Performance of RAG Methods for Conversational AI in the Airport Domain","date":"2025-05-19","arxiv_id":"2505.13006","n_code_links":0,"syntology":null},{"paper":"/paper/faster-video-diffusion-with-trainable-sparse","slug":"faster-video-diffusion-with-trainable-sparse","title":"Faster Video Diffusion with Trainable Sparse Attention","date":"2025-05-19","arxiv_id":"2505.13389","n_code_links":1,"syntology":null},{"paper":null,"slug":"gancompress-gan-enhanced-neural-image","title":"GANCompress: GAN-Enhanced Neural Image Compression with Binary Spherical Quantization","date":"2025-05-19","arxiv_id":"2505.13542","n_code_links":0,"syntology":null},{"paper":null,"slug":"graph-neural-networks-based-anomalous-rssi","title":"Graph Neural Networks Based Anomalous RSSI Detection","date":"2025-05-19","arxiv_id":"2505.15847","n_code_links":0,"syntology":null},{"paper":null,"slug":"guidedmorph-two-stage-deformable-registration","title":"GuidedMorph: Two-Stage Deformable Registration for Breast MRI","date":"2025-05-19","arxiv_id":"2505.13414","n_code_links":0,"syntology":null},{"paper":null,"slug":"hyperdet-source-detection-in-hypergraphs-via","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","date":"2025-05-19","arxiv_id":"2505.12894","n_code_links":0,"syntology":null},{"paper":null,"slug":"joint-velocity-growth-flow-matching-for","title":"Joint Velocity-Growth Flow Matching for Single-Cell Dynamics Modeling","date":"2025-05-19","arxiv_id":"2505.13413","n_code_links":0,"syntology":null},{"paper":"/paper/know-or-not-a-library-for-evaluating-out-of","slug":"know-or-not-a-library-for-evaluating-out-of","title":"Know Or Not: a library for evaluating out-of-knowledge base robustness","date":"2025-05-19","arxiv_id":"2505.13545","n_code_links":1,"syntology":null},{"paper":"/paper/know3-rag-a-knowledge-aware-rag-framework","slug":"know3-rag-a-knowledge-aware-rag-framework","title":"Know3-RAG: A Knowledge-aware RAG Framework with Adaptive Retrieval, Generation, and Filtering","date":"2025-05-19","arxiv_id":"2505.12662","n_code_links":1,"syntology":null},{"paper":"/paper/libog-lifelong-learning-for-black-box","slug":"libog-lifelong-learning-for-black-box","title":"LiBOG: Lifelong Learning for Black-Box Optimizer Generation","date":"2025-05-19","arxiv_id":"2505.13025","n_code_links":1,"syntology":{"ran":7,"of":11,"n_ran_checked":7,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["peijy/libog"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"lidar-mot-detr-a-lidar-based-two-stage","title":"LiDAR MOT-DETR: A LiDAR-based Two-Stage Transformer for 3D Multiple Object Tracking","date":"2025-05-19","arxiv_id":"2505.12753","n_code_links":0,"syntology":null},{"paper":"/paper/long-rvos-a-comprehensive-benchmark-for-long","slug":"long-rvos-a-comprehensive-benchmark-for-long","title":"Long-RVOS: A Comprehensive Benchmark for Long-term Referring Video Object Segmentation","date":"2025-05-19","arxiv_id":"2505.12702","n_code_links":0,"syntology":null},{"paper":"/paper/msvit-improving-spiking-vision-transformer","slug":"msvit-improving-spiking-vision-transformer","title":"MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion","date":"2025-05-19","arxiv_id":"2505.14719","n_code_links":1,"syntology":null},{"paper":"/paper/multi-head-temporal-latent-attention","slug":"multi-head-temporal-latent-attention","title":"Multi-head Temporal Latent Attention","date":"2025-05-19","arxiv_id":"2505.13544","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["d-keqi/mlta"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/multi-resolution-haar-network-enhancing-human","slug":"multi-resolution-haar-network-enhancing-human","title":"Multi-Resolution Haar Network: Enhancing human motion prediction via Haar