{"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/multi-head-attention/papers/22","list_of":"/method/multi-head-attention","method":"Multi-Head Attention","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":22,"pages_in_order":249,"rows_per_page":100,"rows":[2101,2200],"of":24855,"counts":{"archive_papers_tagged":24855,"with_a_code_link":11214,"where_syntology_ran_a_sample":3454,"not_listed_spam_title":0,"listed":24855,"listed_where_code_ran":3454,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2916,"every_run_a_failure_of_syntologys_instrument":538,"listed_with_a_run_with_no_instrument_failure":2916,"listed_every_run_a_failure_of_syntologys_instrument":538,"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/multi-head-attention","prev":"/method/multi-head-attention/papers/21","next":"/method/multi-head-attention/papers/23","papers":[{"paper":null,"slug":"topic-fliprag-topic-orientated-adversarial","title":"Topic-FlipRAG: Topic-Orientated Adversarial Opinion Manipulation Attacks to Retrieval-Augmented Generation Models","date":"2025-02-03","arxiv_id":"2502.01386","n_code_links":0,"syntology":null},{"paper":null,"slug":"toward-neurosymbolic-program-comprehension","title":"Toward Neurosymbolic Program Comprehension","date":"2025-02-03","arxiv_id":"2502.01806","n_code_links":0,"syntology":null},{"paper":"/paper/transformers-trained-on-proteins-can-learn-to","slug":"transformers-trained-on-proteins-can-learn-to","title":"Transformers trained on proteins can learn to attend to Euclidean distance","date":"2025-02-03","arxiv_id":"2502.01533","n_code_links":1,"syntology":null},{"paper":"/paper/videorag-retrieval-augmented-generation-with","slug":"videorag-retrieval-augmented-generation-with","title":"VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos","date":"2025-02-03","arxiv_id":"2502.01549","n_code_links":1,"syntology":null},{"paper":"/paper/deepgate4-efficient-and-effective","slug":"deepgate4-efficient-and-effective","title":"DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scale","date":"2025-02-02","arxiv_id":"2502.01681","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"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":["zyzheng17/DeepGate4-ICLR-25"],"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":"estimating-forest-carbon-stocks-from-high","title":"Estimating forest carbon stocks from high-resolution remote sensing imagery by reducing domain shift with style transfer","date":"2025-02-02","arxiv_id":"2502.00784","n_code_links":0,"syntology":null},{"paper":null,"slug":"explainability-in-practice-a-survey-of","title":"Explainability in Practice: A Survey of Explainable NLP Across Various Domains","date":"2025-02-02","arxiv_id":"2502.00837","n_code_links":0,"syntology":null},{"paper":null,"slug":"libra-measuring-bias-of-large-language-model","title":"LIBRA: Measuring Bias of Large Language Model from a Local Context","date":"2025-02-02","arxiv_id":"2502.01679","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-framework-for-river-connectivity","title":"A framework for river connectivity classification using temporal image processing and attention based neural networks","date":"2025-02-01","arxiv_id":"2502.00474","n_code_links":0,"syntology":null},{"paper":"/paper/a-study-on-the-performance-of-u-net","slug":"a-study-on-the-performance-of-u-net","title":"A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation","date":"2025-02-01","arxiv_id":"2502.00314","n_code_links":1,"syntology":null},{"paper":null,"slug":"benchmark-on-peer-review-toxic-detection-a","title":"Benchmark on Peer Review Toxic Detection: A Challenging Task with a New Dataset","date":"2025-02-01","arxiv_id":"2502.01676","n_code_links":0,"syntology":null},{"paper":null,"slug":"coddllm-empowering-large-language-models-for","title":"CoddLLM: Empowering Large Language Models for Data Analytics","date":"2025-02-01","arxiv_id":"2502.00329","n_code_links":0,"syntology":null},{"paper":null,"slug":"contrastive-forward-forward-a-training","title":"Contrastive Forward-Forward: A Training Algorithm of Vision Transformer","date":"2025-02-01","arxiv_id":"2502.00571","n_code_links":0,"syntology":null},{"paper":"/paper/converting-transformers-into-dgnns-form-1","slug":"converting-transformers-into-dgnns-form-1","title":"Converting Transformers into DGNNs Form","date":"2025-02-01","arxiv_id":"2502.00585","n_code_links":1,"syntology":null},{"paper":null,"slug":"explainable-ai-for-sentiment-analysis-of","title":"Explainable AI for Sentiment Analysis of Human Metapneumovirus (HMPV) Using XLNet","date":"2025-02-01","arxiv_id":"2502.01663","n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-solvers-for-discrete-diffusion-models","title":"Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms","date":"2025-02-01","arxiv_id":"2502.00234","n_code_links":0,"syntology":null},{"paper":"/paper/mambaglue-fast-and-robust-local-feature","slug":"mambaglue-fast-and-robust-local-feature","title":"MambaGlue: Fast and Robust Local Feature Matching With Mamba","date":"2025-02-01","arxiv_id":"2502.00462","n_code_links":1,"syntology":null},{"paper":"/paper/milmer-a-framework-for-multiple-instance","slug":"milmer-a-framework-for-multiple-instance","title":"Milmer: a Framework for Multiple Instance Learning based Multimodal Emotion Recognition","date":"2025-02-01","arxiv_id":"2502.00547","n_code_links":1,"syntology":null},{"paper":"/paper/riddle-me-this-stealthy-membership-inference","slug":"riddle-me-this-stealthy-membership-inference","title":"Riddle Me This! Stealthy Membership Inference for Retrieval-Augmented Generation","date":"2025-02-01","arxiv_id":"2502.00306","n_code_links":1,"syntology":null},{"paper":null,"slug":"sigmoid-self-attention-has-lower-sample","title":"Sigmoid Self-Attention has Lower Sample Complexity than Softmax Self-Attention: A Mixture-of-Experts Perspective","date":"2025-02-01","arxiv_id":"2502.00281","n_code_links":0,"syntology":null},{"paper":"/paper/vertiformer-a-data-efficient-multi-task","slug":"vertiformer-a-data-efficient-multi-task","title":"VertiFormer: A Data-Efficient Multi-Task Transformer for Off-Road Robot Mobility","date":"2025-02-01","arxiv_id":"2502.00543","n_code_links":1,"syntology":null},{"paper":null,"slug":"accelerating-diffusion-transformer-via-error","title":"Accelerating Diffusion Transformer via Error-Optimized Cache","date":"2025-01-31","arxiv_id":"2501.19243","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-ai-solve-the-peer-review-crisis-a-large","title":"Can AI Solve the Peer Review Crisis? A Large Scale Cross Model Experiment of LLMs' Performance and Biases in Evaluating over 1000 Economics Papers","date":"2025-01-31","arxiv_id":"2502.00070","n_code_links":0,"syntology":null},{"paper":"/paper/cerradata-4mm-a-multimodal-benchmark-dataset","slug":"cerradata-4mm-a-multimodal-benchmark-dataset","title":"CerraData-4MM: A multimodal benchmark dataset on Cerrado for land use and land cover classification","date":"2025-01-31","arxiv_id":"2502.00083","n_code_links":1,"syntology":null},{"paper":null,"slug":"contextformer-redefining-efficiency-in","title":"ContextFormer: Redefining Efficiency in Semantic Segmentation","date":"2025-01-31","arxiv_id":"2501.19255","n_code_links":0,"syntology":null},{"paper":"/paper/do-llms-strategically-reveal-conceal-and","slug":"do-llms-strategically-reveal-conceal-and","title":"Do LLMs Strategically Reveal, Conceal, and Infer Information? A Theoretical and Empirical Analysis in The Chameleon