{"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/align/papers/27","list_of":"/method/align","method":"ALIGN","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":27,"pages_in_order":56,"rows_per_page":100,"rows":[2601,2700],"of":5524,"counts":{"archive_papers_tagged":5527,"with_a_code_link":2162,"where_syntology_ran_a_sample":726,"not_listed_spam_title":3,"listed":5524,"listed_where_code_ran":726,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":628,"every_run_a_failure_of_syntologys_instrument":98,"listed_with_a_run_with_no_instrument_failure":628,"listed_every_run_a_failure_of_syntologys_instrument":98,"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/align","prev":"/method/align/papers/26","next":"/method/align/papers/28","papers":[{"paper":"/paper/simplifying-the-theory-on-over-smoothing","slug":"simplifying-the-theory-on-over-smoothing","title":"Simplifying the Theory on Over-Smoothing","date":"2024-07-16","arxiv_id":"2407.11876","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":4,"n_instrument":0,"unverified":0,"pointer_only":4,"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) · 0 unverified","official":{"repos":["roth-andreas/simplifying-over-smoothing"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"arena-learning-build-data-flywheel-for-llms","title":"Arena Learning: Build Data Flywheel for LLMs Post-training via Simulated Chatbot Arena","date":"2024-07-15","arxiv_id":"2407.10627","n_code_links":0,"syntology":null},{"paper":null,"slug":"clave-an-adaptive-framework-for-evaluating","title":"CLAVE: An Adaptive Framework for Evaluating Values of LLM Generated Responses","date":"2024-07-15","arxiv_id":"2407.10725","n_code_links":0,"syntology":null},{"paper":"/paper/idol-unified-dual-modal-latent-diffusion-for","slug":"idol-unified-dual-modal-latent-diffusion-for","title":"IDOL: Unified Dual-Modal Latent Diffusion for Human-Centric Joint Video-Depth Generation","date":"2024-07-15","arxiv_id":"2407.10937","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-to-unlearn-for-robust-machine","title":"Learning to Unlearn for Robust Machine Unlearning","date":"2024-07-15","arxiv_id":"2407.10494","n_code_links":0,"syntology":null},{"paper":"/paper/novicode-generating-programs-from-natural","slug":"novicode-generating-programs-from-natural","title":"NoviCode: Generating Programs from Natural Language Utterances by Novices","date":"2024-07-15","arxiv_id":"2407.10626","n_code_links":1,"syntology":null},{"paper":"/paper/omnigenome-aligning-rna-sequences-with","slug":"omnigenome-aligning-rna-sequences-with","title":"Bridging Sequence-Structure Alignment in RNA Foundation Models","date":"2024-07-15","arxiv_id":"2407.11242","n_code_links":1,"syntology":null},{"paper":"/paper/ovlw-detr-open-vocabulary-light-weighted","slug":"ovlw-detr-open-vocabulary-light-weighted","title":"OVLW-DETR: Open-Vocabulary Light-Weighted Detection Transformer","date":"2024-07-15","arxiv_id":"2407.10655","n_code_links":1,"syntology":null},{"paper":null,"slug":"rotationally-invariant-latent-distances-for","title":"Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances","date":"2024-07-15","arxiv_id":"2407.10844","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-granularity-semantic-revision-for-large","title":"Multi-Granularity Semantic Revision for Large Language Model Distillation","date":"2024-07-14","arxiv_id":"2407.10068","n_code_links":0,"syntology":null},{"paper":null,"slug":"what-makes-and-breaks-safety-fine-tuning","title":"What Makes and Breaks Safety Fine-tuning? A Mechanistic Study","date":"2024-07-14","arxiv_id":"2407.10264","n_code_links":0,"syntology":null},{"paper":"/paper/3d-weakly-supervised-semantic-segmentation","slug":"3d-weakly-supervised-semantic-segmentation","title":"3D Weakly Supervised Semantic Segmentation with 2D Vision-Language Guidance","date":"2024-07-13","arxiv_id":"2407.09826","n_code_links":1,"syntology":null},{"paper":"/paper/diffrect-latent-diffusion-label-rectification","slug":"diffrect-latent-diffusion-label-rectification","title":"DiffRect: Latent Diffusion Label