transform","date":"2025-05-19","arxiv_id":"2505.12631","n_code_links":1,"syntology":null},{"paper":"/paper/mvar-visual-autoregressive-modeling-with","slug":"mvar-visual-autoregressive-modeling-with","title":"MVAR: Visual Autoregressive Modeling with Scale and Spatial Markovian Conditioning","date":"2025-05-19","arxiv_id":"2505.12742","n_code_links":1,"syntology":{"ran":7,"of":8,"n_ran_checked":4,"n_instrument":3,"unverified":1,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["labshuhanggu/mvar"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"omgpt-a-sequence-modeling-framework-for-data","title":"OMGPT: A Sequence Modeling Framework for Data-driven Operational Decision Making","date":"2025-05-19","arxiv_id":"2505.13580","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimizing-retrieval-augmented-generation-for","title":"Optimizing Retrieval Augmented Generation for Object Constraint Language","date":"2025-05-19","arxiv_id":"2505.13129","n_code_links":0,"syntology":null},{"paper":"/paper/panda-a-pretrained-forecast-model-for","slug":"panda-a-pretrained-forecast-model-for","title":"Panda: A pretrained forecast model for universal representation of chaotic dynamics","date":"2025-05-19","arxiv_id":"2505.13755","n_code_links":1,"syntology":null},{"paper":"/paper/pptnet-a-hybrid-periodic-pattern-transformer","slug":"pptnet-a-hybrid-periodic-pattern-transformer","title":"PPTNet: A Hybrid Periodic Pattern-Transformer Architecture for Traffic Flow Prediction and Congestion Identification","date":"2025-05-19","arxiv_id":"2505.13047","n_code_links":1,"syntology":null},{"paper":null,"slug":"prompt-stability-matters-evaluating-and","title":"Prompt Stability Matters: Evaluating and Optimizing Auto-Generated Prompt in General-Purpose Systems","date":"2025-05-19","arxiv_id":"2505.13546","n_code_links":0,"syntology":null},{"paper":null,"slug":"pyramid-sparse-transformer-enhancing-multi","title":"Pyramid Sparse Transformer: Enhancing Multi-Scale Feature Fusion with Dynamic Token Selection","date":"2025-05-19","arxiv_id":"2505.12772","n_code_links":0,"syntology":null},{"paper":null,"slug":"rar-setting-knowledge-tripwires-for-retrieval","title":"RAR: Setting Knowledge Tripwires for Retrieval Augmented Rejection","date":"2025-05-19","arxiv_id":"2505.13581","n_code_links":0,"syntology":null},{"paper":"/paper/revealing-the-deceptiveness-of-knowledge","slug":"revealing-the-deceptiveness-of-knowledge","title":"Revealing the Deceptiveness of Knowledge Editing: A Mechanistic Analysis of Superficial Editing","date":"2025-05-19","arxiv_id":"2505.12636","n_code_links":1,"syntology":null},{"paper":null,"slug":"rl-in-name-only-analyzing-the-structural","title":"RL in Name Only? Analyzing the Structural Assumptions in RL post-training for LLMs","date":"2025-05-19","arxiv_id":"2505.13697","n_code_links":0,"syntology":null},{"paper":null,"slug":"safety-alignment-can-be-not-superficial-with","title":"Safety Alignment Can Be Not Superficial With Explicit Safety Signals","date":"2025-05-19","arxiv_id":"2505.17072","n_code_links":0,"syntology":null},{"paper":null,"slug":"selective-code-generation-for-functional","title":"Selective Code Generation for Functional Guarantees","date":"2025-05-19","arxiv_id":"2505.13553","n_code_links":0,"syntology":null},{"paper":"/paper/self-reinforced-graph-contrastive-learning","slug":"self-reinforced-graph-contrastive-learning","title":"Self-Reinforced Graph Contrastive Learning","date":"2025-05-19","arxiv_id":"2505.13650","n_code_links":1,"syntology":null},{"paper":null,"slug":"simplicity-is-key-an-unsupervised-pretraining","title":"Simplicity is Key: An Unsupervised Pretraining Approach for Sparse Radio Channels","date":"2025-05-19","arxiv_id":"2505.13055","n_code_links":0,"syntology":null},{"paper":null,"slug":"single-image-reflection-removal-via-inter","title":"Single