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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

Very Large-Scale Multi-Agent Simulation in AgentScope

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

Artificial Intelligence

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

Consensus Measures for Unstructured Biomedical Text Annotations

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

Collaborative Adaptation for Recovery from Unforeseen Malfunctions in

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

Report GitHub Issue

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

Numerical Analysis

https://arxiv.org/list/cs.NA/new
all New submissions (showing 18 of 18 entries) [1] arXiv:2608

MDGAM-Based Cooperative Task Scheduling for Communication-Constrained

https://arxiv.org/html/2608.00648v1
In these scenarios, heterogeneous agents and complex task constraints require efficient inter-agent coordination to improve execution efficiency

On the Diversity of Analogy Making in Large Language Models

https://arxiv.org/html/2608.03233v1
org perpetual non-exclusive license arXiv:2608

Computation and Language

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)

Computer Vision and Pattern Recognition

https://arxiv.org/list/cs.CV/new
To address this limitation, we propose Hunyuan3D-Buffalo 1

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https://arxiv.org/pdf/2006.02318
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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

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https://arxiv.org/pdf/2006.00342
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https://arxiv.org/src/2303.12712
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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/src/2405.20232
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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

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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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