{"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/bpe/papers/50","list_of":"/method/bpe","method":"BPE","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":50,"pages_in_order":190,"rows_per_page":100,"rows":[4901,5000],"of":18975,"counts":{"archive_papers_tagged":18975,"with_a_code_link":8675,"where_syntology_ran_a_sample":2895,"not_listed_spam_title":0,"listed":18975,"listed_where_code_ran":2895,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2443,"every_run_a_failure_of_syntologys_instrument":452,"listed_with_a_run_with_no_instrument_failure":2443,"listed_every_run_a_failure_of_syntologys_instrument":452,"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/bpe","prev":"/method/bpe/papers/49","next":"/method/bpe/papers/51","papers":[{"paper":null,"slug":"enhancing-agricultural-machinery-management","title":"Enhancing Agricultural Machinery Management through Advanced LLM Integration","date":"2024-07-30","arxiv_id":"2407.20588","n_code_links":0,"syntology":null},{"paper":"/paper/handdagt-a-denoising-adaptive-graph","slug":"handdagt-a-denoising-adaptive-graph","title":"HandDAGT: A Denoising Adaptive Graph Transformer for 3D Hand Pose Estimation","date":"2024-07-30","arxiv_id":"2407.20542","n_code_links":1,"syntology":null},{"paper":null,"slug":"mimicking-the-mavens-agent-based-opinion","title":"Mimicking the Mavens: Agent-based Opinion Synthesis and Emotion Prediction for Social Media Influencers","date":"2024-07-30","arxiv_id":"2407.20668","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-load-prediction-of-power-network","title":"Robust Load Prediction of Power Network Clusters Based on Cloud-Model-Improved Transformer","date":"2024-07-30","arxiv_id":"2407.20817","n_code_links":0,"syntology":null},{"paper":null,"slug":"spotformer-multi-scale-spatio-temporal","title":"SpotFormer: Multi-Scale Spatio-Temporal Transformer for Facial Expression Spotting","date":"2024-07-30","arxiv_id":"2407.20799","n_code_links":0,"syntology":null},{"paper":"/paper/synthvlm-high-efficiency-and-high-quality","slug":"synthvlm-high-efficiency-and-high-quality","title":"SynthVLM: High-Efficiency and High-Quality Synthetic Data for Vision Language Models","date":"2024-07-30","arxiv_id":"2407.20756","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-study-on-the-implementation-method-of-an","title":"A Study on the Implementation Method of an Agent-Based Advanced RAG System Using Graph","date":"2024-07-29","arxiv_id":"2407.19994","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-unified-graph-transformer-for-overcoming","title":"A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendation","date":"2024-07-29","arxiv_id":"2407.19886","n_code_links":0,"syntology":null},{"paper":null,"slug":"ageval-a-benchmark-for-zero-shot-and-few-shot","title":"AgEval: A Benchmark for Zero-Shot and Few-Shot Plant Stress Phenotyping with Multimodal LLMs","date":"2024-07-29","arxiv_id":"2407.19617","n_code_links":0,"syntology":null},{"paper":"/paper/alen-a-dual-approach-for-uniform-and-non","slug":"alen-a-dual-approach-for-uniform-and-non","title":"ALEN: A Dual-Approach for Uniform and Non-Uniform Low-Light Image Enhancement","date":"2024-07-29","arxiv_id":"2407.19708","n_code_links":1,"syntology":null},{"paper":"/paper/autoscale-automatic-prediction-of-compute","slug":"autoscale-automatic-prediction-of-compute","title":"AutoScale: Scale-Aware Data Mixing for Pre-Training LLMs","date":"2024-07-29","arxiv_id":"2407.20177","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":{"repos":["feiyang-k/autoscale"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":"/paper/cross-layer-feature-pyramid-transformer-for","slug":"cross-layer-feature-pyramid-transformer-for","title":"Cross-Layer Feature Pyramid Transformer for Small Object Detection in Aerial Images","date":"2024-07-29","arxiv_id":"2407.19696","n_code_links":1,"syntology":null},{"paper":"/paper/detecting-and-understanding-vulnerabilities","slug":"detecting-and-understanding-vulnerabilities","title":"Detecting