{"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/10","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":10,"pages_in_order":142,"rows_per_page":100,"rows":[901,1000],"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/9","next":"/task/language-modeling/papers/11","papers":[{"url":"/paper/contextual-augmentation-data-augmentation-by","slug":"contextual-augmentation-data-augmentation-by","title":"Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations","date":"2018-05-16","arxiv_id":"1805.06201","repositories_listed":2,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/contextual-augmentation-data-augmentation-by#ran","syntology_url":"https://syntology.ai/paper/1805.06201","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.06201"}},"official":{"repos":["pfnet-research/contextual_augmentation"],"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/building-language-models-for-text-with-named","slug":"building-language-models-for-text-with-named","title":"Building Language Models for Text with Named Entities","date":"2018-05-13","arxiv_id":"1805.04836","repositories_listed":2,"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/building-language-models-for-text-with-named#ran","syntology_url":"https://syntology.ai/paper/1805.04836","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.04836"}},"official":{"repos":["uclanlp/NamedEntityLanguageModel"],"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/born-again-neural-networks","slug":"born-again-neural-networks","title":"Born Again Neural Networks","date":"2018-05-12","arxiv_id":"1805.04770","repositories_listed":2,"syntology":null},{"url":"/paper/subword-regularization-improving-neural","slug":"subword-regularization-improving-neural","title":"Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates","date":"2018-04-29","arxiv_id":"1804.10959","repositories_listed":2,"syntology":null},{"url":"/paper/colorless-green-recurrent-networks-dream","slug":"colorless-green-recurrent-networks-dream","title":"Colorless green recurrent networks dream hierarchically","date":"2018-03-29","arxiv_id":"1803.11138","repositories_listed":2,"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/colorless-green-recurrent-networks-dream#ran","syntology_url":"https://syntology.ai/paper/1803.11138","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.11138"}},"official":{"repos":["facebookresearch/colorlessgreenRNNs"],"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/network-traffic-anomaly-detection-using","slug":"network-traffic-anomaly-detection-using","title":"Network Traffic Anomaly Detection Using Recurrent Neural Networks","date":"2018-03-28","arxiv_id":"1803.10769","repositories_listed":2,"syntology":null},{"url":"/paper/polisis-automated-analysis-and-presentation","slug":"polisis-automated-analysis-and-presentation","title":"Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning","date":"2018-02-07","arxiv_id":"1802.02561","repositories_listed":2,"syntology":null},{"url":"/paper/discrete-autoencoders-for-sequence-models","slug":"discrete-autoencoders-for-sequence-models","title":"Discrete Autoencoders for Sequence Models","date":"2018-01-29","arxiv_id":"1801.09797","repositories_listed":2,"syntology":null},{"url":"/paper/training-rnns-as-fast-as-cnns","slug":"training-rnns-as-fast-as-cnns","title":"Training RNNs as Fast as CNNs","date":"2018-01-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/letter-based-speech-recognition-with-gated","slug":"letter-based-speech-recognition-with-gated","title":"Letter-Based Speech Recognition with Gated ConvNets","date":"2017-12-22","arxiv_id":"1712.09444","repositories_listed":2,"syntology":null},{"url":"/paper/effective-use-of-bidirectional-language","slug":"effective-use-of-bidirectional-language","title":"Effective Use of Bidirectional Language Modeling for Transfer Learning in Biomedical Named Entity Recognition","date":"2017-11-21","arxiv_id":"1711.07908","repositories_listed":2,"syntology":null},{"url":"/paper/unbounded-cache-model-for-online-language","slug":"unbounded-cache-model-for-online-language","title":"Unbounded cache model for online language modeling with open vocabulary","date":"2017-11-07","arxiv_id":"1711.02604","repositories_listed":2,"syntology":null},{"url":"/paper/rotational-unit-of-memory","slug":"rotational-unit-of-memory","title":"Rotational Unit of