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Freshness: newest result fetched 24 d ago · median age of these results 24 d · 1 of 30 carry a fetch time
https://arxiv.org/list/cs.DC/new
SE)
Automating GPU kernel optimization remains difficult in practice: generated variants can violate correctness constraints, runtime measurements are noisy, and search often stalls early
https://arxiv.org/list/astro-ph.SR/new
SR)
Binary interaction in massive stars is expected to produce a large population of intermediate-mass ($2$-$8$ M$_\odot$) envelope-stripped stars, yet such objects have remained elusive in the Milky Way
https://arxiv.org/list/cs.NA/new
NA)
Imaging a target sample in near-field scanning optical microscopy (NSOM) is fundamentally limited by measurement artifacts and data contamination from multiple probe-sample scattering
https://arxiv.org/html/2607.29442v2
That is, does there exist a tick-by-tick model, replicating the salient features of ultrahigh-frequency prices, whose scaling limit is of the form Yt=Xt+ZtY_{t}=X_{t}+Z_{t} with ZZ a continuous-time rough process
https://arxiv.org/list/cs.DB/new
We propose an architectural pattern for such systems, a trusted kernel with a generative shell, resting on one invariant: a component that can fabricate may influence which question the system answers, never which value it returns
https://arxiv.org/list/cs.OS/new
Applications may therefore slow down before the kernel kills a process
https://arxiv.org/list/cs.AR/new
Our analysis shows that, despite its limited write performance, HBF can significantly improve the batch size, throughput, and flexibility of LLM-serving systems while reducing the minimum GPU requirements, but realizing these benefits critically depends on sustaining HBM-comparable read bandwidth and requires
https://arxiv.org/html/2609.03027v1
EPIC can achieve ��(N​log�N)\mathcal{O}(N\log N) computational scaling for dense aperture arrays and has been demonstrated on the Long Wavelength Array (LWA) station in Sevilleta (USA) using Graphics Processing Unit (GPU)
https://arxiv.org/html/2609.02852v1
However, various strands of evidence indicate that an LLM’s externalized linguistic outputs and mechanistically-extracted linguistic features can be an unreliable lens for understanding internal model computation
https://arxiv.org/html/2511.06451v1
Spectral safety and discrete Green kernel
https://arxiv.org/html/2407.20441v1
Abstract
Recent research endeavours have theoretically shown the beneficial effect of cooperation in multi-agent reinforcement learning (MARL)
https://arxiv.org/list/math.ST/2024-03
00186 [pdf, html, other]
Title: Warped Kernel Estimator for I
https://arxiv.org/list/cs.LG/2024-12
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Machine Learning
Authors and titles for December 2024
Total of 3298 entries : 1-50 51-100 101-150 151-200 ... 3251-3298
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https://arxiv.org/list/eess/new
The design relies on two affine Volterra transformations involving both Fredholm- and Volterra-type integral terms, which results in a four-PIDE kernel equations
https://arxiv.org/list/astro-ph.IM/new