Game","date":"2025-01-31","arxiv_id":"2501.19398","n_code_links":1,"syntology":null},{"paper":"/paper/from-semantic-segmentation-of-natural-images","slug":"from-semantic-segmentation-of-natural-images","title":"From Semantic Segmentation of Natural Images to Medical Image Segmentation Using ViT-Based Architectures","date":"2025-01-31","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"homogeneity-bias-as-differential-sampling","title":"Homogeneity Bias as Differential Sampling Uncertainty in Language Models","date":"2025-01-31","arxiv_id":"2501.19337","n_code_links":0,"syntology":null},{"paper":"/paper/kbqa-o1-agentic-knowledge-base-question","slug":"kbqa-o1-agentic-knowledge-base-question","title":"KBQA-o1: Agentic Knowledge Base Question Answering with Monte Carlo Tree Search","date":"2025-01-31","arxiv_id":"2501.18922","n_code_links":1,"syntology":{"ran":2,"of":8,"n_ran_checked":2,"n_instrument":0,"unverified":6,"pointer_only":0,"phrase":"2 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; 0 where Syntology's instrument failed) · 6 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["lhrlab/kbqa-o1"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"large-language-models-accuracy-in-emulating","title":"Large Language Models' Accuracy in Emulating Human Experts' Evaluation of Public Sentiments about Heated Tobacco Products on Social Media","date":"2025-01-31","arxiv_id":"2502.01658","n_code_links":0,"syntology":null},{"paper":null,"slug":"pixelworld-towards-perceiving-everything-as","title":"PixelWorld: Towards Perceiving Everything as Pixels","date":"2025-01-31","arxiv_id":"2501.19339","n_code_links":0,"syntology":null},{"paper":null,"slug":"privacy-preserving-charge-location-prediction","title":"Privacy Preserving Charge Location Prediction for Electric Vehicles","date":"2025-01-31","arxiv_id":"2502.00068","n_code_links":0,"syntology":null},{"paper":null,"slug":"strassen-attention-unlocking-compositional","title":"Strassen Attention: Unlocking Compositional Abilities in Transformers Based on a New Lower Bound Method","date":"2025-01-31","arxiv_id":"2501.19215","n_code_links":0,"syntology":null},{"paper":null,"slug":"through-the-looking-glass-llm-based-analysis","title":"Through the Looking Glass: LLM-Based Analysis of AR/VR Android Applications Privacy Policies","date":"2025-01-31","arxiv_id":"2501.19223","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-learnable-multi-views-contrastive-framework","title":"A Learnable Multi-views Contrastive Framework with Reconstruction Discrepancy for Medical Time-Series","date":"2025-01-30","arxiv_id":"2501.18367","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-unified-perspective-on-the-dynamics-of-deep","title":"A Unified Perspective on the Dynamics of Deep Transformers","date":"2025-01-30","arxiv_id":"2501.18322","n_code_links":0,"syntology":null},{"paper":null,"slug":"alphaadam-asynchronous-masked-optimization","title":"AlphaAdam:Asynchronous Masked Optimization with Dynamic Alpha for Selective Updates","date":"2025-01-30","arxiv_id":"2501.18094","n_code_links":0,"syntology":null},{"paper":null,"slug":"arbitrary-data-as-images-fusion-of-patient","title":"Arbitrary Data as Images: Fusion of Patient Data Across Modalities and Irregular Intervals with Vision Transformers","date":"2025-01-30","arxiv_id":"2501.18237","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-we-retrieve-everything-all-at-once-arm-an","title":"Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method","date":"2025-01-30","arxiv_id":"2501.18539","n_code_links":0,"syntology":null},{"paper":null,"slug":"deltallm-compress-llms-with-low-rank-deltas","title":"DeltaLLM: Compress LLMs with Low-Rank Deltas between Shared Weights","date":"2025-01-30","arxiv_id":"2501.18596","n_code_links":0,"syntology":null},{"paper":null,"slug":"economic-rationality-under-specialization","title":"Economic