{"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/focus/papers/6","list_of":"/method/focus","method":"Focus","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":6,"pages_in_order":154,"rows_per_page":100,"rows":[501,600],"of":15340,"counts":{"archive_papers_tagged":15340,"with_a_code_link":5193,"where_syntology_ran_a_sample":1419,"not_listed_spam_title":0,"listed":15340,"listed_where_code_ran":1419,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1210,"every_run_a_failure_of_syntologys_instrument":209,"listed_with_a_run_with_no_instrument_failure":1210,"listed_every_run_a_failure_of_syntologys_instrument":209,"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/focus","prev":"/method/focus/papers/5","next":"/method/focus/papers/7","papers":[{"paper":null,"slug":"linear-regression-with-overparameterized","title":"Linear regression with overparameterized linear neural networks: Tight upper and lower bounds for implicit $\\ell^1$-regularization","date":"2025-06-01","arxiv_id":"2506.01143","n_code_links":0,"syntology":null},{"paper":"/paper/pfmbench-protein-foundation-model-benchmark","slug":"pfmbench-protein-foundation-model-benchmark","title":"PFMBench: Protein Foundation Model Benchmark","date":"2025-06-01","arxiv_id":"2506.14796","n_code_links":1,"syntology":null},{"paper":null,"slug":"research-borderlands-analysing-writing-across","title":"Research Borderlands: Analysing Writing Across Research Cultures","date":"2025-06-01","arxiv_id":"2506.00784","n_code_links":0,"syntology":null},{"paper":null,"slug":"scalable-association-of-users-in-cf-mmimo-a","title":"Scalable Association of Users in CF-mMIMO: A Synergy of Communication, Sensing, and JCAS","date":"2025-06-01","arxiv_id":"2506.01060","n_code_links":0,"syntology":null},{"paper":null,"slug":"source-tracing-of-synthetic-speech-systems","title":"Source Tracing of Synthetic Speech Systems Through Paralinguistic Pre-Trained Representations","date":"2025-06-01","arxiv_id":"2506.01157","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-fusion-of-neural-audio-codec-based","title":"Towards Fusion of Neural Audio Codec-based Representations with Spectral for Heart Murmur Classification via Bandit-based Cross-Attention Mechanism","date":"2025-06-01","arxiv_id":"2506.01148","n_code_links":0,"syntology":null},{"paper":null,"slug":"2506-04247","title":"The GAIN Model: A Nature-Inspired Neural Network Framework Based on an Adaptation of the Izhikevich Model","date":"2025-05-31","arxiv_id":"2506.04247","n_code_links":0,"syntology":null},{"paper":null,"slug":"2506-06333","title":"Extending AALpy with Passive Learning: A Generalized State-Merging Approach","date":"2025-05-31","arxiv_id":"2506.06333","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-systematic-review-of-metaheuristics-based","title":"A Systematic Review of Metaheuristics-Based and Machine Learning-Driven Intrusion Detection Systems in IoT","date":"2025-05-31","arxiv_id":"2506.00377","n_code_links":0,"syntology":null},{"paper":null,"slug":"channel-imposed-fusion-a-simple-yet-effective","title":"Channel-Imposed Fusion: A Simple yet Effective Method for Medical Time Series Classification","date":"2025-05-31","arxiv_id":"2506.00337","n_code_links":0,"syntology":null},{"paper":"/paper/chemreservoir-an-open-source-framework-for","slug":"chemreservoir-an-open-source-framework-for","title":"ChemReservoir -- An Open-Source Framework for Chemically-Inspired Reservoir Computing","date":"2025-05-31","arxiv_id":"2506.04249","n_code_links":1,"syntology":null},{"paper":null,"slug":"codesense-a-real-world-benchmark-and-dataset","title":"CodeSense: a Real-World Benchmark and Dataset for Code Semantic Reasoning","date":"2025-05-31","arxiv_id":"2506.00750","n_code_links":0,"syntology":null},{"paper":null,"slug":"disentangling-codemixing-in-chats-the-nus-abc","title":"Disentangling Codemixing in Chats: The NUS ABC