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CY) We show that knowledge distillation (KD) in small instruction-tuned language models has asymmetric effects on bias, and that measuring them correctly requires accounting for where refusal mass moves and what the parser can legitimately score
from the top result below

Computation and Language

https://arxiv.org/list/cs.CL/new
LG) Half a million cuneiform clay tablets survive in museums worldwide, yet modern users can neither read nor write in the world's oldest writing system, leaving a 4,000-year cultural barrier that existing NLP tools have only partially addressed

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
We demonstrate that it can self-assemble functional circuits from scratch and rapidly re-route logic around permanent, previously unseen hardware faults

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https://arxiv.org/pdf/2608.04341
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https://arxiv.org/pdf/2608.02764
~ €'Ñ£ö—³ê° `Á‰çÄ"± m�Óšc3á8{�ck'NÕ: µ‡œãLº¤q]œ\—�s@Î1uœñÑfìq®œ#ô ´™òØ£1²¯Ø> c¥%câðxA{5fv®MKDoußtßõÉP}¿²›tΣ)÷•8sjtíÙ1ñŸ7´µôæ›×&—-âü·âùòlõ×eë±¾}óÏö�í¶�’_±'ºæK¶W×(&H‹—›¤|�üÐŒ§„2šË¿P"§±”ÍFAHŠ3Qˆ„cy

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https://arxiv.org/pdf/2607.28577v1
) /Title (Train Often, Deploy Selectively: Forward-Gated Model Replacement in Crypto Markets) /Trapped /False /arXivID (https://arxiv

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https://arxiv.org/pdf/2607.28410v1
©’ðÐÿ~N·68^Õ°œûõ¹ï�„ÀpÁ@€` bH0„$"HP’~€  ¯Èb¤ `Ft¡3¦“rƾ\^BÐÓ•û‚Áíâa¹ºHÍzôRšÔ”¿MµþÚ+vÙö›"Ë÷[H$!ÜA0Ì˪î?阤%øfªM™?×EIõ¸õšêÆ ƒàþð«~y6¤ô�tË}Ny֤͑'»åYåYýT�ŠN¦««îíb> endobj 15 0 obj > stream xœí\Ys9’~Ÿ_�Çݘ°Q(œ11±’

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https://arxiv.org/pdf/2608.01651v1
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https://arxiv.org/pdf/2607.27188v1
) /Title (Inverse Learning of Latent Risk-Neutral Densities from Irregular Option Quotes) /Trapped /False /arXivID (https://arxiv

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https://arxiv.org/pdf/2608.03063
æ0ù0)ûÂá��ÂãqA€·/nÖG7“K¾Ã~¦áP–6$4-a=CYå‘6,P-ÄuÛ•Ä*óÚOQk¸3W‘÷D“7«UZÆ0úüÒjLâ¬OÈ£óZ³ì:TdÕ›²ÞO\þÓœ|޶O‚“É)Fiû$ê«V ˜�§•B‘öE§LÈ-&âißàoèû¼¡ïó†¾Ïú>oèû|@àóAÈ

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https://arxiv.org/pdf/2608.02758
@Û¬MX–T‰Þ`óëû)iI}¸=Žç g†o)™‘Œp” 9þ‘‚I" ͸"ŠPZ¤ ” AJBE& B¨”ð…Zä@a„eœ‡0–ç„æ„劂0)áSV(ÀáÖ Â)®+ gH €³’0Jx cðYf„qÂ;{ÿžì~¶7ëzòy:ZgÞ�Œi{c®,câ%Л­*Ó�`ÕV÷NW¶wöøL¾�Ý/úf¶W2ø}(^tu0}e¦fY–î]ÝÜÝ3Co¤+´¯iYж³/Ú™g’Ç=nær²ßôÉ\p‹Ö˜EBp

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https://arxiv.org/pdf/2608.02122
) /Title (DEFT: Joint Task Placement and DVFS for Energy-Efficient Multi-GPU Runtimes) /Trapped /False /arXivID (https://arxiv

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https://arxiv.org/pdf/2608.03883
3/ pdfx PDF/X Schema external URL to an online version or preprint AuthoritativeDomain Text http://www