In particular, the maximal light deflection of a given Solar System body can be easily estimated for a given observer
https://arxiv.org/html/2608.03411v1
Pioneering studies have mapped functional specialization within Transformer architectures, identifying Feed-Forward Networks (FFNs) as repositories for parametric knowledge (Meng et al
https://arxiv.org/list/cs.SE/new
Yet it remains unclear when further evolution helps, how successful and failed trajectories shape revision, and whether extra test-time computation can recover the same gains
https://arxiv.org/html/2508.02739v1
2 Preliminary
3 Methodology
K-line Tokenization
Hierarchical Autoregressive Modeling
Model Pre-training
Dataset
Model Training
Inference
4 Experiments
Experimental Setup
Main Results
Prediction Tasks
Generative Tasks
Investment
https://arxiv.org/list/quant-ph/new
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https://arxiv.org/html/2407.05763v1
However, centralized observers for plants modeled by nonlinear systems satisfying Ho¨¨o\ddot{\mathrm{o}}over¨ start_ARG roman_o end_ARGlder condition can be found in the recent literature (see, e
https://arxiv.org/list/cs.LG/2022-12
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Machine Learning
Authors and titles for December 2022
Total of 2004 entries : 1
https://arxiv.org/pdf/2608.03898
�=ØžðòëïiYöÈ{wŽí–ÔzúEn�%ó˜bAÄ|&dÈ&’€…L†>‹˜RŠÅÌ÷$K˜ &ôëë¼-2EÐÛ¯¿ök ?9ð²Îî™´™DÞ˜ „L"žQïËÓó¿ŸžO¨÷¼M‹U±ÿcî #ï à‘ï>©¶%#þI7ϲ|uÛ¿Bš«¬e�ø1?áOø)ÊÏøoüœ?çð+þš¿å)O—yVgMÞðk~]§‹» ‹ð¦íŸkâÇ|QU‰¿ëuÊ—Íon°îÊE¦‡2D\a~Ñis«�ö{}F… ÿ¶Hëªd¼»!wë
https://arxiv.org/src/2608.03994
{’ûUu“lr8¶›Üá€$§»ªººªº¾š³·÷æäÅñÙIxYféÎgŸo?æ¿øôÿî=Ù{üÅþ“ƒG�5ÿôs7BeV‘*q(ÞÑ >wî/MW¥œ+š L–'êZ%ÁØ›žéD¥ri!dQ|3‘‰žêI¬f:Ò*ÅÿådV $4ê6p¨÷ã�.¥çiV¨�,%¿¥ÒLõ4œ–¦!J?Œí®rývŠRg)¡ìÛa°�èkåÏ{OìRA^dË¢f,ˆ²e޵†ÍãY"ë}Ü�îGÿÛ:üg>])þ9kðùÿfÃùß
https://arxiv.org/src/2006.03720
Á`"†aXt࢜G ¿Ô÷aVTê(:–F ÁTWÓÑc#ýZe_òÞÐÔÚ˜‡ÒÙ þ²u'iUipŒŽMEE'ϰäžO›ÌKX ²Á�¨(NDÌIñ&_,æº)n¡dlnTªÐ“w—çGG‡'ïŽ;³q O6ì&*ŠÐ;p´¤uÖ;Æ{s^hÃA³—ÜãXÕÐl
https://arxiv.org/src/2006.00064
rD™h¥ø t Ã'L€0°"Ip}eòdêD󀉆 —`}•[>M)P#Cu¬xHW0B“6¯dP{xÜcÌ`�æÖ‰°“›ÌŽ_JikŠ5áÎùœì>ŠÞ…Š/—ù`Ó“ñNª7°Íšáu¬¸U° Y«Ž™‡æ¦S¸@¾ó6žœhØ“`ÇAZ–Æwè¢9t¢¥†Ý�«O4™)·ìF3I„\Ó°„uÒ™”‚âÁÀÌœ\Çœä_†”Þ]Д«Íït°¸$ÌŽJ0ñj€Ï·|¦At %QïòÄóíÃó'Fä:oê1Óä1ã£Ió’s~_Œ£y•>(ìŽ)ÔâCfXcêFÌš”{Fºj€‰¦ü{œÚ €÷ ¤la~£°ÀħÅvǸŸ\5îÇ3�m6´9(Äx
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
https://arxiv.org/src/2006.01128
háLkol ðÿÑÿ'VVæÿ=þ§ÿkd gcfc``becf`üßzÐÿYYðéÿ”ÏÿE.NÎŽøø ŽvvÎÿŸúý{ÿÿ§"‘¥a e�"™_Y‡bŧǷ3´„âæ†¢“6±5s6Çgdå`ÇÿßE'jaílâˆO'jmàl"lbdglÅËåäìhb`å¾¹£-‡Ý†Ø{W‡ïH_ lÑ‹‡>$1N¢î RïS¿�$|Eì DòÇ«—`ݨǴE‘¹°(M×5nˆÚW‹¾A´�EºQ…õ1Þ6XfÉq�m
https://arxiv.org/src/2006.02759
}†‘û{Ø\»†�fUaÅbTMV ¼•~À8yœš–ˆÆ9}r'e´‘¢ÉŽ]y±DÔ)u³V5ºh㾕z±'&]Ñ'“”ÆPÄÊ0 ´á7N* ¡¼iPÿÎååà3œèqS [÷F@ub¨{@ºÊzêó«èiÍXs™
https://arxiv.org/src/2608.03883
>ã |ró�àLò©@àžŠ“PÜ‹p¯]ûA(">×" äÝ 78ëÜrywÜUb¹gpüþÒÖáŽïE*ƒ8Bº¾ì0¸UÈ0;Õòö’4†iÒƒv;$ƒÿѓٿWlâ°�ੈD nå±E fLÍóbWª4HX¦‚0PÝ–å$XœÓ â!Óa…¸J¶©,+ÁSKƾ%¦q¢Àáåć\ÊÃﯣ?Ãÿ°¿þûŸŠX`wû�)” ¢)ƒ‘ÀÅúqÊ^¿ùùšý§wr'ìÆá–dß"¨óËM7N§49ö*S³8
https://arxiv.org/pdf/2006.02602
,{AûëG™JÐ ÝÅ’ùÈGRRBv$!·Aâ×�›àâ¦&MÜ”YI6[’2WeMÊ¢Š3V�MG©�‡!Œ²ššq #–%tkFؤ íä(Z¶¦ï좳–-Ÿ¤Ñèòs¡Š)„ïÁÙ…ÐH¸]?XÜä´7ó„;ñ�Ô;´\Þy§^ôQø´ùÊ£´Š–¡‡5£qYNo¤æJ’ÓðÊŠ—ù�ûˆQñaÀØeB§½À *¯Ï”Ìuw²ó„–”Z¥_íÞ`iQJ±hèdÐäK?ÍP´6PmÕŠIZ¶z®sÜŠ¢Ô$