{"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/attention/papers/116","list_of":"/method/attention","method":"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":116,"pages_in_order":316,"rows_per_page":100,"rows":[11501,11600],"of":31583,"counts":{"archive_papers_tagged":31583,"with_a_code_link":13473,"where_syntology_ran_a_sample":3998,"not_listed_spam_title":0,"listed":31583,"listed_where_code_ran":3998,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3366,"every_run_a_failure_of_syntologys_instrument":632,"listed_with_a_run_with_no_instrument_failure":3366,"listed_every_run_a_failure_of_syntologys_instrument":632,"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/attention","prev":"/method/attention/papers/115","next":"/method/attention/papers/117","papers":[{"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},{"paper":null,"slug":"retrieval-augmented-generation-or-long","title":"Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach","date":"2024-07-23","arxiv_id":"2407.16833","n_code_links":0,"syntology":null},{"paper":"/paper/robust-privacy-amidst-innovation-with-large","slug":"robust-privacy-amidst-innovation-with-large","title":"Robust Privacy Amidst Innovation with Large Language Models Through a Critical Assessment of the Risks","date":"2024-07-23","arxiv_id":"2407.16166","n_code_links":1,"syntology":null},{"paper":null,"slug":"s-e-pipeline-a-vision-transformer-vit-based","title":"S-E Pipeline: A Vision Transformer (ViT) based Resilient Classification Pipeline for Medical Imaging Against Adversarial Attacks","date":"2024-07-23","arxiv_id":"2407.17587","n_code_links":0,"syntology":null},{"paper":"/paper/safnet-selective-alignment-fusion-network-for","slug":"safnet-selective-alignment-fusion-network-for","title":"SAFNet: Selective Alignment Fusion Network for Efficient HDR Imaging","date":"2024-07-23","arxiv_id":"2407.16308","n_code_links":1,"syntology":{"ran":14,"of":17,"n_ran_checked":5,"n_instrument":9,"unverified":3,"pointer_only":17,"phrase":"14 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; 9 where Syntology's instrument failed) · 3 unverified","official":{"repos":["ltkong218/safnet"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/seds-semantically-enhanced-dual-stream","slug":"seds-semantically-enhanced-dual-stream","title":"SEDS: Semantically Enhanced Dual-Stream Encoder for Sign Language Retrieval","date":"2024-07-23","arxiv_id":"2407.16394","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":1,"n_instrument":2,"unverified":1,"pointer_only":0,"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) · 1 unverified","official":{"repos":["longtaojiang/seds"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"self-reasoning-assistant-learning-for-non","title":"Self-Reasoning Assistant Learning for non-Abelian Gauge Fields Design","date":"2024-07-23","arxiv_id":"2407.16255","n_code_links":0,"syntology":null},{"paper":"/paper/sinder-repairing-the-singular-defects-of","slug":"sinder-repairing-the-singular-defects-of","title":"SINDER: Repairing the Singular Defects of DINOv2","date":"2024-07-23","arxiv_id":"2407.16826","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 1 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; the one sample that ran constructed an object rather than computing a result","official":{"repos":["haoqiwang/sinder"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/spatiotemporal-graph-guided-multi-modal","slug":"spatiotemporal-graph-guided-multi-modal","title":"Spatiotemporal Graph Guided Multi-modal Network for Livestreaming Product Retrieval","date":"2024-07-23","arxiv_id":"2407.16248","n_code_links":1,"syntology":null},{"paper":null,"slug":"splat-a-framework-for-optimised-gpu-code","title":"SPLAT: A framework for optimised GPU code-generation for SParse reguLar ATtention","date":"2024-07-23","arxiv_id":"2407.16847","n_code_links":0,"syntology":null},{"paper":null,"slug":"stock-driven-household-attention","title":"Stock-driven