OptoAgent is a multi‑agent framework called SightSentinel that transforms a classroom wall display into a continuous vision screening tool. It embeds short, calibrated optotype probes into ordinary educational content, and eight specialized agents analyze each encounter to recover viewing distance, recognition accuracy, gaze stability, and other metrics. A quality agent filters out poor data, a longitudinal agent builds a child‑specific baseline, and an orchestrator produces a Vision Concern Score that is routed through a safety gate to avoid diagnosis or prescription.
"whyItMatters":"The system aims to detect vision problems earlier and more reliably than periodic school screenings by leveraging everyday classroom interactions and automated, low‑cost data collection."
By Toqeer Ali Syed, Ali Akarma, Adeel Ahmad, Hammad Muneer
arXiv:2609.14896v1 Announce Type: cross
Abstract: A notable byproduct of LLM alignment training is mode collapse: the progressive loss of output diversity that narrows a model's expressivity at infer...
By Jiayi Yuan, Hangoo Kang, James Jihao Liu, Yejin Choi, Vikram Iyer, Liwei Jiang, Natasha Jaques
arXiv:2609.15065v1 Announce Type: new
Abstract: Autonomous agents operating on multisensory data cannot assume that all sensory modalities remain consistently informative. In real environments, senso...
By Tin Mi\v{s}i\'c, Takato Horii
arXiv:2609.15297v1 Announce Type: new
Abstract: In order to make Reinforcement Learning algorithms applicable in real world scenarios, safety must be ensured even under adverse operating conditions....
By Paul Stahlhofen, Luca Hermes, Tim Kochs, Markus Vieth, Barbara Hammer
arXiv:2609.14528v1 Announce Type: cross
Abstract: Multi-Hop Knowledge Graph Question Answering (KGQA) tasks require models to assemble relational evidence along paths in a KG to answer natural-langua...
By Eduin E. Hernandez, Luis F. Garcia, Nurassyl Askar, Sergio A. Diaz, Stefano Rini
arXiv:2609.14802v1 Announce Type: cross
Abstract: Importance weights are essential in domain adaptation under label shift, yet their utility is often undermined by the finite sample uncertainty assoc...
By Mushan Li, Kihyun Han, Yanyuan Ma
arXiv:2408.09838v3 Announce Type: replace
Abstract: A continual learning agent builds on previous experiences to develop increasingly complex behaviors by adapting to non-stationary and dynamic envir...
By Achref Jaziri, Etienne K\"unzel, Visvanathan Ramesh
arXiv:2609.13733v1 Announce Type: new
Abstract: Stable and reliable 4D spatial understanding is fundamental for autonomous driving systems. While feedforward reconstruction networks can estimate came...
By Meng-Li Shih, Shih-Yang Su, Yuliang Zou, Hao Xiang, Haidong Zhu, Vincent Casser, Brian Curless, Dmitry Kalenichenko, Mingxing Tan, Dragomir Anguelov
arXiv:2609.14523v1 Announce Type: new
Abstract: Change visual question answering (Change VQA) requires understanding semantic changes across bi-temporal remote sensing images. Although vision languag...
By Yakoub Bazi, Mohamad M. Al Rahhal, Mohamed A. Mekhtiche, Mansour Zuair
arXiv:2603.18614v2 Announce Type: replace
Abstract: Tool-augmented large language models (LLMs) must tightly couple multi-step reasoning with external actions, yet existing benchmarks often confound...
By Wanjia Zhao, Ludwig Schmidt, Yejin Choi, James Zou, Vidhisha Balachandran, Lingjiao Chen
arXiv:2605.20941v2 Announce Type: replace
Abstract: Existing neural painting methods are target-driven: given a reference image, strokes are optimized to reconstruct it, fixing the outcome before pai...
By Yunge Wen, Yaluo Wang, Yuancheng Shen, Robert Krueger, Paul Pu Liang
arXiv:2603.26788v3 Announce Type: replace-cross
Abstract: Zero-shot object navigation requires agents to locate unseen targets in unfamiliar environments without prior maps or task-specific training....
By Feng Wu, Wei Zuo, Wenliang Yang, Jun Xiao, Yang Liu, Xinhua Zeng
arXiv:2609.13548v1 Announce Type: new
Abstract: Web agents can utilize reusable tools to reduce the cost and latency of low-level browser interaction, but automatically discovered tool collections ca...
By Xinyun Cao, Adriana Szekeres, Fazle Elahi Faisal
arXiv:2609.13543v1 Announce Type: new
Abstract: LLM agents are predominantly benchmarked on short, single-task trajectories, yet real deployments run for hours under contention, surfacing a different...
By Grace Chang Yuan, Xiaoman Zhang, Sung Eun Kim, Luyang Luo, Pranav Rajpurkar
arXiv:2609.13664v1 Announce Type: new
Abstract: Knowledge graphs are increasingly distributed across autonomous organizations that share an entity space but own disjoint subsets of relations, forming...
By Md Saikat Islam Khan Bappy, Oshani Seneviratne
The paper introduces the Universe of Universes (UoU) framework, treating the ecosystem of major large language models as a structured retrieval corpus and proposing a compositional Automated Reasoning and Machine Learning architecture for cross-model retrieval‑augmented generation. It formally defines the Benefit Yield Function (BYF), measuring marginal performance gain per added model, and identifies an implosion threshold θ* where BYF becomes zero and ensemble performance degrades. The work highlights gaps in current LLM ensemble research, such as lack of performance analysis across full model universes, and connects these findings to implications for DoD AI acquisition policy and testing of AI‑enabled systems.
By Danielle Franklin, Vasu Raj Jain
arXiv:2609.13731v1 Announce Type: new
Abstract: The transition from passive foundation models to autonomous, goal-directed agentic AI systems has introduced unprecedented capabilities by coupling rec...
By Seyedakbar Mostafavi
arXiv:2609.14512v1 Announce Type: new
Abstract: Virtual worlds now host classrooms, meetings, conferences, shops, and social venues, and nearly every interaction they expose assumes a user who can sc...
By Toqeer Ali Syed, Ali Akarma, Adeel Ahmad, Danial Hameed
arXiv:2609.14976v1 Announce Type: new
Abstract: Long-horizon LLM agents accumulate memory across sessions, creating sparse but high-impact risks: stale facts, conflicting updates, cross-user leakage,...
By Jianhua Jiang, Dongbo Yuan, Weihua Li
arXiv:2609.15011v1 Announce Type: new
Abstract: Memory-augmented and tool-using agents expose exact private values when remote LLMs process retrieved memory, tool actions, and intermediate observatio...
By Yu Li, Qikun Cai, Tao Huang, Chen Hou