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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.04046">arXiv:2406.04046</a> <span> [<a href="https://arxiv.org/pdf/2406.04046">pdf</a>, <a href="https://arxiv.org/format/2406.04046">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Complexity">cs.CC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> ActionReasoningBench: Reasoning about Actions with and without Ramification Constraints </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Handa%2C+D">Divij Handa</a>, <a href="/search/cs?searchtype=author&query=Dolin%2C+P">Pavel Dolin</a>, <a href="/search/cs?searchtype=author&query=Kumbhar%2C+S">Shrinidhi Kumbhar</a>, <a href="/search/cs?searchtype=author&query=Son%2C+T+C">Tran Cao Son</a>, <a href="/search/cs?searchtype=author&query=Baral%2C+C">Chitta Baral</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.04046v2-abstract-short" style="display: inline;"> Reasoning about Actions and Change (RAC) has historically played a pivotal role in solving foundational AI problems, such as the frame problem. It has driven advancements in AI fields, such as non-monotonic and commonsense reasoning. RAC remains crucial for AI systems that operate in dynamic environments, engage in interactive scenarios, or rely on commonsense reasoning. Despite substantial advanc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.04046v2-abstract-full').style.display = 'inline'; document.getElementById('2406.04046v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.04046v2-abstract-full" style="display: none;"> Reasoning about Actions and Change (RAC) has historically played a pivotal role in solving foundational AI problems, such as the frame problem. It has driven advancements in AI fields, such as non-monotonic and commonsense reasoning. RAC remains crucial for AI systems that operate in dynamic environments, engage in interactive scenarios, or rely on commonsense reasoning. Despite substantial advances made by Large Language Models (LLMs) in various AI domains, their performance in RAC remains underexplored. To address this gap, we introduce a new diagnostic benchmark, ActionReasoningBench, which encompasses 8 domains and includes questions for up to 19 action sequences. This benchmark rigorously evaluates LLMs across six key RAC dimensions: Fluent Tracking, State Tracking, Action Executability, Effects of Actions, Numerical RAC, and Composite Questions. LLMs demonstrate average accuracy rates of 73.55%, 65.63%, 58.73%, and 62.38% on the former four dimensions, which are frequently discussed in RAC literature. However, the performance on the latter two dimensions, which introduce complex and novel reasoning questions, the average performance of LLMs is lowered to 33.16% and 51.19%, respectively, reflecting a 17.9% performance decline. We also introduce new ramification constraints to capture the indirect effects of actions, providing deeper insights into RAC challenges. Our evaluation of state-of-the-art LLMs, including both open-source and commercial models, reveals challenges across all RAC dimensions, particularly in handling ramifications, with GPT-4o failing to solve any question and o1-preview achieving a score of only 18.4%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.04046v2-abstract-full').style.display = 'none'; document.getElementById('2406.04046v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">35 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.00836">arXiv:2310.00836</a> <span> [<a href="https://arxiv.org/pdf/2310.00836">pdf</a>, <a href="https://arxiv.org/format/2310.00836">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Luo%2C+M">Man Luo</a>, <a href="/search/cs?searchtype=author&query=Kumbhar%2C+S">Shrinidhi Kumbhar</a>, <a href="/search/cs?searchtype=author&query=shen%2C+M">Ming shen</a>, <a href="/search/cs?searchtype=author&query=Parmar%2C+M">Mihir Parmar</a>, <a href="/search/cs?searchtype=author&query=Varshney%2C+N">Neeraj Varshney</a>, <a href="/search/cs?searchtype=author&query=Banerjee%2C+P">Pratyay Banerjee</a>, <a href="/search/cs?searchtype=author&query=Aditya%2C+S">Somak Aditya</a>, <a href="/search/cs?searchtype=author&query=Baral%2C+C">Chitta Baral</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.00836v3-abstract-short" style="display: inline;"> Logical reasoning is fundamental for humans yet presents a substantial challenge in the domain of Artificial Intelligence. Initially, researchers used Knowledge Representation and Reasoning (KR) systems that did not scale and required non-trivial manual effort. Recently, the emergence of large language models (LLMs) has demonstrated the ability to overcome various limitations of formal Knowledge R… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.00836v3-abstract-full').style.display = 