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- Multilingual ReasoningUnderstanding cross-language reasoning patterns in Large Reasoning Models. Use when designing multilingual reasoning systems, building non-English LLM applications, or addressing language-specific reasoning optimization. Triggers on "multilingual reasoning", "cross-language reasoning", "non-English reasoning models", or "language-specific reasoning patterns".Votes: 0GitHub stars: 3
- Graph Laplacian J Divergence**来源论文：** arXiv:2012.11240 - Improving J-divergence of brain connectivity states by graph Laplacian denoisingVotes: 0GitHub stars: 3
- Chart Visual ReasoningSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- A Quantum Like Benchmark For Context Sensitive AssDerived from arXiv:2606.12449 - A quantum-like benchmark for context-sensitive associative memory with adaptive plasticityVotes: 0GitHub stars: 3
- Debate On Graph Reliable And Adaptive Reasoning OfDerived from arXiv:2607.17266 - Debate-on-Graph: Reliable and Adaptive Reasoning of Large Language Model on Uncertain Knowledge GraphVotes: 0GitHub stars: 3
- A Monadbased Clause Architecture For Artificial Age Score Aas In Large Language Models**arXiv ID:** 2512.11835 **Authors:** Seyma Yaman Kayadibi **Published:** 2025-12-03T12:48:40Z **Abstract:** Large language models (LLMs) are often deployed as powerful yet opaque systems, leaving open how their internal memory and "self-like" behavior should be governed in a principled and auditable way. The Artificial Age Score (AAS) was previously introduced and mathematically justified through three theorems that characterise it as a metric of artificial memory aging. Building on this fou...Votes: 0GitHub stars: 3
- Tutorial On Answering Questions About Images With Deep Learning**arXiv ID:** 1610.01076 **Authors:** Mateusz Malinowski, Mario Fritz **Published:** 2016-10-04T16:29:28Z **Abstract:** Together with the development of more accurate methods in Computer Vision and Natural Language Understanding, holistic architectures that answer on questions about the content of real-world images have emerged. In this tutorial, we build a neural-based approach to answer questions about images. We base our tutorial on two datasets: (mostly on) DAQUAR, and (a bit on) VQA. Wit...Votes: 0GitHub stars: 3
- Toward Anthropomorphic Dialogue A Closed Loop FramDerived from arXiv:2607.17191 - Toward Anthropomorphic Dialogue: A Closed-Loop Framework for Human-Like Chat Generation, Evaluation, and Preference AlignmentVotes: 0GitHub stars: 3
- Te Pai Classical SimulationTensor-Network Randomized Time Evolution via Parallelized Approximate Inversion (TE-PAI) for classical simulation of quantum many-body dynamics. MPS TE-PAI achieves 10^3x gate-count reduction and massive parallelization via randomized shallow Trotter-variant circuits. Keywords: tensor network, MPS, time evolution, randomized algorithms, TE-PAI, classical simulation, parallelization, Trotter, quantum many-body.Votes: 0GitHub stars: 3
- Stl Parameter Synthesis NonlinearSynthesize parameters for nonlinear dynamical systems that robustly satisfy continuous-time Signal Temporal Logic (STL) specifications over uncertain initial conditions. Combines gradient-based optimization with set-based reachability verification for provable satisfaction guarantees. Activation: STL parameter synthesis, signal temporal robustness, formal methods control, reachability verification, nonlinear optimal control, interpretable constraints.Votes: 0GitHub stars: 3
- Statistically Significant Stopping Of Neural Network Training**arXiv ID:** 2103.01205 **Authors:** J. K. Terry, Mario Jayakumar, Kusal De Alwis **Published:** 2021-03-01T18:51:16Z **Abstract:** The general approach taken when training deep learning classifiers is to save the parameters after every few iterations, train until either a human observer or a simple metric-based heuristic decides the network isn't learning anymore, and then backtrack and pick the saved parameters with the best validation accuracy. Simple methods are used to determine if a ne...Votes: 0GitHub stars: 3
- State Dependent Observation Noise Active InferenceState-Dependent Observation Noise methodology that reintroduces epistemic value in Linear-Gaussian Active Inference models. This skill provides the mathematical framework and implementation guidance for restoring curiosity-driven behavior in Gaussian agents by introducing state-dependent observation noise covariance R(x). Use when working with active inference, Bayesian filtering, dual control theory, or neural dynamics models where epistemic drive has been lost in standard linear-Gaussian fo...Votes: 0GitHub stars: 3
- Single Atom Reservoir ComputingA Single Atom in Front of a Mirror as Universal Reservoir Computer methodology. Demonstrates universality with minimal quantum setup, providing explicit recipe for target accuracy with specified physical resources and resonator modes.Votes: 0GitHub stars: 3
