A rigor-matched, three-seed audit of two periodic-step, search-based methods that make this decision online at inference time and re-evaluate it every few generation steps: a confidence-gated early-exit baseline (ConfLayers) and genuine self-speculative decoding (SWIFT, Xia et al. 2024).
Comparing machine learning and deep learning models for classifying postural states in VR under visual perturbations suggests that multimodal sensing, temporal deep learning, and explainable AI can support reliable classification of balance-related instability in VR.
N. Anjum, M. Pavel, Robert Gonzalez et al.· 0 citations
This work establishes quantum signal processing (QSP) as a solvable quantum model of the representation-learning regime, and proves a sparse-data guarantee for the full nonlinear gradient flow without freezing or ensemble-averaging the kernel.
ASTRA, an agentic system for ticket resolution in which a central orchestrator coordinates three specialist information-gathering agents and drives a judge-orchestrator refinement loop to produce evidence-backed troubleshooting reports, is proposed.
Shashidhar Reddy Javaji, Mohamed Trabelsi, Jin Cao et al.· 0 citations
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ORDDAR (Observation-Driven Reasoning for Distortion-Resilient Decision, Action, and Cognitive Recovery) is presented, a reasoning framework that models reasoning as cognitive state transitions, detects localized distortions, retrieves related reasoning from prior experiences, and repairs only the affected states.
Deblina Kar, Anant Nawalgaria, S. D. Das Mandal· 0 citations
This work forms LLM-driven equation discovery as an iterative search process that unifies Best-of-N, sequential refinement, tree search, and evolution-style methods under a common compute-allocation view and finds that search width is the dominant allocation parameter.
Hao-Wei Lin, Hubert Lim, Xiang-Yu Wang et al.· 0 citations
It is shown that user-preferring conflict resolution can coexist with a readable internal arbitration signal, and successful intervention depends on the geometry of the readout rather than probe accuracy alone, while directions selected mainly for pooled separability steer poorly.
Enrique Balp-Straffon, Chih-Hao Hsu, Rushiraj Gadhvi et al.· 0 citations
MAGG is proposed, a principled multi-agent framework for constructing Governed Knowledge Graphs that introduces explicit governance decisions for reliable and trustworthy knowledge sharing and demonstrates its effectiveness.
Pranav Bykampadi, Neel Mokaria, Vishesh Narayan et al.· 0 citations
A three-role Monte Carlo Tree Search (MCTS) framework that treats the Lean 4 compiler purely as a reward oracle using compiler output as a scalar signal for UCB-guided tree updates without feeding error content into the generation context is proposed.
AutoScientist-Quant, a self evolving search process that regards quantitative research as one budgeted search problem, is presented, a self evolving search process that regards quantitative research as one budgeted search problem.
Zong-Qian Li, Yaoyiran Li, Yao-Hui Guo et al.· 0 citations
The proposed learning-assisted Tabu Search notably reduces computation time while consistently producing higher-quality solutions than the standard algorithm, highlighting the potential of combining machine learning with metaheuristics by leveraging the implicit knowledge embedded in search trajectories.
Wissem Ahmed Zaid, Alain Hertz, Denny Liu· 0 citations
A novel preference elicitation algorithm for linear utilities that outperforms prior techniques in practice and is applied to heart transplant allocation where a policy must balance competing objectives such as post-transplant outcomes, waitlist mortality, geographic ease, and equity.
Itai Zilberstein, I. Anagnostides, Zachary W. Sollie et al.· 0 citations
