Tootfinder

In der neuen Ausgabe der @… geht es u.a. ausführlich ums #Fediverse. Da war aus meiner Sicht viel Gutes und Interessantes dabei, insbesondere ein Appell an öffentliche Akteure *auch* einen Fediverse-Account zu betreiben (also genau die 1-Stragegie von

The Cognitive Bandwidth Bottleneck: Shifting Long-Horizon Agent from Planning with Actions to Planning with Schemas
Baixuan Xu, Tianshi Zheng, Zhaowei Wang, Hong Ting Tsang, Weiqi Wang, Tianqing Fang, Yangqiu Song
https://arxiv.org/abs/2510.07091

The Cognitive Bandwidth Bottleneck: Shifting Long-Horizon Agent from Planning with Actions to Planning with Schemas
Enabling LLMs to effectively operate long-horizon task which requires long-term planning and multiple interactions is essential for open-world autonomy. Conventional methods adopt planning with actions where a executable action list would be provided as reference. However, this action representation choice would be impractical when the environment action space is combinatorial exploded (e.g., open-ended real world). This naturally leads to a question: As environmental action space scales, what …

Hybrid Terrain-Aware Path Planning: Integrating VD--RRT\(^{*}\) Exploration and VD--D\(^{*}\) Lite Repair
Akshay Naik, William R. Norris, Dustin Nottage, Ahmet Soylemezoglu
https://arxiv.org/abs/2510.12169

Hybrid Terrain-Aware Path Planning: Integrating VD--RRT\(^{*}\) Exploration and VD--D\(^{*}\) Lite Repair
Autonomous ground vehicles operating off-road must plan curvature-feasible paths while accounting for spatially varying soil strength and slope hazards in real time. We present a continuous state--cost metric that combines a Bekker pressure--sinkage model with elevation-derived slope and attitude penalties. The resulting terrain cost field is analytic, bounded, and monotonic in soil modulus and slope, ensuring well-posed discretization and stable updates under sensor noise. This metric is evalu…

FedMon: Federated eBPF Monitoring for Distributed Anomaly Detection in Multi-Cluster Cloud Environments
Sehar Zehra, Hassan Jamil Syed, Ummay Faseeha
https://arxiv.org/abs/2510.10126

FedMon: Federated eBPF Monitoring for Distributed Anomaly Detection in Multi-Cluster Cloud Environments
Kubernetes multi-cluster deployments demand scalable and privacy-preserving anomaly detection. Existing eBPF-based monitors provide low-overhead system and network visibility but are limited to single clusters, while centralized approaches incur bandwidth, privacy, and heterogeneity challenges. We propose FedMon, a federated eBPF framework that unifies kernel-level telemetry with federated learning (FL) for cross-cluster anomaly detection. Lightweight eBPF agents capture syscalls and network ev…

When Greedy Wins: Emergent Exploitation Bias in Meta-Bandit LLM Training
Sanxing Chen, Xiaoyin Chen, Yukun Huang, Roy Xie, Bhuwan Dhingra
https://arxiv.org/abs/2509.24923 https:…

When Greedy Wins: Emergent Exploitation Bias in Meta-Bandit LLM Training
While Large Language Models (LLMs) hold promise to become autonomous agents, they often explore suboptimally in sequential decision-making. Recent work has sought to enhance this capability via supervised fine-tuning (SFT) or reinforcement learning (RL), improving regret on the classic multi-armed bandit task. However, it remains unclear how these learning methods shape exploration strategies and how well they generalize. We investigate both paradigms by training LLMs with SFT on expert traject…

Generative AI-Driven Hierarchical Multi-Agent Framework for Zero-Touch Optical Networks
Yao Zhang, Yuchen Song, Shengnan Li, Yan Shi, Shikui Shen, Xiongyan Tang, Min Zhang, Danshi Wang
https://arxiv.org/abs/2510.05625

