{"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/language-modeling/papers/23","list_of":"/task/language-modeling","task":"Language Modeling","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":23,"pages_in_order":142,"rows_per_page":100,"rows":[2201,2300],"of":14182,"counts":{"archive_papers_tagged":14182,"with_a_code_link":5620,"where_syntology_ran_a_sample":1894,"not_listed_spam_title":0,"listed":14182,"listed_where_code_ran":1894,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1580,"every_run_a_failure_of_syntologys_instrument":314,"listed_with_a_run_with_no_instrument_failure":1580,"listed_every_run_a_failure_of_syntologys_instrument":314,"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/language-modeling","prev":"/task/language-modeling/papers/22","next":"/task/language-modeling/papers/24","papers":[{"url":"/paper/prometheus-chatbot-knowledge-graph","slug":"prometheus-chatbot-knowledge-graph","title":"Prometheus Chatbot: Knowledge Graph Collaborative Large Language Model for Computer Components Recommendation","date":"2024-07-29","arxiv_id":"2407.19643","repositories_listed":1,"syntology":null},{"url":"/paper/a-bayesian-flow-network-framework-for","slug":"a-bayesian-flow-network-framework-for","title":"A Bayesian Flow Network Framework for Chemistry Tasks","date":"2024-07-28","arxiv_id":"2407.20294","repositories_listed":1,"syntology":null},{"url":"/paper/xlip-cross-modal-attention-masked-modelling","slug":"xlip-cross-modal-attention-masked-modelling","title":"MMCLIP: Cross-modal Attention Masked Modelling for Medical Language-Image Pre-Training","date":"2024-07-28","arxiv_id":"2407.19546","repositories_listed":1,"syntology":null},{"url":"/paper/a-role-specific-guided-large-language-model","slug":"a-role-specific-guided-large-language-model","title":"A Role-specific Guided Large Language Model for Ophthalmic Consultation Based on Stylistic Differentiation","date":"2024-07-26","arxiv_id":"2407.18483","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-contrastive-search-uncertainty","slug":"adaptive-contrastive-search-uncertainty","title":"Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text Generation","date":"2024-07-26","arxiv_id":"2407.18698","repositories_listed":1,"syntology":null},{"url":"/paper/demystifying-verbatim-memorization-in-large","slug":"demystifying-verbatim-memorization-in-large","title":"Demystifying Verbatim Memorization in Large Language Models","date":"2024-07-25","arxiv_id":"2407.17817","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/demystifying-verbatim-memorization-in-large#ran","syntology_url":"https://syntology.ai/paper/2407.17817","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.17817"}},"official":{"repos":["explanare/verbatim-memorization"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/exploring-scaling-trends-in-llm-robustness","slug":"exploring-scaling-trends-in-llm-robustness","title":"Scaling Trends in Language Model Robustness","date":"2024-07-25","arxiv_id":"2407.18213","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/exploring-scaling-trends-in-llm-robustness#ran","syntology_url":"https://syntology.ai/paper/2407.18213","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.18213"}},"official":{"repos":["AlignmentResearch/scaling-llm-robustness-paper"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/text-driven-neural-collaborative-filtering","slug":"text-driven-neural-collaborative-filtering","title":"Text-Driven Neural Collaborative Filtering Model for Paper Source Tracing","date":"2024-07-25","arxiv_id":"2407.17722","repositories_listed":1,"syntology":null},{"url":"/paper/can-language-models-evaluate-human-written","slug":"can-language-models-evaluate-human-written","title":"Can Language Models Evaluate Human Written Text? Case Study on Korean Student Writing for Education","date":"2024-07-24","arxiv_id":"2407.17022","repositories_listed":1,"syntology":null},{"url":"/paper/densetrack-drone-based-crowd-tracking-via","slug":"densetrack-drone-based-crowd-tracking-via","title":"DenseTrack: Drone-based Crowd Tracking via Density-aware Motion-appearance Synergy","date":"2024-07-24","arxiv_id":"2407.17272","repositories_listed":1,"syntology":null},{"url":"/paper/dependency-transformer-grammars-integrating","slug":"dependency-transformer-grammars-integrating","title":"Dependency Transformer Grammars: Integrating Dependency Structures into Transformer Language Models","date":"2024-07-24","arxiv_id":"2407.17406","repositories_listed":1,"syntology":null},{"url":"/paper/time-matters-examine-temporal-effects-on","slug":"time-matters-examine-temporal-effects-on","title":"Time Matters: Examine Temporal Effects on Biomedical Language Models","date":"2024-07-24","arxiv_id":"2407.17638","repositories_listed":1,"syntology":null},{"url":"/paper/towards-aligning-language-models-with-textual","slug":"towards-aligning-language-models-with-textual","title":"Towards Aligning Language Models with Textual Feedback","date":"2024-07-24","arxiv_id":"2407.16970","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/towards-aligning-language-models-with-textual#ran","syntology_url":"https://syntology.ai/paper/2407.16970","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.16970"}},"official":{"repos":["sauc-abadal/alt"],"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/train-attention-meta-learning-where-to-focus","slug":"train-attention-meta-learning-where-to-focus","title":"Train-Attention: Meta-Learning Where to Focus in Continual