{"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/23","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":23,"pages_in_order":56,"rows_per_page":100,"rows":[2201,2300],"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/22","next":"/method/align/papers/24","papers":[{"paper":"/paper/broadcast-product-shape-aligned-element-wise","slug":"broadcast-product-shape-aligned-element-wise","title":"Broadcast Product: Shape-aligned Element-wise Multiplication and Beyond","date":"2024-09-26","arxiv_id":"2409.17502","n_code_links":0,"syntology":{"ran":8,"of":14,"n_ran_checked":8,"n_instrument":0,"unverified":6,"pointer_only":14,"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) · 6 unverified","official":null}},{"paper":null,"slug":"enhancing-tourism-recommender-systems-for","title":"Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generation","date":"2024-09-26","arxiv_id":"2409.18003","n_code_links":0,"syntology":null},{"paper":null,"slug":"inference-time-language-model-alignment-via","title":"Inference-Time Language Model Alignment via Integrated Value Guidance","date":"2024-09-26","arxiv_id":"2409.17819","n_code_links":0,"syntology":null},{"paper":"/paper/logic-of-thought-injecting-logic-into","slug":"logic-of-thought-injecting-logic-into","title":"Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models","date":"2024-09-26","arxiv_id":"2409.17539","n_code_links":1,"syntology":null},{"paper":null,"slug":"physics-aligned-schrodinger-bridge","title":"Physics-aligned Schrödinger bridge","date":"2024-09-26","arxiv_id":"2409.17825","n_code_links":0,"syntology":null},{"paper":null,"slug":"ta-cleaner-a-fine-grained-text-alignment","title":"CleanerCLIP: Fine-grained Counterfactual Semantic Augmentation for Backdoor Defense in Contrastive Learning","date":"2024-09-26","arxiv_id":"2409.17601","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptive-self-supervised-learning-strategies","title":"Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalization","date":"2024-09-25","arxiv_id":"2409.16973","n_code_links":0,"syntology":null},{"paper":null,"slug":"characterizing-stable-regions-in-the-residual","title":"Characterizing stable regions in the residual stream of LLMs","date":"2024-09-25","arxiv_id":"2409.17113","n_code_links":0,"syntology":null},{"paper":"/paper/controlcity-a-multimodal-diffusion-model","slug":"controlcity-a-multimodal-diffusion-model","title":"ControlCity: A Multimodal Diffusion Model Based Approach for Accurate Geospatial Data Generation and Urban Morphology Analysis","date":"2024-09-25","arxiv_id":"2409.17049","n_code_links":1,"syntology":null},{"paper":"/paper/graphlora-structure-aware-contrastive-low","slug":"graphlora-structure-aware-contrastive-low","title":"GraphLoRA: Structure-Aware Contrastive Low-Rank Adaptation for Cross-Graph Transfer Learning","date":"2024-09-25","arxiv_id":"2409.16670","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":2,"n_instrument":1,"unverified":1,"pointer_only":4,"phrase":"3 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; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["allminerlab/graphlora"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"mt2kd-towards-a-general-purpose-encoder-for","title":"MT2KD: Towards A General-Purpose Encoder for Speech, Speaker, and Audio Events","date":"2024-09-25","arxiv_id":"2409.17010","n_code_links":0,"syntology":null},{"paper":null,"slug":"sociotechnical-approach-to-enterprise","title":"Sociotechnical Approach to Enterprise Generative Artificial Intelligence (E-GenAI)","date":"2024-09-25","arxiv_id":"2409.17408","n_code_links":0,"syntology":null},{"paper":null,"slug":"finetuning-llms-for-comparative-assessment","title":"Finetuning LLMs for Comparative Assessment