Rationality under Specialization: Evidence of Decision Bias in AI Agents","date":"2025-01-30","arxiv_id":"2501.18190","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-large-language-models-in-1","title":"Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows","date":"2025-01-30","arxiv_id":"2502.00064","n_code_links":0,"syntology":null},{"paper":"/paper/gdformer-going-beyond-subsequence-isolation","slug":"gdformer-going-beyond-subsequence-isolation","title":"GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly Detection","date":"2025-01-30","arxiv_id":"2501.18196","n_code_links":1,"syntology":null},{"paper":null,"slug":"general-embedding-vs-task-specific-embedding","title":"General Embedding vs. Task-Specific Embedding: A Comparative Approach to Enhancing NLP Performance","date":"2025-01-30","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"genie-generative-note-information-extraction","title":"GENIE: Generative Note Information Extraction model for structuring EHR data","date":"2025-01-30","arxiv_id":"2501.18435","n_code_links":0,"syntology":null},{"paper":null,"slug":"in-context-learning-of-polynomial-kernel","title":"On the Role of Transformer Feed-Forward Layers in Nonlinear In-Context Learning","date":"2025-01-30","arxiv_id":"2501.18187","n_code_links":0,"syntology":null},{"paper":null,"slug":"israel-hamas-war-through-telegram-reddit-and","title":"Israel-Hamas war through Telegram, Reddit and Twitter","date":"2025-01-30","arxiv_id":"2502.00060","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-llm-agents-for-automated","title":"Leveraging LLM Agents for Automated Optimization Modeling for SASP Problems: A Graph-RAG based Approach","date":"2025-01-30","arxiv_id":"2501.18320","n_code_links":0,"syntology":null},{"paper":"/paper/matir-a-hybrid-mamba-transformer-image","slug":"matir-a-hybrid-mamba-transformer-image","title":"MatIR: A Hybrid Mamba-Transformer Image Restoration Model","date":"2025-01-30","arxiv_id":"2501.18401","n_code_links":1,"syntology":null},{"paper":"/paper/rbft-robust-fine-tuning-for-retrieval","slug":"rbft-robust-fine-tuning-for-retrieval","title":"RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval Defects","date":"2025-01-30","arxiv_id":"2501.18365","n_code_links":1,"syntology":null},{"paper":null,"slug":"retrieval-augmented-generation-based-llm","title":"Retrieval Augmented Generation Based LLM Evaluation For Protocol State Machine Inference With Chain-of-Thought Reasoning","date":"2025-01-30","arxiv_id":"2502.15727","n_code_links":0,"syntology":null},{"paper":"/paper/sana-1-5-efficient-scaling-of-training-time","slug":"sana-1-5-efficient-scaling-of-training-time","title":"SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer","date":"2025-01-30","arxiv_id":"2501.18427","n_code_links":1,"syntology":null},{"paper":null,"slug":"scalable-and-cost-efficient-ml-inference","title":"Scalable and Cost-Efficient ML Inference: Parallel Batch Processing with Serverless Functions","date":"2025-01-30","arxiv_id":"2502.12017","n_code_links":0,"syntology":null},{"paper":null,"slug":"state-stream-transformer-sst-emergent","title":"State Stream Transformer (SST) : Emergent Metacognitive Behaviours Through Latent State Persistence","date":"2025-01-30","arxiv_id":"2501.18356","n_code_links":0,"syntology":null},{"paper":null,"slug":"structure-development-in-list-sorting","title":"Structure Development in List-Sorting Transformers","date":"2025-01-30","arxiv_id":"2501.18666","n_code_links":0,"syntology":null},{"paper":null,"slug":"survey-and-improvement-strategies-for-gene","title":"Survey and Improvement Strategies for Gene Prioritization with Large Language Models","date":"2025-01-30","arxiv_id":"2501.18794","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-semantic-genetic-programming-for","title":"Transformer