{"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/navigate/papers/7","list_of":"/task/navigate","task":"Navigate","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":7,"pages_in_order":20,"rows_per_page":100,"rows":[601,700],"of":1979,"counts":{"archive_papers_tagged":1982,"with_a_code_link":644,"where_syntology_ran_a_sample":152,"not_listed_spam_title":3,"listed":1979,"listed_where_code_ran":152,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":130,"every_run_a_failure_of_syntologys_instrument":22,"listed_with_a_run_with_no_instrument_failure":130,"listed_every_run_a_failure_of_syntologys_instrument":22,"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/navigate","prev":"/task/navigate/papers/6","next":"/task/navigate/papers/8","papers":[{"url":"/paper/estimating-attention-flow-in-online-video","slug":"estimating-attention-flow-in-online-video","title":"Estimating Attention Flow in Online Video Networks","date":"2019-08-20","arxiv_id":"1908.07123","repositories_listed":1,"syntology":null},{"url":"/paper/lytnet-a-convolutional-neural-network-for","slug":"lytnet-a-convolutional-neural-network-for","title":"LYTNet: A Convolutional Neural Network for Real-Time Pedestrian Traffic Lights and Zebra Crossing Recognition for the Visually Impaired","date":"2019-07-23","arxiv_id":"1907.09706","repositories_listed":1,"syntology":null},{"url":"/paper/effective-and-general-evaluation-for","slug":"effective-and-general-evaluation-for","title":"General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping","date":"2019-07-11","arxiv_id":"1907.05446","repositories_listed":1,"syntology":null},{"url":"/paper/cooperation-aware-reinforcement-learning-for","slug":"cooperation-aware-reinforcement-learning-for","title":"Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic","date":"2019-06-26","arxiv_id":"1906.11021","repositories_listed":1,"syntology":null},{"url":"/paper/finding-the-needle-in-the-haystack-with","slug":"finding-the-needle-in-the-haystack-with","title":"Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias","date":"2019-06-16","arxiv_id":"1906.06766","repositories_listed":1,"syntology":null},{"url":"/paper/adaptively-preconditioned-stochastic-gradient","slug":"adaptively-preconditioned-stochastic-gradient","title":"Adaptively Preconditioned Stochastic Gradient Langevin Dynamics","date":"2019-06-10","arxiv_id":"1906.04324","repositories_listed":1,"syntology":null},{"url":"/paper/comparing-energy-efficiency-of-cpu-gpu-and","slug":"comparing-energy-efficiency-of-cpu-gpu-and","title":"Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels","date":"2019-05-31","arxiv_id":"1906.11879","repositories_listed":1,"syntology":null},{"url":"/paper/successor-options-an-option-discovery","slug":"successor-options-an-option-discovery","title":"Successor Options: An Option Discovery Framework for Reinforcement Learning","date":"2019-05-14","arxiv_id":"1905.05731","repositories_listed":1,"syntology":null},{"url":"/paper/a-deep-generative-model-for-graph-layout","slug":"a-deep-generative-model-for-graph-layout","title":"A Deep Generative Model for Graph Layout","date":"2019-04-27","arxiv_id":"1904.12225","repositories_listed":1,"syntology":null},{"url":"/paper/multi-target-embodied-question-answering","slug":"multi-target-embodied-question-answering","title":"Multi-Target Embodied Question Answering","date":"2019-04-09","arxiv_id":"1904.04686","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-navigate-unseen-environments-back","slug":"learning-to-navigate-unseen-environments-back","title":"Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout","date":"2019-04-08","arxiv_id":"1904.04195","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/learning-to-navigate-unseen-environments-back#ran","syntology_url":"https://syntology.ai/paper/1904.04195","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.04195"}},"official":{"repos":["airsplay/R2R-EnvDrop"],"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/360-panorama-synthesis-from-a-sparse-set-of","slug":"360-panorama-synthesis-from-a-sparse-set-of","title":"360 