Codemixed Corpus","date":"2025-05-31","arxiv_id":"2506.00332","n_code_links":0,"syntology":null},{"paper":null,"slug":"dyna-think-synergizing-reasoning-acting-and","title":"Dyna-Think: Synergizing Reasoning, Acting, and World Model Simulation in AI Agents","date":"2025-05-31","arxiv_id":"2506.00320","n_code_links":0,"syntology":null},{"paper":null,"slug":"finbert2-a-specialized-bidirectional-encoder","title":"FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models","date":"2025-05-31","arxiv_id":"2506.06335","n_code_links":0,"syntology":null},{"paper":"/paper/idpa-instance-decoupled-prompt-attention-for","slug":"idpa-instance-decoupled-prompt-attention-for","title":"iDPA: Instance Decoupled Prompt Attention for Incremental Medical Object Detection","date":"2025-05-31","arxiv_id":"2506.00406","n_code_links":0,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"joint-activity-detection-and-channel-5","title":"Joint Activity Detection and Channel Estimation for Massive Connectivity: Where Message Passing Meets Score-Based Generative Priors","date":"2025-05-31","arxiv_id":"2506.00581","n_code_links":0,"syntology":null},{"paper":null,"slug":"length-aware-speech-translation-for-video","title":"Length Aware Speech Translation for Video Dubbing","date":"2025-05-31","arxiv_id":"2506.00740","n_code_links":0,"syntology":null},{"paper":"/paper/revisiting-llms-as-zero-shot-time-series","slug":"revisiting-llms-as-zero-shot-time-series","title":"Revisiting LLMs as Zero-Shot Time-Series Forecasters: Small Noise Can Break Large Models","date":"2025-05-31","arxiv_id":"2506.00457","n_code_links":1,"syntology":null},{"paper":"/paper/texttt-avrobustbench-benchmarking-the","slug":"texttt-avrobustbench-benchmarking-the","title":"$\\texttt{AVROBUSTBENCH}$: Benchmarking the Robustness of Audio-Visual Recognition Models at Test-Time","date":"2025-05-31","arxiv_id":"2506.00358","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 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) · 0 unverified","official":{"repos":["sarthaxxxxx/av-c-robustness-benchmark"],"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"]}}},{"paper":null,"slug":"a-mathematical-perspective-on-contrastive","title":"A Mathematical Perspective On Contrastive Learning","date":"2025-05-30","arxiv_id":"2505.24134","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-perception-based-l2-speech-intelligibility","title":"A Perception-Based L2 Speech Intelligibility Indicator: Leveraging a Rater's Shadowing and Sequence-to-sequence Voice Conversion","date":"2025-05-30","arxiv_id":"2505.24304","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-reward-driven-automated-webshell-malicious","title":"A Reward-driven Automated Webshell Malicious-code Generator for Red-teaming","date":"2025-05-30","arxiv_id":"2505.24252","n_code_links":0,"syntology":null},{"paper":"/paper/biological-pathway-guided-gene-selection","slug":"biological-pathway-guided-gene-selection","title":"Biological Pathway Guided Gene Selection Through Collaborative Reinforcement Learning","date":"2025-05-30","arxiv_id":"2505.24155","n_code_links":1,"syntology":null},{"paper":"/paper/cartan-networks-group-theoretical-hyperbolic","slug":"cartan-networks-group-theoretical-hyperbolic","title":"Cartan Networks: Group theoretical Hyperbolic Deep Learning","date":"2025-05-30","arxiv_id":"2505.24353","n_code_links":2,"syntology":null},{"paper":null,"slug":"comparing-retrieval-strategies-to-capture","title":"Comparing Retrieval Strategies to Capture Interdisciplinary Scientific Research: A Bibliometric Evaluation of the Integration of Neuroscience and Computer Science","date":"2025-05-30","arxiv_id":"2506.03187","n_code_links":0,"syntology":null},{"paper":null,"slug":"dexmachina-functional-retargeting-for","title":"DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation","date":"2025-05-30","arxiv_id":"2505.24853","n_code_links":0,"syntology":null},{"paper":null,"slug":"disentangling-granularity-an-implicit","title":"Disentangling