{"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/layer-normalization/papers/14","list_of":"/method/layer-normalization","method":"Layer Normalization","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":14,"pages_in_order":250,"rows_per_page":100,"rows":[1301,1400],"of":24980,"counts":{"archive_papers_tagged":24980,"with_a_code_link":11273,"where_syntology_ran_a_sample":3471,"not_listed_spam_title":0,"listed":24980,"listed_where_code_ran":3471,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2923,"every_run_a_failure_of_syntologys_instrument":548,"listed_with_a_run_with_no_instrument_failure":2923,"listed_every_run_a_failure_of_syntologys_instrument":548,"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/layer-normalization","prev":"/method/layer-normalization/papers/13","next":"/method/layer-normalization/papers/15","papers":[{"paper":null,"slug":"georag-a-question-answering-approach-from-a","title":"GeoRAG: A Question-Answering Approach from a Geographical Perspective","date":"2025-04-02","arxiv_id":"2504.01458","n_code_links":0,"syntology":null},{"paper":null,"slug":"gpt-adoption-and-the-impact-of-disclosure","title":"GPT Adoption and the Impact of Disclosure Policies","date":"2025-04-02","arxiv_id":"2504.01566","n_code_links":0,"syntology":null},{"paper":"/paper/large-legal-retrieval-augmented-generation","slug":"large-legal-retrieval-augmented-generation","title":"LARGE: Legal Retrieval Augmented Generation Evaluation Tool","date":"2025-04-02","arxiv_id":"2504.01840","n_code_links":1,"syntology":null},{"paper":null,"slug":"onrl-rag-real-time-personalized-mental-health","title":"OnRL-RAG: Real-Time Personalized Mental Health Dialogue System","date":"2025-04-02","arxiv_id":"2504.02894","n_code_links":0,"syntology":null},{"paper":null,"slug":"pico-jailbreaking-multimodal-large-language","title":"PiCo: Jailbreaking Multimodal Large Language Models via $\\textbf{Pi}$ctorial $\\textbf{Co}$de Contextualization","date":"2025-04-02","arxiv_id":"2504.01444","n_code_links":0,"syntology":null},{"paper":null,"slug":"prompt-guided-attention-head-selection-for","title":"Prompt-Guided Attention Head Selection for Focus-Oriented Image Retrieval","date":"2025-04-02","arxiv_id":"2504.01348","n_code_links":0,"syntology":null},{"paper":"/paper/prompting-medical-vision-language-models-to","slug":"prompting-medical-vision-language-models-to","title":"Prompting Medical Vision-Language Models to Mitigate Diagnosis Bias by Generating Realistic Dermoscopic Images","date":"2025-04-02","arxiv_id":"2504.01838","n_code_links":1,"syntology":null},{"paper":"/paper/quattro-transformer-accelerated-iterative","slug":"quattro-transformer-accelerated-iterative","title":"Quattro: Transformer-Accelerated Iterative Linear Quadratic Regulator Framework for Fast Trajectory Optimization","date":"2025-04-02","arxiv_id":"2504.01806","n_code_links":1,"syntology":null},{"paper":null,"slug":"revisiting-funnel-transformers-for-modern-llm","title":"Revisiting Funnel Transformers for Modern LLM Architectures with Comprehensive Ablations in Training and Inference Configurations","date":"2025-04-02","arxiv_id":"2504.02877","n_code_links":0,"syntology":null},{"paper":null,"slug":"scaling-test-time-inference-with-policy","title":"Scaling Test-Time Inference with Policy-Optimized, Dynamic Retrieval-Augmented Generation via KV Caching and Decoding","date":"2025-04-02","arxiv_id":"2504.01281","n_code_links":0,"syntology":null},{"paper":null,"slug":"strategize-globally-adapt-locally-a-multi","title":"Strategize Globally, Adapt Locally: A Multi-Turn Red Teaming Agent with Dual-Level Learning","date":"2025-04-02","arxiv_id":"2504.01278","n_code_links":0,"syntology":null},{"paper":"/paper/towards-interpretable-soft-prompts","slug":"towards-interpretable-soft-prompts","title":"Towards