Rectification for Semi-supervised Medical Image Segmentation","date":"2024-07-13","arxiv_id":"2407.09918","n_code_links":1,"syntology":null},{"paper":"/paper/aligning-diffusion-behaviors-with-q-functions","slug":"aligning-diffusion-behaviors-with-q-functions","title":"Aligning Diffusion Behaviors with Q-functions for Efficient Continuous Control","date":"2024-07-12","arxiv_id":"2407.09024","n_code_links":1,"syntology":{"ran":8,"of":12,"n_ran_checked":4,"n_instrument":4,"unverified":4,"pointer_only":12,"phrase":"8 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; 4 where Syntology's instrument failed) · 4 unverified","official":{"repos":["thu-ml/efficient-diffusion-alignment"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"enhancing-emotion-recognition-in-incomplete","title":"Enhancing Emotion Recognition in Incomplete Data: A Novel Cross-Modal Alignment, Reconstruction, and Refinement Framework","date":"2024-07-12","arxiv_id":"2407.09029","n_code_links":0,"syntology":null},{"paper":null,"slug":"new-desiderata-for-direct-preference","title":"New Desiderata for Direct Preference Optimization","date":"2024-07-12","arxiv_id":"2407.09072","n_code_links":0,"syntology":null},{"paper":null,"slug":"pronunciation-assessment-with-multi-modal","title":"Pronunciation Assessment with Multi-modal Large Language Models","date":"2024-07-12","arxiv_id":"2407.09209","n_code_links":0,"syntology":null},{"paper":null,"slug":"tcan-animating-human-images-with-temporally","title":"TCAN: Animating Human Images with Temporally Consistent Pose Guidance using Diffusion Models","date":"2024-07-12","arxiv_id":"2407.09012","n_code_links":0,"syntology":null},{"paper":null,"slug":"15m-multimodal-facial-image-text-dataset","title":"15M Multimodal Facial Image-Text Dataset","date":"2024-07-11","arxiv_id":"2407.08515","n_code_links":0,"syntology":null},{"paper":"/paper/addressclip-empowering-vision-language-models","slug":"addressclip-empowering-vision-language-models","title":"AddressCLIP: Empowering Vision-Language Models for City-wide Image Address Localization","date":"2024-07-11","arxiv_id":"2407.08156","n_code_links":1,"syntology":null},{"paper":null,"slug":"bootstrapping-vision-language-models-for-self","title":"Bootstrapping Vision-language Models for Self-supervised Remote Physiological Measurement","date":"2024-07-11","arxiv_id":"2407.08507","n_code_links":0,"syntology":null},{"paper":null,"slug":"chromosomal-structural-abnormality-diagnosis","title":"Chromosomal Structural Abnormality Diagnosis by Homologous Similarity","date":"2024-07-11","arxiv_id":"2407.08204","n_code_links":0,"syntology":null},{"paper":null,"slug":"explainability-of-sub-field-level-crop-yield","title":"Explainability of Sub-Field Level Crop Yield Prediction using Remote Sensing","date":"2024-07-11","arxiv_id":"2407.08274","n_code_links":0,"syntology":null},{"paper":null,"slug":"feature-diversification-and-adaptation-for","title":"Feature Diversification and Adaptation for Federated Domain Generalization","date":"2024-07-11","arxiv_id":"2407.08245","n_code_links":0,"syntology":null},{"paper":null,"slug":"fine-tuning-stable-diffusion-xl-for-stylistic","title":"Fine-Tuning Stable Diffusion XL for Stylistic Icon Generation: A Comparison of Caption Size","date":"2024-07-11","arxiv_id":"2407.08513","n_code_links":0,"syntology":null},{"paper":"/paper/gta-a-benchmark-for-general-tool-agents","slug":"gta-a-benchmark-for-general-tool-agents","title":"GTA: A Benchmark for General Tool Agents","date":"2024-07-11","arxiv_id":"2407.08713","n_code_links":1,"syntology":null},{"paper":"/paper/mavis-mathematical-visual-instruction-tuning","slug":"mavis-mathematical-visual-instruction-tuning","title":"MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data Engine","date":"2024-07-11","arxiv_id":"2407.08739","n_code_links":3,"syntology":null},{"paper":null,"slug":"the-career-interests-of-large-language-models","title":"The Career Interests of Large Language