Knowledge Learning","date":"2024-07-24","arxiv_id":"2407.16920","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":12,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/train-attention-meta-learning-where-to-focus#ran","syntology_url":"https://syntology.ai/paper/2407.16920","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.16920"}},"official":{"repos":["ybseo-academy/TAALM"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/amongagents-evaluating-large-language-models","slug":"amongagents-evaluating-large-language-models","title":"AMONGAGENTS: Evaluating Large Language Models in the Interactive Text-Based Social Deduction Game","date":"2024-07-23","arxiv_id":"2407.16521","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"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) · 1 unverified","sample_list":"/paper/amongagents-evaluating-large-language-models#ran","syntology_url":"https://syntology.ai/paper/2407.16521","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.16521"}},"official":{"repos":["cyzus/among-agents"],"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"]}}},{"url":"/paper/how-to-leverage-personal-textual-knowledge","slug":"how-to-leverage-personal-textual-knowledge","title":"How to Leverage Personal Textual Knowledge for Personalized Conversational Information Retrieval","date":"2024-07-23","arxiv_id":"2407.16192","repositories_listed":1,"syntology":null},{"url":"/paper/inf-llava-dual-perspective-perception-for","slug":"inf-llava-dual-perspective-perception-for","title":"INF-LLaVA: Dual-perspective Perception for High-Resolution Multimodal Large Language Model","date":"2024-07-23","arxiv_id":"2407.16198","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"4 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/inf-llava-dual-perspective-perception-for#ran","syntology_url":"https://syntology.ai/paper/2407.16198","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.16198"}},"official":{"repos":["weihuanglin/inf-llava"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/prealign-boosting-cross-lingual-transfer-by","slug":"prealign-boosting-cross-lingual-transfer-by","title":"PreAlign: Boosting Cross-Lingual Transfer by Early Establishment of Multilingual Alignment","date":"2024-07-23","arxiv_id":"2407.16222","repositories_listed":1,"syntology":{"n":9,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":9,"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) · 5 unverified","sample_list":"/paper/prealign-boosting-cross-lingual-transfer-by#ran","syntology_url":"https://syntology.ai/paper/2407.16222","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.16222"}},"official":{"repos":["saltychtao/prealign"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/tlcr-token-level-continuous-reward-for-fine","slug":"tlcr-token-level-continuous-reward-for-fine","title":"TLCR: Token-Level Continuous Reward for Fine-grained Reinforcement Learning from Human Feedback","date":"2024-07-23","arxiv_id":"2407.16574","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":11,"phrase":"10 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/tlcr-token-level-continuous-reward-for-fine#ran","syntology_url":"https://syntology.ai/paper/2407.16574","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.16574"}},"official":{"repos":["esyoon7/rlhf-tlcr"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/adaclip-adapting-clip-with-hybrid-learnable","slug":"adaclip-adapting-clip-with-hybrid-learnable","title":"AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection","date":"2024-07-22","arxiv_id":"2407.15795","repositories_listed":1,"syntology":{"n":28,"n_ran":20,"n_constructed":10,"n_ran_checked":16,"n_instrument":4,"n_unverified":8,"n_honours":0,"n_violates":1,"n_no_contract":15,"n_pointer_only":4,"phrase":"20 ran (of which 10 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 1 violated, 15 with no contract checked; 4 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/adaclip-adapting-clip-with-hybrid-learnable#ran","syntology_url":"https://syntology.ai/paper/2407.15795","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.15795"}},"official":{"repos":["caoyunkang/adaclip"],"state":"official (archive's flag): 20 ran","n_ran":20,"n_constructed":10,"n_ran_no_instrument_failure":16,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/artist-aesthetically-controllable-text-driven","slug":"artist-aesthetically-controllable-text-driven","title":"DiffArtist: Towards Structure and Appearance Controllable Image Stylization","date":"2024-07-22","arxiv_id":"2407.15842","repositories_listed":1,"syntology":null},{"url":"/paper/dmel-speech-tokenization-made-simple","slug":"dmel-speech-tokenization-made-simple","title":"dMel: Speech Tokenization made