Semantic Genetic Programming for Symbolic Regression","date":"2025-01-30","arxiv_id":"2501.18479","n_code_links":0,"syntology":null},{"paper":"/paper/unraveling-the-capabilities-of-language","slug":"unraveling-the-capabilities-of-language","title":"Unraveling the Capabilities of Language Models in News Summarization","date":"2025-01-30","arxiv_id":"2501.18128","n_code_links":1,"syntology":null},{"paper":"/paper/wildchat-50m-a-deep-dive-into-the-role-of","slug":"wildchat-50m-a-deep-dive-into-the-role-of","title":"WILDCHAT-50M: A Deep Dive Into the Role of Synthetic Data in Post-Training","date":"2025-01-30","arxiv_id":"2501.18511","n_code_links":1,"syntology":{"ran":6,"of":9,"n_ran_checked":6,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"6 ran (of which 0 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) · 3 unverified","official":{"repos":["penfever/wildchat-50m"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/2ssp-a-two-stage-framework-for-structured","slug":"2ssp-a-two-stage-framework-for-structured","title":"2SSP: A Two-Stage Framework for Structured Pruning of LLMs","date":"2025-01-29","arxiv_id":"2501.17771","n_code_links":1,"syntology":null},{"paper":"/paper/contourformer-real-time-contour-based-end-to","slug":"contourformer-real-time-contour-based-end-to","title":"ContourFormer:Real-Time Contour-Based End-to-End Instance Segmentation Transformer","date":"2025-01-29","arxiv_id":"2501.17688","n_code_links":1,"syntology":null},{"paper":null,"slug":"dint-transformer","title":"DINT Transformer","date":"2025-01-29","arxiv_id":"2501.17486","n_code_links":0,"syntology":null},{"paper":null,"slug":"hybrid-graphs-for-table-and-text-based","title":"Hybrid Graphs for Table-and-Text based Question Answering using LLMs","date":"2025-01-29","arxiv_id":"2501.17767","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-in-context-learning-and-retrieval","title":"Leveraging In-Context Learning and Retrieval-Augmented Generation for Automatic Question Generation in Educational Domains","date":"2025-01-29","arxiv_id":"2501.17397","n_code_links":0,"syntology":null},{"paper":null,"slug":"prompt-oriented-output-of-culture-specific","title":"Prompt-oriented Output of Culture-Specific Items in Translated African Poetry by Large Language Model: An Initial Multi-layered Tabular Review","date":"2025-01-29","arxiv_id":"2501.18644","n_code_links":0,"syntology":null},{"paper":"/paper/pulmofusion-advancing-pulmonary-health-with","slug":"pulmofusion-advancing-pulmonary-health-with","title":"PulmoFusion: Advancing Pulmonary Health with Efficient Multi-Modal Fusion","date":"2025-01-29","arxiv_id":"2501.17699","n_code_links":1,"syntology":null},{"paper":"/paper/self-supervised-frameworks-for-speaker","slug":"self-supervised-frameworks-for-speaker","title":"Self-Supervised Frameworks for Speaker Verification via Bootstrapped Positive Sampling","date":"2025-01-29","arxiv_id":"2501.17772","n_code_links":1,"syntology":null},{"paper":null,"slug":"shared-diff-transformer","title":"Shared DIFF Transformer","date":"2025-01-29","arxiv_id":"2501.17900","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-based-time-series-forecasting-for","title":"Transformer Based Time-Series Forecasting for Stock","date":"2025-01-29","arxiv_id":"2502.09625","n_code_links":0,"syntology":null},{"paper":"/paper/transrad-retentive-vision-transformer-for","slug":"transrad-retentive-vision-transformer-for","title":"TransRAD: Retentive Vision Transformer for Enhanced Radar Object Detection","date":"2025-01-29","arxiv_id":"2501.17977","n_code_links":1,"syntology":null},{"paper":null,"slug":"watch-your-stepp-semantic-traversability","title":"Watch Your STEPP: Semantic Traversability Estimation using Pose Projected