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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Fas R1 A Unified Multi Task Mllm For Reasoning FacFAS-R1: A Unified Multi-Task MLLM for Reasoning Face Anti-SpoofingVotes: 0GitHub stars: 3
- Fhe Privacy Preserving LlmFully Homomorphic Encryption (FHE) patterns for privacy-preserving LLM inference. Covers lattice-based cryptography (LWE/RLWE), FHE scheme selection (BFV, BGV, CKKS), and techniques for running large models on encrypted data. Based on implementation of FHE on Llama 3 for secure computation. Use when: building privacy-preserving AI inference systems, implementing homomorphic encryption for ML models, or designing secure computation pipelines for sensitive data. arXiv: 2604.12168Votes: 0GitHub stars: 3
- Fifa World Cup 2026 As Contamination Free Benchmark Llm Forecasting Agents Four Models BooSkill derived from arXiv:2607.17765 - FIFA World Cup 2026 as a Contamination-Free Benchmark for LLM Forecasting Agents: Four Models, a BooVotes: 0GitHub stars: 3
- Filesystem Based Memory For Llm Agents OrganizatioFilesystem-Based Memory for LLM Agents: Organization, Evolution, and SustainabilityVotes: 0GitHub stars: 3
- Fixed Point Compositionality Low Rank Gluing组合性计算的理论框架 - 在抑制主导的阈值线性网络中使用低秩连接规则实现固定点的组合性，揭示大脑如何高效分解复杂任务为可复用的基本单元。Votes: 0GitHub stars: 3
- Fleet Of Agents Coordinated Problem Solving With Large Language Models**arXiv ID:** 2405.06691 **Authors:** Lars Klein, Nearchos Potamitis, Roland Aydin, Robert West, Caglar Gulcehre, Akhil Arora **Published:** 2024-05-07T09:36:23Z **Abstract:** While numerous frameworks have been developed to enhance the reasoning abilities of large language models (LLMs), there is a scarcity of methods that effectively balance the trade-off between cost and quality. In this paper, we introduce Fleet of Agents (FoA), a novel and intuitive yet principled framework utilizing LLM...Votes: 0GitHub stars: 3
- Fully Autonomous Programming Using Iterative Multiagent Debugging With Large Language Models**arXiv ID:** 2503.07693 **Authors:** Anastasiia Grishina, Vadim Liventsev, Aki Härmä, Leon Moonen **Published:** 2025-03-10T16:56:51Z **Abstract:** Program synthesis with Large Language Models (LLMs) suffers from a "near-miss syndrome": the generated code closely resembles a correct solution but fails unit tests due to minor errors. We address this with a multi-agent framework called Synthesize, Execute, Instruct, Debug, and Repair (SEIDR). Effectively applying SEIDR to instruction-tuned LLM...Votes: 0GitHub stars: 3
- Future Confidence Distillation In Large Language ModelsReliable confidence estimation is essential for deploying large language models (LLMs) in confidence-aware systems, where downstream decisions such as retrieval, tool use, and adaptive computation dep. Based on arXiv:2607.07626.Votes: 0GitHub stars: 3
- Generative Language Modeling For Automated TheoremSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Gift Geometry Informed Low Precision Gradient Communication For Llm PretrainingGradient communication is a primary scaling bottleneck in large language model (LLM) pretraining. Communicating gradients in low-precision formats, such as FP8 and NVFP4, can significantly reduce the. Based on arXiv:2607.07494.Votes: 0GitHub stars: 3
- Gpt 2 15b ReleaseSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Gpt 2 6 Month Follow UpSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Gpt 4Skill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Gpt 4o Mini Advancing Cost Efficient IntelligenceSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Gpt 4o System Card External Testers AcknowledgemenSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Gpt 5 Lowers The Cost Of Cell Free Protein SynthesSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Gpt 52 Derives A New Result In Theoretical PhysicsSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Gradrag Cross Component Prompt Adaptation For Coordinated MultiGRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAGVotes: 0GitHub stars: 3
- Graph Rag Knowledge GraphSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Harness Engineering For Llm Driven Gpu Kernel GeneDerived from arXiv:2607.17979 - Harness Engineering for LLM-Driven GPU Kernel GenerationVotes: 0GitHub stars: 3
- Harness Engineering Llm Driven Gpu Kernel GenerationSkill derived from arXiv:2607.17979 - Harness Engineering for LLM-Driven GPU Kernel GenerationVotes: 0GitHub stars: 3
- Hello Gpt 4oSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- How Confessions Can Keep Language Models HonestSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- How Data Shapes Rope Frequency Usage From Positional Scale Matching To LengthRotary Position Embeddings (RoPE) provide transformers with a fixed grid of positional frequencies, yet trained models use these frequencies highly non-uniformly. We study what determines this frequen. Based on arXiv:2607.07678.Votes: 0GitHub stars: 3