Household Attention","date":"2024-07-23","arxiv_id":"2407.16141","n_code_links":0,"syntology":null},{"paper":null,"slug":"synthesizer-sound-matching-using-audio","title":"Synthesizer Sound Matching Using Audio Spectrogram Transformers","date":"2024-07-23","arxiv_id":"2407.16643","n_code_links":0,"syntology":null},{"paper":null,"slug":"taptrv2-attention-based-position-update","title":"TAPTRv2: Attention-based Position Update Improves Tracking Any Point","date":"2024-07-23","arxiv_id":"2407.16291","n_code_links":0,"syntology":null},{"paper":null,"slug":"tookabert-a-step-forward-for-persian-nlu","title":"TookaBERT: A Step Forward for Persian NLU","date":"2024-07-23","arxiv_id":"2407.16382","n_code_links":0,"syntology":null},{"paper":null,"slug":"twin-v2-scaling-ultra-long-user-behavior","title":"TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou","date":"2024-07-23","arxiv_id":"2407.16357","n_code_links":0,"syntology":null},{"paper":null,"slug":"when-where-and-what-an-novel-benchmark-for","title":"When, Where, and What? A Novel Benchmark for Accident Anticipation and Localization with Large Language Models","date":"2024-07-23","arxiv_id":"2407.16277","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-empirical-comparison-of-video-frame","title":"An Empirical Comparison of Video Frame Sampling Methods for Multi-Modal RAG Retrieval","date":"2024-07-22","arxiv_id":"2408.03340","n_code_links":0,"syntology":null},{"paper":"/paper/attention-beats-linear-for-fast-implicit","slug":"attention-beats-linear-for-fast-implicit","title":"Attention Beats Linear for Fast Implicit Neural Representation Generation","date":"2024-07-22","arxiv_id":"2407.15355","n_code_links":1,"syntology":null},{"paper":null,"slug":"attention-is-all-you-need-but-you-don-t-need","title":"Attention Is All You Need But You Don't Need All Of It For Inference of Large Language Models","date":"2024-07-22","arxiv_id":"2407.15516","n_code_links":0,"syntology":null},{"paper":null,"slug":"bidirectional-skip-frame-prediction-for-video","title":"Bidirectional skip-frame prediction for video anomaly detection with intra-domain disparity-driven attention","date":"2024-07-22","arxiv_id":"2407.15424","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-gpt-4-learn-to-analyze-moves-in-research","title":"Can GPT-4 learn to analyse moves in research article abstracts?","date":"2024-07-22","arxiv_id":"2407.15612","n_code_links":0,"syntology":null},{"paper":null,"slug":"carformer-self-driving-with-learned-object","title":"CarFormer: Self-Driving with Learned Object-Centric Representations","date":"2024-07-22","arxiv_id":"2407.15843","n_code_links":0,"syntology":null},{"paper":"/paper/counter-turing-test-ct-2-investigating-ai","slug":"counter-turing-test-ct-2-investigating-ai","title":"Counter Turing Test ($CT^2$): Investigating AI-Generated Text Detection for Hindi -- Ranking LLMs based on Hindi AI Detectability Index ($ADI_{hi}$)","date":"2024-07-22","arxiv_id":"2407.15694","n_code_links":1,"syntology":null},{"paper":null,"slug":"customized-retrieval-augmented-generation-and","title":"Customized Retrieval Augmented Generation and Benchmarking for EDA Tool Documentation QA","date":"2024-07-22","arxiv_id":"2407.15353","n_code_links":0,"syntology":null},{"paper":"/paper/diffx-guide-your-layout-to-cross-modal","slug":"diffx-guide-your-layout-to-cross-modal","title":"DiffX: Guide Your Layout to Cross-Modal Generative Modeling","date":"2024-07-22","arxiv_id":"2407.15488","n_code_links":1,"syntology":null},{"paper":"/paper/dissecting-multiplication-in-transformers","slug":"dissecting-multiplication-in-transformers","title":"Dissecting Multiplication in Transformers: Insights into LLMs","date":"2024-07-22","arxiv_id":"2407.15360","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-multi-disparity-transformer-for","title":"Efficient Multi-disparity Transformer for Light Field Image