'inline'; document.getElementById('2310.00836v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.00836v3-abstract-full" style="display: none;"> Logical reasoning is fundamental for humans yet presents a substantial challenge in the domain of Artificial Intelligence. Initially, researchers used Knowledge Representation and Reasoning (KR) systems that did not scale and required non-trivial manual effort. Recently, the emergence of large language models (LLMs) has demonstrated the ability to overcome various limitations of formal Knowledge Representation (KR) systems. Consequently, there's a growing interest in using LLMs for logical reasoning via natural language. This work strives to understand the proficiency of LLMs in logical reasoning by offering a brief review of the latest progress in this area; with a focus on the logical reasoning datasets, tasks, and the methods adopted to utilize LLMs for reasoning. To offer a thorough analysis, we have compiled a benchmark titled LogiGLUE. This includes 24 varied datasets encompassing deductive, abductive, and inductive reasoning. Utilizing LogiGLUE as a foundation, we have trained an instruction fine-tuned language model, resulting in LogiT5. We study single-task training, multi-task training, and "chain-of-thought" knowledge distillation fine-tuning technique to assess the performance of model across the different logical reasoning categories. We also assess various LLMs using LogiGLUE, and the findings indicate that LLMs excel most in abductive reasoning, followed by deductive reasoning, while they are least effective at inductive reasoning. We aim to shed light on the capabilities and potential pathways for enhancing logical reasoning proficiency in LLMs, paving the way for more advanced and nuanced developments in this critical field. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.00836v3-abstract-full').style.display = 'none'; document.getElementById('2310.00836v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Work in progress</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2003.12476">arXiv:2003.12476</a> <span> [<a href="https://arxiv.org/pdf/2003.12476">pdf</a>, <a href="https://arxiv.org/format/2003.12476">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1038/s41597-020-00638-4">10.1038/s41597-020-00638-4 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Huber%2C+S+P">Sebastiaan. P. Huber</a>, <a href="/search/cs?searchtype=author&query=Zoupanos%2C+S">Spyros Zoupanos</a>, <a href="/search/cs?searchtype=author&query=Uhrin%2C+M">Martin Uhrin</a>, <a href="/search/cs?searchtype=author&query=Talirz%2C+L">Leopold Talirz</a>, <a href="/search/cs?searchtype=author&query=Kahle%2C+L">Leonid Kahle</a>, <a href="/search/cs?searchtype=author&query=H%C3%A4uselmann%2C+R">Rico H盲uselmann</a>, <a href="/search/cs?searchtype=author&query=Gresch%2C+D">Dominik Gresch</a>, <a href="/search/cs?searchtype=author&query=M%C3%BCller%2C+T">Tiziano M眉ller</a>, <a href="/search/cs?searchtype=author&query=Yakutovich%2C+A+V">Aliaksandr V. Yakutovich</a>, <a href="/search/cs?searchtype=author&query=Andersen%2C+C+W">Casper W. Andersen</a>, <a href="/search/cs?searchtype=author&query=Ramirez%2C+F+F">Francisco F. Ramirez</a>, <a href="/search/cs?searchtype=author&query=Adorf%2C+C+S">Carl S. Adorf</a>, <a href="/search/cs?searchtype=author&query=Gargiulo%2C+F">Fernando Gargiulo</a>, <a href="/search/cs?searchtype=author&query=Kumbhar%2C+S">Snehal Kumbhar</a>, <a href="/search/cs?searchtype=author&query=Passaro%2C+E">Elsa Passaro</a>, <a href="/search/cs?searchtype=author&query=Johnston%2C+C">Conrad Johnston</a>, <a href="/search/cs?searchtype=author&query=Merkys%2C+A">Andrius Merkys</a>, <a href="/search/cs?searchtype=author&query=Cepellotti%2C+A">Andrea Cepellotti</a>, <a href="/search/cs?searchtype=author&query=Mounet%2C+N">Nicolas Mounet</a>, <a href="/search/cs?searchtype=author&query=Marzari%2C+N">Nicola Marzari</a>, <a href="/search/cs?searchtype=author&query=Kozinsky%2C+B">Boris Kozinsky</a>, <a href="/search/cs?searchtype=author&query=Pizzi%2C+G">Giovanni Pizzi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2003.12476v1-abstract-short" style="display: inline;"> The ever-growing availability of computing power and the sustained development of advanced computational methods have contributed much to recent scientific progress. These developments present new challenges driven by the sheer amount of calculations and data to manage. Next-generation exascale supercomputers will harden these challenges, such that automated and scalable solutions become crucial.