Image Reflection Removal via inter-layer Complementarity","date":"2025-05-19","arxiv_id":"2505.12641","n_code_links":0,"syntology":null},{"paper":null,"slug":"soundit-geo-contextual-soundscape-to","title":"SounDiT: Geo-Contextual Soundscape-to-Landscape Generation","date":"2025-05-19","arxiv_id":"2505.12734","n_code_links":0,"syntology":null},{"paper":null,"slug":"suicide-risk-assessment-using-multimodal","title":"Suicide Risk Assessment Using Multimodal Speech Features: A Study on the SW1 Challenge Dataset","date":"2025-05-19","arxiv_id":"2505.13069","n_code_links":0,"syntology":null},{"paper":null,"slug":"swin-dit-diffusion-transformer-using-pseudo","title":"Swin DiT: Diffusion Transformer using Pseudo Shifted Windows","date":"2025-05-19","arxiv_id":"2505.13219","n_code_links":0,"syntology":null},{"paper":"/paper/temporal-query-network-for-efficient","slug":"temporal-query-network-for-efficient","title":"Temporal Query Network for Efficient Multivariate Time Series Forecasting","date":"2025-05-19","arxiv_id":"2505.12917","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["ACAT-SCUT/TQNet"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":null,"slug":"the-hidden-structure-improving-legal-document","title":"The Hidden Structure -- Improving Legal Document Understanding Through Explicit Text Formatting","date":"2025-05-19","arxiv_id":"2505.12837","n_code_links":0,"syntology":null},{"paper":null,"slug":"time-frequency-based-attention-cache-memory","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","date":"2025-05-19","arxiv_id":"2505.13094","n_code_links":0,"syntology":null},{"paper":"/paper/towards-a-generalist-code-embedding-model","slug":"towards-a-generalist-code-embedding-model","title":"Towards A Generalist Code Embedding Model Based On Massive Data Synthesis","date":"2025-05-19","arxiv_id":"2505.12697","n_code_links":1,"syntology":null},{"paper":null,"slug":"ts-vlm-text-guided-softsort-pooling-for","title":"TS-VLM: Text-Guided SoftSort Pooling for Vision-Language Models in Multi-View Driving Reasoning","date":"2025-05-19","arxiv_id":"2505.12670","n_code_links":0,"syntology":null},{"paper":null,"slug":"unified-cross-modal-translation-of-score","title":"Unified Cross-modal Translation of Score Images, Symbolic Music, and Performance Audio","date":"2025-05-19","arxiv_id":"2505.12863","n_code_links":0,"syntology":null},{"paper":null,"slug":"unlabeled-data-or-pre-trained-model","title":"Unlabeled Data or Pre-trained Model: Rethinking Semi-Supervised Learning and Pretrain-Finetuning","date":"2025-05-19","arxiv_id":"2505.13317","n_code_links":0,"syntology":null},{"paper":"/paper/unlearning-for-federated-online-learning-to","slug":"unlearning-for-federated-online-learning-to","title":"Unlearning for Federated Online Learning to Rank: A Reproducibility Study","date":"2025-05-19","arxiv_id":"2505.12791","n_code_links":1,"syntology":null},{"paper":"/paper/unpacking-positional-encoding-in-transformers","slug":"unpacking-positional-encoding-in-transformers","title":"Unpacking Positional Encoding in Transformers: A Spectral Analysis of Content-Position Coupling","date":"2025-05-19","arxiv_id":"2505.13027","n_code_links":0,"syntology":{"ran":6,"of":6,"n_ran_checked":1,"n_instrument":5,"unverified":0,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/vesselgpt-autoregressive-modeling-of-vascular","slug":"vesselgpt-autoregressive-modeling-of-vascular","title":"VesselGPT: Autoregressive Modeling of Vascular Geometry","date":"2025-05-19","arxiv_id":"2505.13318","n_code_links":1,"syntology":null},{"paper":"/paper/writevit-handwritten-text-generation-with","slug":"writevit-handwritten-text-generation-with","title":"WriteViT: Handwritten Text