{"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/large-language-model/papers/11","list_of":"/task/large-language-model","task":"Large Language Model","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":11,"pages_in_order":61,"rows_per_page":100,"rows":[1001,1100],"of":6097,"counts":{"archive_papers_tagged":6097,"with_a_code_link":2250,"where_syntology_ran_a_sample":801,"not_listed_spam_title":0,"listed":6097,"listed_where_code_ran":801,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":683,"every_run_a_failure_of_syntologys_instrument":118,"listed_with_a_run_with_no_instrument_failure":683,"listed_every_run_a_failure_of_syntologys_instrument":118,"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/large-language-model","prev":"/task/large-language-model/papers/10","next":"/task/large-language-model/papers/12","papers":[{"url":"/paper/multimodal-llm-enhanced-cross-lingual-cross","slug":"multimodal-llm-enhanced-cross-lingual-cross","title":"Multimodal LLM Enhanced Cross-lingual Cross-modal Retrieval","date":"2024-09-30","arxiv_id":"2409.19961","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":4,"n_instrument":5,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":12,"phrase":"9 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; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/multimodal-llm-enhanced-cross-lingual-cross#ran","syntology_url":"https://syntology.ai/paper/2409.19961","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.19961"}},"official":{"repos":["lijiabei-7/leccr"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/openkd-opening-prompt-diversity-for-zero-and","slug":"openkd-opening-prompt-diversity-for-zero-and","title":"OpenKD: Opening Prompt Diversity for Zero- and Few-shot Keypoint Detection","date":"2024-09-30","arxiv_id":"2409.19899","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":14,"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) · 1 unverified","sample_list":"/paper/openkd-opening-prompt-diversity-for-zero-and#ran","syntology_url":"https://syntology.ai/paper/2409.19899","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.19899"}},"official":{"repos":["alanlusun/openkd"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/robin3d-improving-3d-large-language-model-via","slug":"robin3d-improving-3d-large-language-model-via","title":"Robin3D: Improving 3D Large Language Model via Robust Instruction Tuning","date":"2024-09-30","arxiv_id":"2410.00255","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":5,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"9 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/robin3d-improving-3d-large-language-model-via#ran","syntology_url":"https://syntology.ai/paper/2410.00255","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.00255"}},"official":{"repos":["weitaikang/robin3d"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/videoinsta-zero-shot-long-video-understanding","slug":"videoinsta-zero-shot-long-video-understanding","title":"VideoINSTA: Zero-shot Long Video Understanding via Informative Spatial-Temporal Reasoning with LLMs","date":"2024-09-30","arxiv_id":"2409.20365","repositories_listed":1,"syntology":null},{"url":"/paper/medvilam-a-multimodal-large-language-model","slug":"medvilam-a-multimodal-large-language-model","title":"MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generation","date":"2024-09-29","arxiv_id":"2409.19684","repositories_listed":1,"syntology":null},{"url":"/paper/one-token-to-seg-them-all-language-instructed","slug":"one-token-to-seg-them-all-language-instructed","title":"One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos","date":"2024-09-29","arxiv_id":"2409.19603","repositories_listed":1,"syntology":{"n":16,"n_ran":8,"n_constructed":0,"n_ran_checked":4,"n_instrument":4,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"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) · 8 unverified","sample_list":"/paper/one-token-to-seg-them-all-language-instructed#ran","syntology_url":"https://syntology.ai/paper/2409.19603","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.19603"}},"official":{"repos":["showlab/videolisa"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/see-detail-say-clear-towards-brain-ct-report","slug":"see-detail-say-clear-towards-brain-ct-report","title":"See Detail Say Clear: Towards Brain CT Report Generation via Pathological Clue-driven Representation Learning","date":"2024-09-29","arxiv_id":"2409.19676","repositories_listed":1,"syntology":null},{"url":"/paper/clip-moe-towards-building-mixture-of-experts","slug":"clip-moe-towards-building-mixture-of-experts","title":"CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling","date":"2024-09-28","arxiv_id":"2409.19291","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/clip-moe-towards-building-mixture-of-experts#ran","syntology_url":"https://syntology.ai/paper/2409.19291","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.19291"}},"official":{"repos":["OpenSparseLLMs/CLIP-MoE"],"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/test-case-informed-knowledge-tracing-for-open","slug":"test-case-informed-knowledge-tracing-for-open","title":"Test