Interpretable Soft Prompts","date":"2025-04-02","arxiv_id":"2504.02144","n_code_links":1,"syntology":null},{"paper":null,"slug":"univitar-unified-vision-transformer-with","title":"UniViTAR: Unified Vision Transformer with Native Resolution","date":"2025-04-02","arxiv_id":"2504.01792","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-unified-virtual-mixture-of-experts","title":"A Unified Virtual Mixture-of-Experts Framework:Enhanced Inference and Hallucination Mitigation in Single-Model System","date":"2025-04-01","arxiv_id":"2504.03739","n_code_links":0,"syntology":null},{"paper":null,"slug":"accelerating-causal-network-discovery-of","title":"Accelerating Causal Network Discovery of Alzheimer Disease Biomarkers via Scientific Literature-based Retrieval Augmented Generation","date":"2025-04-01","arxiv_id":"2504.08768","n_code_links":0,"syntology":null},{"paper":null,"slug":"automated-factual-benchmarking-for-in-car","title":"Automated Factual Benchmarking for In-Car Conversational Systems using Large Language Models","date":"2025-04-01","arxiv_id":"2504.01248","n_code_links":0,"syntology":null},{"paper":"/paper/cellvta-enhancing-vision-foundation-models","slug":"cellvta-enhancing-vision-foundation-models","title":"CellVTA: Enhancing Vision Foundation Models for Accurate Cell Segmentation and Classification","date":"2025-04-01","arxiv_id":"2504.00784","n_code_links":1,"syntology":null},{"paper":null,"slug":"collaborative-llm-numerical-reasoning-with","title":"Collaborative LLM Numerical Reasoning with Local Data Protection","date":"2025-04-01","arxiv_id":"2504.00299","n_code_links":0,"syntology":null},{"paper":null,"slug":"detecting-financial-fraud-with-hybrid-deep","title":"Detecting Financial Fraud with Hybrid Deep Learning: A Mix-of-Experts Approach to Sequential and Anomalous Patterns","date":"2025-04-01","arxiv_id":"2504.03750","n_code_links":0,"syntology":null},{"paper":null,"slug":"grade-guard-a-smart-system-for-short-answer","title":"Grade Guard: A Smart System for Short Answer Automated Grading","date":"2025-04-01","arxiv_id":"2504.01253","n_code_links":0,"syntology":null},{"paper":null,"slug":"gs-dravidianlangtech-2025-women-targeted","title":"GS_DravidianLangTech@2025: Women Targeted Abusive Texts Detection on Social Media","date":"2025-04-01","arxiv_id":"2504.02863","n_code_links":0,"syntology":null},{"paper":null,"slug":"llm-assisted-proactive-threat-intelligence","title":"LLM-Assisted Proactive Threat Intelligence for Automated Reasoning","date":"2025-04-01","arxiv_id":"2504.00428","n_code_links":0,"syntology":null},{"paper":"/paper/mergevq-a-unified-framework-for-visual","slug":"mergevq-a-unified-framework-for-visual","title":"MergeVQ: A Unified Framework for Visual Generation and Representation with Disentangled Token Merging and Quantization","date":"2025-04-01","arxiv_id":"2504.00999","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-token-attention","title":"Multi-Token Attention","date":"2025-04-01","arxiv_id":"2504.00927","n_code_links":0,"syntology":null},{"paper":null,"slug":"qsvit-a-methodology-for-quantizing-spiking","title":"QSViT: A Methodology for Quantizing Spiking Vision Transformers","date":"2025-04-01","arxiv_id":"2504.00948","n_code_links":0,"syntology":null},{"paper":null,"slug":"srlcg-self-rectified-large-scale-code","title":"SRLCG: Self-Rectified Large-Scale Code Generation with Multidimensional Chain-of-Thought and Dynamic Backtracking","date":"2025-04-01","arxiv_id":"2504.00532","n_code_links":0,"syntology":null},{"paper":"/paper/transformer-based-named-entity-recognition-2","slug":"transformer-based-named-entity-recognition-2","title":"Transformer-Based Named Entity Recognition for Automated Server