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https://arxiv.org/html/2609.02852v1
Such taint tracking is critical because in many cases we can define, a priori, system state that models should not influence regardless of the model’s ostensible or true planning process
https://arxiv.org/abs/2512.09590
Using an extension of the exponential-affine transformation formula for those processes, we establish in the long run the existence of limiting distributions, which (unlike in the case of classical affine diffusion processes) may depend on the initial state of the process, unless the Volterra kernel coincides with the
https://arxiv.org/abs/2608.01651
We present Bole, a kernel--runtime co-design that enables efficient tree speculation for hybrid-attention LLMs
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/list/math.ST/new
If \(T_\star\) is the selected physical trajectory length and \(a_\star=\sqrt m\,T_\star\), a terminal-depth transfer argument yields restricted conductance and warm-start mixing without lazifying either kernel
https://arxiv.org/list/quant-ph/new
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https://arxiv.org/pdf/2205.01882v5
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https://arxiv.org/pdf/2608.00785
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https://arxiv.org/pdf/2608.02854v1
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https://arxiv.org/html/2407.20187v1
In this paper, we present polynomial-time algorithms which can eliminate majority illusion in a network by altering as few connections as possible
https://arxiv.org/pdf/2608.00426
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https://arxiv.org/pdf/2507.17609
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https://arxiv.org/pdf/2412.20138
TradingAgents: Multi-Agents LLM Financial Trading FrameworkYijia XiaoEdward SunDi LuoWei Wanghttp://creativecommons.org/licenses/by/4.0/q-fin.TRcs.AIcs.CEcs.LG endstream endobj 4 0 obj > endobj 5 0 obj > endobj 6 0 obj > endobj 7 0 obj >
https://arxiv.org/list/math.ST/2024-03
00186 [pdf, html, other]
Title: Warped Kernel Estimator for I
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/abs/2609.14858
Our key insight is that accumulated discovery history can serve as a replay simulator over the realized search space
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/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/2511.06451v1
Spectral safety and discrete Green kernel
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/html/2407.20441v1
I Introduction
Training reinforcement learning (RL) algorithms is well-known to be a critically time consuming task that can require several data samples to achieve a desired level of accuracy
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/2024-12
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Machine Learning
Authors and titles for December 2024
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https://arxiv.org/list/astro-ph.SR/new
1$ is in line with mass-loss rates observed for classical WR stars in the Milky Way and exceeds those measured for intermediate-mass stripped stars in the Magellanic Clouds, with the caveat that our target selection is biased towards systems with stronger emission features
https://arxiv.org/src/2006.02318
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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 $4ê6p¨÷ã�.¥çiV¨�,%¿¥ÒLõ4œ–¦!J?Œí®rývŠRg)¡ìÛa°�èkåÏ{OìRA^dË¢f,ˆ²e޵†ÍãY"ë}Ü�îGÿÛ:üg>])þ9kðùÿfÃùß
https://arxiv.org/src/2006.02759
}†‘û{Ø\»†�fUaÅbTMV ¼•~À8yœš–ˆÆ9}r'e´‘¢ÉŽ]y±DÔ)u³V5ºh㾕z±'&]Ñ'“”ÆPÄÊ0 ´á7N* ¡¼iPÿÎååà3œèqS [÷F@ub¨{@ºÊzêó«èiÍXs™
https://arxiv.org/src/2006.01048
�z'P"²#3Ôf+ Sdy¢ž^tϰ=wÙ�®Üy@ :¤ó–dÿÊÕÞw °¹ØÄq³ ™ºN½Òðû™âzAýpO,‘é÷÷VœA6“&üG6‚Æ9"×Ai6ý£ZÌ þ¢È¿ßÙùÓÂÿ'‡†•åÕÔRâââ’’’ÒÒÂ:ÈÉÉ�{kjjª¨¢EçÍð ¹¹¹ii#©www� «†æ‡h†}û'á”ÿ•5©QõîÌUUUþLK[q~~ž%8–Î�fN„œ’¢&(èWRrt¾ÑSÅ1—&ù599Ùñd±®¶6N]èRcÅ·|h®„ª,,„ð ÝÚÜdwØ_ßßGÇÂJ�ŠÒ11as»µØÿ-™ÊòÄ ˆa~aaRB‚Óùz×ÈÈÈô´B>ê708l6£t%»’ô�$
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
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`ø å³P™úJÖ4