Case-Informed Knowledge Tracing for Open-ended Coding Tasks","date":"2024-09-28","arxiv_id":"2410.10829","repositories_listed":1,"syntology":null},{"url":"/paper/align-2-llava-cascaded-human-and-large","slug":"align-2-llava-cascaded-human-and-large","title":"Align$^2$LLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction Curation","date":"2024-09-27","arxiv_id":"2409.18541","repositories_listed":1,"syntology":null},{"url":"/paper/confidential-prompting-protecting-user","slug":"confidential-prompting-protecting-user","title":"Confidential Prompting: Protecting User Prompts from Cloud LLM Providers","date":"2024-09-27","arxiv_id":"2409.19134","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"8 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/confidential-prompting-protecting-user#ran","syntology_url":"https://syntology.ai/paper/2409.19134","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.19134"}},"official":{"repos":["yale-sys/confidential-prompting"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/control-industrial-automation-system-with","slug":"control-industrial-automation-system-with","title":"Control Industrial Automation System with Large Language Model Agents","date":"2024-09-26","arxiv_id":"2409.18009","repositories_listed":1,"syntology":null},{"url":"/paper/cross-institutional-structured-radiology","slug":"cross-institutional-structured-radiology","title":"Development and Validation of a Dynamic-Template-Constrained Large Language Model for Generating Fully-Structured Radiology Reports","date":"2024-09-26","arxiv_id":"2409.18319","repositories_listed":1,"syntology":null},{"url":"/paper/dualad-dual-layer-planning-for-reasoning-in","slug":"dualad-dual-layer-planning-for-reasoning-in","title":"DualAD: Dual-Layer Planning for Reasoning in Autonomous Driving","date":"2024-09-26","arxiv_id":"2409.18053","repositories_listed":1,"syntology":null},{"url":"/paper/maskllm-learnable-semi-structured-sparsity","slug":"maskllm-learnable-semi-structured-sparsity","title":"MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models","date":"2024-09-26","arxiv_id":"2409.17481","repositories_listed":1,"syntology":{"n":16,"n_ran":5,"n_constructed":1,"n_ran_checked":5,"n_instrument":0,"n_unverified":11,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":16,"phrase":"5 ran (of which 1 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) · 11 unverified","sample_list":"/paper/maskllm-learnable-semi-structured-sparsity#ran","syntology_url":"https://syntology.ai/paper/2409.17481","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.17481"}},"official":{"repos":["nvlabs/maskllm"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":1,"n_ran_no_instrument_failure":5,"n_unverified":11,"ran_from_kinds":["official"]}}},{"url":"/paper/retrospective-comparative-analysis-of","slug":"retrospective-comparative-analysis-of","title":"Retrospective Comparative Analysis of Prostate Cancer In-Basket Messages: Responses from Closed-Domain LLM vs. Clinical Teams","date":"2024-09-26","arxiv_id":"2409.18290","repositories_listed":1,"syntology":null},{"url":"/paper/aapm-large-language-model-agent-based-asset","slug":"aapm-large-language-model-agent-based-asset","title":"Empirical Asset Pricing with Large Language Model Agents","date":"2024-09-25","arxiv_id":"2409.17266","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/aapm-large-language-model-agent-based-asset#ran","syntology_url":"https://syntology.ai/paper/2409.17266","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.17266"}},"official":{"repos":["chengjunyan1/aapm"],"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/counterfactual-token-generation-in-large","slug":"counterfactual-token-generation-in-large","title":"Counterfactual Token Generation in Large Language Models","date":"2024-09-25","arxiv_id":"2409.17027","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":10,"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) · 0 unverified","sample_list":"/paper/counterfactual-token-generation-in-large#ran","syntology_url":"https://syntology.ai/paper/2409.17027","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.17027"}},"official":{"repos":["networks-learning/counterfactual-llms"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dalda-data-augmentation-leveraging-diffusion","slug":"dalda-data-augmentation-leveraging-diffusion","title":"DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance Scaling","date":"2024-09-25","arxiv_id":"2409.16949","repositories_listed":1,"syntology":null},{"url":"/paper/mitigating-the-bias-of-large-language-model","slug":"mitigating-the-bias-of-large-language-model","title":"Mitigating the Bias of Large Language Model