Super-resolution","date":"2024-07-22","arxiv_id":"2407.15329","n_code_links":0,"syntology":null},{"paper":"/paper/estimating-probability-densities-with","slug":"estimating-probability-densities-with","title":"Estimating Probability Densities with Transformer and Denoising Diffusion","date":"2024-07-22","arxiv_id":"2407.15703","n_code_links":1,"syntology":{"ran":5,"of":5,"n_ran_checked":5,"n_instrument":0,"unverified":0,"pointer_only":1,"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) · 0 unverified","official":{"repos":["henrysky/stars_foundation_diffusion"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/gfe-mamba-mamba-based-ad-multi-modal","slug":"gfe-mamba-mamba-based-ad-multi-modal","title":"GFE-Mamba: Mamba-based AD Multi-modal Progression Assessment via Generative Feature Extraction from MCI","date":"2024-07-22","arxiv_id":"2407.15719","n_code_links":1,"syntology":null},{"paper":null,"slug":"impacts-of-anthropomorphizing-large-language","title":"Impacts of Anthropomorphizing Large Language Models in Learning Environments","date":"2024-07-22","arxiv_id":"2408.03945","n_code_links":0,"syntology":null},{"paper":null,"slug":"imposter-ai-adversarial-attacks-with-hidden","title":"Imposter.AI: Adversarial Attacks with Hidden Intentions towards Aligned Large Language Models","date":"2024-07-22","arxiv_id":"2407.15399","n_code_links":0,"syntology":null},{"paper":null,"slug":"in-search-of-quantum-advantage-estimating-the","title":"In Search of Quantum Advantage: Estimating the Number of Shots in Quantum Kernel Methods","date":"2024-07-22","arxiv_id":"2407.15776","n_code_links":0,"syntology":null},{"paper":"/paper/inverted-activations","slug":"inverted-activations","title":"Inverted Activations: Reducing Memory Footprint in Neural Network Training","date":"2024-07-22","arxiv_id":"2407.15545","n_code_links":1,"syntology":null},{"paper":null,"slug":"kwt-tiny-risc-v-accelerated-embedded-keyword","title":"KWT-Tiny: RISC-V Accelerated, Embedded Keyword Spotting Transformer","date":"2024-07-22","arxiv_id":"2407.16026","n_code_links":0,"syntology":null},{"paper":null,"slug":"large-scale-time-varying-portfolio","title":"Large-scale Time-Varying Portfolio Optimisation using Graph Attention Networks","date":"2024-07-22","arxiv_id":"2407.15532","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-to-manipulate-anywhere-a-visual","title":"Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning","date":"2024-07-22","arxiv_id":"2407.15815","n_code_links":0,"syntology":null},{"paper":"/paper/link-polarity-prediction-from-sparse-and","slug":"link-polarity-prediction-from-sparse-and","title":"Link Polarity Prediction from Sparse and Noisy Labels via Multiscale Social Balance","date":"2024-07-22","arxiv_id":"2407.15643","n_code_links":1,"syntology":null},{"paper":"/paper/llmmap-fingerprinting-for-large-language","slug":"llmmap-fingerprinting-for-large-language","title":"LLMmap: Fingerprinting For Large Language Models","date":"2024-07-22","arxiv_id":"2407.15847","n_code_links":1,"syntology":{"ran":9,"of":16,"n_ran_checked":9,"n_instrument":0,"unverified":7,"pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","official":{"repos":["pasquini-dario/LLMmap"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":7,"ran_from_kinds":["official"]}}},{"paper":"/paper/local-all-pair-correspondence-for-point","slug":"local-all-pair-correspondence-for-point","title":"Local All-Pair Correspondence for Point Tracking","date":"2024-07-22","arxiv_id":"2407.15420","n_code_links":2,"syntology":{"ran":8,"of":12,"n_ran_checked":8,"n_instrument":0,"unverified":4,"pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["cvlab-kaist/locotrack"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/mamba-meets-crack-segmentation","slug":"mamba-meets-crack-segmentation","title":"Mamba meets crack segmentation","date":"2024-07-22","arxiv_id":"2407.15714","n_code_links":1,"syntology":null},{"paper":"/paper/mini-sequence-transformer-optimizing","slug":"mini-sequence-transformer-optimizing","title":"Mini-Sequence