Tootfinder

DSCC-HS: A Dynamic Self-Reinforcing Framework for Hallucination Suppression in Large Language Models
Xiao Zheng
https://arxiv.org/abs/2509.13702 https://ar…

DSCC-HS: A Dynamic Self-Reinforcing Framework for Hallucination Suppression in Large Language Models
Large Language Model (LLM) hallucination is a significant barrier to their reliable deployment. Current methods like Retrieval-Augmented Generation (RAG) are often reactive. We introduce **Dynamic Self-reinforcing Calibration for Hallucination Suppression (DSCC-HS)**, a novel, proactive framework that intervenes during autoregressive decoding. Inspired by dual-process cognitive theory, DSCC-HS uses a compact proxy model, trained in adversarial roles as a Factual Alignment Proxy (FAP) and a Hall…

A study published in 2021 presented cuttlefish with a new version of the "marshmallow test",
and the results showed there's more going on in their strange little brains than we ever suspected.
Their ability to learn, anticipate future rewards, and adapt their behavior, the researchers said,
may have evolved to give cuttlefish an edge in the cutthroat eat-or-be-eaten marine world they live

Cephalopods Passed a Cognitive Test Intended For Human Children
An eye-opening experiment on cephalopods reinforces why it is so important for us not to underestimate animal intelligence.

Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks
Francisco Javier Esono Nkulu Andong, Qi Min
https://arxiv.org/abs/2509.10163

Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks
As sixth-generation (6G) networks move toward ultra-dense, intelligent edge environments, efficient resource management under stringent privacy, mobility, and energy constraints becomes critical. This paper introduces a novel Federated Multi-Agent Reinforcement Learning (Fed-MARL) framework that incorporates cross-layer orchestration of both the MAC layer and application layer for energy-efficient, privacy-preserving, and real-time resource management across heterogeneous edge devices. Each age…

BuildingGym: An open-source toolbox for AI-based building energy management using reinforcement learning
Xilei Dai, Ruotian Chen, Songze Guan, Wen-Tai Li, Chau Yuen
https://arxiv.org/abs/2509.11922

BuildingGym: An open-source toolbox for AI-based building energy management using reinforcement learning
Reinforcement learning (RL) has proven effective for AI-based building energy management. However, there is a lack of flexible framework to implement RL across various control problems in building energy management. To address this gap, we propose BuildingGym, an open-source tool designed as a research-friendly and flexible framework for training RL control strategies for common challenges in building energy management. BuildingGym integrates EnergyPlus as its core simulator, making it suitable…

Generalizable Pareto-Optimal Offloading with Reinforcement Learning in Mobile Edge Computing
Ning Yang, Junrui Wen, Meng Zhang, Ming Tang
https://arxiv.org/abs/2509.10474 https:…

Generalizable Pareto-Optimal Offloading with Reinforcement Learning in Mobile Edge Computing
Mobile edge computing (MEC) is essential for next-generation mobile network applications that prioritize various performance metrics, including delays and energy efficiency. However, conventional single-objective scheduling solutions cannot be directly applied to practical systems in which the preferences (i.e., the weights of different objectives) are often unknown or challenging to specify in advance. In this study, we formulate a multi-objective offloading problem for MEC with multiple edges…

Coordinated Reinforcement Learning Prefetching Architecture for Multicore Systems
Mohammed Humaid Siddiqui, Fernando Guzman, Yufei Wu, Ruishu Ann
https://arxiv.org/abs/2509.10719

Coordinated Reinforcement Learning Prefetching Architecture for Multicore Systems
Hardware prefetching is critical to fill the performance gap between CPU speeds and slower memory accesses. With multicore architectures becoming commonplace, traditional prefetchers are severely challenged. Independent core operation creates significant redundancy (up to 20% of prefetch requests are duplicates), causing unnecessary memory bus traffic and wasted bandwidth. Furthermore, cutting-edge prefetchers such as Pythia suffer from about a 10% performance loss when scaling from a single-co…

Using Reinforcement Learning to Optimize the Global and Local Crossing Number
Timo Brand, Henry F\"orster, Stephen Kobourov, Robin Schukrafft, Markus Wallinger, Johannes Zink
https://arxiv.org/abs/2509.06108

Using Reinforcement Learning to Optimize the Global and Local Crossing Number
We present a novel approach to graph drawing based on reinforcement learning for minimizing the global and the local crossing number, that is, the total number of edge crossings and the maximum number of crossings on any edge, respectively. In our framework, an agent learns how to move a vertex based on a given observation vector in order to optimize its position. The agent receives feedback in the form of local reward signals tied to crossing reduction. To generate an initial layout, we use a …

REACH: Reinforcement Learning for Adaptive Microservice Rescheduling in the Cloud-Edge Continuum
Xu Bai, Muhammed Tawfiqul Islam, Rajkumar Buyya, Adel N. Toosi
https://arxiv.org/abs/2510.06675

REACH: Reinforcement Learning for Adaptive Microservice Rescheduling in the Cloud-Edge Continuum
Cloud computing, despite its advantages in scalability, may not always fully satisfy the low-latency demands of emerging latency-sensitive pervasive applications. The cloud-edge continuum addresses this by integrating the responsiveness of edge resources with cloud scalability. Microservice Architecture (MSA) characterized by modular, loosely coupled services, aligns effectively with this continuum. However, the heterogeneous and dynamic computing resource poses significant challenges to the op…

Scaling Up without Fading Out: Goal-Aware Sparse GNN for RL-based Generalized Planning
Sangwoo Jeon, Juchul Shin, Gyeong-Tae Kim, YeonJe Cho, Seongwoo Kim
https://arxiv.org/abs/2508.10747

Scaling Up without Fading Out: Goal-Aware Sparse GNN for RL-based Generalized Planning
Generalized planning using deep reinforcement learning (RL) combined with graph neural networks (GNNs) has shown promising results in various symbolic planning domains described by PDDL. However, existing approaches typically represent planning states as fully connected graphs, leading to a combinatorial explosion in edge information and substantial sparsity as problem scales grow, especially evident in large grid-based environments. This dense representation results in diluted node-level infor…

Reasoning Pattern Matters: Learning to Reason without Human Rationales
Chaoxu Pang, Yixuan Cao, Ping Luo
https://arxiv.org/abs/2510.12643 https://arxiv.org…

Reasoning Pattern Matters: Learning to Reason without Human Rationales
Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities under the widely adopted SFT+RLVR paradigm, which first performs Supervised Fine-Tuning (SFT) on human-annotated reasoning trajectories (rationales) to establish initial reasoning behaviors, then applies Reinforcement Learning with Verifiable Rewards (RLVR) to optimize the model using verifiable signals without golden rationales. However, annotating high-quality rationales for the SFT stage remains prohibitively ex…