Granularity: An Implicit Inductive Bias in Factorized VAEs","date":"2025-05-30","arxiv_id":"2505.24684","n_code_links":0,"syntology":null},{"paper":null,"slug":"domain-pre-training-impact-on-representations","title":"Domain Pre-training Impact on Representations","date":"2025-05-30","arxiv_id":"2505.24455","n_code_links":0,"syntology":null},{"paper":null,"slug":"donate-or-create-comparing-data-collection","title":"Donate or Create? Comparing Data Collection Strategies for Emotion-labeled Multimodal Social Media Posts","date":"2025-05-30","arxiv_id":"2505.24427","n_code_links":0,"syntology":null},{"paper":"/paper/draw-all-your-imagine-a-holistic-benchmark","slug":"draw-all-your-imagine-a-holistic-benchmark","title":"Draw ALL Your Imagine: A Holistic Benchmark and Agent Framework for Complex Instruction-based Image Generation","date":"2025-05-30","arxiv_id":"2505.24787","n_code_links":1,"syntology":{"ran":9,"of":9,"n_ran_checked":9,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":{"repos":["yczhou001/longbench-t2i"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/efficient-raw-image-deblurring-with-adaptive","slug":"efficient-raw-image-deblurring-with-adaptive","title":"Efficient RAW Image Deblurring with Adaptive Frequency Modulation","date":"2025-05-30","arxiv_id":"2505.24407","n_code_links":1,"syntology":null},{"paper":null,"slug":"from-macro-to-micro-probing-dataset-diversity","title":"From Macro to Micro: Probing Dataset Diversity in Language Model Fine-Tuning","date":"2025-05-30","arxiv_id":"2505.24768","n_code_links":0,"syntology":null},{"paper":"/paper/generator-based-inference-gbi","slug":"generator-based-inference-gbi","title":"Generator Based Inference (GBI)","date":"2025-05-30","arxiv_id":"2506.00119","n_code_links":1,"syntology":null},{"paper":"/paper/gridroute-a-benchmark-for-llm-based-route","slug":"gridroute-a-benchmark-for-llm-based-route","title":"GridRoute: A Benchmark for LLM-Based Route Planning with Cardinal Movement in Grid Environments","date":"2025-05-30","arxiv_id":"2505.24306","n_code_links":1,"syntology":null},{"paper":null,"slug":"impact-of-bottleneck-layers-and-skip","title":"Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders","date":"2025-05-30","arxiv_id":"2505.24668","n_code_links":0,"syntology":null},{"paper":null,"slug":"input-to-state-stability-based-chemical","title":"Input-to-state stability-based chemical reaction networks composition for molecular computations","date":"2025-05-30","arxiv_id":"2506.12056","n_code_links":0,"syntology":null},{"paper":"/paper/legaleval-q-a-new-benchmark-for-the-quality","slug":"legaleval-q-a-new-benchmark-for-the-quality","title":"LegalEval-Q: A New Benchmark for The Quality Evaluation of LLM-Generated Legal Text","date":"2025-05-30","arxiv_id":"2505.24826","n_code_links":1,"syntology":null},{"paper":"/paper/lgar-zero-shot-llm-guided-neural-ranking-for","slug":"lgar-zero-shot-llm-guided-neural-ranking-for","title":"LGAR: Zero-Shot LLM-Guided Neural Ranking for Abstract Screening in Systematic Literature Reviews","date":"2025-05-30","arxiv_id":"2505.24757","n_code_links":1,"syntology":null},{"paper":"/paper/llm-inference-enhanced-by-external-knowledge","slug":"llm-inference-enhanced-by-external-knowledge","title":"LLM Inference Enhanced by External Knowledge: A Survey","date":"2025-05-30","arxiv_id":"2505.24377","n_code_links":1,"syntology":null},{"paper":"/paper/mastering-massive-multi-task-reinforcement","slug":"mastering-massive-multi-task-reinforcement","title":"Mastering Massive Multi-Task Reinforcement Learning via Mixture-of-Expert Decision Transformer","date":"2025-05-30","arxiv_id":"2505.24378","n_code_links":1,"syntology":null},{"paper":"/paper/multihoax-a-dataset-of-multi-hop-false","slug":"multihoax-a-dataset-of-multi-hop-false","title":"MultiHoax: A Dataset of Multi-hop False-Premise Questions","date":"2025-05-30","arxiv_id":"2506.00264","n_code_links":1,"syntology":null},{"paper":"/paper/multilingual-gloss-free-sign-language","slug":"multilingual-gloss-free-sign-language","title":"Multilingual Gloss-free Sign Language Translation: Towards Building a Sign Language Foundation Model","date":"2025-05-30","arxiv_id":"2505.24355","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-the-lipschitz-continuity-of-set","title":"On the Lipschitz Continuity of Set Aggregation Functions and Neural Networks for Sets","date":"2025-05-30","arxiv_id":"2505.24403","n_code_links":0,"syntology":null},{"paper":null,"slug":"sard-a-large-scale-synthetic-arabic-ocr","title":"SARD: A Large-Scale Synthetic Arabic OCR Dataset for Book-Style Text Recognition","date":"2025-05-30","arxiv_id":"2505.24600","n_code_links":0,"syntology":null},{"paper":"/paper/segmenting-france-across-four-centuries","slug":"segmenting-france-across-four-centuries","title":"Segmenting France Across Four Centuries","date":"2025-05-30","arxiv_id":"2505.24824","n_code_links":1,"syntology":null},{"paper":"/paper/simulating-training-data-leakage-in-multiple","slug":"simulating-training-data-leakage-in-multiple","title":"Simulating Training Data Leakage in Multiple-Choice Benchmarks for LLM Evaluation","date":"2025-05-30","arxiv_id":"2505.24263","n_code_links":1,"syntology":null},{"paper":"/paper/sorce-small-object-retrieval-in-complex","slug":"sorce-small-object-retrieval-in-complex","title":"SORCE: Small Object Retrieval in Complex Environments","date":"2025-05-30","arxiv_id":"2505.24441","n_code_links":1,"syntology":null},{"paper":null,"slug":"sppsformer-high-quality-superpoint-based","title":"SPPSFormer: High-quality Superpoint-based Transformer for Roof Plane Instance Segmentation from Point Clouds","date":"2025-05-30","arxiv_id":"2505.24475","n_code_links":0,"syntology":null},{"paper":"/paper/statistical-mechanics-of-extensive-width","slug":"statistical-mechanics-of-extensive-width","title":"Statistical mechanics of extensive-width Bayesian neural networks near interpolation","date":"2025-05-30","arxiv_id":"2505.24849","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-hype-index-an-nlp-driven-measure-of","title":"The Hype Index: an NLP-driven Measure of Market News Attention","date":"2025-05-30","arxiv_id":"2506.06329","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-unified-modeling-in-federated-multi","title":"Towards Unified Modeling in Federated Multi-Task Learning via Subspace Decoupling","date":"2025-05-30","arxiv_id":"2505.24185","n_code_links":0,"syntology":null},{"paper":"/paper/trident-enhancing-large-language-model-safety","slug":"trident-enhancing-large-language-model-safety","title":"TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional Diversified Red-Teaming Data Synthesis","date":"2025-05-30","arxiv_id":"2505.24672","n_code_links":1,"syntology":null},{"paper":null,"slug":"unigeo-taming-video-diffusion-for-unified","title":"UniGeo: Taming Video Diffusion for Unified Consistent Geometry Estimation","date":"2025-05-30","arxiv_id":"2505.24521","n_code_links":0,"syntology":null},{"paper":"/paper/videocad-a-large-scale-video-dataset-for","slug":"videocad-a-large-scale-video-dataset-for","title":"VideoCAD: A Large-Scale Video Dataset for Learning UI Interactions and 3D Reasoning from CAD Software","date":"2025-05-30","arxiv_id":"2505.24838","n_code_links":1,"syntology":null},{"paper":"/paper/weakly-supervised-affordance-grounding-guided","slug":"weakly-supervised-affordance-grounding-guided","title":"Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors","date":"2025-05-30","arxiv_id":"2505.24103","n_code_links":1,"syntology":{"ran":4,"of":6,"n_ran_checked":3,"n_instrument":1,"unverified":2,"pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["woyut/wsag-plsp"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"when-harry-meets-superman-the-role-of-the","title":"When