Simple","date":"2024-07-22","arxiv_id":"2407.15835","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":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","sample_list":"/paper/dmel-speech-tokenization-made-simple#ran","syntology_url":"https://syntology.ai/paper/2407.15835","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.15835"}},"official":{"repos":["apple/dmel"],"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/llast-improved-end-to-end-speech-translation","slug":"llast-improved-end-to-end-speech-translation","title":"LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models","date":"2024-07-22","arxiv_id":"2407.15415","repositories_listed":1,"syntology":null},{"url":"/paper/promises-and-pitfalls-of-generative-masked","slug":"promises-and-pitfalls-of-generative-masked","title":"Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines","date":"2024-07-22","arxiv_id":"2407.21046","repositories_listed":1,"syntology":null},{"url":"/paper/slowfast-llava-a-strong-training-free","slug":"slowfast-llava-a-strong-training-free","title":"SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models","date":"2024-07-22","arxiv_id":"2407.15841","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/slowfast-llava-a-strong-training-free#ran","syntology_url":"https://syntology.ai/paper/2407.15841","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.15841"}},"official":{"repos":["apple/ml-slowfast-llava"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/taskgen-a-task-based-memory-infused-agentic","slug":"taskgen-a-task-based-memory-infused-agentic","title":"TaskGen: A Task-Based, Memory-Infused Agentic Framework using StrictJSON","date":"2024-07-22","arxiv_id":"2407.15734","repositories_listed":1,"syntology":null},{"url":"/paper/two-stacks-are-better-than-one-a-comparison","slug":"two-stacks-are-better-than-one-a-comparison","title":"A Comparison of Language Modeling and Translation as Multilingual Pretraining Objectives","date":"2024-07-22","arxiv_id":"2407.15489","repositories_listed":1,"syntology":null},{"url":"/paper/large-language-model-for-verilog-generation","slug":"large-language-model-for-verilog-generation","title":"Large Language Model for Verilog Generation with Code-Structure-Guided Reinforcement Learning","date":"2024-07-21","arxiv_id":"2407.18271","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/large-language-model-for-verilog-generation#ran","syntology_url":"https://syntology.ai/paper/2407.18271","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.18271"}},"official":{"repos":["CatIIIIIIII/veriseek"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/large-vocabulary-forensic-pathological","slug":"large-vocabulary-forensic-pathological","title":"Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning","date":"2024-07-20","arxiv_id":"2407.14904","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-classification-of-news-subjects-in","slug":"automatic-classification-of-news-subjects-in","title":"Automatic Classification of News Subjects in Broadcast News: Application to a Gender Bias Representation Analysis","date":"2024-07-19","arxiv_id":"2407.14180","repositories_listed":1,"syntology":null},{"url":"/paper/compact-language-models-via-pruning-and","slug":"compact-language-models-via-pruning-and","title":"Compact Language Models via Pruning and Knowledge Distillation","date":"2024-07-19","arxiv_id":"2407.14679","repositories_listed":1,"syntology":null},{"url":"/paper/conditioning-chat-gpt-for-information","slug":"conditioning-chat-gpt-for-information","title":"Unipa-GPT: Large Language Models for university-oriented QA in Italian","date":"2024-07-19","arxiv_id":"2407.14246","repositories_listed":1,"syntology":null},{"url":"/paper/longhorn-state-space-models-are-amortized","slug":"longhorn-state-space-models-are-amortized","title":"Longhorn: State Space Models are Amortized Online Learners","date":"2024-07-19","arxiv_id":"2407.14207","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_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","sample_list":"/paper/longhorn-state-space-models-are-amortized#ran","syntology_url":"https://syntology.ai/paper/2407.14207","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.14207"}},"official":{"repos":["Cranial-XIX/longhorn"],"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"]}}},{"url":"/paper/rag-qa-arena-evaluating-domain-robustness-for","slug":"rag-qa-arena-evaluating-domain-robustness-for","title":"RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering","date":"2024-07-19","arxiv_id":"2407.13998","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 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) · 0 unverified","sample_list":"/paper/rag-qa-arena-evaluating-domain-robustness-for#ran","syntology_url":"https://syntology.ai/paper/2407.13998","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.13998"}},"official":{"repos":["awslabs/rag-qa-arena"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/t2v-compbench-a-comprehensive-benchmark-for","slug":"t2v-compbench-a-comprehensive-benchmark-for","title":"T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation","date":"2024-07-19","arxiv_id":"2407.14505","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":7,"n_pointer_only":11,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/t2v-compbench-a-comprehensive-benchmark-for#ran","syntology_url":"https://syntology.ai/paper/2407.14505","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.14505"}},"official":{"repos":["KaiyueSun98/T2V-CompBench"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/alclam-arabic-dialectal-language-model","slug":"alclam-arabic-dialectal-language-model","title":"AlcLaM: Arabic Dialectal Language Model","date":"2024-07-18","arxiv_id":"2407.13097","repositories_listed":1,"syntology":null},{"url":"/paper/earthmarker-a-visual-prompt-learning","slug":"earthmarker-a-visual-prompt-learning","title":"EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote Sensing","date":"2024-07-18","arxiv_id":"2407.13596","repositories_listed":1,"syntology":null},{"url":"/paper/villa-video-reasoning-segmentation-with-large","slug":"villa-video-reasoning-segmentation-with-large","title":"ViLLa: Video Reasoning Segmentation with Large Language