Memory","date":"2017-10-26","arxiv_id":"1710.09537","repositories_listed":2,"syntology":null},{"url":"/paper/dynamic-entity-representations-in-neural","slug":"dynamic-entity-representations-in-neural","title":"Dynamic Entity Representations in Neural Language Models","date":"2017-08-02","arxiv_id":"1708.00781","repositories_listed":2,"syntology":null},{"url":"/paper/bayesian-sparsification-of-recurrent-neural","slug":"bayesian-sparsification-of-recurrent-neural","title":"Bayesian Sparsification of Recurrent Neural Networks","date":"2017-07-31","arxiv_id":"1708.00077","repositories_listed":2,"syntology":null},{"url":"/paper/dual-rectified-linear-units-drelus-a","slug":"dual-rectified-linear-units-drelus-a","title":"Dual Rectified Linear Units (DReLUs): A Replacement for Tanh Activation Functions in Quasi-Recurrent Neural Networks","date":"2017-07-25","arxiv_id":"1707.08214","repositories_listed":2,"syntology":null},{"url":"/paper/yellowfin-and-the-art-of-momentum-tuning","slug":"yellowfin-and-the-art-of-momentum-tuning","title":"YellowFin and the Art of Momentum Tuning","date":"2017-06-12","arxiv_id":"1706.03471","repositories_listed":2,"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/yellowfin-and-the-art-of-momentum-tuning#ran","syntology_url":"https://syntology.ai/paper/1706.03471","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1706.03471"}},"official":null}},{"url":"/paper/accelerating-neural-architecture-search-using","slug":"accelerating-neural-architecture-search-using","title":"Accelerating Neural Architecture Search using Performance Prediction","date":"2017-05-30","arxiv_id":"1705.10823","repositories_listed":2,"syntology":null},{"url":"/paper/recurrent-additive-networks","slug":"recurrent-additive-networks","title":"Recurrent Additive Networks","date":"2017-05-21","arxiv_id":"1705.07393","repositories_listed":2,"syntology":null},{"url":"/paper/regularizing-neural-networks-by-penalizing","slug":"regularizing-neural-networks-by-penalizing","title":"Regularizing Neural Networks by Penalizing Confident Output Distributions","date":"2017-01-23","arxiv_id":"1701.06548","repositories_listed":2,"syntology":null},{"url":"/paper/an-empirical-study-of-language-cnn-for-image","slug":"an-empirical-study-of-language-cnn-for-image","title":"An Empirical Study of Language CNN for Image Captioning","date":"2016-12-21","arxiv_id":"1612.07086","repositories_listed":2,"syntology":null},{"url":"/paper/a-dataset-and-exploration-of-models-for","slug":"a-dataset-and-exploration-of-models-for","title":"A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering","date":"2016-11-23","arxiv_id":"1611.07810","repositories_listed":2,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/a-dataset-and-exploration-of-models-for#ran","syntology_url":"https://syntology.ai/paper/1611.07810","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1611.07810"}},"official":null}},{"url":"/paper/recurrent-neural-network-based-part-of-speech","slug":"recurrent-neural-network-based-part-of-speech","title":"Recurrent Neural Network based Part-of-Speech Tagger for Code-Mixed Social Media Text","date":"2016-11-15","arxiv_id":"1611.04989","repositories_listed":2,"syntology":null},{"url":"/paper/neural-text-generation-from-structured-data","slug":"neural-text-generation-from-structured-data","title":"Neural Text Generation from Structured Data with Application to the Biography Domain","date":"2016-03-24","arxiv_id":"1603.07771","repositories_listed":2,"syntology":null},{"url":"/paper/political-speech-generation","slug":"political-speech-generation","title":"Political Speech Generation","date":"2016-01-13","arxiv_id":"1601.03313","repositories_listed":2,"syntology":null},{"url":"/paper/recurrent-memory-networks-for-language","slug":"recurrent-memory-networks-for-language","title":"Recurrent Memory Networks for Language Modeling","date":"2016-01-06","arxiv_id":"1601.01272","repositories_listed":2,"syntology":null},{"url":"/paper/improving-neural-machine-translation-models","slug":"improving-neural-machine-translation-models","title":"Improving Neural Machine Translation Models with Monolingual Data","date":"2015-11-20","arxiv_id":"1511.06709","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/improving-neural-machine-translation-models#ran","syntology_url":"https://syntology.ai/paper/1511.06709","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1511.06709"}},"official":null}},{"url":"/paper/an-end-to-end-neural-network-for-polyphonic","slug":"an-end-to-end-neural-network-for-polyphonic","title":"An