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.12476v1-abstract-full').style.display = 'inline'; document.getElementById('2003.12476v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2003.12476v1-abstract-full" style="display: none;"> The ever-growing availability of computing power and the sustained development of advanced computational methods have contributed much to recent scientific progress. These developments present new challenges driven by the sheer amount of calculations and data to manage. Next-generation exascale supercomputers will harden these challenges, such that automated and scalable solutions become crucial. In recent years, we have been developing AiiDA (http://www.aiida.net), a robust open-source high-throughput infrastructure addressing the challenges arising from the needs of automated workflow management and data provenance recording. Here, we introduce developments and capabilities required to reach sustained performance, with AiiDA supporting throughputs of tens of thousands processes/hour, while automatically preserving and storing the full data provenance in a relational database making it queryable and traversable, thus enabling high-performance data analytics. AiiDA's workflow language provides advanced automation, error handling features and a flexible plugin model to allow interfacing with any simulation software. The associated plugin registry enables seamless sharing of extensions, empowering a vibrant user community dedicated to making simulations more robust, user-friendly and reproducible. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.12476v1-abstract-full').style.display = 'none'; document.getElementById('2003.12476v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Scientific Data 7, 300 (2020) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1601.06503">arXiv:1601.06503</a> <span> [<a href="https://arxiv.org/pdf/1601.06503">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</span> </div> </div> <p class="title is-5 mathjax"> TiO2 based Nanostructured Memristor for RRAM and Neuromorphic Applications: A Simulation Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dongale%2C+T+D">T. D. Dongale</a>, <a href="/search/cs?searchtype=author&query=Patil%2C+P+J">P. J. Patil</a>, <a href="/search/cs?searchtype=author&query=Desai%2C+N+K">N. K. Desai</a>, <a href="/search/cs?searchtype=author&query=Chougule%2C+P+P">P. P. Chougule</a>, <a href="/search/cs?searchtype=author&query=Kumbhar%2C+S+M">S. M. Kumbhar</a>, <a href="/search/cs?searchtype=author&query=Waifalkar%2C+P+P">P. P. Waifalkar</a>, <a href="/search/cs?searchtype=author&query=Patil%2C+P+B">P. B. Patil</a>, <a href="/search/cs?searchtype=author&query=Vhatkar%2C+R+S">R. S. Vhatkar</a>, <a href="/search/cs?searchtype=author&query=Takale%2C+M+V">M. V. Takale</a>, <a href="/search/cs?searchtype=author&query=Gaikwad%2C+P+K">P. K. Gaikwad</a>, <a href="/search/cs?searchtype=author&query=Kamat%2C+R+K">R. K. Kamat</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1601.06503v1-abstract-short" style="display: inline;"> We report simulation of nanostructured memristor device using piecewise linear and nonlinear window functions for RRAM and neuromorphic applications. The linear drift model of memristor has been exploited for the simulation purpose with the linear and non-linear window function as the mathematical and scripting basis. The results evidences that the piecewise linear window function can aptly simula… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1601.06503v1-abstract-full').style.display = 'inline'; document.getElementById('1601.06503v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1601.06503v1-abstract-full" style="display: none;"> We report simulation of nanostructured memristor device using piecewise linear and nonlinear window functions for RRAM and neuromorphic applications. The linear drift model of memristor has been exploited for the simulation purpose with the linear and non-linear window function as the mathematical and scripting basis. The results evidences that the piecewise linear window function can aptly simulate the memristor characteristics pertaining to RRAM application. However, the nonlinear window function could exhibit the nonlinear phenomenon in simulation only at the lower magnitude of control parameter. This has motivated us to propose a new nonlinear window function for emulating the simulation model of the memristor. Interestingly, the proposed window function is scalable up to f(x)=1 and exhibits the nonlinear behavior at higher magnitude of control parameter. Moreover, the simulation results of proposed nonlinear window function are encouraging and reveals the smooth nonlinear change from LRS to HRS and vice versa and therefore useful for the neuromorphic applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1601.06503v1-abstract-full').style.display = 'none'; document.getElementById('1601.06503v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 January, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 8 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 65Zxx; 74K35; 82Dxx </p> </li> </ol> 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