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SkillsMP は ADu2021/skillXiv から 1,228 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
ADu2021/skillXiv収集済み skill 1,228 件中 40 件を表示しています。
To teach robots complex manipulation tasks, it is now a common practice to fine-tune a pre-trained vision-language-action model (VLA) on task-specific data. However, since this recipe updates existing representations, it is unsuitable for long-term operation…
原文の言語: 英語
Accelerate LLM inference by dynamically skipping transformer layers based on in-context signals, without training auxiliary draft models or changing model weights.
原文の言語: 英語
Represent 3D scenes as compressed light-field tokens for efficient neural rendering. Multi-view images are tokenized via Plücker coordinates, condensed through K-means clustering, and rendered adaptively. Achieves 5-7× data reduction versus MVSplat while…
原文の言語: 英語
Augment verifiable reward RL (RLVR) with contrastive learning to generate dense auxiliary rewards. Enforce proximity among correct reasoning trajectories in embedding space while suppressing errors, amplifying invariant reasoning patterns.
原文の言語: 英語
Replace hard clipping in policy gradients with smooth quadratic penalties derived from Total Variation divergence constraints. Eliminates zero-gradient regions and training instability while maintaining stable policy evolution.
原文の言語: 英語
No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning. From arXiv:2601.06794
原文の言語: 英語
Hybrid multi-agent architecture where orchestrator delegates tasks to GUI Operator or Programmer agent. Coding enables efficiency on computational tasks, achieving 60.76% on OSWorld with 33% fewer steps.
原文の言語: 英語
Dynamically allocate training budget across samples using a capability-oriented value function that measures per-sample training importance based on model capability evolution. Reduces training time via greedy heap-based allocation optimizing…
原文の言語: 英語
Generates complex text-heavy and structured images by converting natural language descriptions into executable code that specifies layouts, then renders and refines. Achieves 68% improvement on structured image generation benchmarks.
原文の言語: 英語
Train dual-brain agents with specialized planner (Cerebrum) and executor (Cerebellum) through decoupled RL, resolving planning-execution trade-off for scientific GUI agents
原文の言語: 英語
Train code and test generators through adversarial co-evolution where test LLM generates adversarial test cases to expose code defects. Prevent self-collusion by separating models and enabling white-box test generation.
原文の言語: 英語
Predict GUI state evolution by generating HTML code rather than pixel images. Combines visual fidelity of pixel-based approaches with structural precision of code-based methods through deterministic rendering. Enables agents to evaluate action consequences…
原文の言語: 英語
Generate physically grounded 4D scenes from natural language through dual-stream architecture separating object detail from scene orchestration. VLM-Motion Critic validates physics parameters iteratively, bridging semantic-physical execution gap.
原文の言語: 英語
Generate Verilog hardware code from natural language using reasoning-enhanced LLMs, combining rule-based testbench generation with round-trip data synthesis and adaptive DAPO reinforcement learning for reliable hardware design.
原文の言語: 英語
Evaluate LLM coding capabilities across three dimensions—problem analysis, code implementation, and test validation—to identify specific reasoning gaps and improve model robustness through ensemble approaches.
原文の言語: 英語
Advanced reasoning approach for optimizing inference efficiency through meta-cognitive planning, enabling agents to make better decisions with reduced computational overhead.
原文の言語: 英語
Align VLA efficiency with human cognition through 3-stage progressive routing: instruction-aware aggregation, instruction-irrelevant pruning, and coupled attention for 2.8x inference speedup
原文の言語: 英語
Dynamically adapt LLM depth per input at test time by skipping, repeating, or reordering layers using MCTS search, correcting 60% of initially wrong predictions and processing 75% of correct predictions with shorter architectures without retraining.
原文の言語: 英語
Iteratively retrieve and reason over long narratives using a dynamic memory workspace that integrates retrieved facts into a shared context for complex multi-hop reasoning.