Transformer: Optimizing Intermediate Memory for Long Sequences Training","date":"2024-07-22","arxiv_id":"2407.15892","n_code_links":1,"syntology":null},{"paper":"/paper/mminstruct-a-high-quality-multi-modal","slug":"mminstruct-a-high-quality-multi-modal","title":"MMInstruct: A High-Quality Multi-Modal Instruction Tuning Dataset with Extensive Diversity","date":"2024-07-22","arxiv_id":"2407.15838","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["yuecao0119/mminstruct"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"morse-bridging-the-gap-in-cybersecurity","title":"MoRSE: Bridging the Gap in Cybersecurity Expertise with Retrieval Augmented Generation","date":"2024-07-22","arxiv_id":"2407.15748","n_code_links":0,"syntology":null},{"paper":null,"slug":"movable-antenna-enhanced-wireless","title":"Movable Antenna-Enhanced Wireless Communications: General Architectures and Implementation Methods","date":"2024-07-22","arxiv_id":"2407.15448","n_code_links":0,"syntology":null},{"paper":"/paper/multi-modality-co-learning-for-efficient-1","slug":"multi-modality-co-learning-for-efficient-1","title":"Multi-Modality Co-Learning for Efficient Skeleton-based Action Recognition","date":"2024-07-22","arxiv_id":"2407.15706","n_code_links":1,"syntology":{"ran":9,"of":10,"n_ran_checked":7,"n_instrument":2,"unverified":1,"pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["liujf69/MMCL-Action"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"nv-retriever-improving-text-embedding-models","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","date":"2024-07-22","arxiv_id":"2407.15831","n_code_links":0,"syntology":null},{"paper":null,"slug":"online-reduced-order-data-enabled-predictive","title":"Online Reduced-Order Data-Enabled Predictive Control","date":"2024-07-22","arxiv_id":"2407.16066","n_code_links":0,"syntology":null},{"paper":null,"slug":"poisoning-with-a-pill-circumventing-detection","title":"Poisoning with A Pill: Circumventing Detection in Federated Learning","date":"2024-07-22","arxiv_id":"2407.15389","n_code_links":0,"syntology":null},{"paper":"/paper/predicting-the-best-of-n-visual-trackers","slug":"predicting-the-best-of-n-visual-trackers","title":"Predicting the Best of N Visual Trackers","date":"2024-07-22","arxiv_id":"2407.15707","n_code_links":1,"syntology":null},{"paper":null,"slug":"problems-in-ai-their-roots-in-philosophy-and","title":"Problems in AI, their roots in philosophy, and implications for science and society","date":"2024-07-22","arxiv_id":"2407.15671","n_code_links":0,"syntology":null},{"paper":"/paper/promises-and-pitfalls-of-generative-masked","slug":"promises-and-pitfalls-of-generative-masked","title":"Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines","date":"2024-07-22","arxiv_id":"2407.21046","n_code_links":1,"syntology":null},{"paper":"/paper/radiorag-factual-large-language-models-for","slug":"radiorag-factual-large-language-models-for","title":"RadioRAG: Factual large language models for enhanced diagnostics in radiology using online retrieval augmented generation","date":"2024-07-22","arxiv_id":"2407.15621","n_code_links":1,"syntology":null},{"paper":null,"slug":"razorattention-efficient-kv-cache-compression","title":"RazorAttention: Efficient KV Cache Compression Through Retrieval Heads","date":"2024-07-22","arxiv_id":"2407.15891","n_code_links":0,"syntology":null},{"paper":null,"slug":"region-guided-attention-network-for-retinal","title":"Region Guided Attention Network for Retinal Vessel Segmentation","date":"2024-07-22","arxiv_id":"2407.18970","n_code_links":0,"syntology":null},{"paper":null,"slug":"roadpainter-points-are-ideal-navigators-for","title":"RoadPainter: Points Are Ideal Navigators for Topology transformER","date":"2024-07-22","arxiv_id":"2407.15349","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-facial-reactions-generation-an-emotion","title":"Robust Facial Reactions Generation: An