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However, the randomness of pure ALOHA may lead to serious packet collisions with an increase in the number of EDs
from the top result below

Matching-Driven Deep Reinforcement Learning for Energy-Efficient

https://arxiv.org/html/2407.13076v1
However, the randomness of pure ALOHA may lead to serious packet collisions with an increase in the number of EDs

Report GitHub Issue

https://arxiv.org/html/2608.03063v1
Consider a shop registered under “clothing� that in fact sells jade via livestreams, luring buyers to pay by a QR code sent in private chat: suspicious wording, but not conclusive on its own

Emulate or Estimate? The Divergent Strengths of Base and Post-Trained

https://arxiv.org/html/2608.03044v1
edu Abstract Large language models are increasingly used to simulate human opinions, but prior work reports conflicting results: some studies find promising alignment with human survey data, while others find persona collapse and weak demographic sensitivity

Language Models Encode the Contextual Truth of Propositions

https://arxiv.org/html/2608.03035v1
edu Prior work has shown that LLMs encode the truth of factual propositions along linear directions in activation space

Decoupling Generation and Selection for Budget-Constrained Faithful

https://arxiv.org/html/2608.03655v1
org perpetual non-exclusive license arXiv:2608

Report GitHub Issue

https://arxiv.org/html/2608.00426v1
org perpetual non-exclusive license arXiv:2608

Aligned in Form, Not in Meaning: The Comprehension–Containment

https://arxiv.org/html/2608.02941v1
edu Abstract We audit five frontier large language models on native Bangla derogatory speech (gali) across six protocols to test a single hypothesis: the Comprehension–Containment Decoupling

Activation-Guided Neuron Intervention to Induce Alzheimer’s-Related

https://arxiv.org/html/2608.03067v1
edu Abstract Changes in spontaneous speech provide an early signal of cognitive dysfunction in Alzheimer’s disease (AD) that large language models (LLMs) can detect

arXiv is now an independent nonprofit! Learn more ×

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

Don’t Let Me Ask for It: LLMs Show Deficiencies in Active Multi-Turn

https://arxiv.org/html/2608.03388v1
The modes vary in whether evidence is provided upfront or across turns, and whether queries are selected by the model or examples are provided by the oracle

Consensus Measures for Unstructured Biomedical Text Annotations

https://arxiv.org/html/2608.03529v1
Because the target concepts are not known in advance, annotators prefer open-ended labels, whose agreement is hard to quantify

On the Diversity of Analogy Making in Large Language Models

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

Looking under the Wrong Lamppost: On the Limitations of Automated

https://arxiv.org/html/2608.03577v1
org/standards/popular/iso-9000-family specifically ISO 9001:2015 Applying the same logic to translation production workflows means that MT and AI must be incorporated sensibly to achieve manufacturing-grade reliability, risk guarantees, and robustness

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https://arxiv.org/html/2608.03275v1
org perpetual non-exclusive license arXiv:2608

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

https://arxiv.org/html/2608.03452v1
Our test case is the Spanish L-shaped morphome, a complex irregular pattern in which the verb’s stem alternates in exactly the first-person singular indicative and all subjunctive forms, and whose membership no phonological, semantic, or syntactic feature predicts

Internalizing Academic Writing Workflows for Introduction Generation via

https://arxiv.org/html/2608.03138v1
1 in human evaluation when scaled to Qwen3-32B

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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/2609.18842
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https://arxiv.org/pdf/2608.02694
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https://arxiv.org/pdf/2608.01934
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https://arxiv.org/src/2405.20232
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https://arxiv.org/pdf/2607.26327v3
­2­ŽÑMGSúdGžcšì–„^n5]MS,å-[kЦ0hÁÊP¨í9bŒ’—FÞÇrÙ'fé{Mš €ÜiFŒCrv ÀÒ ÏÊÅC ôŸxŸâî‹€¿g¢Ò9´©¿þŸbáêègi’2¶èHiq€33O¡š¡È”–$yÝ5Q¦¦ ]/™¨E X¼Z’ÂÓTž×ž¤”EL{]‰,v(·Ê2éC#~:j[,yÀëÛIÙjÒÇzB

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https://arxiv.org/pdf/2608.02872
ãS¿ãÚ÷qsžT™Cxú>Üüöý¦ÜÇ[±—ã¸Ò'(2½÷[ = }ÒñH¿E ]Òµ"]UÒÅ/]òÒå,]G¢»{ »[~Jn̸Éà»8îÞd¸“x0ñðAkúßTۅΖt(õ­‡H‡)0=ÐÚcf‘7&�Â,ëƒÍ™šR|ê>1­‘çQ�Ð#`ÿ (K…§ ™ûþ4d½$jA`YÚ™o¥OÑ–íˇ@ߌÒ&M ÎÍFÏ¿ol¶Usèád7Sp o¦L¨3dõ@TGÚ~®%Ø@,íÂX±Ž·ŒQë¾g|Gb¹$ža#D˜ iNö ®`‡iApЋ`@khžædû®Ý¾âq:¨ £^¿ŠyÌиŸ†6Áñv°1�¨Š¹ÊNÅš°€

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https://arxiv.org/pdf/2006.00064
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https://arxiv.org/pdf/2608.03494
Ûò¶åtøõç[e‹Ôà¤'hc™\$×·fR´ds™0Å„ô™ÇDä3Ÿ¹Žds}‡…L‰�EÌC¯p˜‚D0ßÃ7�È� —…ŽÃ„ba ™ðXä &ˆØq™ðà è„‹ÏôNÄ$ %“Dê &12t“)ôãщðÇC—2,.]íºB—Ij¢/Ô‚ƒG¬�¥]Ç‹˜+ñ\¹.þ)´+ü CæâÑ1Xqƒ�y˜Ž‡Aàb]å0å‚H ¦Tˆ?’)L*—©À÷ÐÈTä`£Òƒèp¨Rh™ïx Ž˜

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https://arxiv.org/pdf/2410.05779
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https://arxiv.org/src/2608.04009
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https://arxiv.org/src/2006.00064
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https://arxiv.org/pdf/2309.08431
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