Provisioning","date":"2025-04-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/wikivideo-article-generation-from-multiple","slug":"wikivideo-article-generation-from-multiple","title":"WikiVideo: Article Generation from Multiple Videos","date":"2025-04-01","arxiv_id":"2504.00939","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-systematic-evaluation-of-llm-strategies-for","title":"A Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG","date":"2025-03-31","arxiv_id":"2503.24307","n_code_links":0,"syntology":null},{"paper":null,"slug":"accelerating-high-efficiency-organic","title":"Accelerating High-Efficiency Organic Photovoltaic Discovery via Pretrained Graph Neural Networks and Generative Reinforcement Learning","date":"2025-03-31","arxiv_id":"2503.23766","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptive-layer-skipping-in-pre-trained-llms","title":"Adaptive Layer-skipping in Pre-trained LLMs","date":"2025-03-31","arxiv_id":"2503.23798","n_code_links":0,"syntology":null},{"paper":"/paper/better-wit-than-wealth-dynamic-parametric","slug":"better-wit-than-wealth-dynamic-parametric","title":"Dynamic Parametric Retrieval Augmented Generation for Test-time Knowledge Enhancement","date":"2025-03-31","arxiv_id":"2503.23895","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, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["trae1oung/dyprag"],"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":null,"slug":"citras-covariate-informed-transformer-for","title":"CITRAS: Covariate-Informed Transformer for Time Series Forecasting","date":"2025-03-31","arxiv_id":"2503.24007","n_code_links":0,"syntology":null},{"paper":null,"slug":"coarse-to-fine-learning-for-multi-pipette","title":"Coarse-to-Fine Learning for Multi-Pipette Localisation in Robot-Assisted In Vivo Patch-Clamp","date":"2025-03-31","arxiv_id":"2504.01044","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparing-representations-of-long-clinical","title":"Comparing representations of long clinical texts for the task of patient note-identification","date":"2025-03-31","arxiv_id":"2503.24006","n_code_links":0,"syntology":null},{"paper":null,"slug":"conformal-uncertainty-quantification-to","title":"Conformal uncertainty quantification to evaluate predictive fairness of foundation AI model for skin lesion classes across patient demographics","date":"2025-03-31","arxiv_id":"2503.23819","n_code_links":0,"syntology":null},{"paper":null,"slug":"crossformer-cross-segment-semantic-fusion-for","title":"CrossFormer: Cross-Segment Semantic Fusion for Document Segmentation","date":"2025-03-31","arxiv_id":"2503.23671","n_code_links":0,"syntology":null},{"paper":null,"slug":"does-reasoning-with-large-language-models","title":"Does \"Reasoning\" with Large Language Models Improve Recognizing, Generating, and Reframing Unhelpful Thoughts?","date":"2025-03-31","arxiv_id":"2504.00163","n_code_links":0,"syntology":null},{"paper":"/paper/easi3r-estimating-disentangled-motion-from","slug":"easi3r-estimating-disentangled-motion-from","title":"Easi3R: Estimating Disentangled Motion from DUSt3R Without Training","date":"2025-03-31","arxiv_id":"2503.24391","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancing-large-language-models-llms-for","title":"Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation","date":"2025-03-31","arxiv_id":"2503.24245","n_code_links":0,"syntology":null},{"paper":"/paper/foundation-models-for-seismic-data-processing","slug":"foundation-models-for-seismic-data-processing","title":"Foundation Models For Seismic Data Processing: An Extensive Review","date":"2025-03-31","arxiv_id":"2503.24166","n_code_links":1,"syntology":null},{"paper":null,"slug":"graph-transformer-based-flood-susceptibility","title":"Graph Transformer-Based Flood Susceptibility Mapping: Application to