{"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":"/task/text-generation/papers/5","list_of":"/task/text-generation","task":"Text Generation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":5,"pages_in_order":54,"rows_per_page":100,"rows":[401,500],"of":5335,"counts":{"archive_papers_tagged":5335,"with_a_code_link":2047,"where_syntology_ran_a_sample":610,"not_listed_spam_title":0,"listed":5335,"listed_where_code_ran":610,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":503,"every_run_a_failure_of_syntologys_instrument":107,"listed_with_a_run_with_no_instrument_failure":503,"listed_every_run_a_failure_of_syntologys_instrument":107,"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":"/task/text-generation","prev":"/task/text-generation/papers/4","next":"/task/text-generation/papers/6","papers":[{"url":"/paper/flow-grpo-training-flow-matching-models-via","slug":"flow-grpo-training-flow-matching-models-via","title":"Flow-GRPO: Training Flow Matching Models via Online RL","date":"2025-05-08","arxiv_id":"2505.05470","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_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) · 4 unverified","sample_list":"/paper/flow-grpo-training-flow-matching-models-via#ran","syntology_url":"https://syntology.ai/paper/2505.05470","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.05470"}},"official":{"repos":["yifan123/flow_grpo"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-safer-pretraining-analyzing-and","slug":"towards-safer-pretraining-analyzing-and","title":"Towards Safer Pretraining: Analyzing and Filtering Harmful Content in Webscale datasets for Responsible LLMs","date":"2025-05-04","arxiv_id":"2505.02009","repositories_listed":1,"syntology":null},{"url":"/paper/ensuring-reproducibility-in-generative-ai","slug":"ensuring-reproducibility-in-generative-ai","title":"Ensuring Reproducibility in Generative AI Systems for General Use Cases: A Framework for Regression Testing and Open Datasets","date":"2025-05-02","arxiv_id":"2505.02854","repositories_listed":1,"syntology":null},{"url":"/paper/unibiomed-a-universal-foundation-model-for","slug":"unibiomed-a-universal-foundation-model-for","title":"UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation","date":"2025-04-30","arxiv_id":"2504.21336","repositories_listed":1,"syntology":null},{"url":"/paper/reviving-any-subset-autoregressive-models","slug":"reviving-any-subset-autoregressive-models","title":"Reviving Any-Subset Autoregressive Models with Principled Parallel Sampling and Speculative Decoding","date":"2025-04-29","arxiv_id":"2504.20456","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/reviving-any-subset-autoregressive-models#ran","syntology_url":"https://syntology.ai/paper/2504.20456","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.20456"}},"official":{"repos":["gabeguo/any-order-speculative-decoding"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/contextual-improving-clinical-text","slug":"contextual-improving-clinical-text","title":"ConTextual: Improving Clinical Text Summarization in LLMs with Context-preserving Token Filtering and Knowledge Graphs","date":"2025-04-23","arxiv_id":"2504.16394","repositories_listed":1,"syntology":null},{"url":"/paper/alignrag-an-adaptable-framework-for-resolving","slug":"alignrag-an-adaptable-framework-for-resolving","title":"AlignRAG: Leveraging Critique Learning for Evidence-Sensitive Retrieval-Augmented Reasoning","date":"2025-04-21","arxiv_id":"2504.14858","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/alignrag-an-adaptable-framework-for-resolving#ran","syntology_url":"https://syntology.ai/paper/2504.14858","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.14858"}},"official":{"repos":["qqw-ing/rag-reasonalignment"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/retrieval-augmented-generation-evaluation-in","slug":"retrieval-augmented-generation-evaluation-in","title":"Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey","date":"2025-04-21","arxiv_id":"2504.14891","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-the-repeat-curse-in-large","slug":"understanding-the-repeat-curse-in-large","title":"Understanding the Repeat Curse in Large Language Models from a Feature