and Understanding Vulnerabilities in Language Models via Mechanistic Interpretability","date":"2024-07-29","arxiv_id":"2407.19842","n_code_links":1,"syntology":null},{"paper":"/paper/efficient-face-super-resolution-via-wavelet","slug":"efficient-face-super-resolution-via-wavelet","title":"Efficient Face Super-Resolution via Wavelet-based Feature Enhancement Network","date":"2024-07-29","arxiv_id":"2407.19768","n_code_links":1,"syntology":null},{"paper":"/paper/emotion-driven-melody-harmonization-via","slug":"emotion-driven-melody-harmonization-via","title":"Emotion-Driven Melody Harmonization via Melodic Variation and Functional Representation","date":"2024-07-29","arxiv_id":"2407.20176","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancing-code-translation-in-language-models","title":"Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation","date":"2024-07-29","arxiv_id":"2407.19619","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-retrieval-augmented-language-model","title":"Improving Retrieval Augmented Language Model with Self-Reasoning","date":"2024-07-29","arxiv_id":"2407.19813","n_code_links":0,"syntology":null},{"paper":null,"slug":"introducing-a-new-hyper-parameter-for-rag","title":"Introducing a new hyper-parameter for RAG: Context Window Utilization","date":"2024-07-29","arxiv_id":"2407.19794","n_code_links":0,"syntology":null},{"paper":null,"slug":"legal-minds-algorithmic-decisions-how-llms","title":"Legal Minds, Algorithmic Decisions: How LLMs Apply Constitutional Principles in Complex Scenarios","date":"2024-07-29","arxiv_id":"2407.19760","n_code_links":0,"syntology":null},{"paper":"/paper/mixture-of-nested-experts-adaptive-processing","slug":"mixture-of-nested-experts-adaptive-processing","title":"Mixture of Nested Experts: Adaptive Processing of Visual Tokens","date":"2024-07-29","arxiv_id":"2407.19985","n_code_links":1,"syntology":{"ran":8,"of":14,"n_ran_checked":7,"n_instrument":1,"unverified":6,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","official":null}},{"paper":null,"slug":"ml-mamba-efficient-multi-modal-large-language","title":"ML-Mamba: Efficient Multi-Modal Large Language Model Utilizing Mamba-2","date":"2024-07-29","arxiv_id":"2407.19832","n_code_links":0,"syntology":null},{"paper":null,"slug":"revolutionizing-urban-safety-perception","title":"Revolutionizing Urban Safety Perception Assessments: Integrating Multimodal Large Language Models with Street View Images","date":"2024-07-29","arxiv_id":"2407.19719","n_code_links":0,"syntology":null},{"paper":null,"slug":"sentiment-analysis-of-lithuanian-online","title":"Sentiment Analysis of Lithuanian Online Reviews Using Large Language Models","date":"2024-07-29","arxiv_id":"2407.19914","n_code_links":0,"syntology":null},{"paper":null,"slug":"survey-and-taxonomy-the-role-of-data-centric","title":"Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecasting","date":"2024-07-29","arxiv_id":"2407.19784","n_code_links":0,"syntology":null},{"paper":null,"slug":"to-accept-or-not-to-accept-an-irt-toe","title":"To accept or not to accept? An IRT-TOE Framework to Understand Educators' Resistance to Generative AI in Higher Education","date":"2024-07-29","arxiv_id":"2407.20130","n_code_links":0,"syntology":null},{"paper":null,"slug":"what-if-red-can-talk-dynamic-dialogue","title":"What if Red Can Talk? Dynamic Dialogue Generation Using Large Language Models","date":"2024-07-29","arxiv_id":"2407.20382","n_code_links":0,"syntology":null},{"paper":null,"slug":"2408-01462","title":"Faculty Perspectives on the Potential of RAG in Computer Science Higher Education","date":"2024-07-28","arxiv_id":"2408.01462","n_code_links":0,"syntology":null},{"paper":null,"slug":"adacoder-adaptive-prompt-compression-for","title":"AdaCoder: Adaptive Prompt Compression for Programmatic Visual Question Answering","date":"2024-07-28","arxiv_id":"2407.19410","n_code_links":0,"syntology":null},{"paper":"/paper/are-llms-good-annotators-for-discourse-level","slug":"are-llms-good-annotators-for-discourse-level","title":"Are