Panorama Synthesis from a Sparse Set of Images with Unknown Field of View","date":"2019-04-06","arxiv_id":"1904.03326","repositories_listed":1,"syntology":null},{"url":"/paper/lumipath-towards-real-time-physically-based","slug":"lumipath-towards-real-time-physically-based","title":"LumiPath -- Towards Real-time Physically-based Rendering on Embedded Devices","date":"2019-03-09","arxiv_id":"1903.03837","repositories_listed":1,"syntology":null},{"url":"/paper/marathon-environments-multi-agent-continuous","slug":"marathon-environments-multi-agent-continuous","title":"Marathon Environments: Multi-Agent Continuous Control Benchmarks in a Modern Video Game Engine","date":"2019-02-25","arxiv_id":"1902.09097","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-navigate-image-manifolds-induced","slug":"learning-to-navigate-image-manifolds-induced","title":"Learning to navigate image manifolds induced by generative adversarial networks for unsupervised video generation","date":"2019-01-23","arxiv_id":"1901.11384","repositories_listed":1,"syntology":null},{"url":"/paper/vision-based-navigation-with-language-based","slug":"vision-based-navigation-with-language-based","title":"Vision-based Navigation with Language-based Assistance via Imitation Learning with Indirect Intervention","date":"2018-12-10","arxiv_id":"1812.04155","repositories_listed":1,"syntology":null},{"url":"/paper/trafficpredict-trajectory-prediction-for","slug":"trafficpredict-trajectory-prediction-for","title":"TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents","date":"2018-11-06","arxiv_id":"1811.02146","repositories_listed":1,"syntology":null},{"url":"/paper/saferoute-learning-to-navigate-streets-safely","slug":"saferoute-learning-to-navigate-streets-safely","title":"SafeRoute: Learning to Navigate Streets Safely in an Urban Environment","date":"2018-11-03","arxiv_id":"1811.01147","repositories_listed":1,"syntology":null},{"url":"/paper/visual-semantic-navigation-using-scene-priors","slug":"visual-semantic-navigation-using-scene-priors","title":"Visual Semantic Navigation using Scene Priors","date":"2018-10-15","arxiv_id":"1810.06543","repositories_listed":1,"syntology":null},{"url":"/paper/image-based-guidance-of-autonomous-aircraft","slug":"image-based-guidance-of-autonomous-aircraft","title":"Image-based Guidance of Autonomous Aircraft for Wildfire Surveillance and Prediction","date":"2018-10-04","arxiv_id":"1810.02455","repositories_listed":1,"syntology":null},{"url":"/paper/ensemble-learning-applied-to-classify-gps","slug":"ensemble-learning-applied-to-classify-gps","title":"Ensemble Learning Applied to Classify GPS Trajectories of Birds into Male or Female","date":"2018-08-26","arxiv_id":"1808.08613","repositories_listed":1,"syntology":null},{"url":"/paper/experiential-robot-learning-with-accelerated","slug":"experiential-robot-learning-with-accelerated","title":"Experiential Robot Learning with Accelerated Neuroevolution","date":"2018-08-16","arxiv_id":"1808.05525","repositories_listed":1,"syntology":null},{"url":"/paper/speakers-account-for-asymmetries-in-visual","slug":"speakers-account-for-asymmetries-in-visual","title":"The division of labor in communication: Speakers help listeners account for asymmetries in visual perspective","date":"2018-07-24","arxiv_id":"1807.09000","repositories_listed":1,"syntology":null},{"url":"/paper/rulematrix-visualizing-and-understanding","slug":"rulematrix-visualizing-and-understanding","title":"RuleMatrix: Visualizing and Understanding Classifiers with Rules","date":"2018-07-17","arxiv_id":"1807.06228","repositories_listed":1,"syntology":{"n":17,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":6,"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) · 6 