Evaluation","date":"2024-09-25","arxiv_id":"2409.16788","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mitigating-the-bias-of-large-language-model#ran","syntology_url":"https://syntology.ai/paper/2409.16788","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.16788"}},"official":{"repos":["Joe-Hall-Lee/Debias"],"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/programming-every-example-lifting-pre","slug":"programming-every-example-lifting-pre","title":"Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale","date":"2024-09-25","arxiv_id":"2409.17115","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/programming-every-example-lifting-pre#ran","syntology_url":"https://syntology.ai/paper/2409.17115","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.17115"}},"official":{"repos":["gair-nlp/prox"],"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/eegunity-open-source-tool-in-facilitating","slug":"eegunity-open-source-tool-in-facilitating","title":"EEGUnity: Open-Source Tool in Facilitating Unified EEG Datasets Towards Large-Scale EEG Model","date":"2024-09-24","arxiv_id":"2410.07196","repositories_listed":1,"syntology":null},{"url":"/paper/effectiveness-of-cross-linguistic-extraction","slug":"effectiveness-of-cross-linguistic-extraction","title":"Effectiveness of Cross-linguistic Extraction of Genetic Information using Generative Large Language Models","date":"2024-09-24","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/objectively-evaluating-the-reliability-of","slug":"objectively-evaluating-the-reliability-of","title":"Objectively Evaluating the Reliability of Cell Type Annotation Using LLM-Based Strategies","date":"2024-09-24","arxiv_id":"2409.15678","repositories_listed":1,"syntology":null},{"url":"/paper/small-language-models-survey-measurements-and","slug":"small-language-models-survey-measurements-and","title":"Small Language Models: Survey, Measurements, and Insights","date":"2024-09-24","arxiv_id":"2409.15790","repositories_listed":1,"syntology":null},{"url":"/paper/alphazip-neural-network-enhanced-lossless","slug":"alphazip-neural-network-enhanced-lossless","title":"AlphaZip: Neural Network-Enhanced Lossless Text Compression","date":"2024-09-23","arxiv_id":"2409.15046","repositories_listed":1,"syntology":null},{"url":"/paper/archon-an-architecture-search-framework-for","slug":"archon-an-architecture-search-framework-for","title":"Archon: An Architecture Search Framework for Inference-Time Techniques","date":"2024-09-23","arxiv_id":"2409.15254","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":0,"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/archon-an-architecture-search-framework-for#ran","syntology_url":"https://syntology.ai/paper/2409.15254","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.15254"}},"official":{"repos":["scalingintelligence/archon"],"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/llamapartialspoof-an-llm-driven-fake-speech","slug":"llamapartialspoof-an-llm-driven-fake-speech","title":"LlamaPartialSpoof: An LLM-Driven Fake Speech Dataset Simulating Disinformation Generation","date":"2024-09-23","arxiv_id":"2409.14743","repositories_listed":1,"syntology":null},{"url":"/paper/an-adapted-large-language-model-facilitates","slug":"an-adapted-large-language-model-facilitates","title":"Diabetica: Adapting Large Language Model to Enhance Multiple Medical Tasks in Diabetes Care and Management","date":"2024-09-20","arxiv_id":"2409.13191","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/an-adapted-large-language-model-facilitates#ran","syntology_url":"https://syntology.ai/paper/2409.13191","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.13191"}},"official":{"repos":["waltonfuture/Diabetica"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/beyond-accuracy-optimization-computer-vision","slug":"beyond-accuracy-optimization-computer-vision","title":"Beyond Accuracy Optimization: Computer Vision Losses for Large Language Model Fine-Tuning","date":"2024-09-20","arxiv_id":"2409.13641","repositories_listed":1,"syntology":null},{"url":"/paper/measuring-copyright-risks-of-large-language","slug":"measuring-copyright-risks-of-large-language","title":"Measuring Copyright Risks of Large Language Model via Partial Information Probing","date":"2024-09-20","arxiv_id":"2409.13831","repositories_listed":1,"syntology":null},{"url":"/paper/shizishangpt-an-agricultural-large-language","slug":"shizishangpt-an-agricultural-large-language","title":"ShizishanGPT: An Agricultural Large Language Model Integrating Tools and Resources","date":"2024-09-20","arxiv_id":"2409.13537","repositories_listed":1,"syntology":null},{"url":"/paper/autoverus-automated-proof-generation-for-rust","slug":"autoverus-automated-proof-generation-for-rust","title":"AutoVerus: Automated Proof Generation for Rust Code","date":"2024-09-19","arxiv_id":"2409.13082","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-perception-of-key-changes-in-remote","slug":"enhancing-perception-of-key-changes-in-remote","title":"Enhancing Perception of Key Changes in Remote Sensing Image Change