Features","date":"2025-01-29","arxiv_id":"2501.17594","n_code_links":0,"syntology":null},{"paper":"/paper/an-attention-locating-algorithm-for","slug":"an-attention-locating-algorithm-for","title":"An Attention-Locating Algorithm for Eliminating Background Effects in Fine-grained Visual Classification","date":"2025-01-28","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"attribution-analysis-of-legal-language-as","title":"Attribution analysis of legal language as used by LLM","date":"2025-01-28","arxiv_id":"2501.17330","n_code_links":0,"syntology":null},{"paper":null,"slug":"balancing-content-size-in-rag-text2sql-system","title":"Balancing Content Size in RAG-Text2SQL System","date":"2025-01-28","arxiv_id":"2502.15723","n_code_links":0,"syntology":null},{"paper":null,"slug":"chinese-stock-prediction-based-on-a-multi","title":"Chinese Stock Prediction Based on a Multi-Modal Transformer Framework: Macro-Micro Information Fusion","date":"2025-01-28","arxiv_id":"2501.16621","n_code_links":0,"syntology":null},{"paper":"/paper/detecting-harassment-and-defamation-in","slug":"detecting-harassment-and-defamation-in","title":"Detecting harassment and defamation in cyberbullying with emotion-adaptive training","date":"2025-01-28","arxiv_id":"2501.16925","n_code_links":1,"syntology":null},{"paper":null,"slug":"flexmotion-lightweight-physics-aware-and","title":"FlexMotion: Lightweight, Physics-Aware, and Controllable Human Motion Generation","date":"2025-01-28","arxiv_id":"2501.16778","n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-quantum-combinatorial-optimization","title":"Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolver","date":"2025-01-28","arxiv_id":"2501.16986","n_code_links":0,"syntology":null},{"paper":"/paper/graph-of-attacks-with-pruning-optimizing","slug":"graph-of-attacks-with-pruning-optimizing","title":"Graph of Attacks with Pruning: Optimizing Stealthy Jailbreak Prompt Generation for Enhanced LLM Content Moderation","date":"2025-01-28","arxiv_id":"2501.18638","n_code_links":1,"syntology":null},{"paper":null,"slug":"graph-transformers-for-inverse-physics","title":"Graph Transformers for inverse physics: reconstructing flows around arbitrary 2D airfoils","date":"2025-01-28","arxiv_id":"2501.17081","n_code_links":0,"syntology":null},{"paper":"/paper/jre-l-journalist-reader-and-editor-llms-in","slug":"jre-l-journalist-reader-and-editor-llms-in","title":"JRE-L: Journalist, Reader, and Editor LLMs in the Loop for Science Journalism for the General Audience","date":"2025-01-28","arxiv_id":"2501.16865","n_code_links":1,"syntology":null},{"paper":"/paper/mamba-shedder-post-transformer-compression","slug":"mamba-shedder-post-transformer-compression","title":"Mamba-Shedder: Post-Transformer Compression for Efficient Selective Structured State Space Models","date":"2025-01-28","arxiv_id":"2501.17088","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":["intellabs/hardware-aware-automated-machine-learning"],"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":"midi-gpt-a-controllable-generative-model-for","title":"MIDI-GPT: A Controllable Generative Model for Computer-Assisted Multitrack Music Composition","date":"2025-01-28","arxiv_id":"2501.17011","n_code_links":0,"syntology":null},{"paper":null,"slug":"multiple-abstraction-level-retrieve-augment","title":"Multiple Abstraction Level Retrieve Augment Generation","date":"2025-01-28","arxiv_id":"2501.16952","n_code_links":0,"syntology":null},{"paper":null,"slug":"open-source-retrieval-augmented-generation","title":"Open-Source Retrieval Augmented Generation Framework for Retrieving Accurate Medication Insights from Formularies for African Healthcare