Tasks","date":"2024-09-24","arxiv_id":"2409.15979","n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-ai-driven-forecasting-of-oil","title":"Generative AI-driven forecasting of oil production","date":"2024-09-24","arxiv_id":"2409.16482","n_code_links":0,"syntology":null},{"paper":"/paper/leveraging-estimated-transferability-over","slug":"leveraging-estimated-transferability-over","title":"Leveraging Estimated Transferability Over Human Intuition for Model Selection in Text Ranking","date":"2024-09-24","arxiv_id":"2409.16198","n_code_links":1,"syntology":null},{"paper":null,"slug":"planning-in-the-dark-llm-symbolic-planning","title":"Planning in the Dark: LLM-Symbolic Planning Pipeline without Experts","date":"2024-09-24","arxiv_id":"2409.15915","n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-text-representation-learning-via","title":"Unsupervised Text Representation Learning via Instruction-Tuning for Zero-Shot Dense Retrieval","date":"2024-09-24","arxiv_id":"2409.16497","n_code_links":0,"syntology":null},{"paper":"/paper/llamapartialspoof-an-llm-driven-fake-speech","slug":"llamapartialspoof-an-llm-driven-fake-speech","title":"LlamaPartialSpoof: An LLM-Driven Fake Speech Dataset Simulating Disinformation Generation","date":"2024-09-23","arxiv_id":"2409.14743","n_code_links":1,"syntology":null},{"paper":null,"slug":"robust-training-objectives-improve-embedding","title":"Robust Training Objectives Improve Embedding-based Retrieval in Industrial Recommendation Systems","date":"2024-09-23","arxiv_id":"2409.14682","n_code_links":0,"syntology":null},{"paper":null,"slug":"speechworthy-instruction-tuned-language","title":"Speechworthy Instruction-tuned Language Models","date":"2024-09-23","arxiv_id":"2409.14672","n_code_links":0,"syntology":null},{"paper":"/paper/toolplanner-a-tool-augmented-llm-for-multi","slug":"toolplanner-a-tool-augmented-llm-for-multi","title":"ToolPlanner: A Tool Augmented LLM for Multi Granularity Instructions with Path Planning and Feedback","date":"2024-09-23","arxiv_id":"2409.14826","n_code_links":1,"syntology":{"ran":5,"of":8,"n_ran_checked":4,"n_instrument":1,"unverified":3,"pointer_only":8,"phrase":"5 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["xiaomi/toolplanner"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"using-similarity-to-evaluate-factual","title":"Using Similarity to Evaluate Factual Consistency in Summaries","date":"2024-09-23","arxiv_id":"2409.15090","n_code_links":0,"syntology":null},{"paper":null,"slug":"biosbm-a-random-graph-model-to-integrate","title":"bioSBM: a random graph model to integrate epigenomic data in chromatin structure prediction","date":"2024-09-22","arxiv_id":"2409.14425","n_code_links":0,"syntology":null},{"paper":null,"slug":"dilatequant-accurate-and-efficient-diffusion","title":"DilateQuant: Accurate and Efficient Diffusion Quantization via Weight Dilation","date":"2024-09-22","arxiv_id":"2409.14307","n_code_links":0,"syntology":null},{"paper":null,"slug":"llms-are-one-shot-url-classifiers-and","title":"LLMs are One-Shot URL Classifiers and Explainers","date":"2024-09-22","arxiv_id":"2409.14306","n_code_links":0,"syntology":null},{"paper":null,"slug":"low-light-enhancement-effect-on","title":"Low-Light Enhancement Effect on Classification and Detection: An Empirical Study","date":"2024-09-22","arxiv_id":"2409.14461","n_code_links":0,"syntology":null},{"paper":"/paper/mqm-ape-toward-high-quality-error-annotation","slug":"mqm-ape-toward-high-quality-error-annotation","title":"MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation Evaluators","date":"2024-09-22","arxiv_id":"2409.14335","n_code_links":1,"syntology":null},{"paper":null,"slug":"ursimulator-human-perception-driven-prompt","title":"URSimulator: Human-Perception-Driven Prompt Tuning for Enhanced