Model","date":"2024-07-18","arxiv_id":"2407.14500","repositories_listed":1,"syntology":null},{"url":"/paper/analyzing-the-generalization-and-reliability","slug":"analyzing-the-generalization-and-reliability","title":"Analyzing the Generalization and Reliability of Steering Vectors","date":"2024-07-17","arxiv_id":"2407.12404","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":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","sample_list":"/paper/analyzing-the-generalization-and-reliability#ran","syntology_url":"https://syntology.ai/paper/2407.12404","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.12404"}},"official":{"repos":["dtch1997/steering-bench"],"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/patch-level-training-for-large-language","slug":"patch-level-training-for-large-language","title":"Beyond Next Token Prediction: Patch-Level Training for Large Language Models","date":"2024-07-17","arxiv_id":"2407.12665","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":0,"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/patch-level-training-for-large-language#ran","syntology_url":"https://syntology.ai/paper/2407.12665","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.12665"}},"official":{"repos":["shaochenze/patchtrain"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/exploring-quantization-for-efficient-pre","slug":"exploring-quantization-for-efficient-pre","title":"Exploring Quantization for Efficient Pre-Training of Transformer Language Models","date":"2024-07-16","arxiv_id":"2407.11722","repositories_listed":1,"syntology":null},{"url":"/paper/how-personality-traits-influence-negotiation","slug":"how-personality-traits-influence-negotiation","title":"How Personality Traits Influence Negotiation Outcomes? A Simulation based on Large Language Models","date":"2024-07-16","arxiv_id":"2407.11549","repositories_listed":1,"syntology":null},{"url":"/paper/invagent-a-large-language-model-based-multi","slug":"invagent-a-large-language-model-based-multi","title":"InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains","date":"2024-07-16","arxiv_id":"2407.11384","repositories_listed":1,"syntology":null},{"url":"/paper/lami-detr-open-vocabulary-detection-with","slug":"lami-detr-open-vocabulary-detection-with","title":"LaMI-DETR: Open-Vocabulary Detection with Language Model Instruction","date":"2024-07-16","arxiv_id":"2407.11335","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 2 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","sample_list":"/paper/lami-detr-open-vocabulary-detection-with#ran","syntology_url":"https://syntology.ai/paper/2407.11335","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.11335"}},"official":{"repos":["eternaldolphin/lami-detr"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/litegpt-large-vision-language-model-for-joint","slug":"litegpt-large-vision-language-model-for-joint","title":"LiteGPT: Large Vision-Language Model for Joint Chest X-ray Localization and Classification Task","date":"2024-07-16","arxiv_id":"2407.12064","repositories_listed":1,"syntology":null},{"url":"/paper/mask-free-neuron-concept-annotation-for","slug":"mask-free-neuron-concept-annotation-for","title":"Mask-Free Neuron Concept Annotation for Interpreting Neural Networks in Medical Domain","date":"2024-07-16","arxiv_id":"2407.11375","repositories_listed":1,"syntology":null},{"url":"/paper/an-actionable-framework-for-assessing-bias","slug":"an-actionable-framework-for-assessing-bias","title":"An Actionable Framework for Assessing Bias and Fairness in Large Language Model Use Cases","date":"2024-07-15","arxiv_id":"2407.10853","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"1 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; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/an-actionable-framework-for-assessing-bias#ran","syntology_url":"https://syntology.ai/paper/2407.10853","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.10853"}},"official":{"repos":["cvs-health/langfair"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/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","repositories_listed":1,"syntology":null},{"url":"/paper/think-on-graph-2-0-deep-and-interpretable","slug":"think-on-graph-2-0-deep-and-interpretable","title":"Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation","date":"2024-07-15","arxiv_id":"2407.10805","repositories_listed":1,"syntology":{"n":16,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 1 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/think-on-graph-2-0-deep-and-interpretable#ran","syntology_url":"https://syntology.ai/paper/2407.10805","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.10805"}},"official":{"repos":["idea-finai/tog-2"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/when-ai-meets-finance-stockagent-large","slug":"when-ai-meets-finance-stockagent-large","title":"When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments","date":"2024-07-15","arxiv_id":"2407.18957","repositories_listed":1,"syntology":null},{"url":"/paper/autograms-autonomous-graphical-agent-modeling","slug":"autograms-autonomous-graphical-agent-modeling","title":"AutoGRAMS: Autonomous Graphical Agent Modeling Software","date":"2024-07-14","arxiv_id":"2407.10049","repositories_listed":1,"syntology":null},{"url":"/paper/chatlogic-integrating-logic-programming-with","slug":"chatlogic-integrating-logic-programming-with","title":"ChatLogic: Integrating Logic Programming with Large Language Models for