Perspective","date":"2025-04-19","arxiv_id":"2504.14218","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/understanding-the-repeat-curse-in-large#ran","syntology_url":"https://syntology.ai/paper/2504.14218","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.14218"}},"official":{"repos":["kaustpradalab/repeat-curse-llm"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/enhancing-multimodal-analogical-reasoning","slug":"enhancing-multimodal-analogical-reasoning","title":"Enhancing multimodal analogical reasoning with Logic Augmented Generation","date":"2025-04-15","arxiv_id":"2504.11190","repositories_listed":1,"syntology":null},{"url":"/paper/reasondrive-efficient-visual-question","slug":"reasondrive-efficient-visual-question","title":"ReasonDrive: Efficient Visual Question Answering for Autonomous Vehicles with Reasoning-Enhanced Small Vision-Language Models","date":"2025-04-14","arxiv_id":"2504.10757","repositories_listed":1,"syntology":null},{"url":"/paper/2504-09184","slug":"2504-09184","title":"Parameterized Synthetic Text Generation with SimpleStories","date":"2025-04-12","arxiv_id":"2504.09184","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-study-of-gpt-4o-image-generation","slug":"an-empirical-study-of-gpt-4o-image-generation","title":"An Empirical Study of GPT-4o Image Generation Capabilities","date":"2025-04-08","arxiv_id":"2504.05979","repositories_listed":1,"syntology":null},{"url":"/paper/retrieval-augmented-generation-with-2","slug":"retrieval-augmented-generation-with-2","title":"Retrieval Augmented Generation with Collaborative Filtering for Personalized Text Generation","date":"2025-04-08","arxiv_id":"2504.05731","repositories_listed":1,"syntology":null},{"url":"/paper/impersona-evaluating-individual-level-lm","slug":"impersona-evaluating-individual-level-lm","title":"IMPersona: Evaluating Individual Level LM Impersonation","date":"2025-04-06","arxiv_id":"2504.04332","repositories_listed":1,"syntology":null},{"url":"/paper/msl-not-all-tokens-are-what-you-need-for","slug":"msl-not-all-tokens-are-what-you-need-for","title":"MSL: Not All Tokens Are What You Need for Tuning LLM as a Recommender","date":"2025-04-05","arxiv_id":"2504.04178","repositories_listed":1,"syntology":null},{"url":"/paper/textcrafter-accurately-rendering-multiple","slug":"textcrafter-accurately-rendering-multiple","title":"TextCrafter: Accurately Rendering Multiple Texts in Complex Visual Scenes","date":"2025-03-30","arxiv_id":"2503.23461","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-study-of-validating-synthetic-1","slug":"an-empirical-study-of-validating-synthetic-1","title":"An Empirical Study of Validating Synthetic Data for Text-Based Person Retrieval","date":"2025-03-28","arxiv_id":"2503.22171","repositories_listed":1,"syntology":null},{"url":"/paper/controlling-large-language-model-with-latent","slug":"controlling-large-language-model-with-latent","title":"Controlling Large Language Model with Latent Actions","date":"2025-03-27","arxiv_id":"2503.21383","repositories_listed":1,"syntology":null},{"url":"/paper/qwen2-5-omni-technical-report","slug":"qwen2-5-omni-technical-report","title":"Qwen2.5-Omni Technical Report","date":"2025-03-26","arxiv_id":"2503.20215","repositories_listed":1,"syntology":null},{"url":"/paper/unified-multimodal-discrete-diffusion","slug":"unified-multimodal-discrete-diffusion","title":"Unified Multimodal Discrete Diffusion","date":"2025-03-26","arxiv_id":"2503.20853","repositories_listed":1,"syntology":null},{"url":"/paper/long-context-autoregressive-video-modeling-1","slug":"long-context-autoregressive-video-modeling-1","title":"Long-Context Autoregressive Video Modeling with Next-Frame Prediction","date":"2025-03-25","arxiv_id":"2503.19325","repositories_listed":1,"syntology":null},{"url":"/paper/improving-rag-for-personalization-with-author","slug":"improving-rag-for-personalization-with-author","title":"Improving RAG for Personalization with Author Features and Contrastive Examples","date":"2025-03-24","arxiv_id":"2504.08745","repositories_listed":1,"syntology":null},{"url":"/paper/reasoning-to-learn-from-latent-thoughts","slug":"reasoning-to-learn-from-latent-thoughts","title":"Reasoning to Learn from Latent Thoughts","date":"2025-03-24","arxiv_id":"2503.18866","repositories_listed":1,"syntology":{"n":20,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":4,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/reasoning-to-learn-from-latent-thoughts#ran","syntology_url":"https://syntology.ai/paper/2503.18866","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.18866"}},"official":null}},{"url":"/paper/mepnet-medical-entity-balanced-prompting","slug":"mepnet-medical-entity-balanced-prompting","title":"MEPNet: Medical Entity-balanced Prompting Network for Brain CT Report Generation","date":"2025-03-22","arxiv_id":"2503.17784","repositories_listed":1,"syntology":null},{"url":"/paper/fuxi-a-benchmark-for-evaluating-language","slug":"fuxi-a-benchmark-for-evaluating-language","title":"Fùxì: A Benchmark for Evaluating Language Models on Ancient Chinese Text Understanding and Generation","date":"2025-03-20","arxiv_id":"2503.15837","repositories_listed":1,"syntology":null},{"url":"/paper/cube-a-roblox-view-of-3d-intelligence","slug":"cube-a-roblox-view-of-3d-intelligence","title":"Cube: A Roblox View of 3D Intelligence","date":"2025-03-19","arxiv_id":"2503.15475","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-failures-in-tool-augmented","slug":"benchmarking-failures-in-tool-augmented","title":"Benchmarking Failures in Tool-Augmented Language Models","date":"2025-03-18","arxiv_id":"2503.14227","repositories_listed":1,"syntology":null},{"url":"/paper/flowtok-flowing-seamlessly-across-text-and","slug":"flowtok-flowing-seamlessly-across-text-and","title":"FlowTok: Flowing Seamlessly Across Text and Image Tokens","date":"2025-03-13","arxiv_id":"2503.10772","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":6,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 6 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified; every one of the 6 samples that ran constructed an object rather than computing a result","sample_list":"/paper/flowtok-flowing-seamlessly-across-text-and#ran","syntology_url":"https://syntology.ai/paper/2503.10772","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.10772"}},"official":{"repos":["bytedance/1d-tokenizer"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/omnimamba-efficient-and-unified-multimodal","slug":"omnimamba-efficient-and-unified-multimodal","title":"OmniMamba: Efficient and Unified Multimodal Understanding and Generation via State Space Models","date":"2025-03-11","arxiv_id":"2503.08686","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":2,"n_instrument":5,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"7 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; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/omnimamba-efficient-and-unified-multimodal#ran","syntology_url":"https://syntology.ai/paper/2503.08686","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.08686"}},"official":{"repos":["hustvl/omnimamba"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/understanding-the-quality-diversity-trade-off","slug":"understanding-the-quality-diversity-trade-off","title":"Understanding the Quality-Diversity Trade-off in Diffusion Language Models","date":"2025-03-11","arxiv_id":"2503.10683","repositories_listed":1,"syntology":null},{"url":"/paper/lost-in-the-middle-in-long-text-generation","slug":"lost-in-the-middle-in-long-text-generation","title":"Lost-in-the-Middle in Long-Text Generation: Synthetic Dataset, Evaluation Framework, and Mitigation","date":"2025-03-10","arxiv_id":"2503.06868","repositories_listed":1,"syntology":null},{"url":"/paper/unleashing-the-potential-of-large-language-3","slug":"unleashing-the-potential-of-large-language-3","title":"Unleashing the Potential of Large Language Models for Text-to-Image Generation through Autoregressive Representation Alignment","date":"2025-03-10","arxiv_id":"2503.07334","repositories_listed":1,"syntology":null},{"url":"/paper/writingbench-a-comprehensive-benchmark-for","slug":"writingbench-a-comprehensive-benchmark-for","title":"WritingBench: A Comprehensive Benchmark for Generative Writing","date":"2025-03-07","arxiv_id":"2503.05244","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/writingbench-a-comprehensive-benchmark-for#ran","syntology_url":"https://syntology.ai/paper/2503.05244","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.05244"}},"official":{"repos":["X-PLUG/WritingBench"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mcitebench-a-benchmark-for-multimodal","slug":"mcitebench-a-benchmark-for-multimodal","title":"MCiteBench: A Multimodal Benchmark for Generating Text with Citations","date":"2025-03-04","arxiv_id":"2503.02589","repositories_listed":1,"syntology":null},{"url":"/paper/q-filters-leveraging-qk-geometry-for","slug":"q-filters-leveraging-qk-geometry-for","title":"Q-Filters: Leveraging QK Geometry for Efficient KV Cache