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https://arxiv.org/pdf/2608.01785
+ÖÑÖ1‡}ª$jšdKmo€Át‹u°ê«bUQÍHF8‘†B™!’0Ή"ÌX¢ —š…rb‰d¡‘ZJ‰ ø‰¦ðÁ‰ÖœPAŒ¡„Jb¹%¸ž;î — HIF(�¬£2fa@â>�$€Î@‘d`˜#­!ð•*M }šÃ'è3ÐAŸ1 Ç‚* søÊX ,Ò‚K Êfœ€3œjxð§xD¸†ý¸""Sˆàèà¦ðl‰0–‘H*øN¤Ì€È#•3Q™áD¢8¸,$BBŠ(ÎM”6°nˆ²Š0‚cDfDs

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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/pdf/2508.11152
document was published isbn Text external ISBN for the publication in which the document was published doi Text external Digital Object Identifier for the document url URL external URL at which the document can be found byteCount Integer internal Approximate file size in octets pageCount Integer internal Number of

1 Introduction

https://arxiv.org/html/2501.13956v1
2 Knowledge Graph Construction 2.1 Episodes 2.2 Semantic Entities and Facts 2.2.1 Entities 2.2.2 Facts 2.2.3 Temporal Extraction and Edge Invalidation 2.3 Communities 3 Memory Retrieval 3.1 Search 3.2 Reranker 4 Experiments 4.1 Choice

Report GitHub Issue

https://arxiv.org/html/2608.02613v1
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends

Report GitHub Issue

https://arxiv.org/html/2608.03048v1
for large language models, as recent recurrent-memory approaches face two inherent challenges: sequential chunk-wise updates can overwrite early critical evidence with later irrelevant content, and serial inter-chunk dependencies limit parallelism and cause latency to increase with context length

Computers and Society Aug 2026

https://arxiv.org/list/cs.CY/current
251-256 Showing up to 50 entries per page: fewer | more | all [1] arXiv:2608

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https://arxiv.org/pdf/2608.02943
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‹'u�bìX X×·§ŠOÄ\*":"»,aPe `Å!’‘$3!¢­EÁ]Ä q/Z— ¸ÖE¥

https://arxiv.org/src/2205.11916v1
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https://arxiv.org/pdf/2608.02244
ˆQîÚÿãóÏEóDü”M‰ïEóê¿ÿ”¦ ²Å\ã³ÑHü,=:ïÙꕞ`ŸôP‚Ë=É£•ž>J ¡­é j—{e¸4æþd™w“iw"íѼÍW“—“̡Ǔéa7Å d´Æ‹ÍgÈ—¸øZ4OEó}w@ÂJë6IºD8ZèìßkI Cÿg¯çïO;Ñ|3ÿ ~çöãñd~%EqŠ(اo˜@ko¥£cLZØöv NO»ñáð�ürOº¨²NKC=*TÙ¾™ÛRu|²ÖJzCD©`˜Ö)ém%ÊÖnKÑétr:™ çÃÉX†]¤[`ˆ2ÁÉ”C¹•ÄÀ d>’ô`wpÍ ªÇaXá–Tݯ¸$cY¥ÊÁ›„Œ_nCU÷ÛÙ “¤wᔇ»Ì¸ÅÙ

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https://arxiv.org/src/2006.01048
¸»»;Óœó~ç¼ß]wî�µæÎ³×Î"ìnº«žzžŸô®*]{CyG]+];+ãÿ�¾è€ Ó_

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https://arxiv.org/pdf/2412.12213
Finance-Informed Neural Network: Learning the Geometry of Option PricingAmine M. AboussalahXuanze LiCheng ChiRaj Patelhttp://creativecommons.org/licenses/by/4.0/cs.LGq-fin.CPstat.ML endstream endobj 4 0 obj > endobj 5 0 obj > endobj 6 0 obj

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https://arxiv.org/src/2403.08299
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https://arxiv.org/pdf/2608.02942
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https://arxiv.org/src/2006.02318
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https://arxiv.org/pdf/2104.00446
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https://arxiv.org/pdf/2609.18842
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https://arxiv.org/pdf/2508.02685
5 %¿÷¢þ 1 0 obj > endobj 2 0 obj > endobj 3 0 obj > stream Benchmarking Classical and Quantum Models for DeFi Yield Prediction on Curve FinanceChi-Sheng ChenAidan Hung-Wen Tsaihttp://arxiv