Captioning","date":"2024-09-19","arxiv_id":"2409.12612","repositories_listed":1,"syntology":null},{"url":"/paper/hllm-enhancing-sequential-recommendations-via","slug":"hllm-enhancing-sequential-recommendations-via","title":"HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling","date":"2024-09-19","arxiv_id":"2409.12740","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/hllm-enhancing-sequential-recommendations-via#ran","syntology_url":"https://syntology.ai/paper/2409.12740","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.12740"}},"official":{"repos":["bytedance/hllm"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/iteration-of-thought-leveraging-inner","slug":"iteration-of-thought-leveraging-inner","title":"Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning","date":"2024-09-19","arxiv_id":"2409.12618","repositories_listed":1,"syntology":null},{"url":"/paper/profiling-patient-transcript-using-large","slug":"profiling-patient-transcript-using-large","title":"Profiling Patient Transcript Using Large Language Model Reasoning Augmentation for Alzheimer's Disease Detection","date":"2024-09-19","arxiv_id":"2409.12541","repositories_listed":1,"syntology":null},{"url":"/paper/scaling-smart-accelerating-large-language","slug":"scaling-smart-accelerating-large-language","title":"Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization","date":"2024-09-19","arxiv_id":"2409.12903","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/scaling-smart-accelerating-large-language#ran","syntology_url":"https://syntology.ai/paper/2409.12903","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.12903"}},"official":{"repos":["apple/ml-hypercloning"],"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"]}}},{"url":"/paper/towards-interactive-and-learnable-cooperative","slug":"towards-interactive-and-learnable-cooperative","title":"Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework","date":"2024-09-19","arxiv_id":"2409.12812","repositories_listed":1,"syntology":null},{"url":"/paper/development-and-bilingual-evaluation-of","slug":"development-and-bilingual-evaluation-of","title":"Development and bilingual evaluation of Japanese medical large language model within reasonably low computational resources","date":"2024-09-18","arxiv_id":"2409.11783","repositories_listed":1,"syntology":null},{"url":"/paper/revealing-the-challenge-of-detecting","slug":"revealing-the-challenge-of-detecting","title":"Revealing and Mitigating the Challenge of Detecting Character Knowledge Errors in LLM Role-Playing","date":"2024-09-18","arxiv_id":"2409.11726","repositories_listed":1,"syntology":null},{"url":"/paper/ruie-retrieval-based-unified-information","slug":"ruie-retrieval-based-unified-information","title":"RUIE: Retrieval-based Unified Information Extraction using Large Language Model","date":"2024-09-18","arxiv_id":"2409.11673","repositories_listed":1,"syntology":null},{"url":"/paper/attention-seeker-dynamic-self-attention","slug":"attention-seeker-dynamic-self-attention","title":"Attention-Seeker: Dynamic Self-Attention Scoring for Unsupervised Keyphrase Extraction","date":"2024-09-17","arxiv_id":"2409.10907","repositories_listed":1,"syntology":null},{"url":"/paper/lola-an-open-source-massively-multilingual","slug":"lola-an-open-source-massively-multilingual","title":"LOLA -- An Open-Source Massively Multilingual Large Language Model","date":"2024-09-17","arxiv_id":"2409.11272","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-large-language-model-uncertainty","slug":"benchmarking-large-language-model-uncertainty","title":"Benchmarking Large Language Model Uncertainty for Prompt Optimization","date":"2024-09-16","arxiv_id":"2409.10044","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-rl-safety-with-counterfactual-llm","slug":"enhancing-rl-safety-with-counterfactual-llm","title":"Enhancing RL Safety with Counterfactual LLM Reasoning","date":"2024-09-16","arxiv_id":"2409.10188","repositories_listed":1,"syntology":null},{"url":"/paper/unveiling-gender-bias-in-large-language","slug":"unveiling-gender-bias-in-large-language","title":"Unveiling Gender Bias in Large Language Models: Using Teacher's Evaluation in Higher Education As an Example","date":"2024-09-15","arxiv_id":"2409.09652","repositories_listed":1,"syntology":null},{"url":"/paper/periguru-a-peripheral-robotic-mobile-app","slug":"periguru-a-peripheral-robotic-mobile-app","title":"PeriGuru: A Peripheral Robotic Mobile App Operation Assistant based on GUI Image Understanding and Prompting with LLM","date":"2024-09-14","arxiv_id":"2409.09354","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-the-influence-of-source-code-on","slug":"rethinking-the-influence-of-source-code-on","title":"Measuring the Influence of Incorrect Code on Test Generation","date":"2024-09-14","arxiv_id":"2409.09464","repositories_listed":1,"syntology":null},{"url":"/paper/symbolic-regression-with-a-learned-concept","slug":"symbolic-regression-with-a-learned-concept","title":"Symbolic Regression with a Learned Concept Library","date":"2024-09-14","arxiv_id":"2409.09359","repositories_listed":1,"syntology":null},{"url":"/paper/large-language-model-can-transcribe-speech-in","slug":"large-language-model-can-transcribe-speech-in","title":"Large Language Model