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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。
Train text-to-image diffusion models directly in frozen DINOv3 feature spaces, eliminating VAE-based compression. Enables high-resolution synthesis by leveraging VFM representations as native latent manifolds with unified cross-modal transformers.
原文の言語: 英語
Generate problem variants from correct model solutions while preserving answer equivalence, enabling self-play training that maintains output diversity and prevents entropy collapse.
原文の言語: 英語
Train LLM-based agents for multi-turn SWE tasks via rejection fine-tuning and DAPO RL, scaling to 131k context length achieving 39% Pass@1.
原文の言語: 英語
Framework orchestrating competitive debate among specialized agents with different reasoning perspectives. Generates multiple fault propagation traces via code dependency graphs, then resolves to consolidated fixes through structured multi-round competition.
原文の言語: 英語
Framework that distills reusable experience from prior agent trajectories enabling continuous learning across issues. Achieves 73% resolution on SWE-Bench by leveraging multi-level experience banks capturing both successful and failed repair attempts.
原文の言語: 英語
Automate construction of GitHub issue resolution benchmarks via multi-agent LLM coordination, binary file recovery, and exit-code-based validation, achieving 337 valid instances at $0.047 per instance.
原文の言語: 英語
Achieve state-of-the-art software issue resolution through lightweight supervised fine-tuning with structured datasets and curriculum learning, plus test-time scaling—reaching 52.6% on SWE-Bench Verified and 58.8% with test-time strategies, outperforming…
原文の言語: 英語
Implement techniques from SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents. LLM agents have demonstrated remarkable capabilities in software development, but their performance is hampered by long interaction contexts, which incur high API costs and…
原文の言語: 英語
Improve SWE agent RL via execution-free reward models optimized for three metrics beyond TTS: discriminative ability (AUC) and calibration (ECE). Shows TTS alone insufficient—models must distinguish correct/incorrect trajectories and align confidence with…
原文の言語: 英語
Automatically generate executable software engineering environments from GitHub pull requests at million-scale using an autonomous building agent. Detects superficial verification patterns to force genuine code execution testing.
原文の言語: 英語
Replace Docker environments with learned LLM surrogates comprising a lightweight sandbox for deterministic file operations, a transition model predicting execution feedback, and a reward model acting as virtual test runner. Reduces training infrastructure…
原文の言語: 英語
Enable multimodal models to dynamically switch between text and vision reasoning modes, allocating computation based on perceived difficulty and image resolution, achieving strong performance on both vision-dense and text-heavy benchmarks.
原文の言語: 英語
Dynamically switch between explicit reasoning and latent computation modes during inference to optimize the trade-off between reasoning quality and token consumption.
原文の言語: 英語
Enable VLMs and LLMs to self-improve at world modeling by treating actions as latent variables and reciprocally optimizing forward and inverse dynamics models using only unlabeled state transitions.
原文の言語: 英語
Adapt web agents to new domains through targeted synthetic data generation and quality-aware refinement—identifying and correcting hallucinations while preserving task consistency to enable efficient adaptation with minimal human supervision.
原文の言語: 英語
Scale visual reasoning via automated synthesis of challenging questions from seed samples, using verification mechanisms to ensure correctness and verify RL training gains on out-of-domain visual math tasks.
原文の言語: 英語
Personalize diffusion models to learn a concept from a single image without overfitting by using timestep-dependent rank masking and orthogonal weight initialization, enabling faithful concept reproduction while maintaining text-guided control.
原文の言語: 英語
Accelerate diffusion language models from many to few generation steps using trajectory self-distillation. Collect clean-noisy state pairs along teacher trajectories, apply reverse-KL mode-seeking distillation, and weight losses by decoding order to…
原文の言語: 英語
Improve language model performance on complex numerical reasoning over tables through three-stage pipeline: decompose questions into sub-questions, sanitize and clean table data, then generate executable Python code for precise computation.