- Illusion Of Equivalency Quantization Effects LlmsShows that post-training quantization evaluation via accuracy/perplexity fails to capture behavioral changes. Introduces correctness agreement metric. Reveals non-linear breakpoints at low bit-widths. Query/key projections more sensitive than value/output. Activation: quantization, LLM deployment, behavioral change, correctness agreement, post-training quantization.Votes: 0GitHub stars: 3
- Improving Instruction Hierarchy In Frontier LlmsSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Internet Explorer Targeted Representation Learning On The Open Web**arXiv ID:** 2302.14051 **Authors:** Alexander C. Li, Ellis Brown, Alexei A. Efros, Deepak Pathak **Published:** 2023-02-27T18:59:55Z **Abstract:** Modern vision models typically rely on fine-tuning general-purpose models pre-trained on large, static datasets. These general-purpose models only capture the knowledge within their pre-training datasets, which are tiny, out-of-date snapshots of the Internet -- where billions of images are uploaded each day. We suggest an alternate approach: rath...Votes: 0GitHub stars: 3
- Intrinsic Computational Functionalism内在计算功能主义方法论 — 从观察者相对映射到观察者独立结构。解决计算理论意识中的观察者相对性问题，提供可操作化的标准框架。Votes: 0GitHub stars: 3
- Ken Utilization Layer Hebbian Replay Within A Students Ken For Adaptive Exercise Recommendation**arXiv ID:** 2507.00032 **Authors:** Grey Kuling, Marinka Zitnik **Published:** 2025-06-18T00:06:28Z **Abstract:** Adaptive exercise recommendation (ER) aims to choose the next activity that matches a learner's evolving Zone of Proximal Development (ZPD). We present KUL-Rec, a biologically inspired ER system that couples a fast Hebbian memory with slow replay-based consolidation to enable continual, few-shot personalization from sparse interactions. The model operates in an embedding space, ...Votes: 0GitHub stars: 3
- Kv Cache Recycling To Expand Usable Context Capacity In Low Parameter Llms**arXiv ID:** 2512.11851 **Authors:** Prashant Pandey **Published:** 2025-12-04T17:04:43Z **Abstract:** Whether attention key value (KV) states computed for one prompt for a small LLM can be reused to accelerate inference on a new similar prompt, giving an increase to the space to its context memory using an approach called token recycling. Using a standard Hugging Face setup with DialoGPT-medium (a 345M parameter GPT-2 style decoder trained on 147M Reddit exchanges, 2005 to 2017) as the test...Votes: 0GitHub stars: 3
- Language Models And Cycle Consistency For Selfreflective Machine Translation**arXiv ID:** 2411.02791 **Authors:** Jianqiao Wangni **Published:** 2024-11-05T04:01:41Z **Abstract:** This paper introduces a novel framework that leverages large language models (LLMs) for machine translation (MT). We start with one conjecture: an ideal translation should contain complete and accurate information for a strong enough LLM to recover the original sentence. We generate multiple translation candidates from a source language A to a target language B, and subsequently translate t...Votes: 0GitHub stars: 3
- Language Models Are Few Shot LearnersSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Language Models Need SleepSleep paradigm for LLMs that enables continual learning through memory consolidation and dreaming phases. Use when: (1) implementing continual learning for LLMs; (2) designing memory consolidation mechanisms; (3) creating autonomous self-improvement systems; (4) addressing catastrophic forgetting in sequential tasks; (5) developing RL-based curriculum generation for synthetic data. Trigger words: sleep paradigm, memory consolidation, dreaming process, knowledge seeding, LLM sleep.Votes: 0GitHub stars: 3
- Large Language Model As Metasurrogate For Datadriven Manytask Optimization A Proofofprinciple Study**arXiv ID:** 2503.08301 **Authors:** Xian-Rong Zhang, Yue-Jiao Gong, Yuan-Ting Zhong, Ting Huang, Jun Zhang **Published:** 2025-03-11T11:13:11Z **Abstract:** In many-task optimization scenarios, surrogate models are valuable for mitigating the computational burden of repeated fitness evaluations across tasks. This study proposes a novel meta-surrogate framework to assist many-task optimization, by leveraging the knowledge transfer strengths and emergent capabilities of large language models ...Votes: 0GitHub stars: 3