the French Riviera and Railway Infrastructure Under Climate Change","date":"2025-03-31","arxiv_id":"2504.03727","n_code_links":0,"syntology":null},{"paper":null,"slug":"judgelrm-large-reasoning-models-as-a-judge","title":"JudgeLRM: Large Reasoning Models as a Judge","date":"2025-03-31","arxiv_id":"2504.00050","n_code_links":0,"syntology":null},{"paper":null,"slug":"large-language-models-pass-the-turing-test","title":"Large Language Models Pass the Turing Test","date":"2025-03-31","arxiv_id":"2503.23674","n_code_links":0,"syntology":null},{"paper":null,"slug":"llm4fs-leveraging-large-language-models-for","title":"LLM4FS: Leveraging Large Language Models for Feature Selection and How to Improve It","date":"2025-03-31","arxiv_id":"2503.24157","n_code_links":0,"syntology":null},{"paper":null,"slug":"neuralatex-a-machine-learning-library-written","title":"NeuRaLaTeX: A machine learning library written in pure LaTeX","date":"2025-03-31","arxiv_id":"2503.24187","n_code_links":0,"syntology":null},{"paper":null,"slug":"rubric-is-all-you-need-enhancing-llm-based","title":"Rubric Is All You Need: Enhancing LLM-based Code Evaluation With Question-Specific Rubrics","date":"2025-03-31","arxiv_id":"2503.23989","n_code_links":0,"syntology":null},{"paper":null,"slug":"synthetic-news-generation-for-fake-news","title":"Synthetic News Generation for Fake News Classification","date":"2025-03-31","arxiv_id":"2503.24206","n_code_links":0,"syntology":null},{"paper":"/paper/text-chunking-for-document-classification-for","slug":"text-chunking-for-document-classification-for","title":"Text Chunking for Document Classification for Urban System Management using Large Language Models","date":"2025-03-31","arxiv_id":"2504.00274","n_code_links":1,"syntology":null},{"paper":null,"slug":"transmamba-flexibly-switching-between","title":"TransMamba: Flexibly Switching between Transformer and Mamba","date":"2025-03-31","arxiv_id":"2503.24067","n_code_links":0,"syntology":null},{"paper":"/paper/ultrarag-a-modular-and-automated-toolkit-for","slug":"ultrarag-a-modular-and-automated-toolkit-for","title":"UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented Generation","date":"2025-03-31","arxiv_id":"2504.08761","n_code_links":1,"syntology":null},{"paper":"/paper/a-lightweight-image-super-resolution","slug":"a-lightweight-image-super-resolution","title":"A Lightweight Image Super-Resolution Transformer Trained on Low-Resolution Images Only","date":"2025-03-30","arxiv_id":"2503.23265","n_code_links":1,"syntology":null},{"paper":null,"slug":"advancing-sentiment-analysis-in-tamil-english","title":"Advancing Sentiment Analysis in Tamil-English Code-Mixed Texts: Challenges and Transformer-Based Solutions","date":"2025-03-30","arxiv_id":"2503.23295","n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-detection-designing-ai-resilient","title":"Beyond Detection: Designing AI-Resilient Assessments with Automated Feedback Tool to Foster Critical Thinking","date":"2025-03-30","arxiv_id":"2503.23622","n_code_links":0,"syntology":null},{"paper":null,"slug":"cadformer-fine-grained-cross-modal-alignment","title":"CADFormer: Fine-Grained Cross-modal Alignment and Decoding Transformer for Referring Remote Sensing Image Segmentation","date":"2025-03-30","arxiv_id":"2503.23456","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-gpt-4-for-robotic-agent-strategy","title":"Exploring GPT-4 for Robotic Agent Strategy with Real-Time State Feedback and a Reactive Behaviour Framework","date":"2025-03-30","arxiv_id":"2503.23601","n_code_links":0,"syntology":null},{"paper":null,"slug":"ferg-llm-feature-engineering-by-reason","title":"FeRG-LLM : Feature Engineering by Reason Generation Large Language