End-to-End Neural Network for Polyphonic Piano Music Transcription","date":"2015-08-07","arxiv_id":"1508.01774","repositories_listed":2,"syntology":null},{"url":"/paper/sublinear-partition-estimation","slug":"sublinear-partition-estimation","title":"Sublinear Partition Estimation","date":"2015-08-07","arxiv_id":"1508.01596","repositories_listed":2,"syntology":null},{"url":"/paper/making-language-model-a-hierarchical","slug":"making-language-model-a-hierarchical","title":"Making Language Model a Hierarchical Classifier and Generator","date":"2025-07-17","arxiv_id":"2507.12930","repositories_listed":1,"syntology":null},{"url":"/paper/assay2mol-large-language-model-based-drug","slug":"assay2mol-large-language-model-based-drug","title":"Assay2Mol: large language model-based drug design using BioAssay context","date":"2025-07-16","arxiv_id":"2507.12574","repositories_listed":1,"syntology":null},{"url":"/paper/describe-anything-model-for-visual-question","slug":"describe-anything-model-for-visual-question","title":"Describe Anything Model for Visual Question Answering on Text-rich Images","date":"2025-07-16","arxiv_id":"2507.12441","repositories_listed":1,"syntology":null},{"url":"/paper/instructflip-exploring-unified-vision","slug":"instructflip-exploring-unified-vision","title":"InstructFLIP: Exploring Unified Vision-Language Model for Face Anti-spoofing","date":"2025-07-16","arxiv_id":"2507.12060","repositories_listed":1,"syntology":null},{"url":"/paper/kodezi-chronos-a-debugging-first-language","slug":"kodezi-chronos-a-debugging-first-language","title":"Kodezi Chronos: A Debugging-First Language Model for Repository-Scale, Memory-Driven Code Understanding","date":"2025-07-14","arxiv_id":"2507.12482","repositories_listed":1,"syntology":null},{"url":"/paper/repairing-language-model-pipelines-by-meta","slug":"repairing-language-model-pipelines-by-meta","title":"Repairing Language Model Pipelines by Meta Self-Refining Competing Constraints at Runtime","date":"2025-07-11","arxiv_id":"2507.10590","repositories_listed":1,"syntology":null},{"url":"/paper/open-source-planning-control-system-with","slug":"open-source-planning-control-system-with","title":"Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery","date":"2025-07-09","arxiv_id":"2507.07257","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/open-source-planning-control-system-with#ran","syntology_url":"https://syntology.ai/paper/2507.07257","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2507.07257"}},"official":{"repos":["cmbagents/cmbagent"],"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":["community","official"]}}},{"url":"/paper/differential-mamba","slug":"differential-mamba","title":"Differential Mamba","date":"2025-07-08","arxiv_id":"2507.06204","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/differential-mamba#ran","syntology_url":"https://syntology.ai/paper/2507.06204","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2507.06204"}},"official":{"repos":["nadavsc/diff-mamba"],"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/evaluating-morphological-alignment-of","slug":"evaluating-morphological-alignment-of","title":"Evaluating Morphological Alignment of Tokenizers in 70 Languages","date":"2025-07-08","arxiv_id":"2507.06378","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"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) · 1 unverified","sample_list":"/paper/evaluating-morphological-alignment-of#ran","syntology_url":"https://syntology.ai/paper/2507.06378","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2507.06378"}},"official":{"repos":["catherinearnett/morphscore"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/agentstealth-reinforcing-large-language-model","slug":"agentstealth-reinforcing-large-language-model","title":"AgentStealth: Reinforcing Large Language Model for Anonymizing User-generated Text","date":"2025-06-26","arxiv_id":"2506.22508","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/agentstealth-reinforcing-large-language-model#ran","syntology_url":"https://syntology.ai/paper/2506.22508","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.22508"}},"official":{"repos":["tsinghua-fib-lab/agentstealth"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/beyond-reactive-safety-risk-aware-llm","slug":"beyond-reactive-safety-risk-aware-llm","title":"Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulation","date":"2025-06-26","arxiv_id":"2506.20949","repositories_listed":1,"syntology":null},{"url":"/paper/detecting-referring-expressions-in-visually","slug":"detecting-referring-expressions-in-visually","title":"Detecting