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https://arxiv.org/html/2608.03154v1
org perpetual non-exclusive license arXiv:2608

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https://arxiv.org/pdf/2205.01882v5
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https://arxiv.org/pdf/2608.02817
�R¼7c…‰å®RpJ –} Ž“�À\Êv>©åp—®fv)»çZ+ÈŠÐPP%:3ÎvÉåÈ÷ⱂT3ÞÙÌ™ŸIêj¸S× œL*û‹Wœ­oÔÌ5#ñF5šÏ½Ö `ظ,àÏ”ß5SY V 3™T4V²q ^6¶dµè�·q^«E�ЭZ6#Ç;mþxå_

Finance-Informed Neural Network: Learning the Geometry of Option Pricing

https://arxiv.org/html/2412.12213v2
Instead of training on observed option prices, FINN is learned through a self-supervised replication objective based on dynamic hedging, ensuring economic consistency by construction

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https://arxiv.org/pdf/2005.14165
{ê´™Ö­ÝÆ­[§uÊxÀ‚µ³~Çtßú�œœ˜ ¯ÁíííYËÍÍÍ;Þi;ëw~~îîð\^^¶p···ïu§WWWí�^__»û¼gggíÜýýý{Ýé—/_Ú;-ŠÂÝà5øôéS;÷ŽwzrròAR�¼

Optimizing Age of Information in Vehicular Edge Computing with Federated

https://arxiv.org/html/2407.02342v1
org) Wen Chen is with the Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China(e-mail: wenchen@sjtu

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https://arxiv.org/pdf/2608.02758
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Preying on Leveraged ETFsPreliminary and incomplete. This project was

https://arxiv.org/html/2608.03703v1
This project was begun on July 20, 2026; the present version is dated August 4, 2026

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https://arxiv.org/src/2005.13905
M¢}œZ]ziŸ¹=ï3Ó]¶/pp‡I)6H®&Ê_öŽÎôæŽNþ€Hí째Gıh® @°¥©¡€%ÐYn—)ºÈ$óµAlWéŠ*tE6ÙJÆ4GÖ 0ôCˆ·®bXE€´>£L®YSÜ

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https://arxiv.org/pdf/2608.03118
ZØz|ÐPG«-Tû*¾öÄö1ÇÆP¡8YîvkÚ©-'NÄŸéá`ÀPMÝŽ$&‡] L—'|*¡ûÍ�¥p7k7m¢E™7·€nÙîrøy09U=–PP#ü'#Þ„v2Zú$íþt;Ʀël�r5÷ð‡ð”

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https://arxiv.org/src/2006.02893
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https://arxiv.org/src/2006.02759
YT,‹ *Šÿ¹ËÌÜ™LÒ²øþ÷þÿÍÍÌ�{Ï=÷ì÷Ü;3==ö¡Biû¸¿òHƒ#;»#þ…Ãø‹ÏÓ3²²²Ò3³²:¦Ç¥¥§eÃ×ñ/ÅŠ!IæƒE9V½ªîÿ‡= ÿ=b€Ê©þ�Ïf—.÷8ƒ³²2ÍùŸž–‘Õ1ñ?=³cfzfZ6ð?£cÇŽq\ÚeÆÃôøÎÿ$NÏz.5•Ã21‚“ä2¯À¹Ä —?p7A ý¼WâRòø`1t¦z=EÍ„ ÇA*·IHJHâzçé58ÐÐü�ð5Çõ e·

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https://arxiv.org/pdf/2006.02759
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https://arxiv.org/src/2006.02833
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https://arxiv.org/src/2608.04008
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https://arxiv.org/pdf/2407.01722
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https://arxiv.org/src/2211.10435v1
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https://arxiv.org/pdf/2006.00342
R-Rv×üúÍD&À‡({:¦ë‰¡D>¿L âÕý*^½}òâÓ“god"W¹(ŒIVŸîV©Y‘¯2eDš¤«O»ÕÏÑ˶¹+OëþðîýëàÌœz?õà ß “Û¾ñŸytXÓIÑn÷¨À?f ,#C� µØ�)èl/à§êTŠUYJé¹ó|ýyW•�“Zÿ$ɹø6¨°a˜E2¦v€PC(èë¦xI6=ÀóTÎ1Q"6E¨dôôiΰp]u£|b(j�‘6—`ä~ºr�·Ú°2püÖIz´ö�‹KšÊL¾bAZ

[2608.03883] DS@GT-ARC at eRisk 2026 Task 3: Sparse, Semantic, and LLM

https://arxiv.org/abs/2608.03883
arXiv is committed to these values and only works with partners that adhere to them

[2104.08663] BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of

https://arxiv.org/abs/2104.08663
Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs

[2608.03860] SciRet: A Compute-Aware Empirical Study of Retrieval and

https://arxiv.org/abs/2608.03860
arXiv is committed to these values and only works with partners that adhere to them

Information Retrieval

https://arxiv.org/list/cs.IR/recent
IR) Tue, 4 Aug 2026 (showing first 34 of 45 entries ) [17] arXiv:2608

Information Retrieval

https://arxiv.org/list/cs.IR/new
IR) Industrial recommenders increasingly adopt the pretrain-then-transfer paradigm, yet behavioral distribution drift raises two questions: what to learn from behavior sequences, and how to transfer the learned knowledge while the pretrained model is continually refreshed

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
, the meaning of a customized field) originates from users' domain knowledge and is rarely documented publicly

Report GitHub Issue

https://arxiv.org/html/2609.02852v1
Report GitHub Issue Description: arXiv is now an independent nonprofit

Global coal trade is resilient to maritime chokepoints

https://arxiv.org/html/2608.00328v2
org perpetual non-exclusive license arXiv:2608

arXiv is now an independent nonprofit! Learn more ×

https://arxiv.org/html/2609.03159v1
Description: arXiv is now an independent nonprofit

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https://arxiv.org/pdf/2608.03157
ÄÌ4ºû4àC0ÎøMÈ8$SÙH‹‘Rp¬Æk4>¢eñ8ŠQ�{Ñ›Pp/ªIŠö1˜œñšMõŠc2ââ…^ø¼Q#ïT#ÑáNrFRÂK“àclƒ ©‘w'�0I»ƒŸ}¢”ˆŸJüœãþUÞÁñ™h+`d@«WÙul“`¯r0=ÍÑhJl“ŒfÇ6'è‘ÏÅhÞ©&8bAŸƒ|¢ˆ šk³JˆžwÔ„ÔîØF�¹À´ÿù’LÅ·J6Q‰¶�‘†,'ø²¯ÎÄDÓUÁ {P½‰™_GObiw‚I®µ‰& zàk‚ýÑ�¾¥HÂF)Òª0^SÑogRÁ€¨“�ƒœ7™C¦¸ÈÁÑ6¿Ø8â¯W— lÀŸ2N *cåOÕ䂨8S

Herding, Momentum, and Reversal in China’s A-Share Market: An Agent

https://arxiv.org/html/2607.27063v1
The local interaction structure is represented by von Neumann and Moore lattices and is later replaced by Erdős–Rényi and Watts–Strogatz networks for robustness

Inverse Learning of Latent Risk-Neutral Densities from Irregular Option

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