Compression","date":"2025-03-04","arxiv_id":"2503.02812","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-personalized-tool-augmented-llms","slug":"evaluating-personalized-tool-augmented-llms","title":"Evaluating Personalized Tool-Augmented LLMs from the Perspectives of Personalization and Proactivity","date":"2025-03-02","arxiv_id":"2503.00771","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/evaluating-personalized-tool-augmented-llms#ran","syntology_url":"https://syntology.ai/paper/2503.00771","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.00771"}},"official":{"repos":["hypasd-art/etapp"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/autohete-an-automatic-and-efficient","slug":"autohete-an-automatic-and-efficient","title":"AutoHete: An Automatic and Efficient Heterogeneous Training System for LLMs","date":"2025-02-27","arxiv_id":"2503.01890","repositories_listed":1,"syntology":null},{"url":"/paper/make-lora-great-again-boosting-lora-with","slug":"make-lora-great-again-boosting-lora-with","title":"Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment","date":"2025-02-24","arxiv_id":"2502.16894","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":3,"n_ran_checked":3,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"6 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; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/make-lora-great-again-boosting-lora-with#ran","syntology_url":"https://syntology.ai/paper/2502.16894","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.16894"}},"official":{"repos":["facico/goat-peft"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/mapping-1000-language-models-via-the-log","slug":"mapping-1000-language-models-via-the-log","title":"Mapping 1,000+ Language Models via the Log-Likelihood Vector","date":"2025-02-22","arxiv_id":"2502.16173","repositories_listed":1,"syntology":null},{"url":"/paper/a-general-pseudonymization-framework-for","slug":"a-general-pseudonymization-framework-for","title":"A General Pseudonymization Framework for Cloud-Based LLMs: Replacing Privacy Information in Controlled Text Generation","date":"2025-02-21","arxiv_id":"2502.15233","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-general-pseudonymization-framework-for#ran","syntology_url":"https://syntology.ai/paper/2502.15233","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.15233"}},"official":{"repos":["mebymeby/pseudonymization-framework"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/almost-ai-almost-human-the-challenge-of","slug":"almost-ai-almost-human-the-challenge-of","title":"Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writing","date":"2025-02-21","arxiv_id":"2502.15666","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-rwkv-based-language-models-for-long","slug":"enhancing-rwkv-based-language-models-for-long","title":"Enhancing RWKV-based Language Models for Long-Sequence Text Generation","date":"2025-02-21","arxiv_id":"2502.15485","repositories_listed":1,"syntology":null},{"url":"/paper/ipad-inverse-prompt-for-ai-detection-a-robust","slug":"ipad-inverse-prompt-for-ai-detection-a-robust","title":"IPAD: Inverse Prompt for AI Detection -- A Robust and Explainable LLM-Generated Text Detector","date":"2025-02-21","arxiv_id":"2502.15902","repositories_listed":1,"syntology":null},{"url":"/paper/machine-generated-text-detection-prevents","slug":"machine-generated-text-detection-prevents","title":"Machine-generated text detection prevents language model collapse","date":"2025-02-21","arxiv_id":"2502.15654","repositories_listed":1,"syntology":null},{"url":"/paper/a-survey-on-data-contamination-for-large","slug":"a-survey-on-data-contamination-for-large","title":"A Survey on Data Contamination for Large Language Models","date":"2025-02-20","arxiv_id":"2502.14425","repositories_listed":1,"syntology":null},{"url":"/paper/middle-layer-representation-alignment-for","slug":"middle-layer-representation-alignment-for","title":"Middle-Layer Representation Alignment for Cross-Lingual Transfer in Fine-Tuned LLMs","date":"2025-02-20","arxiv_id":"2502.14830","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"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) · 0 unverified","sample_list":"/paper/middle-layer-representation-alignment-for#ran","syntology_url":"https://syntology.ai/paper/2502.14830","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.14830"}},"official":{"repos":["dannigt/mid-align"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/natural-language-generation","slug":"natural-language-generation","title":"Natural