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com/modelscope/agentscope/tree/main/examples/paper_large_scale_simulation to inspire further research and development in large-scale multi-agent simulations
from the top result below
https://arxiv.org/html/2407.17789v2
com/modelscope/agentscope/tree/main/examples/paper_large_scale_simulation to inspire further research and development in large-scale multi-agent simulations
https://arxiv.org/html/2608.02613v1
Then it must make an even harder judgment: even if the answer is known, is it actually appropriate to tell Bob
https://arxiv.org/list/cs.AI/new
To bridge this gap, we introduce a novel and comprehensive Interactive SEmantic Enrichment system (ISEE)
https://arxiv.org/list/cs.CV/new
To address this limitation, we propose Hunyuan3D-Buffalo 1
https://arxiv.org/list/eess.IV/recent
Accepted to MICCAI 2026
Subjects: Image and Video Processing (eess
https://arxiv.org/src/2006.02735
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https://arxiv.org/html/2608.02609v1
TabletCraft: Bridging a 4,000-Year Cultural Gap with Bidirectional Akkadian NMT and Cuneiform RenderingAccepted to the 4th Workshop on Cross-Cultural Considerations in NLP (C3NLP) at ACL 2026
https://arxiv.org/pdf/2608.13122
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https://arxiv.org/pdf/1802.00050
t0ó€F�É�–i›iYr´ôË˯Om”(?¹1ôÅâÎb±–¯Šþîùç¿Ç»óð.Ú�wÑîÇùûÃó»ï>TÙ.ŽÂ*ªâÝó ŠyX©|—WQÕîù¸ûgðS»J²,/vàÒË>Ž‚®ÿü«Eðb¸ùÖ›Á´#W4ÚngÁü1�ô4ç®·ãåŠÕ–Õ2ḛšÃ`¹—ÆÏ†›ÆôŸeüÉèqê¥çlZÓëÑvm¸RQ|œzñÒÉ’z¦+–#ÀJ¶•îãÑ>áÑšd\Ï™c‡_“ 74ú`€B® fäîîÄ
https://arxiv.org/pdf/2608.02665
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https://arxiv.org/src/2608.03930
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https://arxiv.org/pdf/2010.15980
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https://arxiv.org/pdf/2608.03014
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https://arxiv.org/pdf/2010.15980v1
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https://arxiv.org/pdf/2608.00458
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https://arxiv.org/pdf/2006.00441
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https://arxiv.org/html/2608.03529v1
There, the annotation space is known in advance, and measuring Inter-Rater Reliability (IRR) on the resulting codified annotations Cohen (1960); Scott (1955); Fleiss (1971) is standard procedure to enable insights into their reliability and quality Artstein and Poesio (2008); Hanegraaf et al
https://arxiv.org/pdf/2006.03994
ØáˆNäຘ[éìAsÔ�ƒAª8Ï B% r{D’„ײÀÝüáF: ¾{üîÿ Û}=endstream endobj 6 0 obj > stream xÚí[koÛ:ýî_Á�í•9|4�v´AÓÛÞÝÞ Pµj[YYîMö×ï™vÜH(¬À#MJ å…v6 …Q7•0šLOk”šð¥CéQzT’Â2ÂJkñ�¿ˆ›¸´¥ÇwTµ¸o�ç”ìYà9T¶ÀsÀ±Àó2‡ªÞh�
https://arxiv.org/html/2608.00648v1
In these scenarios, heterogeneous agents and complex task constraints require efficient inter-agent coordination to improve execution efficiency
https://arxiv.org/html/2410.05779v3
com Both Chao Huang and Yanhua Yu are corresponding authors
https://arxiv.org/pdf/2608.00151
R†5ö9DüÉ‚6, ¥ÎβYƒW‚*e"ѹÚ�Rˆl®ŸßÔF≧Êk–l‘ò+Œ|ûZý-É[~ZзaˆÒd}æX®\>™Ö]«8ðn¤Ÿ¬V‚HIКÓÞb7ÆEèÈb8ÍíãÜd¿ìß%lVª)Pçf e’Z…6z³öà^wºcoM²±z†iÂ& ^ ”ŠÅ$aƒ�ÃZánŸÎ¿Y¿PŒ‡ÍÍÐÒ$™ joVþ²ìßÝá“5ãnYçÊÝ@›Ã
https://arxiv.org/html/2407.14571v1
Many socio-economical critical domains (such as sustainability, public health, and disasters) are characterized by highly complex and dynamic systems, requiring data and model-driven simulations to support decision-making
https://arxiv.org/list/cs.SE/new
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Glob patterns, a domain-specific language for structured string matching, are a foundational yet understudied component of modern software development embedded in everything from build scripts and configuration files to web server routes
https://arxiv.org/html/2407.19144v1/
These malfunctions decrease the team’s ability to accomplish assigned task(s), especially if they occur after the learning algorithms have already converged onto a collaborative strategy
https://arxiv.org/list/cond-mat/new
The mixed anomaly between these is accommodated by the not-on-site action of the symmetries
https://arxiv.org/html/2608.03233v1
Our findings highlight a concerning issue of domain homogeneity, a prevalent tendency for LLMs to generate analogies from a narrow set of target domains, limiting both inter-query and intra-model diversity
https://arxiv.org/pdf/2407.01407