Models","date":"2024-07-11","arxiv_id":"2407.08564","n_code_links":0,"syntology":null},{"paper":"/paper/a-machine-learning-and-explainable-ai","slug":"a-machine-learning-and-explainable-ai","title":"A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery","date":"2024-07-10","arxiv_id":"2407.18935","n_code_links":1,"syntology":null},{"paper":null,"slug":"cross-domain-object-detection-via-multi","title":"Cross Domain Object Detection via Multi-Granularity Confidence Alignment based Mean Teacher","date":"2024-07-10","arxiv_id":"2407.07780","n_code_links":0,"syntology":null},{"paper":null,"slug":"deformable-feature-alignment-and-refinement","title":"Deformable Feature Alignment and Refinement for Moving Infrared Dim-small Target Detection","date":"2024-07-10","arxiv_id":"2407.07289","n_code_links":0,"syntology":null},{"paper":null,"slug":"multilingual-blending-llm-safety-alignment","title":"Multilingual Blending: LLM Safety Alignment Evaluation with Language Mixture","date":"2024-07-10","arxiv_id":"2407.07342","n_code_links":0,"syntology":null},{"paper":null,"slug":"raising-the-ceiling-conflict-free-local","title":"Raising the Ceiling: Conflict-Free Local Feature Matching with Dynamic View Switching","date":"2024-07-10","arxiv_id":"2407.07789","n_code_links":0,"syntology":null},{"paper":null,"slug":"video-in-context-learning","title":"Video In-context Learning","date":"2024-07-10","arxiv_id":"2407.07356","n_code_links":0,"syntology":null},{"paper":null,"slug":"ceia-clip-based-event-image-alignment-for","title":"CEIA: CLIP-Based Event-Image Alignment for Open-World Event-Based Understanding","date":"2024-07-09","arxiv_id":"2407.06611","n_code_links":0,"syntology":null},{"paper":null,"slug":"historical-review-of-variants-of-informal","title":"Historical Review of Variants of Informal Semantics for Logic Programs under Answer Set Semantics: GL'88, GL'91, GK'14, D-V'12","date":"2024-07-09","arxiv_id":"2407.06814","n_code_links":0,"syntology":null},{"paper":null,"slug":"ada-adapter-fast-few-shot-style","title":"Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder","date":"2024-07-08","arxiv_id":"2407.05552","n_code_links":0,"syntology":null},{"paper":"/paper/anole-an-open-autoregressive-native-large","slug":"anole-an-open-autoregressive-native-large","title":"ANOLE: An Open, Autoregressive, Native Large Multimodal Models for Interleaved Image-Text Generation","date":"2024-07-08","arxiv_id":"2407.06135","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancing-vision-language-models-with-scene","title":"Enhancing Vision-Language Models with Scene Graphs for Traffic Accident Understanding","date":"2024-07-08","arxiv_id":"2407.05910","n_code_links":0,"syntology":null},{"paper":null,"slug":"exposing-privacy-gaps-membership-inference","title":"Exposing Privacy Gaps: Membership Inference Attack on Preference Data for LLM Alignment","date":"2024-07-08","arxiv_id":"2407.06443","n_code_links":0,"syntology":null},{"paper":"/paper/generation-and-de-identification-of-indian","slug":"generation-and-de-identification-of-indian","title":"Generation and De-Identification of Indian Clinical Discharge Summaries using LLMs","date":"2024-07-08","arxiv_id":"2407.05887","n_code_links":1,"syntology":null},{"paper":"/paper/leveraging-transformers-for-weakly-supervised","slug":"leveraging-transformers-for-weakly-supervised","title":"Leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videos","date":"2024-07-08","arxiv_id":"2407.06018","n_code_links":1,"syntology":null},{"paper":"/paper/link-representation-learning-for","slug":"link-representation-learning-for","title":"Link Representation Learning for Probabilistic Travel Time Estimation","date":"2024-07-08","arxiv_id":"2407.05895","n_code_links":2,"syntology":null},{"paper":"/paper/transma-an-explainable-multi-modal-deep","slug":"transma-an-explainable-multi-modal-deep","title":"TransMA: an explainable multi-modal deep learning model for predicting properties of ionizable lipid nanoparticles in mRNA delivery","date":"2024-07-08","arxiv_id":"2407.05736","n_code_links":1,"syntology":null},{"paper":null,"slug":"beyond-binary-gender-labels-revealing-gender","title":"Beyond Binary Gender Labels: Revealing Gender Biases