Virtual Urban Renewal via Diffusion Models","date":"2024-09-22","arxiv_id":"2409.14589","n_code_links":0,"syntology":null},{"paper":null,"slug":"zero-shot-skeleton-based-action-recognition-1","title":"Zero-Shot Skeleton-based Action Recognition with Dual Visual-Text Alignment","date":"2024-09-22","arxiv_id":"2409.14336","n_code_links":0,"syntology":null},{"paper":null,"slug":"2409-13972","title":"Can Language Model Understand Word Semantics as A Chatbot? An Empirical Study of Language Model Internal External Mismatch","date":"2024-09-21","arxiv_id":"2409.13972","n_code_links":0,"syntology":null},{"paper":null,"slug":"braindreamer-reasoning-coherent-and","title":"BrainDreamer: Reasoning-Coherent and Controllable Image Generation from EEG Brain Signals via Language Guidance","date":"2024-09-21","arxiv_id":"2409.14021","n_code_links":0,"syntology":null},{"paper":"/paper/burstm-deep-burst-multi-scale-sr-using","slug":"burstm-deep-burst-multi-scale-sr-using","title":"BurstM: Deep Burst Multi-scale SR using Fourier Space with Optical Flow","date":"2024-09-21","arxiv_id":"2409.15384","n_code_links":1,"syntology":null},{"paper":null,"slug":"cus3d-clip-based-unsupervised-3d-segmentation","title":"CUS3D :CLIP-based Unsupervised 3D Segmentation via Object-level Denoise","date":"2024-09-21","arxiv_id":"2409.13982","n_code_links":0,"syntology":null},{"paper":null,"slug":"mssda-multi-sub-source-adaptation-for","title":"MSSDA: Multi-Sub-Source Adaptation for Diabetic Foot Neuropathy Recognition","date":"2024-09-21","arxiv_id":"2409.14154","n_code_links":0,"syntology":null},{"paper":null,"slug":"recovering-global-data-distribution-locally","title":"Recovering Global Data Distribution Locally in Federated Learning","date":"2024-09-21","arxiv_id":"2409.14063","n_code_links":0,"syntology":null},{"paper":null,"slug":"soft-segmented-randomization-enhancing-domain","title":"Soft Segmented Randomization: Enhancing Domain Generalization in SAR-ATR for Synthetic-to-Measured","date":"2024-09-21","arxiv_id":"2409.14060","n_code_links":0,"syntology":null},{"paper":null,"slug":"2409-13559","title":"Efficient Visualization of Neural Networks with Generative Models and Adversarial Perturbations","date":"2024-09-20","arxiv_id":"2409.13559","n_code_links":0,"syntology":null},{"paper":null,"slug":"2409-13870","title":"Instruct-Tuning Pretrained Causal Language Models for Ancient Greek Papyrology and Epigraphy","date":"2024-09-20","arxiv_id":"2409.13870","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-multiple-fill-in-the-blank-exam-approach","title":"A Multiple-Fill-in-the-Blank Exam Approach for Enhancing Zero-Resource Hallucination Detection in Large Language Models","date":"2024-09-20","arxiv_id":"2409.17173","n_code_links":0,"syntology":null},{"paper":null,"slug":"ci-bench-benchmarking-contextual-integrity-of","title":"CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data","date":"2024-09-20","arxiv_id":"2409.13903","n_code_links":0,"syntology":null},{"paper":null,"slug":"do-language-models-practice-what-they-preach","title":"Do language models practice what they preach? Examining language ideologies about gendered language reform encoded in LLMs","date":"2024-09-20","arxiv_id":"2409.13852","n_code_links":0,"syntology":null},{"paper":null,"slug":"large-language-model-should-understand-pinyin","title":"Large Language Model Should Understand Pinyin for Chinese ASR Error Correction","date":"2024-09-20","arxiv_id":"2409.13262","n_code_links":0,"syntology":null},{"paper":"/paper/measuring-error-alignment-for-decision-making","slug":"measuring-error-alignment-for-decision-making","title":"Measuring Error Alignment for Decision-Making Systems","date":"2024-09-20","arxiv_id":"2409.13919","n_code_links":1,"syntology":null},{"paper":null,"slug":"mufu-multilingual-fused-learning-for-low","title":"Mufu: Multilingual Fused Learning for Low-Resource Translation with LLM","date":"2024-09-20","arxiv_id":"2409.13949","n_code_links":0,"syntology":null},{"paper":null,"slug":"plot-text-based-person-search-with-part-slot","title":"PLOT: Text-based Person Search with Part Slot Attention for Corresponding Part Discovery","date":"2024-09-20","arxiv_id":"2409.13475","n_code_links":0,"syntology":null},{"paper":null,"slug":"region-prompt-tuning-fine-grained-scene-text","title":"Region Prompt Tuning: Fine-grained Scene Text Detection Utilizing Region Text Prompt","date":"2024-09-20","arxiv_id":"2409.13576","n_code_links":0,"syntology":null},{"paper":null,"slug":"time-awareness-in-large-language-models","title":"Time Awareness in Large Language Models: Benchmarking Fact Recall Across Time","date":"2024-09-20","arxiv_id":"2409.13338","n_code_links":0,"syntology":null},{"paper":null,"slug":"cameleval-advancing-culturally-aligned-arabic","title":"CamelEval: Advancing Culturally Aligned Arabic Language Models and Benchmarks","date":"2024-09-19","arxiv_id":"2409.12623","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-the-lands-between-a-method-for","title":"Exploring the Lands Between: A Method for Finding Differences between AI-Decisions and Human Ratings through Generated Samples","date":"2024-09-19","arxiv_id":"2409.12801","n_code_links":0,"syntology":null},{"paper":null,"slug":"impact-of-ml-optimization-tactics-on-greener","title":"Impact of ML Optimization Tactics on Greener Pre-Trained ML Models","date":"2024-09-19","arxiv_id":"2409.12878","n_code_links":0,"syntology":null},{"paper":"/paper/language-models-learn-to-mislead-humans-via","slug":"language-models-learn-to-mislead-humans-via","title":"Language Models Learn to Mislead Humans via RLHF","date":"2024-09-19","arxiv_id":"2409.12822","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":["jiaxin-wen/misleadlm"],"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":"preference-alignment-improves-language-model","title":"Preference Alignment Improves Language Model-Based TTS","date":"2024-09-19","arxiv_id":"2409.12403","n_code_links":0,"syntology":null},{"paper":null,"slug":"re-introducing-layernorm-geometric-meaning","title":"Geometric Interpretation of Layer Normalization and a Comparative Analysis with RMSNorm","date":"2024-09-19","arxiv_id":"2409.12951","n_code_links":0,"syntology":null},{"paper":null,"slug":"bundle-adjustment-in-the-eager-mode","title":"Bundle Adjustment in the Eager Mode","date":"2024-09-18","arxiv_id":"2409.12190","n_code_links":0,"syntology":null},{"paper":null,"slug":"finetuning-language-models-to-emit-linguistic","title":"Finetuning Language Models to Emit Linguistic Expressions of Uncertainty","date":"2024-09-18","arxiv_id":"2409.12180","n_code_links":0,"syntology":null},{"paper":null,"slug":"free-vsc-free-semantics-from-visual","title":"Free-VSC: Free Semantics from Visual Foundation Models for Unsupervised Video Semantic Compression","date":"2024-09-18","arxiv_id":"2409.11718","n_code_links":0,"syntology":null},{"paper":null,"slug":"memory-networks-towards-fully-biologically","title":"Memory Networks: Towards Fully Biologically Plausible Learning","date":"2024-09-18","arxiv_id":"2409.17282","n_code_links":0,"syntology":null},{"paper":null,"slug":"sfda-rppg-source-free-domain-adaptive-remote","title":"SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal Consistency","date":"2024-09-18","arxiv_id":"2409.12040","n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-domain-adaptation-via-data","title":"Unsupervised Domain Adaptation Via Data Pruning","date":"2024-09-18","arxiv_id":"2409.12076","n_code_links":0,"syntology":null},{"paper":"/paper/a-physics-informed-neural-network-pinn","slug":"a-physics-informed-neural-network-pinn","title":"A