Language Generation","date":"2025-02-20","arxiv_id":"2502.14437","repositories_listed":1,"syntology":null},{"url":"/paper/nlora-nystrom-initiated-low-rank-adaptation","slug":"nlora-nystrom-initiated-low-rank-adaptation","title":"NLoRA: Nyström-Initiated Low-Rank Adaptation for Large Language Models","date":"2025-02-20","arxiv_id":"2502.14482","repositories_listed":1,"syntology":null},{"url":"/paper/token-level-density-based-uncertainty","slug":"token-level-density-based-uncertainty","title":"Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models","date":"2025-02-20","arxiv_id":"2502.14427","repositories_listed":1,"syntology":null},{"url":"/paper/a-cognitive-writing-perspective-for","slug":"a-cognitive-writing-perspective-for","title":"A Cognitive Writing Perspective for Constrained Long-Form Text Generation","date":"2025-02-18","arxiv_id":"2502.12568","repositories_listed":1,"syntology":null},{"url":"/paper/uniguardian-a-unified-defense-for-detecting","slug":"uniguardian-a-unified-defense-for-detecting","title":"UniGuardian: A Unified Defense for Detecting Prompt Injection, Backdoor Attacks and Adversarial Attacks in Large Language Models","date":"2025-02-18","arxiv_id":"2502.13141","repositories_listed":1,"syntology":null},{"url":"/paper/aakt-enhancing-knowledge-tracing-with","slug":"aakt-enhancing-knowledge-tracing-with","title":"AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling","date":"2025-02-17","arxiv_id":"2502.11817","repositories_listed":1,"syntology":null},{"url":"/paper/generating-text-from-uniform-meaning","slug":"generating-text-from-uniform-meaning","title":"Generating Text from Uniform Meaning Representation","date":"2025-02-17","arxiv_id":"2502.11973","repositories_listed":1,"syntology":null},{"url":"/paper/exposing-numeracy-gaps-a-benchmark-to","slug":"exposing-numeracy-gaps-a-benchmark-to","title":"Exposing Numeracy Gaps: A Benchmark to Evaluate Fundamental Numerical Abilities in Large Language Models","date":"2025-02-16","arxiv_id":"2502.11075","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-graph-driven-retrieval-augmented","slug":"knowledge-graph-driven-retrieval-augmented","title":"Knowledge Graph-Driven Retrieval-Augmented Generation: Integrating Deepseek-R1 with Weaviate for Advanced Chatbot Applications","date":"2025-02-16","arxiv_id":"2502.11108","repositories_listed":1,"syntology":null},{"url":"/paper/relearn-unlearning-via-learning-for-large","slug":"relearn-unlearning-via-learning-for-large","title":"ReLearn: Unlearning via Learning for Large Language Models","date":"2025-02-16","arxiv_id":"2502.11190","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":5,"phrase":"10 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/relearn-unlearning-via-learning-for-large#ran","syntology_url":"https://syntology.ai/paper/2502.11190","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.11190"}},"official":{"repos":["zjunlp/unlearn"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/talk-structurally-act-hierarchically-a","slug":"talk-structurally-act-hierarchically-a","title":"Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems","date":"2025-02-16","arxiv_id":"2502.11098","repositories_listed":1,"syntology":null},{"url":"/paper/multilingual-encoder-knows-more-than-you","slug":"multilingual-encoder-knows-more-than-you","title":"Multilingual Encoder Knows more than You Realize: Shared Weights Pretraining for Extremely Low-Resource Languages","date":"2025-02-15","arxiv_id":"2502.10852","repositories_listed":1,"syntology":null},{"url":"/paper/a-judge-free-llm-open-ended-generation","slug":"a-judge-free-llm-open-ended-generation","title":"A Judge-free LLM Open-ended Generation Benchmark Based on the Distributional Hypothesis","date":"2025-02-13","arxiv_id":"2502.09316","repositories_listed":1,"syntology":null},{"url":"/paper/explanation-based-in-context-demonstrations","slug":"explanation-based-in-context-demonstrations","title":"Explanation based In-Context Demonstrations Retrieval for Multilingual Grammatical Error Correction","date":"2025-02-12","arxiv_id":"2502.08507","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_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) · 1 