Generative AI-Driven Hierarchical Multi-Agent Framework for Zero-Touch Optical Networks
The rapid development of Generative Artificial Intelligence (GenAI) has catalyzed a transformative technological revolution across all walks of life. As the backbone of wideband communication, optical networks are expecting high-level autonomous operation and zero-touch management to accommodate their expanding network scales and escalating transmission bandwidth. The integration of GenAI is deemed as the pivotal solution for realizing zero-touch optical networks. However, the lifecycle managem…

Have We Scene It All? Scene Graph-Aware Deep Point Cloud Compression
Nikolaos Stathoulopoulos, Christoforos Kanellakis, George Nikolakopoulos
https://arxiv.org/abs/2510.08512 ht…

Have We Scene It All? Scene Graph-Aware Deep Point Cloud Compression
Efficient transmission of 3D point cloud data is critical for advanced perception in centralized and decentralized multi-agent robotic systems, especially nowadays with the growing reliance on edge and cloud-based processing. However, the large and complex nature of point clouds creates challenges under bandwidth constraints and intermittent connectivity, often degrading system performance. We propose a deep compression framework based on semantic scene graphs. The method decomposes point cloud…

Beckstrom initially did not want to go to the capital because she was concerned about feeling lonely away from home.
“She hated it. She cried about it,” her boyfriend said.
But with time, she came to enjoy the deployment and bonded with other troops.
In her spare time, he said, she visited monuments and museums, taking pictures and soaking up D.C.’s history.
She was especially interested in the U.S. Holocaust Memorial Museum

National Guard member killed in D.C. shooting remembered for 'heart of gold'
Sarah Beckstrom, the slain West Virginia National Guard member, took a keen interest in criminal justice and dreamed of one day becoming an FBI agent.

Robust forecast aggregation via additional queries
Rafael Frongillo, Mary Monroe, Eric Neyman, Bo Waggoner
https://arxiv.org/abs/2512.05271 https://arxiv.org/pdf/2512.05271 https://arxiv.org/html/2512.05271
arXiv:2512.05271v1 Announce Type: new
Abstract: We study the problem of robust forecast aggregation: combining expert forecasts with provable accuracy guarantees compared to the best possible aggregation of the underlying information. Prior work shows strong impossibility results, e.g. that even under natural assumptions, no aggregation of the experts' individual forecasts can outperform simply following a random expert (Neyman and Roughgarden, 2022).
In this paper, we introduce a more general framework that allows the principal to elicit richer information from experts through structured queries. Our framework ensures that experts will truthfully report their underlying beliefs, and also enables us to define notions of complexity over the difficulty of asking these queries. Under a general model of independent but overlapping expert signals, we show that optimal aggregation is achievable in the worst case with each complexity measure bounded above by the number of agents $n$. We further establish tight tradeoffs between accuracy and query complexity: aggregation error decreases linearly with the number of queries, and vanishes when the "order of reasoning" and number of agents relevant to a query is $\omega(\sqrt{n})$. These results demonstrate that modest extensions to the space of expert queries dramatically strengthen the power of robust forecast aggregation. We therefore expect that our new query framework will open up a fruitful line of research in this area.
toXiv_bot_toot

Hybrid Safety Verification of Multi-Agent Systems using $\psi$-Weighted CBFs and PAC Guarantees
Venkat Margapuri, Garik Kazanjian, Naren Kosaraju
https://arxiv.org/abs/2509.20093

Hybrid Safety Verification of Multi-Agent Systems using $ψ$-Weighted CBFs and PAC Guarantees
This study proposes a hybrid safety verification framework for closed-loop multi-agent systems under bounded stochastic disturbances. The proposed approach augments control barrier functions with a novel $ψ$-weighted formulation that encodes directional control alignment between agents into the safety constraints. Deterministic admissibility is combined with empirical validation via Monte Carlo rollouts, and a PAC-style guarantee is derived based on margin-aware safety violations to provide a …