Emotion-Aware Framework with Modality Compensation","date":"2024-07-22","arxiv_id":"2407.15798","n_code_links":0,"syntology":null},{"paper":null,"slug":"settp-style-extraction-and-tunable-inference","title":"SETTP: Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning","date":"2024-07-22","arxiv_id":"2407.15556","n_code_links":0,"syntology":null},{"paper":"/paper/stamp-outlier-aware-test-time-adaptation-with","slug":"stamp-outlier-aware-test-time-adaptation-with","title":"STAMP: Outlier-Aware Test-Time Adaptation with Stable Memory Replay","date":"2024-07-22","arxiv_id":"2407.15773","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"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":["yuyongcan/stamp"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/stretching-each-dollar-diffusion-training","slug":"stretching-each-dollar-diffusion-training","title":"Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget","date":"2024-07-22","arxiv_id":"2407.15811","n_code_links":1,"syntology":{"ran":4,"of":6,"n_ran_checked":4,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["sonyresearch/micro_diffusion"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/test-time-low-rank-adaptation-via-confidence","slug":"test-time-low-rank-adaptation-via-confidence","title":"Test-Time Low Rank Adaptation via Confidence Maximization for Zero-Shot Generalization of Vision-Language Models","date":"2024-07-22","arxiv_id":"2407.15913","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["razaimam45/ttl-test-time-low-rank-adaptation"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/towards-open-world-object-based-anomaly","slug":"towards-open-world-object-based-anomaly","title":"Towards Open-World Object-based Anomaly Detection via Self-Supervised Outlier Synthesis","date":"2024-07-22","arxiv_id":"2407.15763","n_code_links":1,"syntology":null},{"paper":"/paper/two-stacks-are-better-than-one-a-comparison","slug":"two-stacks-are-better-than-one-a-comparison","title":"A Comparison of Language Modeling and Translation as Multilingual Pretraining Objectives","date":"2024-07-22","arxiv_id":"2407.15489","n_code_links":1,"syntology":null},{"paper":null,"slug":"unlocking-the-potential-benchmarking-large","title":"Unlocking the Potential: Benchmarking Large Language Models in Water Engineering and Research","date":"2024-07-22","arxiv_id":"2407.21045","n_code_links":0,"syntology":null},{"paper":"/paper/vtensor-flexible-virtual-tensor-management","slug":"vtensor-flexible-virtual-tensor-management","title":"vTensor: Flexible Virtual Tensor Management for Efficient LLM Serving","date":"2024-07-22","arxiv_id":"2407.15309","n_code_links":1,"syntology":null},{"paper":null,"slug":"zzu-nlp-at-sighan-2024-dimabsa-task-aspect","title":"ZZU-NLP at SIGHAN-2024 dimABSA Task: Aspect-Based Sentiment Analysis with Coarse-to-Fine In-context Learning","date":"2024-07-22","arxiv_id":"2407.15341","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-multi-level-multi-label-text-classification","title":"A multi-level multi-label text classification dataset of 19th century Ottoman and Russian literary and critical texts","date":"2024-07-21","arxiv_id":"2407.15136","n_code_links":0,"syntology":null},{"paper":"/paper/answer-assemble-ace-understanding-how","slug":"answer-assemble-ace-understanding-how","title":"Answer, Assemble, Ace: Understanding How Transformers Answer Multiple Choice Questions","date":"2024-07-21","arxiv_id":"2407.15018","n_code_links":0,"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":null}},{"paper":null,"slug":"arondight-red-teaming-large-vision-language","title":"Arondight: Red Teaming Large Vision Language Models with Auto-generated Multi-modal Jailbreak Prompts","date":"2024-07-21","arxiv_id":"2407.15050","n_code_links":0,"syntology":null},{"paper":null,"slug":"calibrbev-multi-camera-calibration-via","title":"CalibRBEV: Multi-Camera Calibration via ReversedBird's-eye-view Representations for Autonomous