unverified","sample_list":"/paper/explanation-based-in-context-demonstrations#ran","syntology_url":"https://syntology.ai/paper/2502.08507","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.08507"}},"official":{"repos":["gmago-leway/fewshotgec"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/bridging-llm-generated-code-and-requirements","slug":"bridging-llm-generated-code-and-requirements","title":"Bridging LLM-Generated Code and Requirements: Reverse Generation technique and SBC Metric for Developer Insights","date":"2025-02-11","arxiv_id":"2502.07835","repositories_listed":1,"syntology":null},{"url":"/paper/hallucination-monofacts-and-miscalibration-an","slug":"hallucination-monofacts-and-miscalibration-an","title":"Hallucination, Monofacts, and Miscalibration: An Empirical Investigation","date":"2025-02-11","arxiv_id":"2502.08666","repositories_listed":1,"syntology":null},{"url":"/paper/lantern-enhanced-relaxed-speculative-decoding","slug":"lantern-enhanced-relaxed-speculative-decoding","title":"LANTERN++: Enhancing Relaxed Speculative Decoding with Static Tree Drafting for Visual Auto-regressive Models","date":"2025-02-10","arxiv_id":"2502.06352","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":0,"n_instrument":5,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"5 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; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/lantern-enhanced-relaxed-speculative-decoding#ran","syntology_url":"https://syntology.ai/paper/2502.06352","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.06352"}},"official":{"repos":["jadohu/LANTERN"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/position-it-s-time-to-act-on-the-risk-of","slug":"position-it-s-time-to-act-on-the-risk-of","title":"Position: It's Time to Act on the Risk of Efficient Personalized Text Generation","date":"2025-02-10","arxiv_id":"2502.06560","repositories_listed":1,"syntology":null},{"url":"/paper/smab-mab-based-word-sensitivity-estimation","slug":"smab-mab-based-word-sensitivity-estimation","title":"SMAB: MAB based word Sensitivity Estimation Framework and its Applications in Adversarial Text Generation","date":"2025-02-10","arxiv_id":"2502.07101","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/smab-mab-based-word-sensitivity-estimation#ran","syntology_url":"https://syntology.ai/paper/2502.07101","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.07101"}},"official":{"repos":["skp1999/SMAB"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/few-shot-llm-synthetic-data-with-distribution","slug":"few-shot-llm-synthetic-data-with-distribution","title":"Few-shot_LLM_Synthetic_Data_with_Distribution_Matching","date":"2025-02-09","arxiv_id":"2502.08661","repositories_listed":1,"syntology":null},{"url":"/paper/saving-77-of-the-parameters-in-large-language","slug":"saving-77-of-the-parameters-in-large-language","title":"Saving 77% of the Parameters in Large Language Models Technical Report","date":"2025-02-09","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/unicms-a-unified-consistency-model-for","slug":"unicms-a-unified-consistency-model-for","title":"UniCMs: A Unified Consistency Model For Efficient Multimodal Generation and Understanding","date":"2025-02-08","arxiv_id":"2502.05415","repositories_listed":1,"syntology":null},{"url":"/paper/adparaphrase-paraphrase-dataset-for-analyzing","slug":"adparaphrase-paraphrase-dataset-for-analyzing","title":"AdParaphrase: Paraphrase Dataset for Analyzing Linguistic Features toward Generating Attractive Ad Texts","date":"2025-02-07","arxiv_id":"2502.04674","repositories_listed":1,"syntology":null},{"url":"/paper/beautiful-images-toxic-words-understanding","slug":"beautiful-images-toxic-words-understanding","title":"Beautiful Images, Toxic Words: Understanding and Addressing Offensive Text in Generated Images","date":"2025-02-07","arxiv_id":"2502.05066","repositories_listed":1,"syntology":null},{"url":"/paper/controlled-llm-decoding-via-discrete-auto","slug":"controlled-llm-decoding-via-discrete-auto","title":"Controlled LLM Decoding via Discrete Auto-regressive Biasing","date":"2025-02-06","arxiv_id":"2502.03685","repositories_listed":1,"syntology":null},{"url":"/paper/codesteer-symbolic-augmented-language-models","slug":"codesteer-symbolic-augmented-language-models","title":"CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance","date":"2025-02-04","arxiv_id":"2502.04350","repositories_listed":1,"syntology":null},{"url":"/paper/rankify-a-comprehensive-python-toolkit-for","slug":"rankify-a-comprehensive-python-toolkit-for","title":"Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation","date":"2025-02-04","arxiv_id":"2502.02464","repositories_listed":1,"syntology":null},{"url":"/paper/joint-localization-and-activation-editing-for","slug":"joint-localization-and-activation-editing-for","title":"Joint