Models","date":"2025-03-30","arxiv_id":"2503.23371","n_code_links":0,"syntology":null},{"paper":null,"slug":"hipart-hierarchical-pose-autoregressive","title":"HiPART: Hierarchical Pose AutoRegressive Transformer for Occluded 3D Human Pose Estimation","date":"2025-03-30","arxiv_id":"2503.23331","n_code_links":0,"syntology":null},{"paper":null,"slug":"hyper-rag-combating-llm-hallucinations-using","title":"Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation","date":"2025-03-30","arxiv_id":"2504.08758","n_code_links":0,"syntology":null},{"paper":null,"slug":"javisdit-joint-audio-video-diffusion","title":"JavisDiT: Joint Audio-Video Diffusion Transformer with Hierarchical Spatio-Temporal Prior Synchronization","date":"2025-03-30","arxiv_id":"2503.23377","n_code_links":0,"syntology":null},{"paper":"/paper/large-language-models-are-better-logical","slug":"large-language-models-are-better-logical","title":"Large Language Models Are Better Logical Fallacy Reasoners with Counterargument, Explanation, and Goal-Aware Prompt Formulation","date":"2025-03-30","arxiv_id":"2503.23363","n_code_links":1,"syntology":null},{"paper":"/paper/lavic-adapting-large-vision-language-models","slug":"lavic-adapting-large-vision-language-models","title":"LaViC: Adapting Large Vision-Language Models to Visually-Aware Conversational Recommendation","date":"2025-03-30","arxiv_id":"2503.23312","n_code_links":1,"syntology":null},{"paper":null,"slug":"measuring-online-hate-on-4chan-using-pre","title":"Measuring Online Hate on 4chan using Pre-trained Deep Learning Models","date":"2025-03-30","arxiv_id":"2504.00045","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-stakeholder-disaster-insights-from","title":"Multi-Stakeholder Disaster Insights from Social Media Using Large Language Models","date":"2025-03-30","arxiv_id":"2504.00046","n_code_links":0,"syntology":null},{"paper":null,"slug":"object-isolated-attention-for-consistent","title":"Object Isolated Attention for Consistent Story Visualization","date":"2025-03-30","arxiv_id":"2503.23353","n_code_links":0,"syntology":null},{"paper":"/paper/rare-retrieval-augmented-reasoning-modeling","slug":"rare-retrieval-augmented-reasoning-modeling","title":"RARE: Retrieval-Augmented Reasoning Modeling","date":"2025-03-30","arxiv_id":"2503.23513","n_code_links":1,"syntology":{"ran":13,"of":14,"n_ran_checked":13,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["open-dataflow/rare"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"score-story-coherence-and-retrieval","title":"SCORE: Story Coherence and Retrieval Enhancement for AI Narratives","date":"2025-03-30","arxiv_id":"2503.23512","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-training-free-llm-framework-with","title":"A Training-free LLM Framework with Interaction between Contextually Related Subtasks in Solving Complex Tasks","date":"2025-03-29","arxiv_id":"2503.23053","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-adaptation-for-remote-sensing","title":"Efficient Adaptation For Remote Sensing Visual Grounding","date":"2025-03-29","arxiv_id":"2503.23083","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-knowledge-graph-completion-with","title":"Enhancing Knowledge Graph Completion with Entity Neighborhood and Relation Context","date":"2025-03-29","arxiv_id":"2503.23205","n_code_links":0,"syntology":null},{"paper":"/paper/large-self-supervised-models-bridge-the-gap","slug":"large-self-supervised-models-bridge-the-gap","title":"Large Self-Supervised Models Bridge the Gap in Domain Adaptive Object Detection","date":"2025-03-29","arxiv_id":"2503.23220","n_code_links":1,"syntology":null},{"paper":null,"slug":"mhts-multi-hop-tree-structure-framework-for","title":"MHTS: Multi-Hop Tree Structure Framework for Generating Difficulty-Controllable QA Datasets for RAG