Multi-Step Reasoning","date":"2024-07-14","arxiv_id":"2407.10162","repositories_listed":1,"syntology":null},{"url":"/paper/practical-unlearning-for-large-language","slug":"practical-unlearning-for-large-language","title":"On Large Language Model Continual Unlearning","date":"2024-07-14","arxiv_id":"2407.10223","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":7,"n_instrument":5,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":13,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/practical-unlearning-for-large-language#ran","syntology_url":"https://syntology.ai/paper/2407.10223","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.10223"}},"official":{"repos":["gcyzsl/o3-llm-unlearning"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/iot-lm-large-multisensory-language-models-for","slug":"iot-lm-large-multisensory-language-models-for","title":"IoT-LM: Large Multisensory Language Models for the Internet of Things","date":"2024-07-13","arxiv_id":"2407.09801","repositories_listed":1,"syntology":null},{"url":"/paper/minimizing-plm-based-few-shot-intent","slug":"minimizing-plm-based-few-shot-intent","title":"Minimizing PLM-Based Few-Shot Intent Detectors","date":"2024-07-13","arxiv_id":"2407.09943","repositories_listed":1,"syntology":null},{"url":"/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","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":4,"n_instrument":4,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_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","sample_list":"/paper/aligning-diffusion-behaviors-with-q-functions#ran","syntology_url":"https://syntology.ai/paper/2407.09024","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.09024"}},"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"]}}},{"url":"/paper/astprompter-weakly-supervised-automated","slug":"astprompter-weakly-supervised-automated","title":"ASTPrompter: Weakly Supervised Automated Language Model Red-Teaming to Identify Low-Perplexity Toxic Prompts","date":"2024-07-12","arxiv_id":"2407.09447","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-language-model-creativity-a-case","slug":"benchmarking-language-model-creativity-a-case","title":"Benchmarking Language Model Creativity: A Case Study on Code Generation","date":"2024-07-12","arxiv_id":"2407.09007","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 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) · 0 unverified","sample_list":"/paper/benchmarking-language-model-creativity-a-case#ran","syntology_url":"https://syntology.ai/paper/2407.09007","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.09007"}},"official":{"repos":["JHU-CLSP/NeoCoder"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/daniel-a-fast-document-attention-network-for","slug":"daniel-a-fast-document-attention-network-for","title":"DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documents","date":"2024-07-12","arxiv_id":"2407.09103","repositories_listed":1,"syntology":null},{"url":"/paper/gofa-a-generative-one-for-all-model-for-joint","slug":"gofa-a-generative-one-for-all-model-for-joint","title":"GOFA: A Generative One-For-All Model for Joint Graph Language Modeling","date":"2024-07-12","arxiv_id":"2407.09709","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/gofa-a-generative-one-for-all-model-for-joint#ran","syntology_url":"https://syntology.ai/paper/2407.09709","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.09709"}},"official":{"repos":["jiaruifeng/gofa"],"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/molecule-language-model-with-augmented-pairs","slug":"molecule-language-model-with-augmented-pairs","title":"Vision Language Model is NOT All You Need: Augmentation Strategies for Molecule Language Models","date":"2024-07-12","arxiv_id":"2407.09043","repositories_listed":1,"syntology":null},{"url":"/paper/stepwise-verification-and-remediation-of","slug":"stepwise-verification-and-remediation-of","title":"Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors","date":"2024-07-12","arxiv_id":"2407.09136","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-thermal-infrared-tracking-with","slug":"enhancing-thermal-infrared-tracking-with","title":"Coordinate-Aware Thermal Infrared Tracking Via Natural Language Modeling","date":"2024-07-11","arxiv_id":"2407.08265","repositories_listed":1,"syntology":null},{"url":"/paper/explore-the-potential-of-clip-for-training","slug":"explore-the-potential-of-clip-for-training","title":"Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentation","date":"2024-07-11","arxiv_id":"2407.08268","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 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) · 2 unverified","sample_list":"/paper/explore-the-potential-of-clip-for-training#ran","syntology_url":"https://syntology.ai/paper/2407.08268","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.08268"}},"official":{"repos":["leaves162/cliptrase"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/hypergraph-multi-modal-large-language-model","slug":"hypergraph-multi-modal-large-language-model","title":"Hypergraph Multi-modal Large Language Model: Exploiting EEG and Eye-tracking Modalities to Evaluate Heterogeneous Responses for Video Understanding","date":"2024-07-11","arxiv_id":"2407.08150","repositories_listed":1,"syntology":null},{"url":"/paper/incorporating-large-language-models-into","slug":"incorporating-large-language-models-into","title":"Incorporating Large Language Models into Production Systems