Referring Expressions in Visually Grounded Dialogue with Autoregressive Language Models","date":"2025-06-26","arxiv_id":"2506.21294","repositories_listed":1,"syntology":null},{"url":"/paper/mtsbench-benchmarking-multivariate-time","slug":"mtsbench-benchmarking-multivariate-time","title":"mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale","date":"2025-06-26","arxiv_id":"2506.21550","repositories_listed":1,"syntology":null},{"url":"/paper/sharpzo-hybrid-sharpness-aware-vision","slug":"sharpzo-hybrid-sharpness-aware-vision","title":"SharpZO: Hybrid Sharpness-Aware Vision Language Model Prompt Tuning via Forward-Only Passes","date":"2025-06-26","arxiv_id":"2506.20990","repositories_listed":1,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":2,"n_instrument":4,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"6 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; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/sharpzo-hybrid-sharpness-aware-vision#ran","syntology_url":"https://syntology.ai/paper/2506.20990","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.20990"}},"official":{"repos":["yifanycc/sharpzo"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/a-multi-pass-large-language-model-framework","slug":"a-multi-pass-large-language-model-framework","title":"A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection","date":"2025-06-25","arxiv_id":"2506.20112","repositories_listed":1,"syntology":null},{"url":"/paper/aalc-large-language-model-efficient-reasoning","slug":"aalc-large-language-model-efficient-reasoning","title":"AALC: Large Language Model Efficient Reasoning via Adaptive Accuracy-Length Control","date":"2025-06-25","arxiv_id":"2506.20160","repositories_listed":1,"syntology":null},{"url":"/paper/gptailor-large-language-model-pruning-through","slug":"gptailor-large-language-model-pruning-through","title":"GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching","date":"2025-06-25","arxiv_id":"2506.20480","repositories_listed":1,"syntology":null},{"url":"/paper/language-modeling-by-language-models","slug":"language-modeling-by-language-models","title":"Language Modeling by Language Models","date":"2025-06-25","arxiv_id":"2506.20249","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/language-modeling-by-language-models#ran","syntology_url":"https://syntology.ai/paper/2506.20249","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.20249"}},"official":{"repos":["allenai/genesys"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/narrative-shift-detection-a-hybrid-approach","slug":"narrative-shift-detection-a-hybrid-approach","title":"Narrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language Models","date":"2025-06-25","arxiv_id":"2506.20269","repositories_listed":1,"syntology":null},{"url":"/paper/octothinker-mid-training-incentivizes","slug":"octothinker-mid-training-incentivizes","title":"OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling","date":"2025-06-25","arxiv_id":"2506.20512","repositories_listed":1,"syntology":{"n":17,"n_ran":16,"n_constructed":0,"n_ran_checked":16,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":16,"n_pointer_only":4,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/octothinker-mid-training-incentivizes#ran","syntology_url":"https://syntology.ai/paper/2506.20512","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.20512"}},"official":{"repos":["gair-nlp/octothinker"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":16,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-community-driven-agents-for-machine","slug":"towards-community-driven-agents-for-machine","title":"Towards Community-Driven Agents for Machine Learning