in LLMs through Gender-Neutral Name Predictions","date":"2024-07-07","arxiv_id":"2407.05271","n_code_links":0,"syntology":null},{"paper":"/paper/edge-guided-and-cross-scale-feature-fusion","slug":"edge-guided-and-cross-scale-feature-fusion","title":"Edge-guided and Cross-scale Feature Fusion Network for Efficient Multi-contrast MRI Super-Resolution","date":"2024-07-07","arxiv_id":"2407.05307","n_code_links":1,"syntology":null},{"paper":null,"slug":"unlocking-textual-and-visual-wisdom-open","title":"Unlocking Textual and Visual Wisdom: Open-Vocabulary 3D Object Detection Enhanced by Comprehensive Guidance from Text and Image","date":"2024-07-07","arxiv_id":"2407.05256","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-study-of-test-time-contrastive-concepts-for","title":"Test-time Contrastive Concepts for Open-world Semantic Segmentation","date":"2024-07-06","arxiv_id":"2407.05061","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhance-the-robustness-of-text-centric","title":"Enhance the Robustness of Text-Centric Multimodal Alignments","date":"2024-07-06","arxiv_id":"2407.05036","n_code_links":0,"syntology":null},{"paper":null,"slug":"helios-an-extremely-low-power-event-based","title":"Helios: An extremely low power event-based gesture recognition for always-on smart eyewear","date":"2024-07-06","arxiv_id":"2407.05206","n_code_links":0,"syntology":null},{"paper":null,"slug":"incremental-multiview-point-cloud","title":"Incremental Multiview Point Cloud Registration","date":"2024-07-06","arxiv_id":"2407.05021","n_code_links":0,"syntology":null},{"paper":"/paper/rethinking-the-effectiveness-of-graph","slug":"rethinking-the-effectiveness-of-graph","title":"Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNs","date":"2024-07-06","arxiv_id":"2407.04999","n_code_links":1,"syntology":{"ran":11,"of":14,"n_ran_checked":9,"n_instrument":2,"unverified":3,"pointer_only":1,"phrase":"11 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; 2 where Syntology's instrument failed) · 3 unverified","official":{"repos":["ICLab4DL/GNNBenchEffectiveness"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/rule-reliable-multimodal-rag-for-factuality","slug":"rule-reliable-multimodal-rag-for-factuality","title":"RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models","date":"2024-07-06","arxiv_id":"2407.05131","n_code_links":1,"syntology":{"ran":5,"of":5,"n_ran_checked":1,"n_instrument":4,"unverified":0,"pointer_only":0,"phrase":"5 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; 4 where Syntology's instrument failed) · 0 unverified","official":{"repos":["richard-peng-xia/rule"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"aligning-model-evaluations-with-human","title":"Aligning Model Evaluations with Human Preferences: Mitigating Token Count Bias in Language Model Assessments","date":"2024-07-05","arxiv_id":"2407.12847","n_code_links":0,"syntology":null},{"paper":null,"slug":"dude-dual-distribution-aware-context-prompt","title":"Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model","date":"2024-07-05","arxiv_id":"2407.04489","n_code_links":0,"syntology":null},{"paper":"/paper/mj-bench-is-your-multimodal-reward-model","slug":"mj-bench-is-your-multimodal-reward-model","title":"MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?","date":"2024-07-05","arxiv_id":"2407.04842","n_code_links":1,"syntology":{"ran":11,"of":14,"n_ran_checked":11,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["MJ-Bench/MJ-Bench"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"vrsd-rethinking-similarity-and-diversity-for","title":"VRSD: Rethinking Similarity and Diversity for Retrieval in Large Language Models","date":"2024-07-05","arxiv_id":"2407.04573","n_code_links":0,"syntology":null},{"paper":"/paper/dgr-mil-exploring-diverse-global","slug":"dgr-mil-exploring-diverse-global","title":"DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification","date":"2024-07-04","arxiv_id":"2407.03575","n_code_links":1,"syntology":{"ran":3,"of":9,"n_ran_checked":1,"n_instrument":2,"unverified":6,"pointer_only":0,"phrase":"3 