for Enhanced Task Automation and Flexibility","date":"2024-07-11","arxiv_id":"2407.08550","repositories_listed":1,"syntology":null},{"url":"/paper/seed-story-multimodal-long-story-generation","slug":"seed-story-multimodal-long-story-generation","title":"SEED-Story: Multimodal Long Story Generation with Large Language Model","date":"2024-07-11","arxiv_id":"2407.08683","repositories_listed":1,"syntology":{"n":17,"n_ran":14,"n_constructed":0,"n_ran_checked":8,"n_instrument":6,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":17,"phrase":"14 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; 6 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/seed-story-multimodal-long-story-generation#ran","syntology_url":"https://syntology.ai/paper/2407.08683","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.08683"}},"official":{"repos":["tencentarc/seed-story"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/ida-vlm-towards-movie-understanding-via-id","slug":"ida-vlm-towards-movie-understanding-via-id","title":"IDA-VLM: Towards Movie Understanding via ID-Aware Large Vision-Language Model","date":"2024-07-10","arxiv_id":"2407.07577","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":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","sample_list":"/paper/ida-vlm-towards-movie-understanding-via-id#ran","syntology_url":"https://syntology.ai/paper/2407.07577","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.07577"}},"official":{"repos":["jiyt17/ida-vlm"],"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/paligemma-a-versatile-3b-vlm-for-transfer","slug":"paligemma-a-versatile-3b-vlm-for-transfer","title":"PaliGemma: A versatile 3B VLM for transfer","date":"2024-07-10","arxiv_id":"2407.07726","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/paligemma-a-versatile-3b-vlm-for-transfer#ran","syntology_url":"https://syntology.ai/paper/2407.07726","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.07726"}},"official":{"repos":["google-research/big_vision"],"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/towards-a-text-based-quantitative-and","slug":"towards-a-text-based-quantitative-and","title":"Towards a text-based quantitative and explainable histopathology image analysis","date":"2024-07-10","arxiv_id":"2407.07360","repositories_listed":1,"syntology":null},{"url":"/paper/cola-conditional-dropout-and-language-driven","slug":"cola-conditional-dropout-and-language-driven","title":"CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object Detection","date":"2024-07-09","arxiv_id":"2407.06780","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":3,"n_instrument":5,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/cola-conditional-dropout-and-language-driven#ran","syntology_url":"https://syntology.ai/paper/2407.06780","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.06780"}},"official":{"repos":["ssecv/CoLA"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/copybench-measuring-literal-and-non-literal","slug":"copybench-measuring-literal-and-non-literal","title":"CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation","date":"2024-07-09","arxiv_id":"2407.07087","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/copybench-measuring-literal-and-non-literal#ran","syntology_url":"https://syntology.ai/paper/2407.07087","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.07087"}},"official":{"repos":["chentong0/copy-bench"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fbi-llm-scaling-up-fully-binarized-llms-from","slug":"fbi-llm-scaling-up-fully-binarized-llms-from","title":"FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation","date":"2024-07-09","arxiv_id":"2407.07093","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":8,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":14,"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) · 3 unverified","sample_list":"/paper/fbi-llm-scaling-up-fully-binarized-llms-from#ran","syntology_url":"https://syntology.ai/paper/2407.07093","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.07093"}},"official":{"repos":["liqunma/fbi-llm"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/multimodal-self-instruct-synthetic-abstract","slug":"multimodal-self-instruct-synthetic-abstract","title":"Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Model","date":"2024-07-09","arxiv_id":"2407.07053","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":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","sample_list":"/paper/multimodal-self-instruct-synthetic-abstract#ran","syntology_url":"https://syntology.ai/paper/2407.07053","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.07053"}},"official":{"repos":["zwq2018/multi-modal-self-instruct"],"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/scaling-retrieval-based-language-models-with","slug":"scaling-retrieval-based-language-models-with","title":"Scaling Retrieval-Based Language Models with a Trillion-Token Datastore","date":"2024-07-09","arxiv_id":"2407.12854","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":1,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/scaling-retrieval-based-language-models-with#ran","syntology_url":"https://syntology.ai/paper/2407.12854","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.12854"}},"official":{"repos":["rulinshao/retrieval-scaling"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-single-transformer-for-scalable-vision","slug":"a-single-transformer-for-scalable-vision","title":"SOLO: A