Can Transcribe Speech in Multi-Talker Scenarios with Versatile Instructions","date":"2024-09-13","arxiv_id":"2409.08596","repositories_listed":1,"syntology":null},{"url":"/paper/synsum-synthetic-benchmark-with-structured","slug":"synsum-synthetic-benchmark-with-structured","title":"SynSUM -- Synthetic Benchmark with Structured and Unstructured Medical Records","date":"2024-09-13","arxiv_id":"2409.08936","repositories_listed":1,"syntology":null},{"url":"/paper/vltp-vision-language-guided-token-pruning-for","slug":"vltp-vision-language-guided-token-pruning-for","title":"VLTP: Vision-Language Guided Token Pruning for Task-Oriented Segmentation","date":"2024-09-13","arxiv_id":"2409.08464","repositories_listed":1,"syntology":null},{"url":"/paper/wirelessagent-large-language-model-agents-for","slug":"wirelessagent-large-language-model-agents-for","title":"WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks","date":"2024-09-12","arxiv_id":"2409.07964","repositories_listed":1,"syntology":null},{"url":"/paper/adacad-adaptively-decoding-to-balance","slug":"adacad-adaptively-decoding-to-balance","title":"AdaCAD: Adaptively Decoding to Balance Conflicts between Contextual and Parametric Knowledge","date":"2024-09-11","arxiv_id":"2409.07394","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adacad-adaptively-decoding-to-balance#ran","syntology_url":"https://syntology.ai/paper/2409.07394","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.07394"}},"official":{"repos":["hannight/adacad"],"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","unlocated"]}}},{"url":"/paper/cross-refine-improving-natural-language","slug":"cross-refine-improving-natural-language","title":"Cross-Refine: Improving Natural Language Explanation Generation by Learning in Tandem","date":"2024-09-11","arxiv_id":"2409.07123","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cross-refine-improving-natural-language#ran","syntology_url":"https://syntology.ai/paper/2409.07123","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.07123"}},"official":{"repos":["qiaw99/Cross-Refine"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hesso-towards-automatic-efficient-and-user","slug":"hesso-towards-automatic-efficient-and-user","title":"HESSO: Towards Automatic Efficient and User Friendly Any Neural Network Training and Pruning","date":"2024-09-11","arxiv_id":"2409.09085","repositories_listed":1,"syntology":null},{"url":"/paper/ontology-free-general-domain-knowledge-graph","slug":"ontology-free-general-domain-knowledge-graph","title":"Ontology-Free General-Domain Knowledge Graph-to-Text Generation Dataset Synthesis using Large Language Model","date":"2024-09-11","arxiv_id":"2409.07088","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":7,"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) · 2 unverified","sample_list":"/paper/ontology-free-general-domain-knowledge-graph#ran","syntology_url":"https://syntology.ai/paper/2409.07088","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.07088"}},"official":{"repos":["daehuikim/WikiOFGraph"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/leveraging-content-and-acoustic","slug":"leveraging-content-and-acoustic","title":"Leveraging Content and Acoustic Representations for Speech Emotion Recognition","date":"2024-09-09","arxiv_id":"2409.05566","repositories_listed":1,"syntology":null},{"url":"/paper/texttoucher-fine-grained-text-to-touch","slug":"texttoucher-fine-grained-text-to-touch","title":"TextToucher: Fine-Grained Text-to-Touch Generation","date":"2024-09-09","arxiv_id":"2409.05427","repositories_listed":1,"syntology":null},{"url":"/paper/multi-programming-language-ensemble-for-code","slug":"multi-programming-language-ensemble-for-code","title":"Multi-Programming Language Ensemble for Code Generation in Large Language Model","date":"2024-09-06","arxiv_id":"2409.04114","repositories_listed":1,"syntology":null},{"url":"/paper/sparse-rewards-can-self-train-dialogue-agents","slug":"sparse-rewards-can-self-train-dialogue-agents","title":"Sparse Rewards Can Self-Train Dialogue Agents","date":"2024-09-06","arxiv_id":"2409.04617","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/sparse-rewards-can-self-train-dialogue-agents#ran","syntology_url":"https://syntology.ai/paper/2409.04617","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.04617"}},"official":{"repos":["asappresearch/josh-llm-simulation-training"],"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/large-language-model-based-agents-for","slug":"large-language-model-based-agents-for","title":"Large Language Model-Based Agents for Software Engineering: A Survey","date":"2024-09-04","arxiv_id":"2409.02977","repositories_listed":1,"syntology":null},{"url":"/paper/exploiting-the-vulnerability-of-large","slug":"exploiting-the-vulnerability-of-large","title":"Exploiting the Vulnerability of Large Language Models via Defense-Aware Architectural Backdoor","date":"2024-09-03","arxiv_id":"2409.01952","repositories_listed":1,"syntology":null},{"url":"/paper/fuzzcoder-byte-level-fuzzing-test-via-large","slug":"fuzzcoder-byte-level-fuzzing-test-via-large","title":"FuzzCoder: Byte-level Fuzzing Test via Large Language Model","date":"2024-09-03","arxiv_id":"2409.01944","repositories_listed":1,"syntology":null},{"url":"/paper/lifegpt-topology-agnostic-generative","slug":"lifegpt-topology-agnostic-generative","title":"LifeGPT: Topology-Agnostic Generative Pretrained Transformer Model for Cellular Automata","date":"2024-09-03","arxiv_id":"2409.12182","repositories_listed":1,"syntology":null},{"url":"/paper/agentic-society-merging-skeleton-from-real","slug":"agentic-society-merging-skeleton-from-real","title":"Agentic Society: Merging skeleton from real world and texture from Large Language Model","date":"2024-09-02","arxiv_id":"2409.10550","repositories_listed":1,"syntology":null},{"url":"/paper/co-learning-code-learning-for-multi-agent","slug":"co-learning-code-learning-for-multi-agent","title":"Co-Learning: Code Learning for Multi-Agent Reinforcement Collaborative Framework with Conversational Natural Language Interfaces","date":"2024-09-02","arxiv_id":"2409.00985","repositories_listed":1,"syntology":null},{"url":"/paper/scope-sign-language-contextual-processing","slug":"scope-sign-language-contextual-processing","title":"SCOPE: Sign Language Contextual Processing with Embedding from LLMs","date":"2024-09-02","arxiv_id":"2409.01073","repositories_listed":1,"syntology":null},{"url":"/paper/sam4mllm-enhance-multi-modal-large-language","slug":"sam4mllm-enhance-multi-modal-large-language","title":"SAM4MLLM: Enhance Multi-Modal Large Language Model for Referring Expression Segmentation","date":"2024-09-01","arxiv_id":"2409.10542","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/sam4mllm-enhance-multi-modal-large-language#ran","syntology_url":"https://syntology.ai/paper/2409.10542","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.10542"}},"official":{"repos":["ai-application-and-integration-lab/sam4mllm"],"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/adaptvision-dynamic-input-scaling-in-mllms","slug":"adaptvision-dynamic-input-scaling-in-mllms","title":"AdaptVision: Dynamic Input Scaling in MLLMs for Versatile Scene Understanding","date":"2024-08-30","arxiv_id":"2408.16986","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/adaptvision-dynamic-input-scaling-in-mllms#ran","syntology_url":"https://syntology.ai/paper/2408.16986","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.16986"}},"official":{"repos":["harrytea/adaptvision"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/getting-inspiration-for-feature-elicitation","slug":"getting-inspiration-for-feature-elicitation","title":"Getting Inspiration for Feature Elicitation: App Store- vs. LLM-based Approach","date":"2024-08-30","arxiv_id":"2408.17404","repositories_listed":1,"syntology":null},{"url":"/paper/multimath-bridging-visual-and-mathematical","slug":"multimath-bridging-visual-and-mathematical","title":"MultiMath: Bridging Visual and Mathematical Reasoning for Large Language Models","date":"2024-08-30","arxiv_id":"2409.00147","repositories_listed":1,"syntology":{"n":18,"n_ran":17,"n_constructed":0,"n_ran_checked":9,"n_instrument":8,"n_unverified":1,"n_honours":0,"n_violates":3,"n_no_contract":6,"n_pointer_only":3,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 3 violated, 6 with no contract checked; 8 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/multimath-bridging-visual-and-mathematical#ran","syntology_url":"https://syntology.ai/paper/2409.00147","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.00147"}},"official":{"repos":["pengshuai-rin/multimath"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/gradbias-unveiling-word-influence-on-bias-in","slug":"gradbias-unveiling-word-influence-on-bias-in","title":"GradBias: Unveiling Word Influence on Bias in Text-to-Image Generative Models","date":"2024-08-29","arxiv_id":"2408.16700","repositories_listed":1,"syntology":null},{"url":"/paper/plausible-parrots-msp2023-enhancing-semantic","slug":"plausible-parrots-msp2023-enhancing-semantic","title":"Plausible-Parrots @ MSP2023: Enhancing Semantic Plausibility Modeling using Entity and Event Knowledge","date":"2024-08-29","arxiv_id":"2408.16937","repositories_listed":1,"syntology":null},{"url":"/paper/wet-overcoming-paraphrasing-vulnerabilities","slug":"wet-overcoming-paraphrasing-vulnerabilities","title":"WET: Overcoming Paraphrasing Vulnerabilities in Embeddings-as-a-Service with Linear Transformation Watermarks","date":"2024-08-29","arxiv_id":"2409.04459","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-llm-scheduling-by-learning-to-rank","slug":"efficient-llm-scheduling-by-learning-to-rank","title":"Efficient LLM Scheduling by Learning to Rank","date":"2024-08-28","arxiv_id":"2408.15792","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/efficient-llm-scheduling-by-learning-to-rank#ran","syntology_url":"https://syntology.ai/paper/2408.15792","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.15792"}},"official":{"repos":["hao-ai-lab/vllm-ltr"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/legilimens-practical-and-unified-content","slug":"legilimens-practical-and-unified-content","title":"Legilimens: Practical and Unified Content Moderation for Large Language Model Services","date":"2024-08-28","arxiv_id":"2408.15488","repositories_listed":1,"syntology":null},{"url":"/paper/llm-defenses-are-not-robust-to-multi-turn","slug":"llm-defenses-are-not-robust-to-multi-turn","title":"LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet","date":"2024-08-27","arxiv_id":"2408.15221","repositories_listed":1,"syntology":null},{"url":"/paper/xg-nid-dual-modality-network-intrusion","slug":"xg-nid-dual-modality-network-intrusion","title":"XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language Model","date":"2024-08-27","arxiv_id":"2408.16021","repositories_listed":1,"syntology":null},{"url":"/paper/agentmove-predicting-human-mobility-anywhere","slug":"agentmove-predicting-human-mobility-anywhere","title":"AgentMove: Predicting Human Mobility Anywhere Using Large Language Model based Agentic Framework","date":"2024-08-26","arxiv_id":"2408.13986","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/agentmove-predicting-human-mobility-anywhere#ran","syntology_url":"https://syntology.ai/paper/2408.13986","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.13986"}},"official":{"repos":["tsinghua-fib-lab/agentmove"],"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/grounded-multi-hop-videoqa-in-long-form","slug":"grounded-multi-hop-videoqa-in-long-form","title":"Grounded Multi-Hop VideoQA in Long-Form Egocentric Videos","date":"2024-08-26","arxiv_id":"2408.14469","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/grounded-multi-hop-videoqa-in-long-form#ran","syntology_url":"https://syntology.ai/paper/2408.14469","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.14469"}},"official":null}},{"url":"/paper/mlr-copilot-autonomous-machine-learning","slug":"mlr-copilot-autonomous-machine-learning","title":"MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agents","date":"2024-08-26","arxiv_id":"2408.14033","repositories_listed":1,"syntology":{"n":10,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":10,"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) · 7 unverified","sample_list":"/paper/mlr-copilot-autonomous-machine-learning#ran","syntology_url":"https://syntology.ai/paper/2408.14033","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.14033"}},"official":{"repos":["du-nlp-lab/mlr-copilot"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/question-answering-system-of-bridge-design","slug":"question-answering-system-of-bridge-design","title":"Question answering system of bridge design specification based on large language model","date":"2024-08-26","arxiv_id":"2408.13282","repositories_listed":1,"syntology":null},{"url":"/paper/video-ccam-enhancing-video-language","slug":"video-ccam-enhancing-video-language","title":"Video-CCAM: Enhancing Video-Language Understanding with Causal Cross-Attention Masks for Short and Long Videos","date":"2024-08-26","arxiv_id":"2408.14023","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/video-ccam-enhancing-video-language#ran","syntology_url":"https://syntology.ai/paper/2408.14023","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.14023"}},"official":{"repos":["qq-mm/video-ccam"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/vision-language-and-large-language-model","slug":"vision-language-and-large-language-model","title":"Vision-Language and Large Language Model Performance in Gastroenterology: GPT, Claude, Llama, Phi, Mistral, Gemma, and Quantized Models","date":"2024-08-25","arxiv_id":"2409.00084","repositories_listed":1,"syntology":null},{"url":"/paper/iaa-inner-adaptor-architecture-empowers","slug":"iaa-inner-adaptor-architecture-empowers","title":"IAA: Inner-Adaptor Architecture Empowers Frozen Large Language Model with Multimodal Capabilities","date":"2024-08-23","arxiv_id":"2408.12902","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":6,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"9 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/iaa-inner-adaptor-architecture-empowers#ran","syntology_url":"https://syntology.ai/paper/2408.12902","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.12902"}},"official":{"repos":["360cvgroup/inner-adaptor-architecture"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/limp-large-language-model-enhanced-intent","slug":"limp-large-language-model-enhanced-intent","title":"LIMP: Large Language Model Enhanced Intent-aware Mobility Prediction","date":"2024-08-23","arxiv_id":"2408.12832","repositories_listed":1,"syntology":null},{"url":"/paper/vfm-det-towards-high-performance-vehicle","slug":"vfm-det-towards-high-performance-vehicle","title":"VFM-Det: Towards High-Performance Vehicle Detection via Large Foundation