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; 2 where Syntology's instrument failed) · 6 unverified","official":{"repos":["chongqingnosubway/dgr-mil"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":"/paper/meta-optimized-angular-margin-contrastive","slug":"meta-optimized-angular-margin-contrastive","title":"MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation Learning","date":"2024-07-04","arxiv_id":"2407.03788","n_code_links":1,"syntology":null},{"paper":"/paper/the-mysterious-case-of-neuron-1512-injectable","slug":"the-mysterious-case-of-neuron-1512-injectable","title":"The Mysterious Case of Neuron 1512: Injectable Realignment Architectures Reveal Internal Characteristics of Meta's Llama 2 Model","date":"2024-07-04","arxiv_id":"2407.03621","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-case-study-on-context-aware-neural-machine","title":"A Case Study on Context-Aware Neural Machine Translation with Multi-Task Learning","date":"2024-07-03","arxiv_id":"2407.03076","n_code_links":0,"syntology":null},{"paper":null,"slug":"cogergllm-exploring-large-language-model","title":"CogErgLLM: Exploring Large Language Model Systems Design Perspective Using Cognitive Ergonomics","date":"2024-07-03","arxiv_id":"2407.02885","n_code_links":0,"syntology":null},{"paper":null,"slug":"large-language-models-physics-based-modeling","title":"Large language models, physics-based modeling, experimental measurements: the trinity of data-scarce learning of polymer properties","date":"2024-07-03","arxiv_id":"2407.02770","n_code_links":0,"syntology":null},{"paper":null,"slug":"mudit-musit-alignment-with-colloquial","title":"MuDiT & MuSiT: Alignment with Colloquial Expression in Description-to-Song Generation","date":"2024-07-03","arxiv_id":"2407.03188","n_code_links":0,"syntology":null},{"paper":"/paper/on-the-client-preference-of-llm-fine-tuning","slug":"on-the-client-preference-of-llm-fine-tuning","title":"Towards Federated RLHF with Aggregated Client Preference for LLMs","date":"2024-07-03","arxiv_id":"2407.03038","n_code_links":0,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"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":null}},{"paper":null,"slug":"policymaker-meetings-as-heteroscedasticity","title":"Wild inference for wild SVARs with application to heteroscedasticity-based IV","date":"2024-07-03","arxiv_id":"2407.03265","n_code_links":0,"syntology":null},{"paper":null,"slug":"visual-grounding-with-attention-driven","title":"Visual Grounding with Attention-Driven Constraint Balancing","date":"2024-07-03","arxiv_id":"2407.03243","n_code_links":0,"syntology":null},{"paper":null,"slug":"aligning-human-motion-generation-with-human","title":"Aligning Human Motion Generation with Human Perceptions","date":"2024-07-02","arxiv_id":"2407.02272","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-end-to-end-speech-summarization-using","title":"An End-to-End Speech Summarization Using Large Language Model","date":"2024-07-02","arxiv_id":"2407.02005","n_code_links":0,"syntology":null},{"paper":"/paper/axial-attention-based-explainability-for","slug":"axial-attention-based-explainability-for","title":"AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans","date":"2024-07-02","arxiv_id":"2407.02418","n_code_links":1,"syntology":null},{"paper":null,"slug":"camera-lidar-cross-modality-gait-recognition","title":"Camera-LiDAR Cross-modality Gait Recognition","date":"2024-07-02","arxiv_id":"2407.02038","n_code_links":0,"syntology":null},{"paper":null,"slug":"cfinbench-a-comprehensive-chinese-financial","title":"CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models","date":"2024-07-02","arxiv_id":"2407.02301","n_code_links":0,"syntology":null},{"paper":"/paper/glyphdraw2-automatic-generation-of-complex","slug":"glyphdraw2-automatic-generation-of-complex","title":"GlyphDraw2: Automatic Generation of Complex Glyph Posters with Diffusion Models and Large Language Models","date":"2024-07-02","arxiv_id":"2407.02252","n_code_links":1,"syntology":null},{"paper":null,"slug":"investigating-event-based-cameras-for-video","title":"Investigating