Engineering","date":"2025-06-25","arxiv_id":"2506.20640","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/towards-community-driven-agents-for-machine#ran","syntology_url":"https://syntology.ai/paper/2506.20640","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.20640"}},"official":{"repos":["comind-ml/comind"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/pre-trained-llm-is-a-semantic-aware-and","slug":"pre-trained-llm-is-a-semantic-aware-and","title":"Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster","date":"2025-06-22","arxiv_id":"2506.18034","repositories_listed":1,"syntology":null},{"url":"/paper/sharegpt-4o-image-aligning-multimodal-models","slug":"sharegpt-4o-image-aligning-multimodal-models","title":"ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image Generation","date":"2025-06-22","arxiv_id":"2506.18095","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/sharegpt-4o-image-aligning-multimodal-models#ran","syntology_url":"https://syntology.ai/paper/2506.18095","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.18095"}},"official":{"repos":["freedomintelligence/sharegpt-4o-image"],"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","unlocated"]}}},{"url":"/paper/lmr-bench-evaluating-llm-agent-s-ability-on","slug":"lmr-bench-evaluating-llm-agent-s-ability-on","title":"LMR-BENCH: Evaluating LLM Agent's Ability on Reproducing Language Modeling Research","date":"2025-06-19","arxiv_id":"2506.17335","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/lmr-bench-evaluating-llm-agent-s-ability-on#ran","syntology_url":"https://syntology.ai/paper/2506.17335","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.17335"}},"official":{"repos":["du-nlp-lab/lmr-bench"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/watermarking-autoregressive-image-generation","slug":"watermarking-autoregressive-image-generation","title":"Watermarking Autoregressive Image Generation","date":"2025-06-19","arxiv_id":"2506.16349","repositories_listed":1,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":8,"n_instrument":3,"n_unverified":0,"n_honours":2,"n_violates":1,"n_no_contract":5,"n_pointer_only":8,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 2 honoured, 1 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/watermarking-autoregressive-image-generation#ran","syntology_url":"https://syntology.ai/paper/2506.16349","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.16349"}},"official":{"repos":["facebookresearch/wmar"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["found_in_text","official","unlocated"]}}},{"url":"/paper/ras-eval-a-comprehensive-benchmark-for","slug":"ras-eval-a-comprehensive-benchmark-for","title":"RAS-Eval: A Comprehensive Benchmark for Security Evaluation of LLM Agents in Real-World Environments","date":"2025-06-18","arxiv_id":"2506.15253","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/ras-eval-a-comprehensive-benchmark-for#ran","syntology_url":"https://syntology.ai/paper/2506.15253","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.15253"}},"official":{"repos":["lanzer-tree/ras-eval"],"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/show-o2-improved-native-unified-multimodal","slug":"show-o2-improved-native-unified-multimodal","title":"Show-o2: Improved Native Unified Multimodal Models","date":"2025-06-18","arxiv_id":"2506.15564","repositories_listed":1,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":5,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/show-o2-improved-native-unified-multimodal#ran","syntology_url":"https://syntology.ai/paper/2506.15564","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.15564"}},"official":{"repos":["showlab/show-o"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bmfm-rna-an-open-framework-for-building-and","slug":"bmfm-rna-an-open-framework-for-building-and","title":"BMFM-RNA: An Open Framework for Building and Evaluating Transcriptomic Foundation Models","date":"2025-06-17","arxiv_id":"2506.14861","repositories_listed":1,"syntology":null},{"url":"/paper/from-bytes-to-ideas-language-modeling-with","slug":"from-bytes-to-ideas-language-modeling-with","title":"From Bytes to Ideas: Language Modeling with Autoregressive U-Nets","date":"2025-06-17","arxiv_id":"2506.14761","repositories_listed":1,"syntology":{"n":16,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":1,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/from-bytes-to-ideas-language-modeling-with#ran","syntology_url":"https://syntology.ai/paper/2506.14761","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.14761"}},"official":{"repos":["facebookresearch/lingua"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/from-what-to-respond-to-when-to-respond","slug":"from-what-to-respond-to-when-to-respond","title":"From