Evaluation","date":"2025-03-29","arxiv_id":"2504.08756","n_code_links":0,"syntology":null},{"paper":"/paper/multimodal-machine-learning-with-large","slug":"multimodal-machine-learning-with-large","title":"Multimodal machine learning with large language embedding model for polymer property prediction","date":"2025-03-29","arxiv_id":"2503.22962","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-geomagnetic-storm-and-kp-prediction-using","title":"The geomagnetic storm and Kp prediction using Wasserstein transformer","date":"2025-03-29","arxiv_id":"2503.23102","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-realization-of-tones-in-spontaneous","title":"The realization of tones in spontaneous spoken Taiwan Mandarin: a corpus-based survey and theory-driven computational modeling","date":"2025-03-29","arxiv_id":"2503.23163","n_code_links":0,"syntology":null},{"paper":"/paper/z-saslm-zero-shot-style-aligned-sli-blending-1","slug":"z-saslm-zero-shot-style-aligned-sli-blending-1","title":"Z-SASLM: Zero-Shot Style-Aligned SLI Blending Latent Manipulation","date":"2025-03-29","arxiv_id":"2503.23234","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-advanced-ensemble-deep-learning-framework","title":"An Advanced Ensemble Deep Learning Framework for Stock Price Prediction Using VAE, Transformer, and LSTM Model","date":"2025-03-28","arxiv_id":"2503.22192","n_code_links":0,"syntology":null},{"paper":"/paper/annopage-dataset-dataset-of-non-textual","slug":"annopage-dataset-dataset-of-non-textual","title":"AnnoPage Dataset: Dataset of Non-Textual Elements in Documents with Fine-Grained Categorization","date":"2025-03-28","arxiv_id":"2503.22526","n_code_links":0,"syntology":null},{"paper":null,"slug":"bridging-the-dimensional-chasm-uncover-layer","title":"Bridging the Dimensional Chasm: Uncover Layer-wise Dimensional Reduction in Transformers through Token Correlation","date":"2025-03-28","arxiv_id":"2503.22547","n_code_links":0,"syntology":null},{"paper":null,"slug":"camera-model-identification-with-spair-swin","title":"Camera Model Identification with SPAIR-Swin and Entropy based Non-Homogeneous Patches","date":"2025-03-28","arxiv_id":"2503.22120","n_code_links":0,"syntology":null},{"paper":"/paper/correlation-attention-masked-temporal","slug":"correlation-attention-masked-temporal","title":"Correlation-Attention Masked Temporal Transformer for User Identity Linkage Using Heterogeneous Mobility Data","date":"2025-03-28","arxiv_id":"2504.01979","n_code_links":1,"syntology":null},{"paper":null,"slug":"deepoformer-deep-operator-learning-with","title":"DeepOFormer: Deep Operator Learning with Domain-informed Features for Fatigue Life Prediction","date":"2025-03-28","arxiv_id":"2503.22475","n_code_links":0,"syntology":null},{"paper":null,"slug":"dremnet-an-interpretable-denoising-framework","title":"DREMnet: An Interpretable Denoising Framework for Semi-Airborne Transient Electromagnetic Signal","date":"2025-03-28","arxiv_id":"2503.22223","n_code_links":0,"syntology":null},{"paper":null,"slug":"edgeinfinite-a-memory-efficient-infinite","title":"EdgeInfinite: A Memory-Efficient Infinite-Context Transformer for Edge Devices","date":"2025-03-28","arxiv_id":"2503.22196","n_code_links":0,"syntology":null},{"paper":"/paper/historical-ink-exploring-large-language","slug":"historical-ink-exploring-large-language","title":"Historical Ink: Exploring Large Language Models for Irony Detection in 19th-Century Spanish","date":"2025-03-28","arxiv_id":"2503.22585","n_code_links":1,"syntology":null},{"paper":null,"slug":"how-well-can-vison-language-models-understand","title":"How Well Can Vison-Language Models Understand Humans' Intention? An Open-ended Theory of Mind Question Evaluation Benchmark","date":"2025-03-28","arxiv_id":"2503.22093","n_code_links":0,"syntology":null},{"paper":null,"slug":"integrating-artificial-intelligence-with","title":"Integrating