unverified","sample_list":"/paper/rulematrix-visualizing-and-understanding#ran","syntology_url":"https://syntology.ai/paper/1807.06228","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.06228"}},"official":null}},{"url":"/paper/talk-the-walk-navigating-new-york-city","slug":"talk-the-walk-navigating-new-york-city","title":"Talk the Walk: Navigating New York City through Grounded Dialogue","date":"2018-07-09","arxiv_id":"1807.03367","repositories_listed":1,"syntology":null},{"url":"/paper/conditional-affordance-learning-for-driving","slug":"conditional-affordance-learning-for-driving","title":"Conditional Affordance Learning for Driving in Urban Environments","date":"2018-06-18","arxiv_id":"1806.06498","repositories_listed":1,"syntology":{"n":16,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":11,"n_pointer_only":1,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 1 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/conditional-affordance-learning-for-driving#ran","syntology_url":"https://syntology.ai/paper/1806.06498","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.06498"}},"official":{"repos":["xl-sr/CAL"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/attention-based-natural-language-grounding-by","slug":"attention-based-natural-language-grounding-by","title":"Attention Based Natural Language Grounding by Navigating Virtual Environment","date":"2018-04-23","arxiv_id":"1804.08454","repositories_listed":1,"syntology":null},{"url":"/paper/lost-appearance-invariant-place-recognition","slug":"lost-appearance-invariant-place-recognition","title":"LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics","date":"2018-04-16","arxiv_id":"1804.05526","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":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) · 0 unverified","sample_list":"/paper/lost-appearance-invariant-place-recognition#ran","syntology_url":"https://syntology.ai/paper/1804.05526","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.05526"}},"official":{"repos":["oravus/lostX"],"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/semi-parametric-topological-memory-for","slug":"semi-parametric-topological-memory-for","title":"Semi-parametric Topological Memory for Navigation","date":"2018-03-01","arxiv_id":"1803.00653","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/semi-parametric-topological-memory-for#ran","syntology_url":"https://syntology.ai/paper/1803.00653","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.00653"}},"official":null}},{"url":"/paper/a-critical-investigation-of-deep","slug":"a-critical-investigation-of-deep","title":"A Critical Investigation of Deep Reinforcement Learning for Navigation","date":"2018-02-07","arxiv_id":"1802.02274","repositories_listed":1,"syntology":null},{"url":"/paper/conditional-probability-models-for-deep-image","slug":"conditional-probability-models-for-deep-image","title":"Conditional Probability Models for Deep Image Compression","date":"2018-01-12","arxiv_id":"1801.04260","repositories_listed":1,"syntology":null},{"url":"/paper/iqa-visual-question-answering-in-interactive","slug":"iqa-visual-question-answering-in-interactive","title":"IQA: Visual Question Answering in Interactive Environments","date":"2017-12-09","arxiv_id":"1712.03316","repositories_listed":1,"syntology":null},{"url":"/paper/one-shot-reinforcement-learning-for-robot","slug":"one-shot-reinforcement-learning-for-robot","title":"One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay","date":"2017-11-28","arxiv_id":"1711.10137","repositories_listed":1,"syntology":null},{"url":"/paper/teaching-a-machine-to-read-maps-with-deep","slug":"teaching-a-machine-to-read-maps-with-deep","title":"Teaching a Machine to Read Maps with Deep Reinforcement Learning","date":"2017-11-20","arxiv_id":"1711.07479","repositories_listed":1,"syntology":null},{"url":"/paper/run-skeleton-run-skeletal-model-in-a-physics","slug":"run-skeleton-run-skeletal-model-in-a-physics","title":"Run, skeleton, run: skeletal model in a physics-based