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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/2608.03529v1
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/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/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.NA/new
all
New submissions (showing 18 of 18 entries)
[1] arXiv:2608
https://arxiv.org/list/cs.CV/new
To address this limitation, we propose Hunyuan3D-Buffalo 1
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/2608.03233v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/list/cs.CL/new
Users can read ancient tablets (Akkadian to English) and compose new messages as cuneiform clay tablets (English to Akkadian to cuneiform to rendered tablet)
https://arxiv.org/list/cs.AI/new
Existing tool-use agents typically rely on LLMs to infer tool compositions from textual descriptions, which can lead to inefficient exploration and unreliab
https://arxiv.org/pdf/2006.02318
3/ pdfx PDF/X Schema external URL to an online version or preprint AuthoritativeDomain Text http://www
https://arxiv.org/pdf/2508.11152
com three Proceedings of ACM Conference (Conference'17) book 1 1 9 9 Tianjiao ZhaoJingrao LyuStokes JonesHarrison GarberStefano PasqualiDhagash Mehtahttp://arxiv
https://arxiv.org/pdf/2006.00342
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https://arxiv.org/src/2303.12712
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https://arxiv.org/src/2405.20232
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https://arxiv.org/src/1910.03771
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https://arxiv.org/pdf/2609.18842
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https://arxiv.org/pdf/2006.00064
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https://arxiv.org/pdf/2205.11916v2
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https://arxiv.org/pdf/2608.03494
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https://arxiv.org/pdf/2410.05779
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https://arxiv.org/pdf/2608.01934
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https://arxiv.org/src/2006.00064
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https://arxiv.org/pdf/2608.02976
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https://arxiv.org/src/2608.04003
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https://arxiv.org/pdf/2309.08431
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https://arxiv.org/pdf/2506.01844
SmolVLA: A Vision-Language-Action Model for Affordable and Efficient RoboticsMustafa ShukorDana AubakirovaFrancesco CapuanoPepijn KooijmansSteven PalmaAdil ZouitineMichel AractingiCaroline PascalMartino RussiAndres MarafiotiSimon
https://arxiv.org/src/2212.07841
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https://arxiv.org/pdf/2607.26327v3
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https://arxiv.org/pdf/2608.02872
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