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https://arxiv.org/pdf/2608.02122
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https://arxiv.org/pdf/2608.01785
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https://arxiv.org/pdf/2407.00125
) /Title (A Survey on Failure Analysis and Fault Injection in AI Systems) /Trapped /False /arXivID (https://arxiv

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https://arxiv.org/src/2006.01048
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https://arxiv.org/pdf/2608.00785
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https://arxiv.org/pdf/2005.13905
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https://arxiv.org/src/2608.03994
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https://arxiv.org/src/2006.00064
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https://arxiv.org/src/2006.02759
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https://arxiv.org/src/2006.01128
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https://arxiv.org/src/2104.08691
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https://arxiv.org/src/2608.03883
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https://arxiv.org/src/2006.01331
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https://arxiv.org/pdf/2006.01128
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https://arxiv.org/pdf/2006.00342
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https://arxiv.org/pdf/2608.03118
Ó>~ý|)•Š:›{÷iªJ¥3�/S©¬–,a)Ò0Ť‘L3•¦Ì0m5³ÌjÁˤd‰0¸áMBà_áEâý[Åý;Ǻè /b [&²õŽÉDcPƤÐôÂ¤Ì ^˜L*%“:¥Pcñª&—šÉ “‚Î4I0βT€8éXŠ4²4µ¸�åÐI°Të�,5‚Yj1OªðÌ4YêÜÐ5£Ì—)ªdJ82~þêÓ_ù(Lº»8ß¼®òÅt¸øÎãèÇ�ý껳Ù|µÄ�µ`øå³P™úJÖ4�

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
PL) Knowledge graph engineering often distributes accepted state, observations, constraints, processes, and hypothetical scenarios across artifacts whose combined execution contract remains external

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https://arxiv.org/pdf/2608.02854v1
Q ‚±SœãðcàYÀ8‡TÀD©t¢)„O™Ds#XB+PL 8À5†�½Â+P™'FC\5Ä7M

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https://arxiv.org/pdf/2205.01882v5
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https://arxiv.org/pdf/2608.02976
Zýáü oŒÍøe§SÕ�¦©ôðI ×´ÓOÂ\õïèÀIpïÀ‹%ª&1™$a©K¹ËÈCH»0G�Ž_¥3¶{‡bý¹Xx”­ O±jlbLTxT2¦S$‹A™×ŒÛxI V‚¬öuYã

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https://arxiv.org/src/2006.03720
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‹Ptá^ì|‡ô Ÿúoÿ ?ú¬px8:�†ÐÞïFŸ±ýß“)óIãÄŠû,’2ÙwSÿ¿é§ÛítN�ß}

https://arxiv.org/src/2006.02085
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https://arxiv.org/pdf/2006.02602
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https://arxiv.org/pdf/2608.02666
'±üd$ ëÿìWõUóÒäˆ)‚]ŠÍîêêºWu“ÿˆ¾‰þÝ}ñ£‹~ø1JõïÇД&YÁ{þS¥.i³è‡¿E¿xŽŽi’¦y=ÿUµöÂ%¯]REY»yþ[t÷üœE.zþKô]ÿäê::º(Þòò�—.Yïy9Œ_bÞ¾\_odäGvºaë-KF�üŽ�){:^>^oÊu¦ _^GmýÏŸ£o£¿oÞ¤SÐ9èTQ©NáR»2)Û¢Œ\[™NUçtÊE¤:µód¾€Šý¼MÕ&UYb½-Ä@uÙæ�%

Probing Character-level Transformers for the Spanish L-shaped Morphome

https://arxiv.org/html/2608.03452v1
de Abstract When a transformer learns an irregular morphological pattern, what has it learned

Matching-Driven Deep Reinforcement Learning for Energy-Efficient

https://arxiv.org/html/2407.13076v1
Gateway LoRa Networks Ziqi Lin, Xu Zhang, Shimin Gong,  Lanhua Li, Zhou Su,  and Bo Gu This work was supported in part by the National Science Foundation of China (NSFC) under Grant U20A20175 and in part by the National Key R&D Program of China under Grant 2020YFB1713800

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https://arxiv.org/pdf/2002.02145
O¶ÃsÔ‰îÑÑc‚{«3®rôy^ý¹ê=i,WÞŸÛh `üZ¥¯‡syÛ®×l(5®ÚÏåzS­™ÛµdÙ:D3w�í´b½ìf/×Þ¡K³:$ó ç[“gß53—Ùýy÷­ £cÉ·O¸ÐmCÇÃÁ¨x²´ª/Âù²%@ϤÛÏ\�ÎúJ…"ó÷Èo ½™ƒõ©£B]OA;¿’r¯d]IPÃ_ïË Ï׸iÕótm3%($ShàWuYËÄÒ[”WNÁ&šÜ†š èôM1PA¡e£æKŠˆ{n1‚�Ý_QìM«‹]pHʺAjÕìw>‚øiĨpBrŽ|_ô>Bš7çæÈW(·6š×BÖ*öµ#Õ‡0hU•þÓÖ{ÞO�Q©Wijã°yô'PÈ}Ül

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https://arxiv.org/pdf/2608.03898
�=ØžðòëïiYöÈ{wŽí–ÔzúE­n�%ó˜bAÄ|&dÈ&’€…L†>‹˜RŠÅÌ÷$K˜ &ôëë¼-2EÐÛ¯¿ök ?9ð²Îî™´™DÞ˜ „L"žQïËÓó¿ŸžO¨÷¼M‹U±ÿcî #ïà‘ï>©¶%#þI7ϲ|uÛ¿Bš«¬e�ø1?áOø)ÊÏøoüœ?çð+þš¿å)O—yVgMÞðk~]§‹» ‹ð¦íŸkâÇ|QU‰¿ëuÊ—Íon°îÊE¦‡2D\a~Ñis«�ö{}F…ÿ¶Hëªd¼»!wë

Inverse Learning of Latent Risk-Neutral Densities from Irregular Option

https://arxiv.org/html/2607.27188v1
org perpetual non-exclusive license arXiv:2607

Train Often, Deploy Selectively: Forward-Gated Model Replacement in

https://arxiv.org/html/2607.28577v1
org perpetual non-exclusive license arXiv:2607