simulation","date":"2017-11-18","arxiv_id":"1711.06922","repositories_listed":1,"syntology":null},{"url":"/paper/learning-with-latent-language","slug":"learning-with-latent-language","title":"Learning with Latent Language","date":"2017-11-01","arxiv_id":"1711.00482","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-with-latent-language#ran","syntology_url":"https://syntology.ai/paper/1711.00482","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.00482"}},"official":{"repos":["jacobandreas/l3"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/socially-compliant-navigation-through-raw","slug":"socially-compliant-navigation-through-raw","title":"Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning","date":"2017-10-06","arxiv_id":"1710.02543","repositories_listed":1,"syntology":null},{"url":"/paper/like-trainer-like-bot-inheritance-of-bias-in","slug":"like-trainer-like-bot-inheritance-of-bias-in","title":"Like trainer, like bot? Inheritance of bias in algorithmic content moderation","date":"2017-07-05","arxiv_id":"1707.01477","repositories_listed":1,"syntology":null},{"url":"/paper/failing-to-learn-autonomously-identifying","slug":"failing-to-learn-autonomously-identifying","title":"Failing to Learn: Autonomously Identifying Perception Failures for Self-driving Cars","date":"2017-06-30","arxiv_id":"1707.00051","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-fly-by-crashing","slug":"learning-to-fly-by-crashing","title":"Learning to Fly by Crashing","date":"2017-04-19","arxiv_id":"1704.05588","repositories_listed":1,"syntology":null},{"url":"/paper/sparse-depth-sensing-for-resource-constrained","slug":"sparse-depth-sensing-for-resource-constrained","title":"Sparse Depth Sensing for Resource-Constrained Robots","date":"2017-03-04","arxiv_id":"1703.01398","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-navigate-in-complex-environments","slug":"learning-to-navigate-in-complex-environments","title":"Learning to Navigate in Complex Environments","date":"2016-11-11","arxiv_id":"1611.03673","repositories_listed":1,"syntology":null},{"url":"/paper/unrealcv-connecting-computer-vision-to-unreal","slug":"unrealcv-connecting-computer-vision-to-unreal","title":"UnrealCV: Connecting Computer Vision to Unreal Engine","date":"2016-09-05","arxiv_id":"1609.01326","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/unrealcv-connecting-computer-vision-to-unreal#ran","syntology_url":"https://syntology.ai/paper/1609.01326","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1609.01326"}},"official":null}},{"url":"/paper/incorporating-clicks-attention-and","slug":"incorporating-clicks-attention-and","title":"Incorporating Clicks, Attention and Satisfaction into a Search Engine Result Page Evaluation Model","date":"2016-09-02","arxiv_id":"1609.0552","repositories_listed":1,"syntology":null},{"url":null,"slug":"vision-based-perception-for-autonomous","title":"Vision-based Perception for Autonomous Vehicles in Obstacle Avoidance Scenarios","date":"2025-07-16","arxiv_id":"2507.12449","repositories_listed":0,"syntology":null},{"url":null,"slug":"cogddn-a-cognitive-demand-driven-navigation","title":"CogDDN: A Cognitive Demand-Driven Navigation with Decision Optimization and Dual-Process Thinking","date":"2025-07-15","arxiv_id":"2507.11334","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-multi-stage-fall-detection","title":"Privacy-Preserving Multi-Stage Fall Detection Framework with Semi-supervised Federated Learning and Robotic Vision Confirmation","date":"2025-07-14","arxiv_id":"2507.10474","repositories_listed":0,"syntology":null},{"url":null,"slug":"automating-md-simulations-for-proteins-using","title":"Automating MD simulations for Proteins using Large language Models: NAMD-Agent","date":"2025-07-10","arxiv_id":"2507.07887","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-learning","title":"Graph Learning","date":"2025-07-08","arxiv_id":"2507.05636","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-hand-gesture-recognition-with-deep","title":"Visual Hand Gesture Recognition with