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Rung 3 would further apply the reverting rather than the total component (×τ^\times\hat{\tau}, Lemma 2), and it is not defined at this evaluation point: τ^\hat{\tau} is negative below a participation of roughly 0
from the top result below
https://arxiv.org/html/2608.03703v1
begun on July 20, 2026; the present version is dated August 4, 2026. Claude Opus 5 and Fable 5 provided research assistance throughout, spanning code development, data construction and empirical analysis, and the drafting of the manuscript.
https://arxiv.org/html/2608.03452v1
Probing five architectures, twelve trained models each, under lemma-disjoint cross-validation with controls and surface baselines, we show that the models encode the L-shaped class itself, not just its visible alternations
https://arxiv.org/html/2407.20187v1
This network characteristic, whereby local observations can lead to erroneous conclusions regarding a global state, was dubbed majority illusion by Lerman et al
https://arxiv.org/html/2407.00575v3
Gradient play converges to a unique Nash equilibrium (NE) that does not optimize the global objective
https://arxiv.org/html/2604.05327v4
3 Proof of Theorem 4
C Local Asymptotics with Global Priors
C
https://arxiv.org/list/cs.CL/new
MemArena fills these gaps with a single-world conversational benchmark built with its MASim agent simulator, for 50 agents over 15 days (10
https://arxiv.org/html/2607.29442v2
We find that rough noise, while present, is not universal: even when detected, the roughness index is often mild, and it is most pronounced on days dominated by short-run price reversals
https://arxiv.org/html/2407.08254v2
It is based on the use of a global reward function for the estimation of each agent’s local contribution, and regret matching for coordination
https://arxiv.org/html/2407.04974v1
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Abstract
This study proposes the use of a social learning method to estimate a global state within a multi-agent off-policy actor-critic algorithm for reinforcement learning (RL) operating in a partially observable environment
https://arxiv.org/html/2511.06451v1
1 Data Protocol (arXiv Release)
Synthetic risk–neutral generator
https://arxiv.org/pdf/1811.12628
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https://arxiv.org/list/math/new
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New submissions (showing 219 of 219 entries)
[1] arXiv:2608
https://arxiv.org/list/cs.AI/new
A core calculus gives an effect-confinement lemma and six safety properties
https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/html/2607.22511v2
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https://arxiv.org/pdf/2006.02318
3/ pdfx PDF/X Schema external URL to an online version or preprint AuthoritativeDomain Text http://www
https://arxiv.org/list/cond-mat/new
The mixed anomaly between these is accommodated by the not-on-site action of the symmetries
https://arxiv.org/list/cs.AR/new
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New submissions (showing 4 of 4 entries)
[1] arXiv:2608
https://arxiv.org/html/2607.24970v2
Pooling all raw draws produces an ordinary coupon collector, and the first global appearance of every school must generate an IA application
https://arxiv.org/html/2508.20101v1
We illustrate the proposed methodology in an empirical application to a panel of 50 global firms with daily stock returns
https://arxiv.org/html/2607.26405v1
Notably, this reduces realized costs by ∼\sim13% relative to the status quo of vehicle-currency routing, with gains stable through episodes of global financial stress
https://arxiv.org/html/2607.28847v1
2 Proof of Lemma 1 (Optimality of Boundary Crossing)
D
https://arxiv.org/html/2407.00419v1
Two natural assumptions in such settings are that: 1) all agents in the population are individually rational learners, and 2) when any two members of the population are paired together, with high-probability they will achieve at least the same utility as they would under some Pareto efficient equilibrium strategy
https://arxiv.org/src/2006.02759
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https://arxiv.org/html/2407.15077v2
During centralized training, agents can access to other agents’ information and the global state, while during decentralized execution, agents execute decisions independently based on their individual policies
https://arxiv.org/html/2508.08980v3
org perpetual non-exclusive license
arXiv:2508
https://arxiv.org/html/2407.05763v1
A strongly connected nonlinear distributed observer is designed under the assumption of global observability
https://arxiv.org/html/2608.03504v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/pdf/2608.03118
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https://arxiv.org/html/2608.02943v1
org perpetual non-exclusive license
arXiv:2608