Physics Informed Neural Network (PINN) Methodology for Coupled Moving Boundary PDEs","date":"2024-09-17","arxiv_id":"2409.10910","n_code_links":1,"syntology":null},{"paper":null,"slug":"calibrated-multivariate-regression-with-1","title":"Calibrated Multivariate Regression with Localized PIT Mappings","date":"2024-09-17","arxiv_id":"2409.10855","n_code_links":0,"syntology":null},{"paper":null,"slug":"coca-regaining-safety-awareness-of-multimodal","title":"CoCA: Regaining Safety-awareness of Multimodal Large Language Models with Constitutional Calibration","date":"2024-09-17","arxiv_id":"2409.11365","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-spatially-aware-language-and-audio","title":"Learning Spatially-Aware Language and Audio Embeddings","date":"2024-09-17","arxiv_id":"2409.11369","n_code_links":0,"syntology":null},{"paper":null,"slug":"llm-as-a-judge-reward-model-what-they-can-and","title":"LLM-as-a-Judge & Reward Model: What They Can and Cannot Do","date":"2024-09-17","arxiv_id":"2409.11239","n_code_links":0,"syntology":null},{"paper":"/paper/measuring-and-enhancing-trustworthiness-of","slug":"measuring-and-enhancing-trustworthiness-of","title":"Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse","date":"2024-09-17","arxiv_id":"2409.11242","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["declare-lab/trust-align"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/monokan-certified-monotonic-kolmogorov-arnold","slug":"monokan-certified-monotonic-kolmogorov-arnold","title":"MonoKAN: Certified Monotonic Kolmogorov-Arnold Network","date":"2024-09-17","arxiv_id":"2409.11078","n_code_links":1,"syntology":null},{"paper":null,"slug":"oneencoder-a-lightweight-framework-for","title":"OneEncoder: A Lightweight Framework for Progressive Alignment of Modalities","date":"2024-09-17","arxiv_id":"2409.11059","n_code_links":0,"syntology":null},{"paper":null,"slug":"zero-resource-hallucination-detection-for","title":"Zero-resource Hallucination Detection for Text Generation via Graph-based Contextual Knowledge Triples Modeling","date":"2024-09-17","arxiv_id":"2409.11283","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-offline-adaptation-framework-for","title":"An Offline Adaptation Framework for Constrained Multi-Objective Reinforcement Learning","date":"2024-09-16","arxiv_id":"2409.09958","n_code_links":0,"syntology":null},{"paper":"/paper/benchmarking-large-language-model-uncertainty","slug":"benchmarking-large-language-model-uncertainty","title":"Benchmarking Large Language Model Uncertainty for Prompt Optimization","date":"2024-09-16","arxiv_id":"2409.10044","n_code_links":1,"syntology":null},{"paper":null,"slug":"machine-listening-in-a-neonatal-intensive","title":"Machine listening in a neonatal intensive care unit","date":"2024-09-16","arxiv_id":"2409.11439","n_code_links":0,"syntology":null},{"paper":"/paper/motif-motion-instruction-fine-tuning","slug":"motif-motion-instruction-fine-tuning","title":"MotIF: Motion Instruction Fine-tuning","date":"2024-09-16","arxiv_id":"2409.10683","n_code_links":1,"syntology":{"ran":11,"of":12,"n_ran_checked":8,"n_instrument":3,"unverified":1,"pointer_only":12,"phrase":"11 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; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["Minyoung1005/motif"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"partial-distribution-matching-via-partial","title":"Partial Distribution Matching via Partial Wasserstein Adversarial Networks","date":"2024-09-16","arxiv_id":"2409.10499","n_code_links":0,"syntology":null},{"paper":"/paper/semantics-preserving-emoji-recommendation","slug":"semantics-preserving-emoji-recommendation","title":"Semantics