Artificial Intelligence with Human Expertise: An In-depth Analysis of ChatGPT's Capabilities in Generating Metamorphic Relations","date":"2025-03-28","arxiv_id":"2503.22141","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-llms-for-predicting-unknown","title":"Leveraging LLMs for Predicting Unknown Diagnoses from Clinical Notes","date":"2025-03-28","arxiv_id":"2503.22092","n_code_links":0,"syntology":null},{"paper":null,"slug":"scenario-dreamer-vectorized-latent-diffusion","title":"Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments","date":"2025-03-28","arxiv_id":"2503.22496","n_code_links":0,"syntology":null},{"paper":null,"slug":"understanding-inequality-of-llm-fact-checking","title":"Understanding Inequality of LLM Fact-Checking over Geographic Regions with Agent and Retrieval models","date":"2025-03-28","arxiv_id":"2503.22877","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-evaluation-of-llms-and-google-translate","title":"An evaluation of LLMs and Google Translate for translation of selected Indian languages via sentiment and semantic analyses","date":"2025-03-27","arxiv_id":"2503.21393","n_code_links":0,"syntology":null},{"paper":"/paper/as-easy-as-pie-understanding-when-pruning","slug":"as-easy-as-pie-understanding-when-pruning","title":"As easy as PIE: understanding when pruning causes language models to disagree","date":"2025-03-27","arxiv_id":"2503.21714","n_code_links":1,"syntology":null},{"paper":null,"slug":"collab-controlled-decoding-using-mixture-of","title":"Collab: Controlled Decoding using Mixture of Agents for LLM Alignment","date":"2025-03-27","arxiv_id":"2503.21720","n_code_links":0,"syntology":null},{"paper":"/paper/dynamictrl-rethinking-the-basic-structure-and","slug":"dynamictrl-rethinking-the-basic-structure-and","title":"DynamiCtrl: Rethinking the Basic Structure and the Role of Text for High-quality Human Image Animation","date":"2025-03-27","arxiv_id":"2503.21246","n_code_links":1,"syntology":null},{"paper":"/paper/elementwise-layer-normalization","slug":"elementwise-layer-normalization","title":"The Mathematical Relationship Between Layer Normalization and Dynamic Activation Functions","date":"2025-03-27","arxiv_id":"2503.21708","n_code_links":2,"syntology":null},{"paper":null,"slug":"hybrid-emotion-recognition-enhancing-customer","title":"Hybrid Emotion Recognition: Enhancing Customer Interactions Through Acoustic and Textual Analysis","date":"2025-03-27","arxiv_id":"2503.21927","n_code_links":0,"syntology":null},{"paper":"/paper/hypergraphrag-retrieval-augmented-generation","slug":"hypergraphrag-retrieval-augmented-generation","title":"HyperGraphRAG: Retrieval-Augmented Generation with Hypergraph-Structured Knowledge Representation","date":"2025-03-27","arxiv_id":"2503.21322","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":["LHRLAB/HyperGraphRAG"],"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":"integrating-travel-behavior-forecasting-and","title":"Integrating Travel Behavior Forecasting and Generative Modeling for Predicting Future Urban Mobility and Spatial Transformations","date":"2025-03-27","arxiv_id":"2503.21158","n_code_links":0,"syntology":null},{"paper":null,"slug":"meminsight-autonomous-memory-augmentation-for","title":"MemInsight: Autonomous Memory Augmentation for LLM Agents","date":"2025-03-27","arxiv_id":"2503.21760","n_code_links":0,"syntology":null},{"paper":null,"slug":"molecular-quantum-transformer","title":"Molecular Quantum Transformer","date":"2025-03-27","arxiv_id":"2503.21686","n_code_links":0,"syntology":null}],"record_sha256":"83ca4597f40b8102d065b61cab0e1b7b43040953c7fe7e41c74dd4721f2003bb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}