LLMs Good Annotators for Discourse-level Event Relation Extraction?","date":"2024-07-28","arxiv_id":"2407.19568","n_code_links":1,"syntology":null},{"paper":"/paper/depth-wise-convolutions-in-vision","slug":"depth-wise-convolutions-in-vision","title":"Depth-Wise Convolutions in Vision Transformers for Efficient Training on Small Datasets","date":"2024-07-28","arxiv_id":"2407.19394","n_code_links":1,"syntology":{"ran":13,"of":16,"n_ran_checked":11,"n_instrument":2,"unverified":3,"pointer_only":16,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 2 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","official":{"repos":["ztx-100/efficient_vit_with_dw"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"exploring-the-adversarial-robustness-of-clip","title":"Exploring the Adversarial Robustness of CLIP for AI-generated Image Detection","date":"2024-07-28","arxiv_id":"2407.19553","n_code_links":0,"syntology":null},{"paper":null,"slug":"is-generative-ai-an-existential-threat-to","title":"Is Generative AI an Existential Threat to Human Creatives? Insights from Financial Economics","date":"2024-07-28","arxiv_id":"2407.19586","n_code_links":0,"syntology":null},{"paper":null,"slug":"look-hear-gaze-prediction-for-speech-directed","title":"Look Hear: Gaze Prediction for Speech-directed Human Attention","date":"2024-07-28","arxiv_id":"2407.19605","n_code_links":0,"syntology":null},{"paper":"/paper/motamot-a-dataset-for-revealing-the-supremacy","slug":"motamot-a-dataset-for-revealing-the-supremacy","title":"Motamot: A Dataset for Revealing the Supremacy of Large Language Models over Transformer Models in Bengali Political Sentiment Analysis","date":"2024-07-28","arxiv_id":"2407.19528","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-modal-imaging-genomics-transformer","title":"Multi-modal Imaging Genomics Transformer: Attentive Integration of Imaging with Genomic Biomarkers for Schizophrenia Classification","date":"2024-07-28","arxiv_id":"2407.19385","n_code_links":0,"syntology":null},{"paper":null,"slug":"nvc-1b-a-large-neural-video-coding-model","title":"NVC-1B: A Large Neural Video Coding Model","date":"2024-07-28","arxiv_id":"2407.19402","n_code_links":0,"syntology":null},{"paper":null,"slug":"official-nv-a-news-video-dataset-for","title":"Official-NV: An LLM-Generated News Video Dataset for Multimodal Fake News Detection","date":"2024-07-28","arxiv_id":"2407.19493","n_code_links":0,"syntology":null},{"paper":null,"slug":"aresnet-vit-a-hybrid-cnn-transformer-network","title":"AResNet-ViT: A Hybrid CNN-Transformer Network for Benign and Malignant Breast Nodule Classification in Ultrasound Images","date":"2024-07-27","arxiv_id":"2407.19316","n_code_links":0,"syntology":null},{"paper":null,"slug":"channel-boosted-cnn-transformer-based-multi","title":"Channel Boosted CNN-Transformer-based Multi-Level and Multi-Scale Nuclei Segmentation","date":"2024-07-27","arxiv_id":"2407.19186","n_code_links":0,"syntology":null},{"paper":null,"slug":"fine-grained-scene-graph-generation-via","title":"Fine-Grained Scene Graph Generation via Sample-Level Bias Prediction","date":"2024-07-27","arxiv_id":"2407.19259","n_code_links":0,"syntology":null},{"paper":null,"slug":"integrating-large-language-models-into-a-tri","title":"Integrating Large Language Models into a Tri-Modal Architecture for Automated Depression Classification on the DAIC-WOZ","date":"2024-07-27","arxiv_id":"2407.19340","n_code_links":0,"syntology":null},{"paper":"/paper/matrrec-uniting-mamba-and-transformer-for","slug":"matrrec-uniting-mamba-and-transformer-for","title":"MaTrRec: Uniting Mamba and Transformer for Sequential Recommendation","date":"2024-07-27","arxiv_id":"2407.19239","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":["unintelligentmumu/matrrec"],"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":"2407-21059","title":"Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworks","date":"2024-07-26","arxiv_id":"2407.21059","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-reliable-common-sense-reasoning-socialbot","title":"A