Generation with Vision Transformer","date":"2025-05-19","arxiv_id":"2505.13235","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-survey-of-attacks-on-large-language-models","title":"A Survey of Attacks on Large Language Models","date":"2025-05-18","arxiv_id":"2505.12567","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-survey-on-side-information-driven-session","title":"A Survey on Side Information-driven Session-based Recommendation: From a Data-centric Perspective","date":"2025-05-18","arxiv_id":"2505.12279","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-enhanced-u-net-for-accurate","title":"Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans","date":"2025-05-18","arxiv_id":"2505.12298","n_code_links":0,"syntology":null},{"paper":"/paper/bensparx-a-robust-explainable-machine","slug":"bensparx-a-robust-explainable-machine","title":"BenSParX: A Robust Explainable Machine Learning Framework for Parkinson's Disease Detection from Bengali Conversational Speech","date":"2025-05-18","arxiv_id":"2505.12192","n_code_links":1,"syntology":null},{"paper":null,"slug":"bishop-sparsified-bundling-spiking","title":"Bishop: Sparsified Bundling Spiking Transformers on Heterogeneous Cores with Error-Constrained Pruning","date":"2025-05-18","arxiv_id":"2505.12281","n_code_links":0,"syntology":null},{"paper":null,"slug":"context-aware-autoregressive-models-for-multi","title":"Context-Aware Autoregressive Models for Multi-Conditional Image Generation","date":"2025-05-18","arxiv_id":"2505.12274","n_code_links":0,"syntology":null},{"paper":null,"slug":"ctlformer-a-hybrid-denoising-model-combining","title":"CTLformer: A Hybrid Denoising Model Combining Convolutional Layers and Self-Attention for Enhanced CT Image Reconstruction","date":"2025-05-18","arxiv_id":"2505.12203","n_code_links":0,"syntology":null},{"paper":"/paper/disco-reinforcing-large-reasoning-models-with","slug":"disco-reinforcing-large-reasoning-models-with","title":"DisCO: Reinforcing Large Reasoning Models with Discriminative Constrained Optimization","date":"2025-05-18","arxiv_id":"2505.12366","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["optimization-ai/disco"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/enhancing-visual-grounding-for-gui-agents-via","slug":"enhancing-visual-grounding-for-gui-agents-via","title":"Enhancing Visual Grounding for GUI Agents via Self-Evolutionary Reinforcement Learning","date":"2025-05-18","arxiv_id":"2505.12370","n_code_links":2,"syntology":null},{"paper":"/paper/eulearn-a-3d-database-for-learning-euler","slug":"eulearn-a-3d-database-for-learning-euler","title":"EuLearn: A 3D database for learning Euler characteristics","date":"2025-05-18","arxiv_id":"2505.13539","n_code_links":1,"syntology":null},{"paper":null,"slug":"evaloop-assessing-llm-robustness-in","title":"EVALOOP: Assessing LLM Robustness in Programming from a Self-consistency Perspective","date":"2025-05-18","arxiv_id":"2505.12185","n_code_links":0,"syntology":null},{"paper":null,"slug":"from-n-gram-to-attention-how-model","title":"From n-gram to Attention: How Model Architectures Learn and Propagate Bias in Language Modeling","date":"2025-05-18","arxiv_id":"2505.12381","n_code_links":0,"syntology":null},{"paper":"/paper/gates-cost-aware-dynamic-workflow-scheduling","slug":"gates-cost-aware-dynamic-workflow-scheduling","title":"GATES: Cost-aware Dynamic Workflow Scheduling via Graph Attention Networks and Evolution Strategy","date":"2025-05-18","arxiv_id":"2505.12355","n_code_links":1,"syntology":{"ran":4,"of":6,"n_ran_checked":4,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["yashen998/gates"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"guiding-diffusion-with-deep-geometric-moments","title":"Guiding Diffusion