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hiyenwong/ai_collectionEs werden 40 von 4.114 gesammelten Skills angezeigt.
TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale - Training robust autonomous driving agents requires a simulator that is fast enough for reinforcement learning at scale, realistic enough to ground beh...
Quellsprache: Englisch
Unveiling Complex Collective Behaviors from Simple Rewards - Multi-agent Reinforcement Learning (MARL) holds great potential for robot swarms, but the black-box nature of neural policies complicates strategic an...
Quellsprache: Englisch
Dynamic neural manifolds methodology for flexible closed-loop control on neuromorphic hardware. Uses ring attractor networks with sensory-modulated control neurons (speed, shape, selection) to drive subspace rotations and fine-grained trajectory control in…
Quellsprache: Englisch
Human-AI Agent Interaction as a Neuroplastic Training Environment - Interaction with AI agents has become one of the most frequent activities of everyday digital life. Whether conversing with an assistant, working with...
Quellsprache: Englisch
Knowledge- and Gradient-Guided Reinforcement Learning for Parametrized Action Markov Decision Processes - In this paper, we study Reinforcement Learning in Parametrized Action Markov Decision Processes (PAMDP), where each decision consists of a symbolic ac...
Quellsprache: Englisch
LLMs Can See the Smoke but not the Fire: Evaluating Abductive Reasoning with Elenchos - Large language models (LLMs) excel at pattern recognition and text generation, but their capacity for abductive inference - inferring latent hypothese...
Quellsprache: Englisch
Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution - Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task a...
Quellsprache: Englisch
MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations - Long-term memory has become a foundational capability for LLM-based agents that accompany users across extended, multi-session interactions. Existing ...
Quellsprache: Englisch
Tracing Agentic Failure from the Flow of Success - Failure attribution for LLM-based agentic systems, i.e., identifying which steps in a failure trajectory caused the task to fail, is critical for debu...
Quellsprache: Englisch
Who Grades the Grader? Co-Evolving Evaluation Metrics and Skills for Self-Improving LLM Agents - Self-evolving agent systems improve by creating, revising, and retiring their own skills, but every such loop rests on a hidden assumption: a reliable...
Quellsprache: Englisch
Win by Silence: Deletion Non-Monotonicity, Autonomous Exploitation, and Typed-State Gating in LLM Plan Evaluation - Plan evaluators can reward a strategic plan for becoming less explicit. This paper studies that failure in a staged expected-value scorer for…
Quellsprache: Englisch
A Learning-Rate-Gated Failure of GRPO in a Small Language and Vision-Language Model Web Agent: A Controlled Null and Its Mechanism - Reinforcement learning with verifiable rewards, and Group Relative Policy Optimization (GRPO) in particular, is now run…
Quellsprache: Englisch
Can Induced Emotion Bias LLM Behaviors in Sequential Decision Making? - As Large Language Models (LLMs) are increasingly deployed as autonomous agents in high-stakes domains, understanding contextual factors that may modul...
Quellsprache: Englisch
Constraint-Aware Aggregation for Federated Reinforcement Learning in Microgrid Energy Coordination - Federated Reinforcement Learning (FedRL) enables coordination of distributed energy resources without sharing raw local data, but standard aggregation...
Quellsprache: Englisch
PalmClaw: A Native On-Device Agent Framework for Mobile Phones - Large Language Model (LLM) agents have moved beyond generating responses to executing multi-step tasks by calling tools, observing the results, and it...
Quellsprache: Englisch
Research paper: StructAgent: Harness Long-horizon Digital Agents with Unified Causal Structure. Brief summary of key findings and contributions.
Quellsprache: Englisch
LLM interpretability methodology from Anthropic's "A global workspace in language models" (Jul 2026). Use when probing what a language model is "thinking but not saying" — its consciously-accessible / broadcast internal representations — via the Jacobian lens…
Quellsprache: Englisch
Alignment training methodology from Anthropic's "Teaching Claude why" research (May 2026). Use when fine-tuning or RLHF-aligning LLMs against adversarial/misalignment evals, or designing OOD-generalizing safety training data. Covers four lessons:…
Quellsprache: Englisch
Dendritic In-Context Learning (DendriCL): a single-layer compartmental spiking neural network whose apical/dendritic subthreshold dynamics implement online Widrow-Hoff LMS, giving in-context learning without attention, depth, or inference-time synaptic…
Quellsprache: Englisch
Dynamic neural manifolds methodology for flexible closed-loop control on neuromorphic hardware. From arXiv:2607.07373 (von Seeler et al., Jul 2026).
Quellsprache: Englisch
Methodology from Anthropic policy analysis examining possible trajectories for US-China AI competition by 2028 — focusing on compute advantage, export controls, distillation attacks, and two scenarios for democratic vs. authoritarian AI leadership.