Reliable Common-Sense Reasoning Socialbot Built Using LLMs and Goal-Directed ASP","date":"2024-07-26","arxiv_id":"2407.18498","n_code_links":0,"syntology":null},{"paper":null,"slug":"bctr-bidirectional-conditioning-transformer","title":"BCTR: Bidirectional Conditioning Transformer for Scene Graph Generation","date":"2024-07-26","arxiv_id":"2407.18715","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-companion-learning-enhancing","title":"Deep Companion Learning: Enhancing Generalization Through Historical Consistency","date":"2024-07-26","arxiv_id":"2407.18821","n_code_links":0,"syntology":null},{"paper":"/paper/gpt-deciphering-fedspeak-quantifying-dissent","slug":"gpt-deciphering-fedspeak-quantifying-dissent","title":"GPT Deciphering Fedspeak: Quantifying Dissent Among Hawks and Doves","date":"2024-07-26","arxiv_id":"2407.19110","n_code_links":1,"syntology":null},{"paper":null,"slug":"human-artificial-intelligence-teaming-for","title":"Human-artificial intelligence teaming for scientific information extraction from data-driven additive manufacturing research using large language models","date":"2024-07-26","arxiv_id":"2407.18827","n_code_links":0,"syntology":null},{"paper":"/paper/is-larger-always-better-evaluating-and","slug":"is-larger-always-better-evaluating-and","title":"ClinicRealm: Re-evaluating Large Language Models with Conventional Machine Learning for Non-Generative Clinical Prediction Tasks","date":"2024-07-26","arxiv_id":"2407.18525","n_code_links":1,"syntology":{"ran":8,"of":8,"n_ran_checked":6,"n_instrument":2,"unverified":0,"pointer_only":8,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["yhzhu99/ehr-llm-benchmark"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/multimodal-emotion-recognition-using-audio","slug":"multimodal-emotion-recognition-using-audio","title":"Multimodal Emotion Recognition using Audio-Video Transformer Fusion with Cross Attention","date":"2024-07-26","arxiv_id":"2407.18552","n_code_links":1,"syntology":null},{"paper":"/paper/officebench-benchmarking-language-agents","slug":"officebench-benchmarking-language-agents","title":"OfficeBench: Benchmarking Language Agents across Multiple Applications for Office Automation","date":"2024-07-26","arxiv_id":"2407.19056","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":["zlwang-cs/OfficeBench"],"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":"qt-tdm-planning-with-transformer-dynamics","title":"QT-TDM: Planning With Transformer Dynamics Model and Autoregressive Q-Learning","date":"2024-07-26","arxiv_id":"2407.18841","n_code_links":0,"syntology":null},{"paper":null,"slug":"reaper-reasoning-based-retrieval-planning-for","title":"REAPER: Reasoning based Retrieval Planning for Complex RAG Systems","date":"2024-07-26","arxiv_id":"2407.18553","n_code_links":0,"syntology":null},{"paper":null,"slug":"skin-cancer-detection-utilizing-deep-learning","title":"Skin Cancer Detection utilizing Deep Learning: Classification of Skin Lesion Images using a Vision Transformer","date":"2024-07-26","arxiv_id":"2407.18554","n_code_links":0,"syntology":null},{"paper":null,"slug":"tagify-llm-powered-tagging-interface-for","title":"TAGIFY: LLM-powered Tagging Interface for Improved Data Findability on OGD portals","date":"2024-07-26","arxiv_id":"2407.18764","n_code_links":0,"syntology":null},{"paper":"/paper/towards-a-transformer-based-pre-trained-model","slug":"towards-a-transformer-based-pre-trained-model","title":"Towards a Transformer-Based Pre-trained Model for IoT Traffic Classification","date":"2024-07-26","arxiv_id":"2407.19051","n_code_links":1,"syntology":null},{"paper":null,"slug":"using-gpt-4-to-guide-causal-machine-learning","title":"Using GPT-4 to guide causal machine learning","date":"2024-07-26","arxiv_id":"2407.18607","n_code_links":0,"syntology":null},{"paper":null,"slug":"using-large-language-models-for-the","title":"Using