Single Transformer for Scalable Vision-Language Modeling","date":"2024-07-08","arxiv_id":"2407.06438","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-single-transformer-for-scalable-vision#ran","syntology_url":"https://syntology.ai/paper/2407.06438","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.06438"}},"official":{"repos":["yangyi-chen/solo"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/debunc-mitigating-hallucinations-in-large","slug":"debunc-mitigating-hallucinations-in-large","title":"DebUnc: Improving Large Language Model Agent Communication With Uncertainty Metrics","date":"2024-07-08","arxiv_id":"2407.06426","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":4,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"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) · 0 unverified","sample_list":"/paper/debunc-mitigating-hallucinations-in-large#ran","syntology_url":"https://syntology.ai/paper/2407.06426","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.06426"}},"official":{"repos":["lukeyoffe/debunc"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/large-language-model-recall-uncertainty-is","slug":"large-language-model-recall-uncertainty-is","title":"Large Language Model Recall Uncertainty is Modulated by the Fan Effect","date":"2024-07-08","arxiv_id":"2407.06349","repositories_listed":1,"syntology":null},{"url":"/paper/mst5-multilingual-question-answering-over","slug":"mst5-multilingual-question-answering-over","title":"MST5 -- Multilingual Question Answering over Knowledge Graphs","date":"2024-07-08","arxiv_id":"2407.06041","repositories_listed":1,"syntology":null},{"url":"/paper/psycollm-enhancing-llm-for-psychological","slug":"psycollm-enhancing-llm-for-psychological","title":"PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluation","date":"2024-07-08","arxiv_id":"2407.05721","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-hallucination-detection-through","slug":"enhancing-hallucination-detection-through","title":"Enhancing Hallucination Detection through Perturbation-Based Synthetic Data Generation in System Responses","date":"2024-07-07","arxiv_id":"2407.05474","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 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) · 0 unverified","sample_list":"/paper/enhancing-hallucination-detection-through#ran","syntology_url":"https://syntology.ai/paper/2407.05474","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.05474"}},"official":{"repos":["asappresearch/halugen"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/just-read-twice-closing-the-recall-gap-for","slug":"just-read-twice-closing-the-recall-gap-for","title":"Just read twice: closing the recall gap for recurrent language models","date":"2024-07-07","arxiv_id":"2407.05483","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"3 ran (of which 0 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) · 2 unverified","sample_list":"/paper/just-read-twice-closing-the-recall-gap-for#ran","syntology_url":"https://syntology.ai/paper/2407.05483","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.05483"}},"official":{"repos":["HazyResearch/prefix-linear-attention"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/language-models-encode-collaborative-signals","slug":"language-models-encode-collaborative-signals","title":"Language Representations Can be What Recommenders Need: Findings and Potentials","date":"2024-07-07","arxiv_id":"2407.05441","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 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) · 0 unverified","sample_list":"/paper/language-models-encode-collaborative-signals#ran","syntology_url":"https://syntology.ai/paper/2407.05441","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.05441"}},"official":{"repos":["lehengthu/alpharec"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/beyond-perplexity-multi-dimensional-safety","slug":"beyond-perplexity-multi-dimensional-safety","title":"Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM Compression","date":"2024-07-06","arxiv_id":"2407.04965","repositories_listed":1,"syntology":{"n":16,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":16,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/beyond-perplexity-multi-dimensional-safety#ran","syntology_url":"https://syntology.ai/paper/2407.04965","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.04965"}},"official":{"repos":["zhichaoxu-shufe/beyond-perplexity-compression-safety-eval"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/large-language-models-are-good-medical-coders","slug":"large-language-models-are-good-medical-coders","title":"Large language models are good medical coders, if provided with tools","date":"2024-07-06","arxiv_id":"2407.12849","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_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","sample_list":"/paper/large-language-models-are-good-medical-coders#ran","syntology_url":"https://syntology.ai/paper/2407.12849","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.12849"}},"official":{"repos":["ainativehealth/goodmedicalcoder"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/shine-saliency-aware-hierarchical-negative","slug":"shine-saliency-aware-hierarchical-negative","title":"SHINE: Saliency-aware HIerarchical NEgative Ranking for Compositional Temporal Grounding","date":"2024-07-06","arxiv_id":"2407.05118","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":8,"n_pointer_only":13,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 