Deep Learning: A Comprehensive Review of Methods, Datasets, Challenges and Future Research Directions","date":"2025-07-06","arxiv_id":"2507.04465","repositories_listed":0,"syntology":null},{"url":null,"slug":"out-of-distribution-detection-in-3d","title":"Out-of-distribution detection in 3D applications: a review","date":"2025-07-01","arxiv_id":"2507.00570","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-an-evolutionary-search-engine-for","title":"Assessing an evolutionary search engine for small language models, prompts, and evaluation metrics","date":"2025-06-26","arxiv_id":"2506.21512","repositories_listed":0,"syntology":null},{"url":null,"slug":"triz-agents-a-multi-agent-llm-approach-for","title":"TRIZ Agents: A Multi-Agent LLM Approach for TRIZ-Based Innovation","date":"2025-06-23","arxiv_id":"2506.18783","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-sonification-methods-for-the-mindcube","title":"Two Sonification Methods for the MindCube","date":"2025-06-22","arxiv_id":"2506.18196","repositories_listed":0,"syntology":null},{"url":null,"slug":"call-to-speak-to-someone-at-frontiertm","title":"Call To Speak To Someone At Frontier™️ Airlines Through Various Contact Options: The Ultimate Step Guide","date":"2025-06-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"storysage-conversational-autobiography","title":"StorySage: Conversational Autobiography Writing Powered by a Multi-Agent Framework","date":"2025-06-17","arxiv_id":"2506.14159","repositories_listed":0,"syntology":null},{"url":null,"slug":"inverse-design-of-the-transmission-matrix-in","title":"Inverse design of the transmission matrix in a random system using Reinforcement Learning","date":"2025-06-16","arxiv_id":"2506.13057","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-the-interaction-of-linguistic","title":"Investigating the interaction of linguistic and mathematical reasoning in language models using multilingual number puzzles","date":"2025-06-16","arxiv_id":"2506.13886","repositories_listed":0,"syntology":null},{"url":null,"slug":"open-set-lidar-panoptic-segmentation-guided","title":"Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learning","date":"2025-06-16","arxiv_id":"2506.13265","repositories_listed":0,"syntology":null},{"url":null,"slug":"feeling-machines-ethics-culture-and-the-rise","title":"Feeling Machines: Ethics, Culture, and the Rise of Emotional AI","date":"2025-06-14","arxiv_id":"2506.12437","repositories_listed":0,"syntology":null},{"url":null,"slug":"agent-rlvr-training-software-engineering","title":"Agent-RLVR: Training Software Engineering Agents via Guidance and Environment Rewards","date":"2025-06-13","arxiv_id":"2506.11425","repositories_listed":0,"syntology":null},{"url":null,"slug":"build-the-web-for-agents-not-agents-for-the","title":"Build the web for agents, not agents for the web","date":"2025-06-12","arxiv_id":"2506.10953","repositories_listed":0,"syntology":null},{"url":null,"slug":"fluoroscopic-shape-and-pose-tracking-of","title":"Fluoroscopic Shape and Pose Tracking of Catheters with Custom Radiopaque Markers","date":"2025-06-11","arxiv_id":"2506.09934","repositories_listed":0,"syntology":null},{"url":null,"slug":"scholarsearch-benchmarking-scholar-searching","title":"ScholarSearch: Benchmarking Scholar Searching Ability of LLMs","date":"2025-06-11","arxiv_id":"2506.13784","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-sample-complexity-of-online-strategic","title":"The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability","date":"2025-06-11","arxiv_id":"2506.09940","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-vision-language-navigation","title":"Generating Vision-Language Navigation Instructions Incorporated Fine-Grained Alignment Annotations","date":"2025-06-10","arxiv_id":"2506.08566","repositories_listed":0,"syntology":null},{"url":null,"slug":"automating-exploratory-multiomics-research","title":"Automating Exploratory Multiomics Research via Language