Preserving Emoji Recommendation with Large Language Models","date":"2024-09-16","arxiv_id":"2409.10760","n_code_links":1,"syntology":null},{"paper":null,"slug":"solvr-submap-oriented-lidar-visual-re","title":"SOLVR: Submap Oriented LiDAR-Visual Re-Localisation","date":"2024-09-16","arxiv_id":"2409.10247","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-a-unified-theory-for-semiparametric","title":"Towards a Unified Theory for Semiparametric Data Fusion with Individual-Level Data","date":"2024-09-16","arxiv_id":"2409.09973","n_code_links":0,"syntology":null},{"paper":null,"slug":"latent-diffusion-models-for-controllable-rna","title":"Latent Diffusion Models for Controllable RNA Sequence Generation","date":"2024-09-15","arxiv_id":"2409.09828","n_code_links":0,"syntology":null},{"paper":"/paper/unveiling-gender-bias-in-large-language","slug":"unveiling-gender-bias-in-large-language","title":"Unveiling Gender Bias in Large Language Models: Using Teacher's Evaluation in Higher Education As an Example","date":"2024-09-15","arxiv_id":"2409.09652","n_code_links":1,"syntology":null},{"paper":null,"slug":"valuecompass-a-framework-of-fundamental","title":"ValueCompass: A Framework for Measuring Contextual Value Alignment Between Human and LLMs","date":"2024-09-15","arxiv_id":"2409.09586","n_code_links":0,"syntology":null},{"paper":null,"slug":"see-semantically-aligned-eeg-to-text","title":"SEE: Semantically Aligned EEG-to-Text Translation","date":"2024-09-14","arxiv_id":"2409.16312","n_code_links":0,"syntology":null},{"paper":null,"slug":"are-existing-road-design-guidelines-suitable","title":"Are Existing Road Design Guidelines Suitable for Autonomous Vehicles?","date":"2024-09-13","arxiv_id":"2409.10562","n_code_links":0,"syntology":null},{"paper":null,"slug":"incorporation-of-verifier-functionality-in","title":"Incorporation of Verifier Functionality in the Software for Operations and Network Attack Results Review and the Autonomous Penetration Testing System","date":"2024-09-13","arxiv_id":"2409.09174","n_code_links":0,"syntology":null},{"paper":null,"slug":"proactive-recommendation-in-social-networks","title":"Proactive Recommendation in Social Networks: Steering User Interest via Neighbor Influence","date":"2024-09-13","arxiv_id":"2409.08934","n_code_links":0,"syntology":null},{"paper":"/paper/xted-cross-domain-policy-adaptation-via","slug":"xted-cross-domain-policy-adaptation-via","title":"xTED: Cross-Domain Adaptation via Diffusion-Based Trajectory Editing","date":"2024-09-13","arxiv_id":"2409.08687","n_code_links":1,"syntology":null},{"paper":null,"slug":"comalign-compositional-alignment-in-vision","title":"ComAlign: Compositional Alignment in Vision-Language Models","date":"2024-09-12","arxiv_id":"2409.08206","n_code_links":0,"syntology":null},{"paper":null,"slug":"data-driven-virtual-test-bed-of-the-blown","title":"Harnessing On-Machine Metrology Data for Prints with a Surrogate Model for Laser Powder Directed Energy Deposition","date":"2024-09-12","arxiv_id":"2409.09092","n_code_links":0,"syntology":null},{"paper":"/paper/ezigen-enhancing-zero-shot-subject-driven","slug":"ezigen-enhancing-zero-shot-subject-driven","title":"EZIGen: Enhancing zero-shot personalized image generation with precise subject encoding and decoupled guidance","date":"2024-09-12","arxiv_id":"2409.08091","n_code_links":1,"syntology":null},{"paper":null,"slug":"ifadapter-instance-feature-control-for","title":"IFAdapter: Instance Feature Control for Grounded Text-to-Image Generation","date":"2024-09-12","arxiv_id":"2409.08240","n_code_links":0,"syntology":null},{"paper":"/paper/improving-text-guided-object-inpainting-with","slug":"improving-text-guided-object-inpainting-with","title":"Improving Text-guided Object Inpainting