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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。
Allocate LLM reasoning budget optimally via value tree search: use residual value prediction to estimate step utility, then dynamically shift exploration-exploitation balance as budget depletes. Outperform high-budget baselines at 1/4 cost.
原文の言語: 英語
Compress long prompts to 1/26th of original size while maintaining retrieval accuracy using hierarchical page-level pooling, without model fine-tuning.
原文の言語: 英語
Reinforcement learning (RL) is central to post-training, particularly for agentic models that require specialized reasoning behaviors. In this setting, model merging offers a practical mechanism for integrating multiple RL-trained agents from different tasks…
原文の言語: 英語
We introduce Being-H0.5, a foundational Vision-Language-Action (VLA) model designed for robust cross-embodiment generalization across diverse robotic platforms. While existing VLAs often struggle with morphological heterogeneity and data scarcity, we propose…
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
Align diffusion models to hierarchical fine-grained criteria rather than binary preferences. Decompose expert knowledge into attribute hierarchies and apply Complex Preference Optimization to simultaneously maximize positive attributes while minimizing…
原文の言語: 英語
Improve long-context performance by incorporating imaginary components discarded in standard RoPE implementations. Use phase information from complex-valued attention for richer positional encoding—especially valuable as context length increases beyond normal…
原文の言語: 英語
Generate high-quality synthetic training data that enables 7.7x faster training than web data, with smaller models achieving better performance through strategic content rephasing and data optimization.
原文の言語: 英語
Connects multimodal language models with diffusion models using patch-level CLIP embeddings as shared latent variables, enabling controllable image generation with minimal training overhead.
原文の言語: 英語
Build fully ternary quantized vision-language-action models for robotic manipulation, achieving 11x memory reduction and 4.4x speedup while maintaining task performance on edge devices.
原文の言語: 英語
Improve computer-use agent performance by running multiple rollouts and selecting the best trajectory using narrative-level reasoning. The Behavior Judge (BJudge) converts raw execution traces into behavior narratives, enabling intelligent trajectory…
原文の言語: 英語
Deploy efficient MoE models on resource-constrained edge devices by learning chunk-level activation sparsity that achieves 3.67× speedup. Use when you need to compress LLMs for on-device inference while maintaining reasoning quality and supporting speculative…
原文の言語: 英語
Improve credit assignment in multi-objective RL by decomposing advantages into segment-specific values. Use Outcome-Conditioned Baselines to reduce cross-objective interference without expensive rollouts, enabling better training signals for multi-step…
原文の言語: 英語
Optimize language model policies layer-by-layer rather than monolithically to understand internal reasoning structure. Decompose models into per-layer and per-module policies via residual streams, analyze entropy patterns revealing exploration→convergence…
原文の言語: 英語
Overcome reasoning model training plateaus by increasing rollouts per prompt (N=512) rather than training steps, addressing unsampled coupling that destabilizes learning. Theoretical analysis shows broad exploration eliminates plateau bottleneck.
原文の言語: 英語
Build web agents using human-inspired browser actions (scrolling, clicking, typing) operated directly on raw HTML via Playwright. Combine supervised fine-tuning and rejection fine-tuning with explicit memory for strong generalization on web tasks.
原文の言語: 英語
Automates artistic typography customization through self-distilled learning and localized style injection. Generates stylized text images by encoding reference style and injecting it into diffusion denoising. Use for digital design workflows, text-based…
原文の言語: 英語
Replace discrete token prediction with continuous vector prediction by training a high-fidelity autoencoder to compress K tokens into single latent vectors, enabling K-fold sequence length reduction while maintaining likelihood-free generation through…
原文の言語: 英語
Adapt large reasoning models for optimization tasks using expert-guided hint correction. Generate high-quality training data with minimal expert intervention (<2.6% token modification). Trigger: fine-tune reasoning models on domain-specific tasks without…
原文の言語: 英語
Protects computer use agents from prompt injection by using single-shot execution planning that generates complete control flow graphs before UI observation, preventing instruction hijacking while maintaining 57% performance on frontier models.