Workers","date":"2025-01-28","arxiv_id":"2502.15722","n_code_links":0,"syntology":null},{"paper":"/paper/saferag-benchmarking-security-in-retrieval","slug":"saferag-benchmarking-security-in-retrieval","title":"SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model","date":"2025-01-28","arxiv_id":"2501.18636","n_code_links":1,"syntology":null},{"paper":null,"slug":"scenario-understanding-of-traffic-scenes","title":"Scenario Understanding of Traffic Scenes Through Large Visual Language Models","date":"2025-01-28","arxiv_id":"2501.17131","n_code_links":0,"syntology":null},{"paper":"/paper/vit-2spn-vision-transformer-based-dual-stream","slug":"vit-2spn-vision-transformer-based-dual-stream","title":"ViT-2SPN: Vision Transformer-based Dual-Stream Self-Supervised Pretraining Networks for Retinal OCT Classification","date":"2025-01-28","arxiv_id":"2501.17260","n_code_links":1,"syntology":null},{"paper":null,"slug":"whispers-of-sound-enhancing-information","title":"Multimodal Magic Elevating Depression Detection with a Fusion of Text and Audio Intelligence","date":"2025-01-28","arxiv_id":"2501.16813","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comprehensive-study-on-fine-tuning-large","title":"A Comprehensive Study on Fine-Tuning Large Language Models for Medical Question Answering Using Classification Models and Comparative Analysis","date":"2025-01-27","arxiv_id":"2501.17190","n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-domain-semantic-segmentation-with-large","title":"Cross-Domain Semantic Segmentation with Large Language Model-Assisted Descriptor Generation","date":"2025-01-27","arxiv_id":"2501.16467","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-and-exploring-mild-cognitive","title":"Enhancing and Exploring Mild Cognitive Impairment Detection with W2V-BERT-2.0","date":"2025-01-27","arxiv_id":"2501.16201","n_code_links":0,"syntology":null},{"paper":null,"slug":"kernels-of-selfhood-gpt-4o-shows-humanlike","title":"Kernels of Selfhood: GPT-4o shows humanlike patterns of cognitive consistency moderated by free choice","date":"2025-01-27","arxiv_id":"2502.07088","n_code_links":0,"syntology":null},{"paper":"/paper/lctg-bench-llm-controlled-text-generation","slug":"lctg-bench-llm-controlled-text-generation","title":"LCTG Bench: LLM Controlled Text Generation Benchmark","date":"2025-01-27","arxiv_id":"2501.15875","n_code_links":1,"syntology":null},{"paper":null,"slug":"lemmahead-rag-assisted-proof-generation-using","title":"LemmaHead: RAG Assisted Proof Generation Using Large Language Models","date":"2025-01-27","arxiv_id":"2501.15797","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-video-vision-transformer-for","title":"Leveraging Video Vision Transformer for Alzheimer's Disease Diagnosis from 3D Brain MRI","date":"2025-01-27","arxiv_id":"2501.15733","n_code_links":0,"syntology":null},{"paper":null,"slug":"object-detection-for-medical-image-analysis","title":"Object Detection for Medical Image Analysis: Insights from the RT-DETR Model","date":"2025-01-27","arxiv_id":"2501.16469","n_code_links":0,"syntology":null},{"paper":"/paper/parametric-retrieval-augmented-generation","slug":"parametric-retrieval-augmented-generation","title":"Parametric Retrieval Augmented Generation","date":"2025-01-27","arxiv_id":"2501.15915","n_code_links":1,"syntology":null},{"paper":null,"slug":"pdc-vit-source-camera-identification-using","title":"PDC-ViT : Source Camera Identification using Pixel Difference Convolution and Vision Transformer","date":"2025-01-27","arxiv_id":"2501.16227","n_code_links":0,"syntology":null},{"paper":null,"slug":"phase-transitions-in-large-language-models","title":"Phase Transitions in Large Language Models and the $O(N)$ Model","date":"2025-01-27","arxiv_id":"2501.16241","n_code_links":0,"syntology":null}],"record_sha256":"a3884fbf0b715572e8119d5979898dc09cc5fbfdd2ef049ef307823ac0b339b7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}