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For an explicit family of completely positive lattice generators with finite-range couplings, the first-moment equations undergo a supercritical instability at a nonzero wave number and admit analytic site- and bond-centered commensurate stripe branches
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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/html/2608.03153v1
If large entry-level gaps and substantial selection bias were to show up anywhere, this economy is a plausible place to expect them
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/2407.10811v1
II RELATED WORK
II-A Traditional methods
II-B RL methods.
III PRELIMINARIES
IV METHOD
IV-A RL Setup
IV-B Network design
IV-C Training
IV-D Theoretical Analysis
V EXPERIMENT
V-A Towards Real-world Dataset and Simulation
V-B Benchmark
https://arxiv.org/html/2412.12213v2
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https://arxiv.org/html/2407.02342v1
com/qiongwu86/Optimizing-AoI-in-VEC-with-Federated-Graph-Neural-Network-Multi-Agent-Reinforcement-Learnin
https://arxiv.org/html/2407.13076v1
Gateway LoRa Networks
Ziqi Lin, Xu Zhang, Shimin Gong, Lanhua Li, Zhou Su, and Bo Gu This work was supported in part by the National Science Foundation of China (NSFC) under Grant U20A20175 and in part by the National Key R&D Program of China under Grant 2020YFB1713800
https://arxiv.org/html/2407.06328v2
In such dynamic game settings, if agents have limited capabilities for observing the game’s state, they must estimate it and use this estimate to infer expected payoffs and select one of available strategies
https://arxiv.org/html/2407.20187v1
If there are more blue nodes than red nodes then we say blue is the majority
https://arxiv.org/html/2608.02613v1
(3) Search latency bites only at very small reader: on a Spark GB10 edge node, memory-search adds a moderate and fixed 8787/77/4848 ms (BM25-RAG/Memobase/MemSearch) that composes a small part of TTFT for most reader-backend combinations
https://arxiv.org/html/2608.03486v1
Much of the literature has focused on linear specifications, where the reduced-form innovations are assumed to be linear combinations of the structural shocks and identification may be achieved, for example, through exclusion restrictions, external instruments, heteroskedasticity, or non-Gaussianity (see, e
https://arxiv.org/html/2309.08431v3
Concentration risk refers to the decrease in fee revenue if the marginal exchange rate (akin to the midprice in a limit order book) in the pool exits the LP’s range of liquidity
https://arxiv.org/html/2407.04889v1
Therefore, if an agent is good at anticipating the behavior of the other agents, in particular how they will make decisions in each round as a function of their experience that far, it could try to judiciously make its own decisions over the rounds of the interaction so as to influence the other agents to behave in a
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/html/2608.02625v1
Motivated in part by the success of diffusion models in the vision domain [13, 25], recent work has explored whether diffusion-style denoising can offer advantages that differ from those of autoregressive transformers used in most commercial models such as ChatGPT [24], Claude [1], DeepSeek [10], etc
https://arxiv.org/html/2407.00419v1
W say that a population of adaptive agents is socially intelligent if its members are (1) individually Hannan-consistent and (2) compatible in the sense that any pair of agents will perform nearly as well as some Pareto-optimal Nash equilibrium of the matrix game
https://arxiv.org/html/2407.08254v2
We prove that our approach guarantees that the average joint action of all agents converges to a Nash equilibrium (NE) if every agent applies the same RM procedure in any cooperative game with a submodular utility function
https://arxiv.org/html/2407.20441v1
Our main contribution is to provide a finite-time analysis of AsyncMATD that clearly reveals the effect of the delay sequence on the resulting convergence rate; see Theorem 1
https://arxiv.org/html/2407.18010v2
1 Introduction
2 Formulation
3 Existence of a Value and Saddle Point Equilibrium
4 Learning in Unknown Environments
4.1 Convergence using Linear Function Approximators
5 Intervention Budget Constraints
6 Discussion on Subcases
7 Related
https://arxiv.org/html/2608.02971v1
Field-order agreement, response dispersion, reduced-form agreement, and row-specific reliability diagnostics identify unstable outputs; shared model lineages are aggregated through dependency components
https://arxiv.org/html/2407.04503v4
Tools to Evaluate LLMs in Multi-turn Settings
1 Introduction
2 Related work
Biases in LLMs outputs
Transmission chains featuring artificial agents
Iterated Learning
Evaluation of LLMs in social contexts
3 Methods
3.1 Experimental
https://arxiv.org/pdf/2006.02318
org/pdfua/ns/id/ pdfuaid PDF/UA ID Schema internal Part of PDF/UA standard part Integer PRISM metadata http://prismstandard
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
https://arxiv.org/list/cs.CV/new
0, a unified framework supporting 3D understanding, text-to-3D generation, instruction-guided 3D editing, and text-grounded part generation within a single architecture
https://arxiv.org/list/cs.NA/new
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New submissions (showing 18 of 18 entries)
[1] arXiv:2608
https://arxiv.org/pdf/2607.26327v3
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https://arxiv.org/src/1910.03771
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https://arxiv.org/pdf/2609.18842
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https://arxiv.org/pdf/2608.02872
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https://arxiv.org/src/2608.04007
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