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-\mathbb{E}_{\mathbf{b}\sim\mathcal{D}}\sum_{t=1}^{T}\left[\log p(b_{t}^{c}|\mathbf{b}_{<t})+\log p(b_{t}^{f}|\mathbf{b}_{<t},b_{t}^{c})\right]caligraphic_L start_POSTSUBSCRIPT ar end_POSTSUBSCRIPT = - blackboard_E start_POSTSUBSCRIPT bold_b ∼ caligraphic_D end_POSTSUBSCRIPT ∑ start_POSTSUBSCRIPT italic_t = 1
from the top result below
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/2407.02342v1
com/qiongwu86/Optimizing-AoI-in-VEC-with-Federated-Graph-Neural-Network-Multi-Agent-Reinforcement-Learnin
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/2407.08254v2
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/2407.20187v1
If there are more blue nodes than red nodes then we say blue is the majority
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/list/math/new
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New submissions (showing 219 of 219 entries)
[1] arXiv:2608
https://arxiv.org/html/2407.20441v1
I Introduction
II System Model and Problem Formulation
III Main result
IV Proof of the main result
V Simulations
VI Conclusion and Future Work
Finite-Time Analysis of Asynchronous Multi-Agent TD Learning
Nicolò Dal Fabbro, Arman Adibi
https://arxiv.org/html/2407.13076v1
Transmission Parameter Allocation in Multi-Gateway LoRa Networks
I INTRODUCTION
II RELATED WORK
III SYSTEM MODEL
III-A The Traffic Pattern of LoRa Networks
III-B Capture Effect and Quasi-Orthogonality between SFs
III-C Packet Delivery
https://arxiv.org/html/2407.04974v1
Observable Environments
I Introduction
II State of the art
II-A Settings
II-B State estimation in model-based learning
II-C State estimation in model-free RL
II-C1 Neural Network-based Approaches
II-C2 Non-neural-based Approach
II-D
https://arxiv.org/html/2607.22511v2
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https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/html/2407.17766v1
Further, Control Barrier Functions (CBFs) [15, 16] offer a method to design controllers ensuring safety by maintaining the forward invariance property in the set, ensuring that if an agent starts within a safety set, it remains within this set indefinitely, effectively preventing it to move away from the safe zone
https://arxiv.org/html/2608.02854v1
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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/2607.28410v1
org perpetual non-exclusive license
arXiv:2607
https://arxiv.org/html/2508.20108v2
Normalization for Accurate Prediction
1 Introduction
2 Related Works
2.1 Geometric Brownian Motion
2.2 Deep Learning-Based Stock Price Prediction
2.3 Methods for Addressing Distribution Shift
3 Proposed Method
3.1 Overview
3.2 Return
https://arxiv.org/html/2402.03300v3
com/deepseek-ai/DeepSeek-Math
Abstract
Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature
https://arxiv.org/html/2608.02612v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.02668v1
com/hazdzz/deep_connection \ncudata[Correspondence]Jie Zhang (),
Correspondence: Cheng-Fang Su ()
“Geometry is not true, it is advantageous
https://arxiv.org/html/2407.00762v1
mathbb{R}^{n}blackboard_R start_POSTSUPERSCRIPT italic_n end_POSTSUPERSCRIPT, as illustrated in Figure 1
https://arxiv.org/html/2407.18010v2
start_POSTSUPERSCRIPT italic_i end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT = italic_S start_POSTSUPERSCRIPT italic_i end_POSTSUPERSCRIPT ( italic_ω ) : blackboard_R start_POSTSUBSCRIPT > 0 end_POSTSUBSCRIPT × roman_Ω → blackboard_R which is a stochastic process
https://arxiv.org/html/2407.05763v1
A system is said to be finite-time stable if it is Lyapunov stable and its states reach zeros at a finite instant of time (which may depend on the initial state of the system)
https://arxiv.org/html/2407.19144v1/
These malfunctions decrease the team’s ability to accomplish assigned task(s), especially if they occur after the learning algorithms have already converged onto a collaborative strategy
https://arxiv.org/html/2407.05132v1
Roads provide a diminishing utility for a growing number of users; if too many vehicles enter the network, traffic slows down and congestion arises
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/2607.24970v2
one-to-one random markets, IA’s expected average rank is asymptotically logn\log n, just like DA’s
https://arxiv.org/list/cs.DS/new
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New submissions (showing 7 of 7 entries)
[1] arXiv:2608
https://arxiv.org/html/0804.0969v1
org perpetual non-exclusive license
arXiv:0804
https://arxiv.org/html/2608.03529v1
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