Large Language Models for the Interpretation of Building Regulations","date":"2024-07-26","arxiv_id":"2407.21060","n_code_links":0,"syntology":null},{"paper":"/paper/adversarial-robust-decision-transformer","slug":"adversarial-robust-decision-transformer","title":"Adversarially Robust Decision Transformer","date":"2024-07-25","arxiv_id":"2407.18414","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":["xiaohangt/ardt"],"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":"closing-the-gap-between-open-source-and","title":"Closing the gap between open-source and commercial large language models for medical evidence summarization","date":"2024-07-25","arxiv_id":"2408.00588","n_code_links":0,"syntology":null},{"paper":"/paper/cost-effective-instruction-learning-for","slug":"cost-effective-instruction-learning-for","title":"Cost-effective Instruction Learning for Pathology Vision and Language Analysis","date":"2024-07-25","arxiv_id":"2407.17734","n_code_links":1,"syntology":null},{"paper":"/paper/cswin-unet-transformer-unet-with-cross-shaped","slug":"cswin-unet-transformer-unet-with-cross-shaped","title":"CSWin-UNet: Transformer UNet with Cross-Shaped Windows for Medical Image Segmentation","date":"2024-07-25","arxiv_id":"2407.18070","n_code_links":1,"syntology":null},{"paper":"/paper/detection-of-manatee-vocalisations-using-the","slug":"detection-of-manatee-vocalisations-using-the","title":"Detection of manatee vocalisations using the Audio Spectrogram Transformer","date":"2024-07-25","arxiv_id":"2407.18083","n_code_links":1,"syntology":null},{"paper":null,"slug":"hg-pipe-vision-transformer-acceleration-with","title":"HG-PIPE: Vision Transformer Acceleration with Hybrid-Grained Pipeline","date":"2024-07-25","arxiv_id":"2407.17879","n_code_links":0,"syntology":null},{"paper":null,"slug":"is-the-digital-forensics-and-incident","title":"Is the Digital Forensics and Incident Response Pipeline Ready for Text-Based Threats in LLM Era?","date":"2024-07-25","arxiv_id":"2407.17870","n_code_links":0,"syntology":null},{"paper":null,"slug":"keep-the-cost-down-a-review-on-methods-to","title":"Keep the Cost Down: A Review on Methods to Optimize LLM' s KV-Cache Consumption","date":"2024-07-25","arxiv_id":"2407.18003","n_code_links":0,"syntology":null},{"paper":"/paper/peft-u-parameter-efficient-fine-tuning-for","slug":"peft-u-parameter-efficient-fine-tuning-for","title":"PEFT-U: Parameter-Efficient Fine-Tuning for User Personalization","date":"2024-07-25","arxiv_id":"2407.18078","n_code_links":1,"syntology":{"ran":3,"of":5,"n_ran_checked":3,"n_instrument":0,"unverified":2,"pointer_only":5,"phrase":"3 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; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["ChrisIsKing/Parameter-Efficient-Personalization"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/personagym-evaluating-persona-agents-and-llms","slug":"personagym-evaluating-persona-agents-and-llms","title":"PersonaGym: Evaluating Persona Agents and LLMs","date":"2024-07-25","arxiv_id":"2407.18416","n_code_links":1,"syntology":null},{"paper":"/paper/positive-text-reframing-under-multi-strategy","slug":"positive-text-reframing-under-multi-strategy","title":"Positive Text Reframing under Multi-strategy Optimization","date":"2024-07-25","arxiv_id":"2407.17940","n_code_links":1,"syntology":null},{"paper":"/paper/self-training-with-direct-preference","slug":"self-training-with-direct-preference","title":"Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning","date":"2024-07-25","arxiv_id":"2407.18248","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":["tianduowang/dpo-st"],"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":null,"slug":"the-geometry-of-queries-query-based","title":"The Geometry of Queries: Query-Based Innovations in Retrieval-Augmented Generation","date":"2024-07-25","arxiv_id":"2407.18044","n_code_links":0,"syntology":null},{"paper":null,"slug":"trajectory-aligned-space-time-tokens-for-few","title":"Trajectory-aligned