with Deep Geometric Moments: Balancing Fidelity and Variation","date":"2025-05-18","arxiv_id":"2505.12486","n_code_links":0,"syntology":null},{"paper":null,"slug":"k-mshc-unmasking-minimally-sufficient-head","title":"$K$-MSHC: Unmasking Minimally Sufficient Head Circuits in Large Language Models with Experiments on Syntactic Classification Tasks","date":"2025-05-18","arxiv_id":"2505.12268","n_code_links":0,"syntology":null},{"paper":"/paper/kgalign-joint-semantic-structural-knowledge","slug":"kgalign-joint-semantic-structural-knowledge","title":"KGAlign: Joint Semantic-Structural Knowledge Encoding for Multimodal Fake News Detection","date":"2025-05-18","arxiv_id":"2505.14714","n_code_links":1,"syntology":null},{"paper":null,"slug":"mutual-evidential-deep-learning-for-medical","title":"Mutual Evidential Deep Learning for Medical Image Segmentation","date":"2025-05-18","arxiv_id":"2505.12418","n_code_links":0,"syntology":null},{"paper":null,"slug":"near-optimal-sample-complexities-of","title":"Near-Optimal Sample Complexities of Divergence-based S-rectangular Distributionally Robust Reinforcement Learning","date":"2025-05-18","arxiv_id":"2505.12202","n_code_links":0,"syntology":null},{"paper":"/paper/poisonarena-uncovering-competing-poisoning","slug":"poisonarena-uncovering-competing-poisoning","title":"PoisonArena: Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation","date":"2025-05-18","arxiv_id":"2505.12574","n_code_links":1,"syntology":null},{"paper":null,"slug":"ragxplain-from-explainable-evaluation-to","title":"RAGXplain: From Explainable Evaluation to Actionable Guidance of RAG Pipelines","date":"2025-05-18","arxiv_id":"2505.13538","n_code_links":0,"syntology":null},{"paper":"/paper/schoenbat-rethinking-attention-with","slug":"schoenbat-rethinking-attention-with","title":"SchoenbAt: Rethinking Attention with Polynomial basis","date":"2025-05-18","arxiv_id":"2505.12252","n_code_links":1,"syntology":null},{"paper":null,"slug":"senseflow-a-physics-informed-and-self","title":"SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation","date":"2025-05-18","arxiv_id":"2505.12302","n_code_links":0,"syntology":null},{"paper":null,"slug":"smfusion-semantic-preserving-fusion-of","title":"SMFusion: Semantic-Preserving Fusion of Multimodal Medical Images for Enhanced Clinical Diagnosis","date":"2025-05-18","arxiv_id":"2505.12251","n_code_links":0,"syntology":null},{"paper":null,"slug":"spikex-exploring-accelerator-architecture-and","title":"SpikeX: Exploring Accelerator Architecture and Network-Hardware Co-Optimization for Sparse Spiking Neural Networks","date":"2025-05-18","arxiv_id":"2505.12292","n_code_links":0,"syntology":null},{"paper":null,"slug":"star-stage-wise-attention-guided-token","title":"STAR: Stage-Wise Attention-Guided Token Reduction for Efficient Large Vision-Language Models Inference","date":"2025-05-18","arxiv_id":"2505.12359","n_code_links":0,"syntology":null},{"paper":null,"slug":"stereographic-multi-try-metropolis-algorithms","title":"Stereographic Multi-Try Metropolis Algorithms for Heavy-tailed Sampling","date":"2025-05-18","arxiv_id":"2505.12487","n_code_links":0,"syntology":null},{"paper":"/paper/temporal-spectral-spatial-unified-remote","slug":"temporal-spectral-spatial-unified-remote","title":"Temporal-Spectral-Spatial Unified Remote Sensing Dense Prediction","date":"2025-05-18","arxiv_id":"2505.12280","n_code_links":1,"syntology":null},{"paper":null,"slug":"vectors-from-larger-language-models-predict","title":"Vectors from Larger Language Models Predict Human Reading Time and fMRI Data More Poorly when Dimensionality Expansion is Controlled","date":"2025-05-18","arxiv_id":"2505.12196","n_code_links":0,"syntology":null},{"paper":"/paper/video-gpt-via-next-clip-diffusion","slug":"video-gpt-via-next-clip-diffusion","title":"Video-GPT