Harry Meets Superman: The Role of The Interlocutor in Persona-Based Dialogue Generation","date":"2025-05-30","arxiv_id":"2505.24613","n_code_links":0,"syntology":null},{"paper":"/paper/a-divide-and-conquer-approach-for-global","slug":"a-divide-and-conquer-approach-for-global","title":"A Divide-and-Conquer Approach for Global Orientation of Non-Watertight Scene-Level Point Clouds Using 0-1 Integer Optimization","date":"2025-05-29","arxiv_id":"2505.23469","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-unified-framework-for-human-ai","title":"A Unified Framework for Human AI Collaboration in Security Operations Centers with Trusted Autonomy","date":"2025-05-29","arxiv_id":"2505.23397","n_code_links":0,"syntology":null},{"paper":null,"slug":"accelerated-training-of-federated-learning","title":"Accelerated Training of Federated Learning via Second-Order Methods","date":"2025-05-29","arxiv_id":"2505.23588","n_code_links":0,"syntology":null},{"paper":null,"slug":"arc-argument-representation-and-coverage","title":"ARC: Argument Representation and Coverage Analysis for Zero-Shot Long Document Summarization with Instruction Following LLMs","date":"2025-05-29","arxiv_id":"2505.23654","n_code_links":0,"syntology":null},{"paper":null,"slug":"argus-vision-centric-reasoning-with-grounded","title":"Argus: Vision-Centric Reasoning with Grounded Chain-of-Thought","date":"2025-05-29","arxiv_id":"2505.23766","n_code_links":0,"syntology":null},{"paper":null,"slug":"bridging-geometric-and-semantic-foundation","title":"Bridging Geometric and Semantic Foundation Models for Generalized Monocular Depth Estimation","date":"2025-05-29","arxiv_id":"2505.23400","n_code_links":0,"syntology":null},{"paper":null,"slug":"bridging-the-gap-between-semantic-and-user","title":"Bridging the Gap Between Semantic and User Preference Spaces for Multi-modal Music Representation Learning","date":"2025-05-29","arxiv_id":"2505.23298","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-emotion-fool-anti-spoofing","title":"Can Emotion Fool Anti-spoofing?","date":"2025-05-29","arxiv_id":"2505.23962","n_code_links":0,"syntology":null},{"paper":null,"slug":"clip-ae-clip-assisted-cross-view-audio-visual","title":"CLIP-AE: CLIP-assisted Cross-view Audio-Visual Enhancement for Unsupervised Temporal Action Localization","date":"2025-05-29","arxiv_id":"2505.23524","n_code_links":0,"syntology":null},{"paper":null,"slug":"dc-eemf-pushing-depth-of-field-limit-of","title":"Dc-EEMF: Pushing depth-of-field limit of photoacoustic microscopy via decision-level constrained learning","date":"2025-05-29","arxiv_id":"2506.03181","n_code_links":0,"syntology":null},{"paper":null,"slug":"deepfiltergan-a-full-band-real-time-speech","title":"DeepFilterGAN: A Full-band Real-time Speech Enhancement System with GAN-based Stochastic Regeneration","date":"2025-05-29","arxiv_id":"2505.23515","n_code_links":0,"syntology":null},{"paper":"/paper/denoiserotator-enhance-pruning-robustness-for","slug":"denoiserotator-enhance-pruning-robustness-for","title":"DenoiseRotator: Enhance Pruning Robustness for LLMs via Importance Concentration","date":"2025-05-29","arxiv_id":"2505.23049","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"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) · 1 unverified","official":{"repos":["axel-gu/denoiserotator"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"diversity-of-transformer-layers-one-aspect-of","title":"Diversity of Transformer Layers: One Aspect of Parameter Scaling Laws","date":"2025-05-29","arxiv_id":"2505.24009","n_code_links":0,"syntology":null},{"paper":null,"slug":"dsagl-dual-stream-attention-guided-learning","title":"DSAGL: Dual-Stream Attention-Guided Learning for Weakly Supervised Whole Slide Image