Event-Based Cameras for Video Frame Interpolation in Sports","date":"2024-07-02","arxiv_id":"2407.02370","n_code_links":0,"syntology":null},{"paper":null,"slug":"sadl-an-effective-in-context-learning-method","title":"SADL: An Effective In-Context Learning Method for Compositional Visual QA","date":"2024-07-02","arxiv_id":"2407.01983","n_code_links":0,"syntology":null},{"paper":"/paper/scaledreamer-scalable-text-to-3d-synthesis","slug":"scaledreamer-scalable-text-to-3d-synthesis","title":"ScaleDreamer: Scalable Text-to-3D Synthesis with Asynchronous Score Distillation","date":"2024-07-02","arxiv_id":"2407.02040","n_code_links":1,"syntology":{"ran":10,"of":10,"n_ran_checked":9,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 4 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["theericma/scaledreamer"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"spectral-graph-reasoning-network-for","title":"Spectral Graph Reasoning Network for Hyperspectral Image Classification","date":"2024-07-02","arxiv_id":"2407.02647","n_code_links":0,"syntology":null},{"paper":null,"slug":"understanding-alignment-in-multimodal-llms-a","title":"Understanding Alignment in Multimodal LLMs: A Comprehensive Study","date":"2024-07-02","arxiv_id":"2407.02477","n_code_links":0,"syntology":null},{"paper":null,"slug":"unleash-the-power-of-local-representations","title":"Unleash the Power of Local Representations for Few-Shot Classification","date":"2024-07-02","arxiv_id":"2407.01967","n_code_links":0,"syntology":null},{"paper":null,"slug":"wtu-eval-a-whether-or-not-tool-usage","title":"WTU-EVAL: A Whether-or-Not Tool Usage Evaluation Benchmark for Large Language Models","date":"2024-07-02","arxiv_id":"2407.12823","n_code_links":0,"syntology":null},{"paper":"/paper/aligning-target-aware-molecule-diffusion","slug":"aligning-target-aware-molecule-diffusion","title":"Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization","date":"2024-07-01","arxiv_id":"2407.01648","n_code_links":1,"syntology":{"ran":7,"of":8,"n_ran_checked":6,"n_instrument":1,"unverified":1,"pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 2 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["minkaixu/alidiff"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"cross-modal-attention-alignment-network-with","title":"Cross-Modal Attention Alignment Network with Auxiliary Text Description for zero-shot sketch-based image retrieval","date":"2024-07-01","arxiv_id":"2407.00979","n_code_links":0,"syntology":null},{"paper":null,"slug":"dabit-depth-and-blur-informed-transformer-for","title":"DaBiT: Depth and Blur informed Transformer for Joint Refocusing and Super-Resolution","date":"2024-07-01","arxiv_id":"2407.01230","n_code_links":0,"syntology":null},{"paper":"/paper/entropic-optimal-transport-eigenmaps-for","slug":"entropic-optimal-transport-eigenmaps-for","title":"Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional Datasets","date":"2024-07-01","arxiv_id":"2407.01718","n_code_links":1,"syntology":null},{"paper":"/paper/the-house-always-wins-a-framework-for","slug":"the-house-always-wins-a-framework-for","title":"View From Above: A Framework for Evaluating Distribution Shifts in Model Behavior","date":"2024-07-01","arxiv_id":"2407.00948","n_code_links":1,"syntology":{"ran":5,"of":5,"n_ran_checked":5,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":{"repos":["Bluefin-Tuna/ApartResearch"],"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":null,"slug":"unaligning-everything-or-aligning-any-text-to","title":"Unaligning Everything: Or Aligning Any Text to Any Image in Multimodal Models","date":"2024-07-01","arxiv_id":"2407.01157","n_code_links":0,"syntology":null},{"paper":"/paper/zeroddi-a-zero-shot-drug-drug-interaction","slug":"zeroddi-a-zero-shot-drug-drug-interaction","title":"ZeroDDI: A Zero-Shot Drug-Drug Interaction Event Prediction Method with Semantic Enhanced Learning and Dual-Modal Uniform Alignment","date":"2024-07-01","arxiv_id":"2407.00891","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"pointer_only":2,"phrase":"1 