Driving.","date":"2024-07-21","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/decoding-multilingual-moral-preferences","slug":"decoding-multilingual-moral-preferences","title":"Decoding Multilingual Moral Preferences: Unveiling LLM's Biases Through the Moral Machine Experiment","date":"2024-07-21","arxiv_id":"2407.15184","n_code_links":1,"syntology":null},{"paper":"/paper/efficient-visual-transformer-by-learnable","slug":"efficient-visual-transformer-by-learnable","title":"Efficient Visual Transformer by Learnable Token Merging","date":"2024-07-21","arxiv_id":"2407.15219","n_code_links":1,"syntology":null},{"paper":null,"slug":"evidence-based-temporal-fact-verification","title":"Evidence-Based Temporal Fact Verification","date":"2024-07-21","arxiv_id":"2407.15291","n_code_links":0,"syntology":null},{"paper":"/paper/farewell-to-length-extrapolation-a-training","slug":"farewell-to-length-extrapolation-a-training","title":"ReAttention: Training-Free Infinite Context with Finite Attention Scope","date":"2024-07-21","arxiv_id":"2407.15176","n_code_links":0,"syntology":{"ran":5,"of":7,"n_ran_checked":4,"n_instrument":1,"unverified":2,"pointer_only":0,"phrase":"5 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":null,"slug":"improving-prediction-of-need-for-mechanical","title":"Improving Prediction of Need for Mechanical Ventilation using Cross-Attention","date":"2024-07-21","arxiv_id":"2407.15885","n_code_links":0,"syntology":null},{"paper":"/paper/mask-guided-gated-convolution-for-amodal","slug":"mask-guided-gated-convolution-for-amodal","title":"Mask Guided Gated Convolution for Amodal Content Completion","date":"2024-07-21","arxiv_id":"2407.15203","n_code_links":1,"syntology":null},{"paper":null,"slug":"point-transformer-v3-extreme-1st-place","title":"Point Transformer V3 Extreme: 1st Place Solution for 2024 Waymo Open Dataset Challenge in Semantic Segmentation","date":"2024-07-21","arxiv_id":"2407.15282","n_code_links":0,"syntology":null},{"paper":"/paper/prior-knowledge-integration-via-llm-encoding","slug":"prior-knowledge-integration-via-llm-encoding","title":"Prior Knowledge Integration via LLM Encoding and Pseudo Event Regulation for Video Moment Retrieval","date":"2024-07-21","arxiv_id":"2407.15051","n_code_links":1,"syntology":{"ran":8,"of":11,"n_ran_checked":5,"n_instrument":3,"unverified":3,"pointer_only":9,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","official":{"repos":["fletcherjiang/llmepet"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"semi-supervised-pipe-video-temporal-defect","title":"Semi-Supervised Pipe Video Temporal Defect Interval Localization","date":"2024-07-21","arxiv_id":"2407.15170","n_code_links":0,"syntology":null},{"paper":null,"slug":"token-picker-accelerating-attention-in-text","title":"Token-Picker: Accelerating Attention in Text Generation with Minimized Memory Transfer via Probability Estimation","date":"2024-07-21","arxiv_id":"2407.15131","n_code_links":0,"syntology":null},{"paper":"/paper/toward-adaptive-reasoning-in-large-language","slug":"toward-adaptive-reasoning-in-large-language","title":"Toward Adaptive Reasoning in Large Language Models with Thought Rollback","date":"2024-07-21","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"2408-00798","title":"Golden-Retriever: High-Fidelity Agentic Retrieval Augmented Generation for Industrial Knowledge Base","date":"2024-07-20","arxiv_id":"2408.00798","n_code_links":0,"syntology":null},{"paper":null,"slug":"all-against-some-efficient-integration-of","title":"All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks","date":"2024-07-20","arxiv_id":"2407.14996","n_code_links":0,"syntology":null},{"paper":"/paper/automatic-generation-of-fashion-images-using","slug":"automatic-generation-of-fashion-images-using","title":"Automatic Generation of Fashion Images using Prompting in Generative Machine Learning