原文の言語: 英語
Improve LLM agents operating under strict feasibility constraints (budget limits, tool usage caps) by separating planning from execution. Generate multiple candidate plans, merge into plan graph, then use external solver (ILP) to find optimal feasible path.…
原文の言語: 英語
Design capability-adaptive curricula for code generation by constructing per-problem test suites (basic, intermediate, complex, edge cases) and dynamically weighting training signals based on model capability rather than problem difficulty alone. Optimal…
原文の言語: 英語
Token-adaptive preference optimization framework using min-max formulation to reduce multimodal LLM hallucination. Achieves 50% hallucination reduction using min-max distributional robustness while preserving visual grounding.
原文の言語: 英語
Build process reward models for tabular reasoning by grounding intermediate reasoning steps in executable tool operations. Train on 60K+ step-level annotations combining verification rationales with tool execution feedback. TaTToo achieves 30.9% improvement…
原文の言語: 英語
Route queries to multiple specialized agents dynamically using reasoning-aware routing that generates natural-language justification before predicting candidate agents. Enables seamless addition of new agents without system redesign. Routes aggregate…
原文の言語: 英語
Leverages temporal dynamics in diffusion models by aggregating predictions across denoising steps for improved inference quality without retraining.
原文の言語: 英語
Adapt pretrained video diffusion models to conditional tasks using only 10-30 samples without architectural changes.
原文の言語: 英語
Improves self-rewarding language models through temporal framework that prevents representational collapse by anchoring rejected responses and guiding chosen responses across training phases.
原文の言語: 英語
Evaluate agents on 89 challenging terminal-based tasks where frontier models score below 65%, providing realistic assessment of command-line interface automation capability. Use when benchmarking agents designed for system administration, automation, or…
原文の言語: 英語
Learn when to terminate chain-of-thought reasoning by predicting answer arrival patterns. Train an early-exit mechanism on empirical first-answer positions to reduce reasoning length by 14-55% while maintaining or improving accuracy.
原文の言語: 英語
Apply tabu search enhanced with short-term tabu lists and long-term elite memory for simulation optimization with expensive, noisy evaluations. Balances exploration (avoiding cycling) and exploitation (leveraging best solutions). Use for multimodal landscapes…
原文の言語: 英語
Applies test-driven development to agent prompts by iteratively refining prompts against behavioral test suites until compliance is achieved. Enables measurable agent behavior validation through semantic mutation testing and specification evolution.
原文の言語: 英語
Adapt vision-language models at inference without labeled data by generating multiple predictions and rewarding high-frequency outputs. Trigger: improve VLM accuracy on deployment with self-generated supervision signals.
原文の言語: 英語
Scale model performance at test time by generating multiple reasoning trajectories and selecting the best using a self-supervised process reward model. MetaStone-S1 achieves 32B-equivalent performance using only 32B parameters and 53M for trajectory scoring,…
原文の言語: 英語
Generate high-quality videos through iterative test-time refinement: agents critique outputs on visual/audio/context fidelity and a reasoning agent synthesizes feedback to improve prompts across multiple generations.
原文の言語: 英語
Enables agents to synthesize, verify, and evolve executable tools during inference rather than relying on static tool libraries, improving reasoning on heterogeneous scientific domains through dynamic tool adaptation.
原文の言語: 英語
Restore degraded images while preserving textual fidelity using TeReDiff, a multi-task diffusion framework integrating text spotting with U-Net features and VLM-verified dataset curation.
原文の言語: 英語
Modern CI/CD pipelines integrating agent-generated code exhibit a structural failure in responsibility attribution. Decisions are executed through formally correct approval processes, yet no entity possesses both the authority to approve those decisions and…
原文の言語: 英語
Systematic evaluation toolkit for assessing large language models across multiple dimensions, enabling comprehensive benchmarking of agent capabilities and comparative analysis of model performance.
原文の言語: 英語
Reduce reasoning model inference cost by selectively applying latent iterations—use a lightweight neural decider to identify hard tokens and refine only those with focused LoRA modules, achieving 94% token exemption with 8-11% accuracy gain.
原文の言語: 英語
Dynamically allocate reasoning budgets per-task: 36.8% length reduction while improving accuracy 8.4% by learning which problems warrant deep reasoning vs. quick answers. Use when optimizing reasoning efficiency across variable-difficulty problems.
原文の言語: 英語