原文の言語: 英語
Organize agent memory as an event graph with explicit logical relationships rather than flat embeddings. Framework incrementally segments experiences into events and links them through causal, temporal, and logical relations. Enables agents to navigate memory…
原文の言語: 英語
Build generalist judge models for evaluating LLM outputs using verifiable rewards and policy gradient training. Create a 7B model competitive with much larger judges through reward-guided optimization and critical thinking decomposition. Use when you need…
原文の言語: 英語
Lightweight verifier model for evaluating LLM outputs across multiple domains, handling diverse answer types through meta-error pattern analysis.
原文の言語: 英語
Improve RL sample efficiency through co-evolution of policy and experience extractor, enabling dynamic experience replay that adapts to the agent's skill level.
原文の言語: 英語
Decompose complex image editing instructions into simpler sub-tasks with automatically generated control guidance. Handles multi-object edits, preserves identity of surrounding regions, and eliminates manual mask creation.
原文の言語: 英語
Convert easy, high-accuracy training prompts into harder compositional problems by sequentially chaining multiple prompts together. Use Composition-RL to maintain effective learning signals during RL training when many prompts achieve near-perfect accuracy,…
原文の言語: 英語
Enable computer-use agents to flexibly choose between GUI primitives (click, type) and high-level tool calls, reducing cascading errors by 22% and improving execution speed by 11%.
原文の言語: 英語
Enable AI agents to safely explore action outcomes before execution by predicting UI state changes in desktop applications. Two-stage approach: first predict textual description of what changes, then synthesize visual representation of resulting screen.…
原文の言語: 英語
Learns richer spatial representations by training on both 2D and 3D data simultaneously. Combines 3D intra-modal self-distillation with 2D-3D cross-modal joint embedding, achieving 14.2% and 4.8% improvements over single-modality baselines in scene…
原文の言語: 英語
Improve language model reasoning using only model confidence as reward signals, eliminating need for labels or preference models while achieving substantial gains with minimal data.
原文の言語: 英語
Learn optimal configurations for agentic AI systems through hierarchical RL that treats configuration as a query-wise decision problem. Structure policy selects workflows/tools/budgets while prompt policy composes specific instructions, achieving 25% accuracy…
原文の言語: 英語
Improve student model robustness under covariate shift by using diffusion-based augmentation that targets spurious features via teacher-student disagreement.
原文の言語: 英語
Improves speculative decoding acceptance rates by exposing target model's intermediate reasoning through contemplate tokens. Achieves 8-11% acceptance rate improvement over EAGLE through future-direction guidance without extra forward passes.
原文の言語: 英語
Build AI code agents that scale to massive repositories with long-context reasoning and persistent memory. Confucius SDK achieves 59% Resolve@1 on SWE-Bench-Pro—ideal when AI needs to handle real-world codebases with complex toolchains.
原文の言語: 英語
Train adaptive ODE solvers that generate fast diffusion previews while maintaining consistency with full-step refinement. Learn context-aware integration coefficients through PPO without distilling base models. Achieve high-quality previews in few steps with…
原文の言語: 英語
Train a specialized continue-thinking token via reinforcement learning to extend reasoning depth during inference, achieving superior performance over fixed-token baselines.
原文の言語: 英語
Enable dynamic control over reasoning depth during inference using Reasoning Control Fields that guide long chain-of-thought generation based on execution and quality parameters.
原文の言語: 英語
Joint optimization of policy and reward models in LLM reinforcement learning by leveraging rule-based reward precision and dynamically constructing training pairs to prevent reward hacking and improve performance.
原文の言語: 英語
Enhance spatial reasoning in multimodal LLMs by integrating depth and segmentation as auxiliary modalities with adaptive reasoning strategies. COOPER achieves 6.91% improvement in spatial understanding—when you need 3D-aware vision-language capabilities.
原文の言語: 英語
Implement techniques from CooperBench: Why Coding Agents Cannot be Your Teammates Yet. Resolving team conflicts requires not only task-specific competence, but also social intelligence to find common ground and build consensus
原文の言語: 英語
Enhance reasoning models by integrating executable code within thinking traces, enabling grounded computation verification and reducing hallucination in mathematical and logical reasoning.
原文の言語: 英語