Classification","date":"2025-05-29","arxiv_id":"2505.23341","n_code_links":0,"syntology":null},{"paper":"/paper/dsr-bench-evaluating-the-structural-reasoning","slug":"dsr-bench-evaluating-the-structural-reasoning","title":"DSR-Bench: Evaluating the Structural Reasoning Abilities of LLMs via Data Structures","date":"2025-05-29","arxiv_id":"2505.24069","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancing-llm-based-code-generation-with","title":"Enhancing LLM-Based Code Generation with Complexity Metrics: A Feedback-Driven Approach","date":"2025-05-29","arxiv_id":"2505.23953","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-financial-tail-risk-forecasts","title":"Evaluating financial tail risk forecasts: Testing Equal Predictive Ability","date":"2025-05-29","arxiv_id":"2505.23333","n_code_links":0,"syntology":null},{"paper":null,"slug":"from-parameters-to-prompts-understanding-and","title":"From Parameters to Prompts: Understanding and Mitigating the Factuality Gap between Fine-Tuned LLMs","date":"2025-05-29","arxiv_id":"2505.23410","n_code_links":0,"syntology":null},{"paper":null,"slug":"from-theory-to-application-fine-tuning-large","title":"From Theory to Application: Fine-Tuning Large EEG Model with Real-World Stress Data","date":"2025-05-29","arxiv_id":"2505.23042","n_code_links":0,"syntology":null},{"paper":null,"slug":"geoman-temporally-consistent-human-geometry","title":"GeoMan: Temporally Consistent Human Geometry Estimation using Image-to-Video Diffusion","date":"2025-05-29","arxiv_id":"2505.23085","n_code_links":0,"syntology":null},{"paper":null,"slug":"global-optimization-of-graph-acquisition","title":"Global optimization of graph acquisition functions for neural architecture search","date":"2025-05-29","arxiv_id":"2505.23640","n_code_links":0,"syntology":null},{"paper":null,"slug":"higarment-cross-modal-harmony-based-diffusion","title":"HiGarment: Cross-modal Harmony Based Diffusion Model for Flat Sketch to Realistic Garment Image","date":"2025-05-29","arxiv_id":"2505.23186","n_code_links":0,"syntology":null},{"paper":"/paper/is-your-model-fairly-certain-uncertainty","slug":"is-your-model-fairly-certain-uncertainty","title":"Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs","date":"2025-05-29","arxiv_id":"2505.23996","n_code_links":1,"syntology":null},{"paper":"/paper/kgmark-a-diffusion-watermark-for-knowledge","slug":"kgmark-a-diffusion-watermark-for-knowledge","title":"KGMark: A Diffusion Watermark for Knowledge Graphs","date":"2025-05-29","arxiv_id":"2505.23873","n_code_links":1,"syntology":null},{"paper":null,"slug":"machine-learning-based-anomaly-detection-of","title":"Machine Learning-Based Anomaly Detection of Correlated Sensor Data: An Integrated Principal Component Analysis-Autoencoder Approach","date":"2025-05-29","arxiv_id":"2505.24044","n_code_links":0,"syntology":null},{"paper":null,"slug":"mamba-integrated-with-physics-principles","title":"Mamba Integrated with Physics Principles Masters Long-term Chaotic System Forecasting","date":"2025-05-29","arxiv_id":"2505.23863","n_code_links":0,"syntology":null},{"paper":null,"slug":"maskadapt-unsupervised-geometry-aware-domain","title":"MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking","date":"2025-05-29","arxiv_id":"2505.24026","n_code_links":0,"syntology":null},{"paper":null,"slug":"maximum-likelihood-learning-of-latent","title":"Maximum Likelihood Learning of Latent Dynamics Without Reconstruction","date":"2025-05-29","arxiv_id":"2505.23569","n_code_links":0,"syntology":null},{"paper":"/paper/mmboundary-advancing-mllm-knowledge-boundary","slug":"mmboundary-advancing-mllm-knowledge-boundary","title":"MMBoundary: Advancing MLLM Knowledge Boundary Awareness through Reasoning Step Confidence Calibration","date":"2025-05-29","arxiv_id":"2505.23224","n_code_links":1,"syntology":null},{"paper":null,"slug":"nonlinear-oscillatory-response-of-automated","title":"Nonlinear