Models","date":"2024-08-23","arxiv_id":"2408.13031","repositories_listed":1,"syntology":null},{"url":"/paper/evidence-backed-fact-checking-using-rag-and","slug":"evidence-backed-fact-checking-using-rag-and","title":"Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs","date":"2024-08-22","arxiv_id":"2408.12060","repositories_listed":1,"syntology":null},{"url":"/paper/first-teach-a-reliable-large-language-model","slug":"first-teach-a-reliable-large-language-model","title":"FIRST: Teach A Reliable Large Language Model Through Efficient Trustworthy Distillation","date":"2024-08-22","arxiv_id":"2408.12168","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/first-teach-a-reliable-large-language-model#ran","syntology_url":"https://syntology.ai/paper/2408.12168","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.12168"}},"official":{"repos":["shumkashun/first"],"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/a-quick-trustworthy-spectral-detection-q-a","slug":"a-quick-trustworthy-spectral-detection-q-a","title":"A Quick, trustworthy spectral knowledge Q&A system leveraging retrieval-augmented generation on LLM","date":"2024-08-21","arxiv_id":"2408.11557","repositories_listed":1,"syntology":null},{"url":"/paper/biorecap-an-r-package-for-summarizing-biorxiv","slug":"biorecap-an-r-package-for-summarizing-biorxiv","title":"biorecap: an R package for summarizing bioRxiv preprints with a local LLM","date":"2024-08-21","arxiv_id":"2408.11707","repositories_listed":1,"syntology":null},{"url":"/paper/cipher-cybersecurity-intelligent-penetration","slug":"cipher-cybersecurity-intelligent-penetration","title":"CIPHER: Cybersecurity Intelligent Penetration-testing Helper for Ethical Researcher","date":"2024-08-21","arxiv_id":"2408.11650","repositories_listed":1,"syntology":null},{"url":"/paper/critique-out-loud-reward-models","slug":"critique-out-loud-reward-models","title":"Critique-out-Loud Reward Models","date":"2024-08-21","arxiv_id":"2408.11791","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"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) · 1 unverified","sample_list":"/paper/critique-out-loud-reward-models#ran","syntology_url":"https://syntology.ai/paper/2408.11791","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.11791"}},"official":{"repos":["zankner/cloud"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/design-principle-transfer-in-neural","slug":"design-principle-transfer-in-neural","title":"Design Principle Transfer in Neural Architecture Search via Large Language Models","date":"2024-08-21","arxiv_id":"2408.11330","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":3,"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/design-principle-transfer-in-neural#ran","syntology_url":"https://syntology.ai/paper/2408.11330","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.11330"}},"official":{"repos":["milkmilk511/lapt"],"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/mscpt-few-shot-whole-slide-image","slug":"mscpt-few-shot-whole-slide-image","title":"MSCPT: Few-shot Whole Slide Image Classification with Multi-scale and Context-focused Prompt Tuning","date":"2024-08-21","arxiv_id":"2408.11505","repositories_listed":1,"syntology":{"n":8,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":8,"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) · 6 unverified","sample_list":"/paper/mscpt-few-shot-whole-slide-image#ran","syntology_url":"https://syntology.ai/paper/2408.11505","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.11505"}},"official":{"repos":["hanminghao/mscpt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/proteingpt-multimodal-llm-for-protein","slug":"proteingpt-multimodal-llm-for-protein","title":"ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding","date":"2024-08-21","arxiv_id":"2408.11363","repositories_listed":1,"syntology":{"n":14,"n_ran":12,"n_constructed":0,"n_ran_checked":10,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":8,"n_pointer_only":3,"phrase":"12 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/proteingpt-multimodal-llm-for-protein#ran","syntology_url":"https://syntology.ai/paper/2408.11363","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.11363"}},"official":{"repos":["proteingpt/proteingpt"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/hired-attention-guided-token-dropping-for","slug":"hired-attention-guided-token-dropping-for","title":"HiRED: Attention-Guided Token Dropping for Efficient Inference of High-Resolution Vision-Language Models","date":"2024-08-20","arxiv_id":"2408.10945","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/hired-attention-guided-token-dropping-for#ran","syntology_url":"https://syntology.ai/paper/2408.10945","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.10945"}},"official":{"repos":["hasanar1f/hired"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/mistral-splade-llms-for-for-better-learned","slug":"mistral-splade-llms-for-for-better-learned","title":"Mistral-SPLADE: LLMs for better Learned Sparse Retrieval","date":"2024-08-20","arxiv_id":"2408.11119","repositories_listed":1,"syntology":null}],"record_sha256":"cb32c3c960d403cefc12a5bcd6fe5718ae9532b8e259c9a6866d910f9578904f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}