Space-time Tokens for Few-shot Action Recognition","date":"2024-07-25","arxiv_id":"2407.18249","n_code_links":0,"syntology":null},{"paper":null,"slug":"trust-or-escalate-llm-judges-with-provable","title":"Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreement","date":"2024-07-25","arxiv_id":"2407.18370","n_code_links":0,"syntology":null},{"paper":null,"slug":"2407-21056","title":"What Matters in Explanations: Towards Explainable Fake Review Detection Focusing on Transformers","date":"2024-07-24","arxiv_id":"2407.21056","n_code_links":0,"syntology":null},{"paper":null,"slug":"bailicai-a-domain-optimized-retrieval","title":"Bailicai: A Domain-Optimized Retrieval-Augmented Generation Framework for Medical Applications","date":"2024-07-24","arxiv_id":"2407.21055","n_code_links":0,"syntology":null},{"paper":"/paper/case-enhanced-vision-transformer-improving","slug":"case-enhanced-vision-transformer-improving","title":"Case-Enhanced Vision Transformer: Improving Explanations of Image Similarity with a ViT-based Similarity Metric","date":"2024-07-24","arxiv_id":"2407.16981","n_code_links":1,"syntology":null},{"paper":"/paper/dependency-transformer-grammars-integrating","slug":"dependency-transformer-grammars-integrating","title":"Dependency Transformer Grammars: Integrating Dependency Structures into Transformer Language Models","date":"2024-07-24","arxiv_id":"2407.17406","n_code_links":1,"syntology":null},{"paper":"/paper/dynamic-graph-transformer-with-correlated","slug":"dynamic-graph-transformer-with-correlated","title":"Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding","date":"2024-07-24","arxiv_id":"2407.16959","n_code_links":1,"syntology":null},{"paper":"/paper/embedding-free-transformer-with-inference","slug":"embedding-free-transformer-with-inference","title":"Embedding-Free Transformer with Inference Spatial Reduction for Efficient Semantic Segmentation","date":"2024-07-24","arxiv_id":"2407.17261","n_code_links":1,"syntology":null},{"paper":"/paper/i-could-ve-asked-that-reformulating","slug":"i-could-ve-asked-that-reformulating","title":"I Could've Asked That: Reformulating Unanswerable Questions","date":"2024-07-24","arxiv_id":"2407.17469","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-icd-coding-using-chapter-based","title":"Improving ICD coding using Chapter based Named Entities and Attentional Models","date":"2024-07-24","arxiv_id":"2407.17230","n_code_links":0,"syntology":null},{"paper":"/paper/loformer-local-frequency-transformer-for","slug":"loformer-local-frequency-transformer-for","title":"LoFormer: Local Frequency Transformer for Image Deblurring","date":"2024-07-24","arxiv_id":"2407.16993","n_code_links":2,"syntology":null},{"paper":"/paper/must-multi-scale-transformers-for-surgical","slug":"must-multi-scale-transformers-for-surgical","title":"MuST: Multi-Scale Transformers for Surgical Phase Recognition","date":"2024-07-24","arxiv_id":"2407.17361","n_code_links":1,"syntology":null},{"paper":null,"slug":"reporting-and-analysing-the-environmental","title":"Reporting and Analysing the Environmental Impact of Language Models on the Example of Commonsense Question Answering with External Knowledge","date":"2024-07-24","arxiv_id":"2408.01453","n_code_links":0,"syntology":null},{"paper":null,"slug":"testing-large-language-models-on-driving","title":"Testing Large Language Models on Driving Theory Knowledge and Skills for Connected Autonomous Vehicles","date":"2024-07-24","arxiv_id":"2407.17211","n_code_links":0,"syntology":null},{"paper":null,"slug":"analyzing-the-polysemy-evolution-using","title":"Analyzing Polysemy Evolution Using Semantic Cells","date":"2024-07-23","arxiv_id":"2407.16110","n_code_links":0,"syntology":null},{"paper":null,"slug":"artificial-intelligence-in-extracting","title":"Artificial Intelligence in Extracting Diagnostic Data from Dental