Localization and Activation Editing for Low-Resource Fine-Tuning","date":"2025-02-03","arxiv_id":"2502.01179","repositories_listed":1,"syntology":null},{"url":"/paper/learnable-polynomial-trigonometric-and","slug":"learnable-polynomial-trigonometric-and","title":"Polynomial, trigonometric, and tropical activations","date":"2025-02-03","arxiv_id":"2502.01247","repositories_listed":1,"syntology":null},{"url":"/paper/m-extending-memoryllm-with-scalable-long-term","slug":"m-extending-memoryllm-with-scalable-long-term","title":"M+: Extending MemoryLLM with Scalable Long-Term Memory","date":"2025-02-01","arxiv_id":"2502.00592","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"3 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/m-extending-memoryllm-with-scalable-long-term#ran","syntology_url":"https://syntology.ai/paper/2502.00592","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.00592"}},"official":{"repos":["wangyu-ustc/memoryllm"],"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","unlocated"]}}},{"url":"/paper/beyond-checkmate-exploring-the-creative","slug":"beyond-checkmate-exploring-the-creative","title":"Beyond checkmate: exploring the creative chokepoints in AI text","date":"2025-01-31","arxiv_id":"2501.19301","repositories_listed":1,"syntology":null},{"url":"/paper/differentially-private-steering-for-large","slug":"differentially-private-steering-for-large","title":"Differentially Private Steering for Large Language Model Alignment","date":"2025-01-30","arxiv_id":"2501.18532","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/differentially-private-steering-for-large#ran","syntology_url":"https://syntology.ai/paper/2501.18532","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.18532"}},"official":{"repos":["ukplab/iclr2025-psa"],"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"]}}},{"url":"/paper/how-to-select-datapoints-for-efficient-human","slug":"how-to-select-datapoints-for-efficient-human","title":"How to Select Datapoints for Efficient Human Evaluation of NLG Models?","date":"2025-01-30","arxiv_id":"2501.18251","repositories_listed":1,"syntology":null},{"url":"/paper/lctg-bench-llm-controlled-text-generation","slug":"lctg-bench-llm-controlled-text-generation","title":"LCTG Bench: LLM Controlled Text Generation Benchmark","date":"2025-01-27","arxiv_id":"2501.15875","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-and-improving-graph-to-text","slug":"evaluating-and-improving-graph-to-text","title":"Evaluating and Improving Graph to Text Generation with Large Language Models","date":"2025-01-24","arxiv_id":"2501.14497","repositories_listed":1,"syntology":null},{"url":"/paper/expert-effective-and-explainable-evaluation","slug":"expert-effective-and-explainable-evaluation","title":"ExPerT: Effective and Explainable Evaluation of Personalized Long-Form Text Generation","date":"2025-01-24","arxiv_id":"2501.14956","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_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) · 0 unverified","sample_list":"/paper/expert-effective-and-explainable-evaluation#ran","syntology_url":"https://syntology.ai/paper/2501.14956","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.14956"}},"official":{"repos":["alirezasalemi7/ExPerT"],"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"]}}},{"url":"/paper/pix2cap-coco-advancing-visual-comprehension","slug":"pix2cap-coco-advancing-visual-comprehension","title":"Pix2Cap-COCO: Advancing Visual Comprehension via Pixel-Level Captioning","date":"2025-01-23","arxiv_id":"2501.13893","repositories_listed":1,"syntology":null},{"url":"/paper/agentic-retrieval-augmented-generation-a","slug":"agentic-retrieval-augmented-generation-a","title":"Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG","date":"2025-01-15","arxiv_id":"2501.09136","repositories_listed":1,"syntology":null},{"url":"/paper/personalized-graph-based-retrieval-for-large","slug":"personalized-graph-based-retrieval-for-large","title":"Personalized Graph-Based Retrieval for Large Language Models","date":"2025-01-04","arxiv_id":"2501.02157","repositories_listed":1,"syntology":null},{"url":"/paper/who-wrote-this-zero-shot-statistical-tests","slug":"who-wrote-this-zero-shot-statistical-tests","title":"Zero-Shot