with Semantic Pre-inpainting","date":"2024-09-12","arxiv_id":"2409.08260","n_code_links":1,"syntology":{"ran":7,"of":9,"n_ran_checked":5,"n_instrument":2,"unverified":2,"pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 2 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["nnn-s/catdiffusion"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/real-or-robotic-assessing-whether-llms","slug":"real-or-robotic-assessing-whether-llms","title":"Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Dialogue","date":"2024-09-12","arxiv_id":"2409.08330","n_code_links":1,"syntology":{"ran":7,"of":9,"n_ran_checked":7,"n_instrument":0,"unverified":2,"pointer_only":9,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["davidjurgens/human-llm-similarity"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/a-fine-grained-sentiment-analysis-of-app","slug":"a-fine-grained-sentiment-analysis-of-app","title":"How Effectively Do LLMs Extract Feature-Sentiment Pairs from App Reviews?","date":"2024-09-11","arxiv_id":"2409.07162","n_code_links":1,"syntology":null},{"paper":"/paper/data-augmentation-via-latent-diffusion-for","slug":"data-augmentation-via-latent-diffusion-for","title":"Data Augmentation via Latent Diffusion for Saliency Prediction","date":"2024-09-11","arxiv_id":"2409.07307","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":3,"phrase":"2 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; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["ivrl/augsal"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/foundation-models-boost-low-level-perceptual","slug":"foundation-models-boost-low-level-perceptual","title":"Foundation Models Boost Low-Level Perceptual Similarity Metrics","date":"2024-09-11","arxiv_id":"2409.07650","n_code_links":1,"syntology":null},{"paper":"/paper/information-extraction-from-visually-rich-1","slug":"information-extraction-from-visually-rich-1","title":"Information Extraction from Visually Rich Documents Using Directed Weighted Graph Neural Network","date":"2024-09-11","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/pite-pixel-temporal-alignment-for-large-video","slug":"pite-pixel-temporal-alignment-for-large-video","title":"PiTe: Pixel-Temporal Alignment for Large Video-Language Model","date":"2024-09-11","arxiv_id":"2409.07239","n_code_links":1,"syntology":{"ran":3,"of":6,"n_ran_checked":3,"n_instrument":0,"unverified":3,"pointer_only":6,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","official":{"repos":["yliu-cs/pite"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"what-to-align-in-multimodal-contrastive","title":"What to align in multimodal contrastive learning?","date":"2024-09-11","arxiv_id":"2409.07402","n_code_links":0,"syntology":null},{"paper":null,"slug":"adversarial-attacks-to-multi-modal-models","title":"Adversarial Attacks to Multi-Modal Models","date":"2024-09-10","arxiv_id":"2409.06793","n_code_links":0,"syntology":null},{"paper":null,"slug":"connecting-concept-convexity-and-human","title":"Connecting Concept Convexity and Human-Machine Alignment in Deep Neural Networks","date":"2024-09-10","arxiv_id":"2409.06362","n_code_links":0,"syntology":null},{"paper":null,"slug":"e2llm-encoder-elongated-large-language-models","title":"E2LLM: Encoder Elongated Large Language Models for Long-Context Understanding and Reasoning","date":"2024-09-10","arxiv_id":"2409.06679","n_code_links":0,"syntology":null},{"paper":null,"slug":"length-desensitization-in-directed-preference","title":"Length Desensitization in Direct Preference Optimization","date":"2024-09-10","arxiv_id":"2409.06411","n_code_links":0,"syntology":null}],"record_sha256":"c05ec653f1c9a2e1ed28fef2f536e9a8ff7b1a2cc46c0c8f92b066210e942310","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}