Menu
Skills in this repository
SkillsMP has collected 1,228 skills from ADu2021/skillXiv. Open a skill to review its source and details.
ADu2021/skillXivShowing 28 of 1,228 collected skills.
Break through supervised fine-tuning saturation by mixing logits from weaker model checkpoints into strong model training targets. Amplifies hard negatives that strong models have already suppressed, enabling continued learning after standard training…
Enable safe and efficient AI agent interactions with websites via declarative HTML extensions—define agent-executable tools and context using <tool> and <context> tags instead of relying on brittle UI inference.
Create principle-guided reward models for web automation agents that evaluate reasoning process quality rather than just outcomes. Implement domain-specific principles (HTML understanding, interaction patterns, state tracking) to guide agent behavior in web…
Optimize open-source LLM web agent training through systematic analysis of supervised fine-tuning vs. reinforcement learning trade-offs. Achieve 45% lower compute cost by branching into RL at strategic SFT checkpoints.
Enable web agents to improve over time by curating episodic memory from navigation trajectories and dynamically injecting task-specific guidance at runtime—no retraining required, persistent improvement across sessions.
Bridge deterministic web code and generative LLMs via hybrid world models. State and physics defined by TypeScript code, LLMs generate narrative/aesthetics on top. Uses typed interfaces, deterministic hashing, graceful degradation—enabling scalable…
Systematically train 8B web agents to solve complex information-seeking tasks through model-based exploration and long-to-short query evolution, achieving state-of-the-art performance via SFT+RL training pipeline with progressive context expansion to 128K…
Training platform with thousands of realistic web navigation tasks for developing visual web agents, supporting scalable training and evaluation of agents that can generalize to arbitrary websites.
Enable autonomous web agents to navigate partially observable environments through action-aware tree search. Handle destructive actions via snapshot validation, implement speculative backtracking with parallel tabs, and dynamically prioritize actions based on…
Develops multimodal research agents that combine visual and textual reasoning for complex information-seeking tasks, using synthetic training trajectories and reinforcement learning.
Train autoregressive world models on 1M+ real web interactions for accurate browser state prediction. Enables agent training with 100× more data than prior approaches, achieving GPT-4o comparable performance with format flexibility and cross-domain…
Improve downstream task performance by increasing weight decay during pretraining (0.3-1.0 vs. default 0.1), enabling better model plasticity and representation structure.
Research contribution advancing agent and reasoning capabilities through novel approaches to model development, training, and evaluation.
Dynamically fork sub-agents at any step based on task requirements instead of using fixed agent counts. Linearize hierarchical trajectories into unified sequences for GRPO training. Demonstrates 6.36x more sub-agents than baselines via learned orchestration.
Train Vision-Language-Action models for robotic control through world model simulation without real-world interaction—using pixel-based world models aligned with VLA features to enable self-correction and robust policy optimization.
Accelerates iterative world model inference by classifying tokens by temporal curvature (predictability) and applying differentiated caching: stable tokens reused, linear tokens extrapolated, chaotic tokens updated. Achieves 3.7x speedup with 98% rollout…
Improve long-horizon world model fidelity using RL with clip-level rollouts and complementary reward functions for action accuracy and visual quality. Breaks computational constraints by evaluating candidate clips incrementally rather than full sequences,…
Quantify confidence in video generation by estimating latent-space uncertainty and mapping to interpretable heatmaps. Detect untrustworthy regions in generated frames for robot planning and policy evaluation—critical when video hallucinations could cause…
Build unified world models using flow-based video generation architecture that handles Text2World, Image2World, and Video2World in a single model, trained on 200M video clips with RL post-training for improved instruction-following and video quality.
Derives closed-form optimal linear blockwise transforms combining fixed Hadamard matrices with data-dependent components from second-order statistics, providing provably near-optimal quantization for both weights and activations with GPU efficiency.
Systematic evaluation toolkit for assessing large language models across multiple dimensions, enabling comprehensive benchmarking of agent capabilities and comparative analysis of model performance.
Use soft-prompted transformer architecture for multi-robot learning. Add learnable embodiment-specific prompt embeddings to handle different robot types while maintaining single shared backbone. Scale to 0.9B parameters across 6 simulators and 3 real robots.
Reduce KV cache memory by 7.7-10x through quantization and rematerialization of input activations instead of caching Keys and Values, trading recomputation for memory efficiency.
MoE-enhanced YOLOv9-Tiny using lightweight routers to adaptively weight specialized expert outputs at each feature resolution, improving detection quality while maintaining end-to-end differentiability. Deploy for efficient multi-scale object detection with…
Build scalable LLM agent frameworks with automated generation of tools, prompts, and configurations, plus continuous optimization through experience accumulation and reinforcement learning—achieving 71.47% on WebWalkerQA and 72.8% on GAIA.
Unified text-image token processing in a compact 6B-parameter transformer enabling sub-second inference on enterprise GPUs through hierarchical distillation and single-stream architecture. Use when generating high-quality images with tight latency budgets or…
Generate diverse human motions from text descriptions using million-scale datasets and wavelet-enhanced quantization. Achieves state-of-the-art zero-shot generalization on out-of-domain motions, compositional descriptions, and complex choreography through…
Reduce token count by 15-40% at inference through context-adaptive compression, merging frequent token sequences into hypertokens using online Lempel-Ziv-Welch compression without retraining entire models.