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; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["wzy-sarah/zeroddi"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-deep-generative-framework-for-joint","title":"A Deep Generative Framework for Joint Households and Individuals Population Synthesis","date":"2024-06-30","arxiv_id":"2407.01643","n_code_links":0,"syntology":null},{"paper":null,"slug":"bapo-base-anchored-preference-optimization","title":"BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization","date":"2024-06-30","arxiv_id":"2407.00693","n_code_links":0,"syntology":null},{"paper":null,"slug":"causality-driven-sequence-segmentation-for","title":"Causality-driven Sequence Segmentation for Enhancing Multiphase Industrial Process Data Analysis and Soft Sensing","date":"2024-06-30","arxiv_id":"2407.05954","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-real-time-music-accompaniment","title":"Improving Real-Time Music Accompaniment Separation with MMDenseNet","date":"2024-06-30","arxiv_id":"2407.00657","n_code_links":0,"syntology":null},{"paper":"/paper/step-controlled-dpo-leveraging-stepwise-error","slug":"step-controlled-dpo-leveraging-stepwise-error","title":"Step-Controlled DPO: Leveraging Stepwise Error for Enhanced Mathematical Reasoning","date":"2024-06-30","arxiv_id":"2407.00782","n_code_links":1,"syntology":{"ran":6,"of":6,"n_ran_checked":5,"n_instrument":1,"unverified":0,"pointer_only":6,"phrase":"6 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["mathllm/Step-Controlled_DPO"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/revisiting-constant-negative-rewards-for-goal","slug":"revisiting-constant-negative-rewards-for-goal","title":"Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning","date":"2024-06-29","arxiv_id":"2407.00324","n_code_links":1,"syntology":null},{"paper":null,"slug":"evaluating-human-alignment-and-model","title":"Evaluating Human Alignment and Model Faithfulness of LLM Rationale","date":"2024-06-28","arxiv_id":"2407.00219","n_code_links":0,"syntology":null},{"paper":"/paper/gm-df-generalized-multi-scenario-deepfake","slug":"gm-df-generalized-multi-scenario-deepfake","title":"GM-DF: Generalized Multi-Scenario Deepfake Detection","date":"2024-06-28","arxiv_id":"2406.20078","n_code_links":1,"syntology":null},{"paper":null,"slug":"metadesigner-advancing-artistic-typography","title":"MetaDesigner: Advancing Artistic Typography Through AI-Driven, User-Centric, and Multilingual WordArt Synthesis","date":"2024-06-28","arxiv_id":"2406.19859","n_code_links":0,"syntology":null},{"paper":"/paper/parallax-tolerant-image-stitching-via","slug":"parallax-tolerant-image-stitching-via","title":"Parallax-tolerant Image Stitching via Segmentation-guided Multi-homography Warping","date":"2024-06-28","arxiv_id":"2406.19922","n_code_links":1,"syntology":null},{"paper":null,"slug":"simulating-financial-market-via-large","title":"Simulating Financial Market via Large Language Model based Agents","date":"2024-06-28","arxiv_id":"2406.19966","n_code_links":0,"syntology":null},{"paper":"/paper/stllava-med-self-training-large-language-and","slug":"stllava-med-self-training-large-language-and","title":"STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical Question-Answering","date":"2024-06-28","arxiv_id":"2406.19973","n_code_links":1,"syntology":{"ran":9,"of":19,"n_ran_checked":5,"n_instrument":4,"unverified":10,"pointer_only":0,"phrase":"9 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 4 where Syntology's instrument failed) · 10 unverified","official":{"repos":["heliossun/stllava-med"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":10,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"aligning-teacher-with-student-preferences-for","title":"Aligning Teacher with Student Preferences for Tailored Training Data Generation","date":"2024-06-27","arxiv_id":"2406.19227","n_code_links":0,"syntology":null}],"record_sha256":"69334df4c57778ffceb20efc4829e8e08d75b1a7f539c7e8a82d0b6b99e0fca7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}