Quellsprache: Englisch
分析AI能否具有意识的物理主义框架，区分模拟与实例化的本体论边界，提出制图者依赖的计算理论
Quellsprache: Mehrsprachig
Expert guidance for web accessibility (WCAG 2.2). Treat a11y violations as compile errors. Use when implementing accessibility features, auditing a11y compliance, or working with screen readers. Triggers on: accessibility, wcag, a11y, aria, screen reader,…
Quellsprache: Englisch
Active Predictive Filtering paradigm for Spiking Transformers. Inspired by the brain's predictive coding mechanism, actively suppresses predictable signals and focuses on salient visual features. Activation: active predictive filtering, spiking transformer,…
Quellsprache: Englisch
Proposes that active sensing (energy expenditure for information) is not driven by sensory goals but is necessary for task-level control. Integrates empirical data and control theory to explain explore-exploit mode switching in biological sensorimotor…
Quellsprache: Englisch
Theoretical framework proposing that active sensing (movement for information) is not driven by sensory goals but is necessary for task-level control, with explore/exploit mode switching. Based on arXiv:2605.22988 (May 2026). Use when studying active sensing,…
Quellsprache: Englisch
Adaptive acquisition function selection for discrete black-box optimization (BOCS + GP-LCB hybrid). Based on arXiv:2605.10856 (Shikanai & Ohzeki, 2026). Use when optimizing QUBO, HUBO, or any discrete-variable black-box problem where BOCS stagnates. Combines…
Quellsprache: Englisch
Theory of phase-locked activity in delayed spiking networks using the Haken Lighthouse model — an analytically tractable event-based framework bridging integrate-and-fire networks and coupled phase oscillators. Derives self-consistency conditions for…
Quellsprache: Englisch
Two-stage adaptive confidence-gated neural decoding framework for quantum error correction — lightweight neural fast-path with high-confidence fallback to classical refinement. Use when designing real-time QEC decoding, neural-classical hybrid inference,…
Quellsprache: Englisch
Adaptive distributionally robust optimal control for handling Knightian uncertainty in stochastic systems. Addresses epistemic uncertainty through adaptive DROC methods. Use when: (1) Designing robust control systems with distribution uncertainty, (2)…
Quellsprache: Englisch
Adaptive flow routing methodology for uncovering latent communication patterns in brain networks. Models information flow through structural connectivity to reveal hidden communication pathways. Activation: brain network communication, flow routing, latent…
Quellsprache: Englisch
Adaptive-Frequency Resonate-and-Fire (ARF) neurons for spectral estimation of streaming signals. Neuromorphic-inspired method that dynamically adjusts internal frequency to match dominant frequency components, enabling real-time range/velocity estimation in…
Quellsprache: Englisch
MorphSNN: Adaptive Graph Diffusion and Structural Plasticity for Spiking Neural - Bio-inspired undirected diffusion for signal propagation in . Activation triggers: adaptive, graph, diffusion, neuroscience, SNN.
Quellsprache: Englisch
Adaptive Hybrid Quantum-Classical Feature Fusion methodology for medical image classification. Addresses optimization asymmetries between quantum and classical paradigms using Temperature-Scaled Hybrid Fusion (TSHF), Dynamic Hybrid Fusion (DHF), and Static…
Quellsprache: Englisch
Adaptive quantum-classical feature fusion methodologies for medical AI diagnosis. Covers Temperature-Scaled Hybrid Fusion (TSHF), tensor-network compression with quantum refinement, and multi-head quantum-aware encoding. Use when building hybrid…
Quellsprache: Englisch
Adaptive Spiking Neuron (ASN) methodology for vision and language modeling. Implements trainable membrane potential dynamics with adaptive firing mechanisms for efficient Spiking Neural Networks (SNNs). Activation: adaptive spiking neuron, ASN, spiking neural…
Quellsprache: Englisch
Adaptive Spiking Neuron (ASN) methodology for vision and language modeling. Trainable membrane potential dynamics, integer training + spike inference, NASN variant with normalization. Activation: adaptive spiking neuron, asn, nasn, trainable spiking neuron,…
Quellsprache: Englisch
Adaptive Spiking Neuron (ASN) methodology for vision and language modeling - a general-purpose spiking neuron family evaluated on 19 datasets across 5 distinct tasks. Use when: (1) Implementing energy-efficient vision models with SNNs, (2) Building language…
Quellsprache: Englisch
Energy-efficient Spiking Transformer using attention-driven sparse spike propagation. Reduces FLOPs 87.7-97.5% vs dense Transformers by replacing softmax with temporal spike coding. Activation: spiking transformer, energy-efficient vision, spike-based…
Quellsprache: Englisch
Theoretical framework comparing in-context learning (fixed queries) vs agentic learning (adaptive queries) under neural network realizability constraints. Use when: analyzing when adaptive querying helps or hurts, comparing ICL with agentic RL, understanding…
Quellsprache: Englisch