Oscillatory Response of Automated Vehicle Car-following: Theoretical Analysis with Traffic State and Control Input Limits","date":"2025-05-29","arxiv_id":"2505.24029","n_code_links":0,"syntology":null},{"paper":null,"slug":"pbebench-a-multi-step-programming-by-examples","title":"PBEBench: A Multi-Step Programming by Examples Reasoning Benchmark inspired by Historical Linguistics","date":"2025-05-29","arxiv_id":"2505.23126","n_code_links":0,"syntology":null},{"paper":null,"slug":"quantum-computing-and-artificial-intelligence","title":"Quantum computing and artificial intelligence: status and perspectives","date":"2025-05-29","arxiv_id":"2505.23860","n_code_links":0,"syntology":null},{"paper":null,"slug":"rsfake-1m-a-large-scale-dataset-for-detecting","title":"RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries","date":"2025-05-29","arxiv_id":"2505.23283","n_code_links":0,"syntology":null},{"paper":null,"slug":"sc-lora-balancing-efficient-fine-tuning-and","title":"SC-LoRA: Balancing Efficient Fine-tuning and Knowledge Preservation via Subspace-Constrained LoRA","date":"2025-05-29","arxiv_id":"2505.23724","n_code_links":0,"syntology":null},{"paper":null,"slug":"seg-sr-integrating-semantic-knowledge-into","title":"SeG-SR: Integrating Semantic Knowledge into Remote Sensing Image Super-Resolution via Vision-Language Model","date":"2025-05-29","arxiv_id":"2505.23010","n_code_links":0,"syntology":null},{"paper":null,"slug":"semantics-aware-human-motion-generation-from","title":"Semantics-Aware Human Motion Generation from Audio Instructions","date":"2025-05-29","arxiv_id":"2505.23465","n_code_links":0,"syntology":null},{"paper":"/paper/sns-bench-vl-benchmarking-multimodal-large","slug":"sns-bench-vl-benchmarking-multimodal-large","title":"SNS-Bench-VL: Benchmarking Multimodal Large Language Models in Social Networking Services","date":"2025-05-29","arxiv_id":"2505.23065","n_code_links":1,"syntology":null},{"paper":null,"slug":"socratic-prmbench-benchmarking-process-reward","title":"Socratic-PRMBench: Benchmarking Process Reward Models with Systematic Reasoning Patterns","date":"2025-05-29","arxiv_id":"2505.23474","n_code_links":0,"syntology":null},{"paper":"/paper/the-panaceas-for-improving-low-rank","slug":"the-panaceas-for-improving-low-rank","title":"The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning","date":"2025-05-29","arxiv_id":"2505.23176","n_code_links":1,"syntology":{"ran":6,"of":9,"n_ran_checked":3,"n_instrument":3,"unverified":3,"pointer_only":9,"phrase":"6 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","official":{"repos":["leopold1423/fedmud-icml25"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/thinkgeo-evaluating-tool-augmented-agents-for","slug":"thinkgeo-evaluating-tool-augmented-agents-for","title":"ThinkGeo: Evaluating Tool-Augmented Agents for Remote Sensing Tasks","date":"2025-05-29","arxiv_id":"2505.23752","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":["mbzuai-oryx/thinkgeo"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"urbancraft-urban-view-extrapolation-via","title":"UrbanCraft: Urban View Extrapolation via Hierarchical Sem-Geometric Priors","date":"2025-05-29","arxiv_id":"2505.23434","n_code_links":0,"syntology":null},{"paper":"/paper/vf-eval-evaluating-multimodal-llms-for","slug":"vf-eval-evaluating-multimodal-llms-for","title":"VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos","date":"2025-05-29","arxiv_id":"2505.23693","n_code_links":1,"syntology":null},{"paper":null,"slug":"3dllm-mem-long-term-spatial-temporal-memory","title":"3DLLM-Mem: Long-Term Spatial-Temporal Memory for Embodied 3D Large Language Model","date":"2025-05-28","arxiv_id":"2505.22657","n_code_links":0,"syntology":null}],"record_sha256":"118e564ceea898fae1ce07dcdbdbfd4cac685e6c1eb54eaeb9eadb5398ba33e4","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}