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Report GitHub Issue

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

[2512.09590] On Inhomogeneous Affine Volterra Processes: Stationarity

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

[2608.01651] Bole: Efficient Tree Speculation for Hybrid-Attention

https://arxiv.org/abs/2608.01651
We present Bole, a kernel--runtime co-design that enables efficient tree speculation for hybrid-attention LLMs

Instrumentation and Methods for Astrophysics

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

Statistics Theory

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

Quantum Physics

https://arxiv.org/list/quant-ph/new
Skip to main content System maintenance August 4th and 5th Learn more × Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantum Physics New submissions Cross-lists Replacements See recent articles

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https://arxiv.org/pdf/2205.01882v5
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https://arxiv.org/pdf/2608.00785
*¥ä¸"ïÑ¥îå4‘XÚâ¢Ô]滉¼³` Yd)©$’Þéw©(P©¯\éw™÷»¼ó²’´Åe©»$ºS IG–R¯¸Ç¼´Å%‘:V‹ûéŸÍ¶g¿Á{*ÆŠ´ÑŠ$ðïðv˜búyxZü¹ç1ì—ËphNß"lŽÍ€ >´›ïþ=ÔÝðÈêó9œ¶ÍŸï^1ãÏõ1”>"½þñ©m‡S;„wün ¶´ ´ºÒ*áj§ò[¶çGx»Âð.‹Ÿ±¾¹½Ú›�7ûp•� Ïi¸†®

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https://arxiv.org/pdf/2608.02854v1
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Eliminating Majority Illusion is Easy

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

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https://arxiv.org/pdf/2608.00426
®gH™Ã‘¥|ýv“ A*Þ*—GÓènœ>Ýh€à°„$чO®ˆJ:#Ú$]rødࢠKeÂѵL!"p 8 H&+‡)ê¥ð ,€)KAY+„+\Jžt 0Ÿà‚èD  `I„ÔÀwøGÀHm4=pÁ‘W&Z‹  þÐDƒ>º60©„!’²DJ‚ �WÉ!H B  K‰~àOœR"U y  ö@¹4(R‚RþR•„„�Tm@O'šh¦‰æ§2�C#A˜hC ¡’‰ß!ñ”’TCp=5

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https://arxiv.org/pdf/2507.17609
כּžñÖöìfóëKêÅ#ÉòÜXÜÎRE>¤(Ršc©x%M%*ªT%+FY¥*¦EUWÜÐJWB˜ÊT’Ô%•¢ð‹VªV0ÖBW”Wš‘ŠŠJצ¢²2’�,`fðr>ó(p°šV8�sø $!XÅ`Š$ð›Ão¿Q�šWL‚¿A”æ¤b Oø òŒºQ0 ‡(W•i@s%ào`ÚT\€¥+j%+d%ŠVY%ÐáGH «’²€Š–¢>Âb ¢ †W~„" Õ°®� (KIŽו”ø5` vt€

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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 >

Statistics Theory Mar 2024

https://arxiv.org/list/math.ST/2024-03
00186 [pdf, html, other] Title: Warped Kernel Estimator for I

Hardware Architecture

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

Computer Science > Computation and Language

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

Report GitHub Issue

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)

Software Engineering

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

1 Introduction

https://arxiv.org/html/2511.06451v1
Spectral safety and discrete Green kernel

1 Introduction

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

Finite-Time Analysis of Asynchronous Multi-Agent TD Learning

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

Homogeneous Distributed Observers for Quasilinear Systems

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

Machine Learning Dec 2024

https://arxiv.org/list/cs.LG/2024-12
Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Machine Learning Authors and titles for December 2024 Total of 3298 entries : 1-50 51-100 101-150 151-200 ... 3251-3298 Showing up

Solar and Stellar Astrophysics

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

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https://arxiv.org/src/2006.02318
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https://arxiv.org/src/2608.03994
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https://arxiv.org/src/2006.02759
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https://arxiv.org/src/2006.01048
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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

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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`øå³P™úJÖ4