What to Respond to When to Respond: Timely Response Generation for Open-domain Dialogue Agents","date":"2025-06-17","arxiv_id":"2506.14285","repositories_listed":1,"syntology":null},{"url":"/paper/interpreting-biomedical-vlms-on-high","slug":"interpreting-biomedical-vlms-on-high","title":"Interpreting Biomedical VLMs on High-Imbalance Out-of-Distributions: An Insight into BiomedCLIP on Radiology","date":"2025-06-17","arxiv_id":"2506.14136","repositories_listed":1,"syntology":null},{"url":"/paper/lightweight-relevance-grader-in-rag","slug":"lightweight-relevance-grader-in-rag","title":"Lightweight Relevance Grader in RAG","date":"2025-06-17","arxiv_id":"2506.14084","repositories_listed":1,"syntology":null},{"url":"/paper/rmit-adm-s-at-the-sigir-2025-liverag","slug":"rmit-adm-s-at-the-sigir-2025-liverag","title":"RMIT-ADM+S at the SIGIR 2025 LiveRAG Challenge","date":"2025-06-17","arxiv_id":"2506.14516","repositories_listed":1,"syntology":null},{"url":"/paper/sampling-from-your-language-model-one-byte-at","slug":"sampling-from-your-language-model-one-byte-at","title":"Sampling from Your Language Model One Byte at a Time","date":"2025-06-17","arxiv_id":"2506.14123","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":0,"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/sampling-from-your-language-model-one-byte-at#ran","syntology_url":"https://syntology.ai/paper/2506.14123","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.14123"}},"official":{"repos":["sewoonglab/byte-sampler"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/emonews-a-spoken-dialogue-system-for","slug":"emonews-a-spoken-dialogue-system-for","title":"EmoNews: A Spoken Dialogue System for Expressive News Conversations","date":"2025-06-16","arxiv_id":"2506.13894","repositories_listed":1,"syntology":null},{"url":"/paper/seqpe-transformer-with-sequential-position","slug":"seqpe-transformer-with-sequential-position","title":"SeqPE: Transformer with Sequential Position Encoding","date":"2025-06-16","arxiv_id":"2506.13277","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":2,"n_no_contract":5,"n_pointer_only":12,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 2 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/seqpe-transformer-with-sequential-position#ran","syntology_url":"https://syntology.ai/paper/2506.13277","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.13277"}},"official":{"repos":["ghrua/seqpe"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/value-free-policy-optimization-via-reward","slug":"value-free-policy-optimization-via-reward","title":"Value-Free Policy Optimization via Reward Partitioning","date":"2025-06-16","arxiv_id":"2506.13702","repositories_listed":1,"syntology":null},{"url":"/paper/vis-shepherd-constructing-critic-for-llm","slug":"vis-shepherd-constructing-critic-for-llm","title":"VIS-Shepherd: Constructing Critic for LLM-based Data Visualization Generation","date":"2025-06-16","arxiv_id":"2506.13326","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":6,"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/vis-shepherd-constructing-critic-for-llm#ran","syntology_url":"https://syntology.ai/paper/2506.13326","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.13326"}},"official":{"repos":["bopan3/vis-shepherd"],"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/flexrag-a-flexible-and-comprehensive","slug":"flexrag-a-flexible-and-comprehensive","title":"FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation","date":"2025-06-14","arxiv_id":"2506.12494","repositories_listed":1,"syntology":null},{"url":"/paper/tagrouter-learning-route-to-llms-through-tags","slug":"tagrouter-learning-route-to-llms-through-tags","title":"TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation Tasks","date":"2025-06-14","arxiv_id":"2506.12473","repositories_listed":1,"syntology":null},{"url":"/paper/improving-large-language-model-safety-with","slug":"improving-large-language-model-safety-with","title":"Improving Large Language Model Safety with Contrastive Representation Learning","date":"2025-06-13","arxiv_id":"2506.11938","repositories_listed":1,"syntology":null},{"url":"/paper/2506-10678","slug":"2506-10678","title":"Automated Validation of Textual Constraints Against AutomationML via LLMs and SHACL","date":"2025-06-12","arxiv_id":"2506.10678","repositories_listed":1,"syntology":null},{"url":"/paper/neuralnexus-at-bea-2025-shared-task-retrieval","slug":"neuralnexus-at-bea-2025-shared-task-retrieval","title":"NeuralNexus