Models","date":"2025-06-09","arxiv_id":"2506.07591","repositories_listed":0,"syntology":null},{"url":null,"slug":"econwebarena-benchmarking-autonomous-agents","title":"EconWebArena: Benchmarking Autonomous Agents on Economic Tasks in Realistic Web Environments","date":"2025-06-09","arxiv_id":"2506.08136","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-scoring-with-3d-gaussian","title":"Hierarchical Scoring with 3D Gaussian Splatting for Instance Image-Goal Navigation","date":"2025-06-09","arxiv_id":"2506.07338","repositories_listed":0,"syntology":null},{"url":null,"slug":"secondary-stakeholders-in-ai-fighting-for","title":"Secondary Stakeholders in AI: Fighting for, Brokering, and Navigating Agency","date":"2025-06-08","arxiv_id":"2506.07281","repositories_listed":0,"syntology":null},{"url":null,"slug":"adam-assisted-fully-informed-particle-swarm","title":"Adam assisted Fully informed Particle Swarm Optimzation ( Adam-FIPSO ) based Parameter Prediction for the Quantum Approximate Optimization Algorithm (QAOA)","date":"2025-06-07","arxiv_id":"2506.06790","repositories_listed":0,"syntology":null},{"url":null,"slug":"drivesuprim-towards-precise-trajectory","title":"DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning","date":"2025-06-07","arxiv_id":"2506.06659","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-framework-for-robot-lawnmower","title":"End-to-End Framework for Robot Lawnmower Coverage Path Planning using Cellular Decomposition","date":"2025-06-06","arxiv_id":"2506.06028","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-the-buzz-a-pragmatic-take-on-inference","title":"Beyond the Buzz: A Pragmatic Take on Inference Disaggregation","date":"2025-06-05","arxiv_id":"2506.05508","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretation-meets-safety-a-survey-on","title":"Interpretation Meets Safety: A Survey on Interpretation Methods and Tools for Improving LLM Safety","date":"2025-06-05","arxiv_id":"2506.05451","repositories_listed":0,"syntology":null},{"url":null,"slug":"olfactory-inertial-odometry-sensor","title":"Olfactory Inertial Odometry: Sensor Calibration and Drift Compensation","date":"2025-06-05","arxiv_id":"2506.04539","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-theory-to-practice-real-world-use-cases","title":"From Theory to Practice: Real-World Use Cases on Trustworthy LLM-Driven Process Modeling, Prediction and Automation","date":"2025-06-04","arxiv_id":"2506.03801","repositories_listed":0,"syntology":null},{"url":null,"slug":"preface-to-the-special-issue-of-the-tal","title":"Preface to the Special Issue of the TAL Journal on Scholarly Document Processing","date":"2025-06-04","arxiv_id":"2506.03587","repositories_listed":0,"syntology":null},{"url":null,"slug":"sgn-cirl-scene-graph-based-navigation-with","title":"SGN-CIRL: Scene Graph-based Navigation with Curriculum, Imitation, and Reinforcement Learning","date":"2025-06-04","arxiv_id":"2506.04505","repositories_listed":0,"syntology":null},{"url":null,"slug":"impact-of-rankings-and-personalized","title":"Impact of Rankings and Personalized Recommendations in Marketplaces","date":"2025-06-03","arxiv_id":"2506.03369","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolvenav-self-improving-embodied-reasoning","title":"EvolveNav: Self-Improving Embodied Reasoning for LLM-Based Vision-Language Navigation","date":"2025-06-02","arxiv_id":"2506.01551","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimization-strategies-for-variational","title":"Optimization Strategies for Variational Quantum Algorithms in Noisy Landscapes","date":"2025-06-02","arxiv_id":"2506.01715","repositories_listed":0,"syntology":null},{"url":null,"slug":"position-olfaction-standardization-is","title":"Position: Olfaction Standardization is Essential for the Advancement of Embodied Artificial Intelligence","date":"2025-05-31","arxiv_id":"2506.00398","repositories_listed":0,"syntology":null},{"url":null,"slug":"conceptual-framework-toward-embodied","title":"Conceptual