via Next Clip Diffusion","date":"2025-05-18","arxiv_id":"2505.12489","n_code_links":1,"syntology":null},{"paper":null,"slug":"voicecloak-a-multi-dimensional-defense","title":"VoiceCloak: A Multi-Dimensional Defense Framework against Unauthorized Diffusion-based Voice Cloning","date":"2025-05-18","arxiv_id":"2505.12332","n_code_links":0,"syntology":null},{"paper":null,"slug":"accelerating-diffusion-based-super-resolution","title":"Accelerating Diffusion-based Super-Resolution with Dynamic Time-Spatial Sampling","date":"2025-05-17","arxiv_id":"2505.12048","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptmol-adaptive-fusion-from-sequence-string","title":"AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery","date":"2025-05-17","arxiv_id":"2505.11878","n_code_links":0,"syntology":null},{"paper":null,"slug":"black-box-adversaries-from-latent-space","title":"Black-box Adversaries from Latent Space: Unnoticeable Attacks on Human Pose and Shape Estimation","date":"2025-05-17","arxiv_id":"2505.12009","n_code_links":0,"syntology":null},{"paper":null,"slug":"chain-of-model-learning-for-language-model","title":"Chain-of-Model Learning for Language Model","date":"2025-05-17","arxiv_id":"2505.11820","n_code_links":0,"syntology":null},{"paper":null,"slug":"cl-cagan-capsule-differential-adversarial","title":"CL-CaGAN: Capsule differential adversarial continuous learning for cross-domain hyperspectral anomaly detection","date":"2025-05-17","arxiv_id":"2505.11793","n_code_links":0,"syntology":null},{"paper":"/paper/draftattention-fast-video-diffusion-via-low","slug":"draftattention-fast-video-diffusion-via-low","title":"DraftAttention: Fast Video Diffusion via Low-Resolution Attention Guidance","date":"2025-05-17","arxiv_id":"2505.14708","n_code_links":1,"syntology":null},{"paper":"/paper/elite-embedding-less-retrieval-with-iterative","slug":"elite-embedding-less-retrieval-with-iterative","title":"ELITE: Embedding-Less retrieval with Iterative Text Exploration","date":"2025-05-17","arxiv_id":"2505.11908","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancing-complex-instruction-following-for","title":"Enhancing Complex Instruction Following for Large Language Models with Mixture-of-Contexts Fine-tuning","date":"2025-05-17","arxiv_id":"2505.11922","n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-rope-attention-combining-the-polynomial","title":"Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform","date":"2025-05-17","arxiv_id":"2505.11892","n_code_links":0,"syntology":null},{"paper":"/paper/fastcar-cache-attentive-replay-for-fast-auto","slug":"fastcar-cache-attentive-replay-for-fast-auto","title":"FastCar: Cache Attentive Replay for Fast Auto-Regressive Video Generation on the Edge","date":"2025-05-17","arxiv_id":"2505.14709","n_code_links":1,"syntology":null},{"paper":"/paper/fl-plas-federated-learning-with-partial-layer","slug":"fl-plas-federated-learning-with-partial-layer","title":"FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients","date":"2025-05-17","arxiv_id":"2505.12019","n_code_links":1,"syntology":null},{"paper":null,"slug":"geomano-geometric-mamba-neural-operator-for","title":"GeoMaNO: Geometric Mamba Neural Operator for Partial Differential Equations","date":"2025-05-17","arxiv_id":"2505.12020","n_code_links":0,"syntology":null},{"paper":null,"slug":"induction-head-toxicity-mechanistically","title":"Induction Head Toxicity Mechanistically Explains Repetition Curse in Large Language Models","date":"2025-05-17","arxiv_id":"2505.13514","n_code_links":0,"syntology":null}],"record_sha256":"59addc44298e1291caf2246b13a061888a9d56c64c9f4b36f35ce30c340506da","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}