at BEA 2025 Shared Task: Retrieval-Augmented Prompting for Mistake Identification in AI Tutors","date":"2025-06-12","arxiv_id":"2506.10627","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-the-gap-between-open-source-and","slug":"bridging-the-gap-between-open-source-and","title":"Bridging the Gap Between Open-Source and Proprietary LLMs in Table QA","date":"2025-06-11","arxiv_id":"2506.09657","repositories_listed":1,"syntology":null},{"url":"/paper/colmbo-speaker-language-model-for-descriptive","slug":"colmbo-speaker-language-model-for-descriptive","title":"CoLMbo: Speaker Language Model for Descriptive Profiling","date":"2025-06-11","arxiv_id":"2506.09375","repositories_listed":1,"syntology":null},{"url":"/paper/intent-factored-generation-unleashing-the","slug":"intent-factored-generation-unleashing-the","title":"Intent Factored Generation: Unleashing the Diversity in Your Language Model","date":"2025-06-11","arxiv_id":"2506.09659","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/intent-factored-generation-unleashing-the#ran","syntology_url":"https://syntology.ai/paper/2506.09659","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.09659"}},"official":{"repos":["flairox/ifg"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/2506-08967","slug":"2506-08967","title":"Step-Audio-AQAA: a Fully End-to-End Expressive Large Audio Language Model","date":"2025-06-10","arxiv_id":"2506.08967","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/2506-08967#ran","syntology_url":"https://syntology.ai/paper/2506.08967","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.08967"}},"official":null}},{"url":"/paper/joformer-journey-based-transformer-theory-and","slug":"joformer-journey-based-transformer-theory-and","title":"JoFormer (Journey-based Transformer): Theory and Empirical Analysis on the Tiny Shakespeare Dataset","date":"2025-06-10","arxiv_id":"2506.08652","repositories_listed":1,"syntology":null},{"url":"/paper/propmend-hypernetworks-for-knowledge","slug":"propmend-hypernetworks-for-knowledge","title":"PropMEND: Hypernetworks for Knowledge Propagation in LLMs","date":"2025-06-10","arxiv_id":"2506.08920","repositories_listed":1,"syntology":null},{"url":"/paper/xgraphrag-interactive-visual-analysis-for","slug":"xgraphrag-interactive-visual-analysis-for","title":"XGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented Generation","date":"2025-06-10","arxiv_id":"2506.13782","repositories_listed":1,"syntology":null},{"url":"/paper/2506-10024","slug":"2506-10024","title":"Private Memorization Editing: Turning Memorization into a Defense to Strengthen Data Privacy in Large Language Models","date":"2025-06-09","arxiv_id":"2506.10024","repositories_listed":1,"syntology":null},{"url":"/paper/a-hybrid-ga-llm-framework-for-structured-task","slug":"a-hybrid-ga-llm-framework-for-structured-task","title":"A Hybrid GA LLM Framework for Structured Task Optimization","date":"2025-06-09","arxiv_id":"2506.07483","repositories_listed":1,"syntology":null},{"url":"/paper/diffusion-sequence-models-for-enhanced","slug":"diffusion-sequence-models-for-enhanced","title":"Diffusion Sequence Models for Enhanced Protein Representation and Generation","date":"2025-06-09","arxiv_id":"2506.08293","repositories_listed":1,"syntology":null},{"url":"/paper/annodpo-protein-functional-annotation","slug":"annodpo-protein-functional-annotation","title":"AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization","date":"2025-06-08","arxiv_id":"2506.07035","repositories_listed":1,"syntology":null},{"url":"/paper/towards-universal-offline-black-box","slug":"towards-universal-offline-black-box","title":"Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings","date":"2025-06-08","arxiv_id":"2506.07109","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-misuse-mitigation-against-covert","slug":"benchmarking-misuse-mitigation-against-covert","title":"Benchmarking Misuse Mitigation Against Covert Adversaries","date":"2025-06-06","arxiv_id":"2506.06414","repositories_listed":1,"syntology":null},{"url":"/paper/dam-dynamic-attention-mask-for-long-context","slug":"dam-dynamic-attention-mask-for-long-context","title":"DAM: Dynamic Attention Mask for Long-Context Large Language Model Inference