Statistical Tests for LLM-Generated Text Detection using Finite Sample Concentration Inequalities","date":"2025-01-04","arxiv_id":"2501.02406","repositories_listed":1,"syntology":null},{"url":"/paper/mitigating-hallucination-for-large-vision","slug":"mitigating-hallucination-for-large-vision","title":"Mitigating Hallucination for Large Vision Language Model by Inter-Modality Correlation Calibration Decoding","date":"2025-01-03","arxiv_id":"2501.01926","repositories_listed":1,"syntology":null},{"url":"/paper/think-more-hallucinate-less-mitigating","slug":"think-more-hallucinate-less-mitigating","title":"Think More, Hallucinate Less: Mitigating Hallucinations via Dual Process of Fast and Slow Thinking","date":"2025-01-02","arxiv_id":"2501.01306","repositories_listed":1,"syntology":null},{"url":"/paper/large-language-models-are-read-write-policy","slug":"large-language-models-are-read-write-policy","title":"Large Language Models Are Read/Write Policy-Makers for Simultaneous Generation","date":"2025-01-01","arxiv_id":"2501.00868","repositories_listed":1,"syntology":null},{"url":"/paper/disentangling-preference-representation-and","slug":"disentangling-preference-representation-and","title":"Disentangling Preference Representation and Text Generation for Efficient Individual Preference Alignment","date":"2024-12-30","arxiv_id":"2412.20834","repositories_listed":1,"syntology":null},{"url":"/paper/facilitating-large-language-model-russian","slug":"facilitating-large-language-model-russian","title":"Facilitating large language model Russian adaptation with Learned Embedding Propagation","date":"2024-12-30","arxiv_id":"2412.21140","repositories_listed":1,"syntology":null},{"url":"/paper/mllm-sul-multimodal-large-language-model-for","slug":"mllm-sul-multimodal-large-language-model-for","title":"MLLM-SUL: Multimodal Large Language Model for Semantic Scene Understanding and Localization in Traffic Scenarios","date":"2024-12-27","arxiv_id":"2412.19406","repositories_listed":1,"syntology":null},{"url":"/paper/where-am-i-cross-view-geo-localization-with","slug":"where-am-i-cross-view-geo-localization-with","title":"Where am I? Cross-View Geo-localization with Natural Language Descriptions","date":"2024-12-22","arxiv_id":"2412.17007","repositories_listed":1,"syntology":null},{"url":"/paper/empra-embedding-perturbation-rank-attack","slug":"empra-embedding-perturbation-rank-attack","title":"EMPRA: Embedding Perturbation Rank Attack against Neural Ranking Models","date":"2024-12-20","arxiv_id":"2412.16382","repositories_listed":1,"syntology":null},{"url":"/paper/a-survey-of-rwkv","slug":"a-survey-of-rwkv","title":"A Survey of RWKV","date":"2024-12-19","arxiv_id":"2412.14847","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-transfer-of-debiasing-and","slug":"cross-lingual-transfer-of-debiasing-and","title":"Cross-Lingual Transfer of Debiasing and Detoxification in Multilingual LLMs: An Extensive Investigation","date":"2024-12-18","arxiv_id":"2412.14050","repositories_listed":1,"syntology":null},{"url":"/paper/llava-uhd-v2-an-mllm-integrating-high","slug":"llava-uhd-v2-an-mllm-integrating-high","title":"LLaVA-UHD v2: an MLLM Integrating High-Resolution Feature Pyramid via Hierarchical Window Transformer","date":"2024-12-18","arxiv_id":"2412.13871","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"phrase":"7 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; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/llava-uhd-v2-an-mllm-integrating-high#ran","syntology_url":"https://syntology.ai/paper/2412.13871","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.13871"}},"official":{"repos":["thunlp/llava-uhd"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/typhoon-2-a-family-of-open-text-and","slug":"typhoon-2-a-family-of-open-text-and","title":"Typhoon 2: A Family of Open Text and Multimodal Thai Large Language Models","date":"2024-12-18","arxiv_id":"2412.13702","repositories_listed":1,"syntology":null},{"url":"/paper/dndscore-decontextualization-and","slug":"dndscore-decontextualization-and","title":"DnDScore: Decontextualization and Decomposition for Factuality Verification in Long-Form Text Generation","date":"2024-12-17","arxiv_id":"2412.13175","repositories_listed":1,"syntology":null}],"record_sha256":"e68d45cc2da698a055fcaa80df87f8dbe9cb32ad039b1a064d17925acce7741a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}