- Safety Liveness Control ContractsDesign layered control architectures (LCAs) using safety-liveness decomposition via heterogeneous assume-guarantee contracts. Use when: (1) designing hierarchical control systems with discrete planning + continuous execution, (2) enforcing safety constraints while achieving long-horizon objectives, (3) co-designing multi-layer controllers with formal guarantees, (4) building reference governor bridges between MPC planners and low-level controllers. Based on arXiv:2605.04222.Votes: 0GitHub stars: 3
- Rrc Ranking Reward ConstructionRRC for ranking-based reward construction.Votes: 0GitHub stars: 3
- Robust Regret ControlFramework for distributionally robust regret optimization (DRRO) in stochastic control systems, combining robust control with regret minimization under uncertainty. Use when designing control systems with ambiguous disturbances, robust LQR, or optimizing control under unknown probability distributions.Votes: 0GitHub stars: 3
- Rewardguided Iterative Refinement In Diffusion Models At Testtime With Applications To Protein And Dna Design**arXiv ID:** 2502.14944 **Authors:** Masatoshi Uehara, Xingyu Su, Yulai Zhao, Xiner Li, Aviv Regev, Shuiwang Ji, Sergey Levine, Tommaso Biancalani **Published:** 2025-02-20T17:48:45Z **Abstract:** To fully leverage the capabilities of diffusion models, we are often interested in optimizing downstream reward functions during inference. While numerous algorithms for reward-guided generation have been recently proposed due to their significance, current approaches predominantly focus on single-...Votes: 0GitHub stars: 3
- Qumvqd Quantum ChemistryQumode-based Variational Quantum Deflation (QumVQD) framework for excited-state quantum chemistry on bosonic quantum processors. Computes electronic and vibrational excited state energies with 1-2 orders lower gate counts than qubit-based methods. Keywords: quantum chemistry, excited states, qumode, bosonic quantum processor, variational quantum deflation, VQD, vibrational structure, electronic structure.Votes: 0GitHub stars: 3
- Quantum Gaussian State LearningSample-optimal learning of bosonic Gaussian quantum states. Provides sharp bounds on sample complexity for characterizing unknown n-mode Gaussian states: Omega(n^3/epsilon^2) for Gaussian measurements, Omega(n^2/epsilon^2) for arbitrary measurements. Proves non-Gaussian measurements required for optimal learning of passive Gaussian states. Use when: quantum state tomography, bosonic Gaussian states, quantum learning theory, sample complexity bounds, quantum sensing benchmarking, Wigner distri...Votes: 0GitHub stars: 3
- Q Photonas Quantum NasQ-PhotoNAS: Hybrid Quantum Neural Architecture Search framework for photonic quantum-classical models using genetic algorithm and learnable phase encodingVotes: 0GitHub stars: 3
- Predictive Coding Equilibrium Propagation ImagenetTraining Predictive Coding Networks on ImageNet using Equilibrium Propagation. Biologically plausible training framework for PCNs achieving near-backpropagation performance. Use when: (1) Training predictive coding networks at scale, (2) Implementing biologically plausible learning rules, (3) Scaling equilibrium propagation beyond small tasks, (4) Energy-based model training without backpropagation. Keywords: predictive coding network, PCN, equilibrium propagation, EP, energy-based model, Ima...Votes: 0GitHub stars: 3
- Physics Informed Llm Quantum ControlPhysics-informed LLM framework (VF-QCTRL) for general quantum control synthesis combining symbolic reasoning with optimization. Proposes analytic control ansätze and refines parameters through feedback loops. Use when designing LLM-driven quantum control, physics-informed prompt engineering, analytic pulse synthesis, or training-free control protocol design across quantum systems. Activation: physics-informed LLM, quantum control synthesis, VF-QCTRL, analytic pulse design, symbolic reasoning ...Votes: 0GitHub stars: 3
- Penalty Free Qaoa Protein FoldingPenalty-free QAOA methodology for lattice protein folding using conflict graph independent set formulation. Use when: (1) quantum optimization for protein folding or molecular structure prediction, (2) QAOA without penalty terms, (3) conflict graph maximum independent set (MIS) mixer design, (4) quantum annealing approaches to biological optimization problems. Activation: penalty-free QAOA, protein folding, lattice protein, conflict graph, independent set mixer, QAOA bio-physics, quantum prot...Votes: 0GitHub stars: 3
- Leact Learning To Reason From Expert ActionsLeAct framework for recovering chain-of-thought reasoning from expert systems that only produce actions without explicit reasoning traces, treating CoT as a latent variable optimized via action probability scoring.Votes: 0GitHub stars: 3