Acceleration","date":"2025-06-06","arxiv_id":"2506.11104","repositories_listed":1,"syntology":null},{"url":"/paper/masked-language-models-are-good-heterogeneous","slug":"masked-language-models-are-good-heterogeneous","title":"Masked Language Models are Good Heterogeneous Graph Generalizers","date":"2025-06-06","arxiv_id":"2506.06157","repositories_listed":1,"syntology":null},{"url":"/paper/exp4fuse-a-rank-fusion-framework-for-enhanced","slug":"exp4fuse-a-rank-fusion-framework-for-enhanced","title":"Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query Expansion","date":"2025-06-05","arxiv_id":"2506.04760","repositories_listed":1,"syntology":null},{"url":"/paper/halos-hierarchical-asynchronous-local-sgd","slug":"halos-hierarchical-asynchronous-local-sgd","title":"HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training","date":"2025-06-05","arxiv_id":"2506.04531","repositories_listed":1,"syntology":null},{"url":"/paper/mesanet-sequence-modeling-by-locally-optimal","slug":"mesanet-sequence-modeling-by-locally-optimal","title":"MesaNet: Sequence Modeling by Locally Optimal Test-Time Training","date":"2025-06-05","arxiv_id":"2506.05233","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/mesanet-sequence-modeling-by-locally-optimal#ran","syntology_url":"https://syntology.ai/paper/2506.05233","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.05233"}},"official":{"repos":["fla-org/flash-linear-attention"],"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/openmaskdino3d-reasoning-3d-segmentation-via","slug":"openmaskdino3d-reasoning-3d-segmentation-via","title":"OpenMaskDINO3D : Reasoning 3D Segmentation via Large Language Model","date":"2025-06-05","arxiv_id":"2506.04837","repositories_listed":1,"syntology":null},{"url":"/paper/laf-grpo-in-situ-navigation-instruction","slug":"laf-grpo-in-situ-navigation-instruction","title":"LaF-GRPO: In-Situ Navigation Instruction Generation for the Visually Impaired via GRPO with LLM-as-Follower Reward","date":"2025-06-04","arxiv_id":"2506.04070","repositories_listed":1,"syntology":null},{"url":"/paper/poss-position-specialist-generates-better","slug":"poss-position-specialist-generates-better","title":"POSS: Position Specialist Generates Better Draft for Speculative Decoding","date":"2025-06-04","arxiv_id":"2506.03566","repositories_listed":1,"syntology":null},{"url":"/paper/think-like-a-person-before-responding-a-multi","slug":"think-like-a-person-before-responding-a-multi","title":"Think Like a Person Before Responding: A Multi-Faceted Evaluation of Persona-Guided LLMs for Countering Hate","date":"2025-06-04","arxiv_id":"2506.04043","repositories_listed":1,"syntology":null},{"url":"/paper/a-smart-multimodal-healthcare-copilot-with","slug":"a-smart-multimodal-healthcare-copilot-with","title":"A Smart Multimodal Healthcare Copilot with Powerful LLM Reasoning","date":"2025-06-03","arxiv_id":"2506.02470","repositories_listed":1,"syntology":null},{"url":"/paper/trajectory-prediction-meets-large-language","slug":"trajectory-prediction-meets-large-language","title":"Trajectory Prediction Meets Large Language Models: A Survey","date":"2025-06-03","arxiv_id":"2506.03408","repositories_listed":1,"syntology":null},{"url":"/paper/parameter-efficient-fine-tuning-llama-3-1-for","slug":"parameter-efficient-fine-tuning-llama-3-1-for","title":"Parameter Efficient Fine Tuning Llama 3.1 for Answering Arabic Legal Questions: A Case Study on Jordanian Laws","date":"2025-06-02","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/reasoning-table-exploring-reinforcement","slug":"reasoning-table-exploring-reinforcement","title":"Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning","date":"2025-06-02","arxiv_id":"2506.01710","repositories_listed":1,"syntology":null},{"url":"/paper/gigaam-efficient-self-supervised-learner-for","slug":"gigaam-efficient-self-supervised-learner-for","title":"GigaAM: Efficient Self-Supervised Learner for Speech Recognition","date":"2025-06-01","arxiv_id":"2506.01192","repositories_listed":1,"syntology":null},{"url":"/paper/infinity-parser-layout-aware-reinforcement","slug":"infinity-parser-layout-aware-reinforcement","title":"Infinity Parser: Layout Aware Reinforcement Learning for Scanned Document Parsing","date":"2025-06-01","arxiv_id":"2506.03197","repositories_listed":1,"syntology":null}],"record_sha256":"fb5c712ef28c1a7a84087c6b5dc02ccf1a13be73296a8fc6fcc98420d74914dc","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}