原文の言語: 英語
Implement techniques from Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs. Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them, which is essential…
原文の言語: 英語
Generate images with unified control over identity, spatial position, pose, and layout: encode diverse control modalities (spatial canvas, pose canvas, box canvas) into single RGB image, train diffusion model jointly across all control types, and enable…
原文の言語: 英語
CapImagine teaches models to explicitly imagine through text rather than latent reasoning, significantly improving visual reasoning performance.
原文の言語: 英語
Enhance LLM reasoning by combining contrastive learning on reasoning representations with reinforced fine-tuning, leveraging both annotated chains and unsupervised signals.
原文の言語: 英語
Train reusable pre-computed KV cache representations of large text corpora for efficient retrieval, achieving 38.6x memory reduction and 26.4x throughput improvement.
原文の言語: 英語
Replace token-insertion for fusing vision and language with efficient cross-attention that maintains separate text self-attention. Enables text tokens to attend images within local windows, preserves gist tokens from prior images, and maintains near-constant…
原文の言語: 英語
Defend against indirect prompt injection attacks by detecting dominance shifts using leave-one-out attribution, enabling selective sanitization without sacrificing latency or utility.
原文の言語: 英語
Implement CASTLE, a causal attention mechanism that dynamically updates key representations as context expands. Reduces validation loss by 0.006-0.037 across model scales while maintaining O(L²d) training complexity and O(td) decoding speed. Deploy for…
原文の言語: 英語
Extract sparse causal concept graphs from LLM activations using SAE and DAGMA, then validate through ablation to identify causally influential features. Bridges mechanistic interpretability with causal inference for understanding reasoning flow.
原文の言語: 英語
Control policy entropy dynamics in RL by reweighting gradients from clipped tokens. CE-GPPO preserves out-of-clip gradients with beta parameters to stabilize exploration-exploitation balance, preventing entropy collapse while maintaining training stability in…
原文の言語: 英語
Implement techniques from CGPT: Cluster-Guided Partial Tables with LLM-Generated Supervision for Table Retrieval. General-purpose embedding models have demonstrated strong performance in text retrieval but remain suboptimal for table retrieval, where highly…
原文の言語: 英語
Train single models to simulate multi-agent collaboration through distillation from complex multi-agent systems and agentic RL, creating efficient Agent Foundation Models for tool use and web navigation.
原文の言語: 英語
Enable language models to dynamically switch between four cognitive modes (spatial, convergent, divergent, algorithmic) during problem-solving. Meta-agent observes state and selects optimal mode per step, improving reasoning across math, coding, and spatial…
原文の言語: 英語
Analyze when CoT reasoning succeeds or fails using DataAlchemy synthetic environment and distribution discrepancy measurement.
原文の言語: 英語
Improve diffusion model sampling by planning content-adaptive denoising trajectories. Extract Diffusion DNA signatures quantifying per-stage difficulty, then apply graph planning to allocate computation to challenging generative phases.
原文の言語: 英語
Train search agents using citation-aware rubric rewards that decompose complex questions into verifiable single-hop facts. Agents learn to chain evidence through explicit source citations, preventing hallucinations and shortcut exploitation. Citation-aware…
原文の言語: 英語
Implement techniques from ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch. Chart reasoning is a critical capability for Vision Language Models (VLMs)
原文の言語: 英語
Harmonize supervised fine-tuning and reinforcement learning through dynamic weighting, balancing expert imitation and on-policy exploration to prevent response pattern disruption.
原文の言語: 英語
Research contribution advancing agent and reasoning capabilities through novel approaches to model development, training, and evaluation.
原文の言語: 英語
Unify retrieval and generation in RAG systems by compressing documents into shared continuous embeddings that serve both retrieval and generation: implement joint training with differentiable selection, achieving up to 16× context compression while improving…
原文の言語: 英語