Records","date":"2024-07-23","arxiv_id":"2407.21050","n_code_links":0,"syntology":null},{"paper":"/paper/channel-partitioned-windowed-attention-and","slug":"channel-partitioned-windowed-attention-and","title":"Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution","date":"2024-07-23","arxiv_id":"2407.16232","n_code_links":0,"syntology":null},{"paper":"/paper/data-mixture-inference-what-do-bpe-tokenizers","slug":"data-mixture-inference-what-do-bpe-tokenizers","title":"Data Mixture Inference: What do BPE Tokenizers Reveal about their Training Data?","date":"2024-07-23","arxiv_id":"2407.16607","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"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":["alisawuffles/tokenizer-attack"],"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":"diffusion-transformer-captures-spatial","title":"Diffusion Transformer Captures Spatial-Temporal Dependencies: A Theory for Gaussian Process Data","date":"2024-07-23","arxiv_id":"2407.16134","n_code_links":0,"syntology":null},{"paper":null,"slug":"do-llms-know-when-to-not-answer-investigating","title":"Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language Models","date":"2024-07-23","arxiv_id":"2407.16221","n_code_links":0,"syntology":null},{"paper":"/paper/enhancing-llm-s-cognition-via-structurization","slug":"enhancing-llm-s-cognition-via-structurization","title":"Enhancing LLM's Cognition via Structurization","date":"2024-07-23","arxiv_id":"2407.16434","n_code_links":1,"syntology":{"ran":5,"of":7,"n_ran_checked":5,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["alibaba/struxgpt"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"exploring-the-neural-burden-in-pruned-models","title":"Exploring The Neural Burden In Pruned Models: An Insight Inspired By Neuroscience","date":"2024-07-23","arxiv_id":"2407.16716","n_code_links":0,"syntology":null},{"paper":null,"slug":"hsvlt-hierarchical-scale-aware-vision","title":"HSVLT: Hierarchical Scale-Aware Vision-Language Transformer for Multi-Label Image Classification","date":"2024-07-23","arxiv_id":"2407.16244","n_code_links":0,"syntology":null},{"paper":"/paper/hytas-a-hyperspectral-image-transformer","slug":"hytas-a-hyperspectral-image-transformer","title":"HyTAS: A Hyperspectral Image Transformer Architecture Search Benchmark and Analysis","date":"2024-07-23","arxiv_id":"2407.16269","n_code_links":1,"syntology":null},{"paper":null,"slug":"lawluo-a-chinese-law-firm-co-run-by-llm","title":"LawLuo: A Multi-Agent Collaborative Framework for Multi-Round Chinese Legal Consultation","date":"2024-07-23","arxiv_id":"2407.16252","n_code_links":0,"syntology":null},{"paper":"/paper/lawma-the-power-of-specialization-for-legal","slug":"lawma-the-power-of-specialization-for-legal","title":"Lawma: The Power of Specialization for Legal Tasks","date":"2024-07-23","arxiv_id":"2407.16615","n_code_links":0,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/origen-enhancing-rtl-code-generation-with","slug":"origen-enhancing-rtl-code-generation-with","title":"OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection","date":"2024-07-23","arxiv_id":"2407.16237","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":1,"n_instrument":0,"unverified":2,"pointer_only":3,"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) · 2 unverified","official":{"repos":["pku-liang/origen"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/patched-rtc-evaluating-llms-for-diverse","slug":"patched-rtc-evaluating-llms-for-diverse","title":"Patched RTC: evaluating LLMs for diverse software development tasks","date":"2024-07-23","arxiv_id":"2407.16557","n_code_links":1,"syntology":null},{"paper":null,"slug":"redagent-red-teaming-large-language-models","title":"RedAgent: Red Teaming Large Language Models with Context-aware Autonomous Language Agent","date":"2024-07-23","arxiv_id":"2407.16667","n_code_links":0,"syntology":null}],"record_sha256":"7b9b1fa3a8a8886cd1ad33e5b513f8f8af5884271dd297505be75d8896430581","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}