1 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/shine-saliency-aware-hierarchical-negative#ran","syntology_url":"https://syntology.ai/paper/2407.05118","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.05118"}},"official":{"repos":["zxccade/shine"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/crafting-large-language-models-for-enhanced","slug":"crafting-large-language-models-for-enhanced","title":"Crafting Large Language Models for Enhanced Interpretability","date":"2024-07-05","arxiv_id":"2407.04307","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/crafting-large-language-models-for-enhanced#ran","syntology_url":"https://syntology.ai/paper/2407.04307","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.04307"}},"official":null}},{"url":"/paper/poprero-a-new-dataset-for-popularity","slug":"poprero-a-new-dataset-for-popularity","title":"PoPreRo: A New Dataset for Popularity Prediction of Romanian Reddit Posts","date":"2024-07-05","arxiv_id":"2407.04541","repositories_listed":1,"syntology":null},{"url":"/paper/written-term-detection-improves-spoken-term","slug":"written-term-detection-improves-spoken-term","title":"Written Term Detection Improves Spoken Term Detection","date":"2024-07-05","arxiv_id":"2407.04601","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-language-model-context-windows-a","slug":"evaluating-language-model-context-windows-a","title":"Evaluating Language Model Context Windows: A \"Working Memory\" Test and Inference-time Correction","date":"2024-07-04","arxiv_id":"2407.03651","repositories_listed":1,"syntology":null},{"url":"/paper/historical-ink-19th-century-latin-american","slug":"historical-ink-19th-century-latin-american","title":"Historical Ink: 19th Century Latin American Spanish Newspaper Corpus with LLM OCR Correction","date":"2024-07-04","arxiv_id":"2407.12838","repositories_listed":1,"syntology":null},{"url":"/paper/integrating-randomness-in-large-language","slug":"integrating-randomness-in-large-language","title":"Integrating Randomness in Large Language Models: A Linear Congruential Generator Approach for Generating Clinically Relevant Content","date":"2024-07-04","arxiv_id":"2407.03582","repositories_listed":1,"syntology":null},{"url":"/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","repositories_listed":1,"syntology":null},{"url":"/paper/minigpt-med-large-language-model-as-a-general","slug":"minigpt-med-large-language-model-as-a-general","title":"MiniGPT-Med: Large Language Model as a General Interface for Radiology Diagnosis","date":"2024-07-04","arxiv_id":"2407.04106","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"5 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/minigpt-med-large-language-model-as-a-general#ran","syntology_url":"https://syntology.ai/paper/2407.04106","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.04106"}},"official":{"repos":["vision-cair/minigpt-med"],"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"]}}},{"url":"/paper/mixture-of-a-million-experts","slug":"mixture-of-a-million-experts","title":"Mixture of A Million Experts","date":"2024-07-04","arxiv_id":"2407.04153","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mixture-of-a-million-experts#ran","syntology_url":"https://syntology.ai/paper/2407.04153","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.04153"}},"official":null}},{"url":"/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","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-guided-optimization-on-large","slug":"uncertainty-guided-optimization-on-large","title":"Uncertainty-Guided Optimization on Large Language Model Search Trees","date":"2024-07-04","arxiv_id":"2407.03951","repositories_listed":1,"syntology":null},{"url":"/paper/wilddesed-an-llm-powered-dataset-for-wild","slug":"wilddesed-an-llm-powered-dataset-for-wild","title":"WildDESED: An LLM-Powered Dataset for Wild Domestic Environment Sound Event Detection System","date":"2024-07-04","arxiv_id":"2407.03656","repositories_listed":1,"syntology":null},{"url":"/paper/internlm-xcomposer-2-5-a-versatile-large","slug":"internlm-xcomposer-2-5-a-versatile-large","title":"InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output","date":"2024-07-03","arxiv_id":"2407.03320","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":2,"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/internlm-xcomposer-2-5-a-versatile-large#ran","syntology_url":"https://syntology.ai/paper/2407.03320","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.03320"}},"official":{"repos":["internlm/internlm-xcomposer"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/a-bounding-box-is-worth-one-token","slug":"a-bounding-box-is-worth-one-token","title":"A Bounding Box is Worth One Token: Interleaving Layout and Text in a Large Language Model for Document Understanding","date":"2024-07-02","arxiv_id":"2407.01976","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":1,"n_no_contract":4,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 1 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-bounding-box-is-worth-one-token#ran","syntology_url":"https://syntology.ai/paper/2407.01976","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.01976"}},"official":{"repos":["laytextllm/laytextllm"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}}],"record_sha256":"1caf50b846575f704217571c0a092f6ee1eddc7ebf1685279fee96c9182d757f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}