- Large Language Models As Evolution Strategies**arXiv ID:** 2402.18381 **Authors:** Robert Tjarko Lange, Yingtao Tian, Yujin Tang **Published:** 2024-02-28T15:02:17Z **Abstract:** Large Transformer models are capable of implementing a plethora of so-called in-context learning algorithms. These include gradient descent, classification, sequence completion, transformation, and improvement. In this work, we investigate whether large language models (LLMs), which never explicitly encountered the task of black-box optimization, are in princip...Votes: 0GitHub stars: 3
- Learning Evolution Via Optimization Knowledge Adaptation**arXiv ID:** 2501.02200 **Authors:** Chao Wang, Lingling Li, Licheng Jiao, Jiaxuan Zhao, Fang Liu, Shuyuan Yang **Published:** 2025-01-04T05:35:21Z **Abstract:** The iterative search process of evolutionary algorithms (EAs) encapsulates optimization knowledge within historical populations and fitness evaluations. Effective utilization of this knowledge is crucial for facilitating knowledge transfer and online adaptation. However, current research typically addresses these goals in isolation ...Votes: 0GitHub stars: 3
- Learning To Reason With LlmsSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Llama CppRuns LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.Votes: 0GitHub stars: 3
- Llm As A VerifierGeneral-purpose verification framework using probabilistic logit expectation for continuous scoring, enabling multi-dimensional scaling of verification along granularity, repeated evaluation, and criteria decomposition.Votes: 0GitHub stars: 3
- Llm As General VerifierLLM-as-a-Verifier general-purpose verification framework using probabilistic verification and multi-round self-correction for improving LLM output reliability across reasoning, coding, and mathematical tasks.Votes: 0GitHub stars: 3
- Llm Binary DeobfuscationDeobfuscating binary code remains a fundamental challenge in reverse engineering, as obfuscation is widely used to hinder analysis and conceal program logic. Although large language models (LLMs) have... Activation: LLM, reverse engineeringVotes: 0GitHub stars: 3
- Llm Code Generation SurveySkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Llm Confidence BasSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Llm Decision Centric DesignDecision-Centric framework for LLM systems that separates decision signals from action policies. Apply this when designing LLM control flow, routing, adaptive inference, or building diagnosable agent systems.Votes: 0GitHub stars: 3
- Llm Dt Resilient ModelingDesign principles for building resilient LLM-assisted Digital Twin modeling workflows with human oversight — orthogonalized structural modeling and parameter fitting, intermediate representation (IR) for interpretability, and density-preserving IR choice.Votes: 0GitHub stars: 3
- Llm Interaction AwarenessProbe and measure interaction awareness in language models using user-turn generation. Use when evaluating LLM conversation quality beyond task accuracy, measuring whether models encode awareness of what follows their responses, or designing collaboration-oriented post-training. Based on arXiv:2604.02315 - User Turn Generation as a Probe of Interaction Awareness in Language Models.Votes: 0GitHub stars: 3
- Llm Judge Theory Agnostic Personality RecognitionJAM: theory-agnostic framework for personality recognition using LLM-as-a-Judge for adaptive metric alignment. Attention-Pooled Graph Prototypical Network with Cross-Theory Harmonization. LLM operates in before-the-loop and in-the-loop configurations for ambiguous sample identification. Use when working with llm-as-judge, personality-recognition, theory-agnostic.Votes: 0GitHub stars: 3
- Llm Orchestrated SystemsLLM-powered orchestration for complex engineering systems using Model Context Protocol (MCP). Enables natural language interaction with specialized engineering workflows including power grid simulation, quantum system management, and industrial control systems. Use when: (1) building LLM orchestrators for engineering tools, (2) integrating MCP with simulation software, (3) democratizing access to complex analysis workflows, (4) creating natural language interfaces for control systems, (5) aut...Votes: 0GitHub stars: 3
- Llm Reorganize Representational Geometry IclLarge language models reorganize representational geometry during in-context learning, showing that ICL depends on successful online untangling of task-relevant representations with geometric reorganization increasing separability.Votes: 0GitHub stars: 3
- Llm Trading Agent AlignmentBehavioral alignment and representation dynamics analysis for LLM trading agents — pre-failure signatures, risk-feedback alignment, and manifold diagnostics for auditable financial decision-making. Use when building or analyzing LLM-based trading agents, studying agent behavioral alignment, detecting pre-failure signatures in financial LLM systems, or implementing structured risk feedback for trading agents.Votes: 0GitHub stars: 3