Models","date":"2024-07-20","arxiv_id":"2407.14944","n_code_links":1,"syntology":null},{"paper":null,"slug":"cort-class-oriented-real-time-tracking-for","title":"CORT: Class-Oriented Real-time Tracking for Embedded Systems","date":"2024-07-20","arxiv_id":"2407.17521","n_code_links":0,"syntology":null},{"paper":null,"slug":"differential-privacy-of-cross-attention-with","title":"Differential Privacy of Cross-Attention with Provable Guarantee","date":"2024-07-20","arxiv_id":"2407.14717","n_code_links":0,"syntology":null},{"paper":"/paper/dual-high-order-total-variation-model-for","slug":"dual-high-order-total-variation-model-for","title":"Dual High-Order Total Variation Model for Underwater Image Restoration","date":"2024-07-20","arxiv_id":"2407.14868","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancing-microgrid-performance-prediction","title":"Enhancing Microgrid Performance Prediction with Attention-based Deep Learning Models","date":"2024-07-20","arxiv_id":"2407.14984","n_code_links":0,"syntology":null},{"paper":"/paper/fairvit-fair-vision-transformer-via-adaptive","slug":"fairvit-fair-vision-transformer-via-adaptive","title":"FairViT: Fair Vision Transformer via Adaptive Masking","date":"2024-07-20","arxiv_id":"2407.14799","n_code_links":1,"syntology":null},{"paper":null,"slug":"gaitma-pose-guided-multi-modal-feature-fusion","title":"GaitMA: Pose-guided Multi-modal Feature Fusion for Gait Recognition","date":"2024-07-20","arxiv_id":"2407.14812","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-context-aware-preference-modeling","title":"Improving Context-Aware Preference Modeling for Language Models","date":"2024-07-20","arxiv_id":"2407.14916","n_code_links":0,"syntology":null},{"paper":null,"slug":"mapping-patient-trajectories-understanding","title":"Mapping Patient Trajectories: Understanding and Visualizing Sepsis Prognostic Pathways from Patients Clinical Narratives","date":"2024-07-20","arxiv_id":"2407.21039","n_code_links":0,"syntology":null},{"paper":"/paper/metaaug-meta-data-augmentation-for-post","slug":"metaaug-meta-data-augmentation-for-post","title":"MetaAug: Meta-Data Augmentation for Post-Training Quantization","date":"2024-07-20","arxiv_id":"2407.14726","n_code_links":2,"syntology":null},{"paper":null,"slug":"rgb2point-3d-point-cloud-generation-from","title":"RGB2Point: 3D Point Cloud Generation from Single RGB Images","date":"2024-07-20","arxiv_id":"2407.14979","n_code_links":0,"syntology":null},{"paper":null,"slug":"roipoly-vectorized-building-outline","title":"RoIPoly: Vectorized Building Outline Extraction Using Vertex and Logit Embeddings","date":"2024-07-20","arxiv_id":"2407.14920","n_code_links":0,"syntology":null},{"paper":"/paper/step-by-step-reasoning-to-solve-grid-puzzles","slug":"step-by-step-reasoning-to-solve-grid-puzzles","title":"Step-by-Step Reasoning to Solve Grid Puzzles: Where do LLMs Falter?","date":"2024-07-20","arxiv_id":"2407.14790","n_code_links":1,"syntology":null},{"paper":null,"slug":"technical-report-improving-the-properties-of","title":"Technical report: Improving the properties of molecules generated by LIMO","date":"2024-07-20","arxiv_id":"2407.14968","n_code_links":0,"syntology":null},{"paper":null,"slug":"travellm-could-you-plan-my-new-public-transit","title":"TraveLLM: Could you plan my new public transit route in face of a network disruption?","date":"2024-07-20","arxiv_id":"2407.14926","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comparative-study-of-transfer-learning-for","title":"A Comparative Study of Transfer Learning for Emotion Recognition using CNN and Modified VGG16 Models","date":"2024-07-19","arxiv_id":"2407.14576","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-mirror-descent-based-algorithm-for","title":"A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent","date":"2024-07-19","arxiv_id":"2407.14111","n_code_links":0,"syntology":null}],"record_sha256":"636b66303b6362e8c6690214e6fa0a9a6bf312b177ce4cb6dff7b4cee94b0c42","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}