Framework Toward Embodied Collective Adaptive Intelligence","date":"2025-05-29","arxiv_id":"2505.23153","repositories_listed":0,"syntology":null},{"url":null,"slug":"critical-batch-size-revisited-a-simple","title":"Critical Batch Size Revisited: A Simple Empirical Approach to Large-Batch Language Model Training","date":"2025-05-29","arxiv_id":"2505.23971","repositories_listed":0,"syntology":null},{"url":null,"slug":"inference-time-scaling-of-diffusion-models","title":"Inference-time Scaling of Diffusion Models through Classical Search","date":"2025-05-29","arxiv_id":"2505.23614","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-coordinated-badminton-skills-for","title":"Learning coordinated badminton skills for legged manipulators","date":"2025-05-29","arxiv_id":"2505.22974","repositories_listed":0,"syntology":null},{"url":null,"slug":"sc-lora-balancing-efficient-fine-tuning-and","title":"SC-LoRA: Balancing Efficient Fine-tuning and Knowledge Preservation via Subspace-Constrained LoRA","date":"2025-05-29","arxiv_id":"2505.23724","repositories_listed":0,"syntology":null},{"url":null,"slug":"vlm-rrt-vision-language-model-guided-rrt","title":"VLM-RRT: Vision Language Model Guided RRT Search for Autonomous UAV Navigation","date":"2025-05-29","arxiv_id":"2505.23267","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomalies-by-synthesis-anomaly-detection","title":"Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation","date":"2025-05-28","arxiv_id":"2505.22805","repositories_listed":0,"syntology":null},{"url":null,"slug":"idse-navigating-design-space-exploration-in","title":"iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs","date":"2025-05-28","arxiv_id":"2505.22086","repositories_listed":0,"syntology":null},{"url":null,"slug":"conversational-exploration-of-literature","title":"Conversational Exploration of Literature Landscape with LitChat","date":"2025-05-25","arxiv_id":"2505.23789","repositories_listed":0,"syntology":null},{"url":null,"slug":"c-3-bench-the-things-real-disturbing-llm","title":"$C^3$-Bench: The Things Real Disturbing LLM based Agent in Multi-Tasking","date":"2025-05-24","arxiv_id":"2505.18746","repositories_listed":0,"syntology":null},{"url":null,"slug":"distribution-aware-mobility-assisted","title":"Distribution-Aware Mobility-Assisted Decentralized Federated Learning","date":"2025-05-24","arxiv_id":"2505.18866","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-discovery-engine-a-framework-for-ai","title":"The Discovery Engine: A Framework for AI-Driven Synthesis and Navigation of Scientific Knowledge Landscapes","date":"2025-05-23","arxiv_id":"2505.17500","repositories_listed":0,"syntology":null},{"url":null,"slug":"agentic-feature-augmentation-unifying","title":"Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories","date":"2025-05-21","arxiv_id":"2505.15076","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-task-capable-active-matter-learning-to","title":"Toward Task Capable Active Matter: Learning to Avoid Clogging in Confined Collectives via Collisions","date":"2025-05-21","arxiv_id":"2505.15033","repositories_listed":0,"syntology":null},{"url":null,"slug":"navbench-a-unified-robotics-benchmark-for","title":"NavBench: A Unified Robotics Benchmark for Reinforcement Learning-Based Autonomous Navigation","date":"2025-05-20","arxiv_id":"2505.14526","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-10875","title":"A Light and Smart Wearable Platform with Multimodal Foundation Model for Enhanced Spatial Reasoning in People with Blindness and Low Vision","date":"2025-05-16","arxiv_id":"2505.10875","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-10982","title":"Facets in Argumentation: A Formal Approach to Argument Significance","date":"2025-05-16","arxiv_id":"2505.10982","repositories_listed":0,"syntology":null}],"record_sha256":"3f7f518684b320e7325f40ebb8b7c575613a791377735a7f691baf535cc4e866","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}