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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。
Route reasoning between discrete token space (when uncertain) and latent soft embeddings (when confident). Use maximum next-token probability as a routing threshold to dynamically select the reasoning space, improving accuracy under latent reasoning while…
原文の言語: 英語
Control LLM reasoning effort through discrete modes (High/Medium/Low) using budget-aware supervised fine-tuning and adaptive reward shaping, enabling compression-performance tradeoffs.
原文の言語: 英語
Improve agent reasoning for spatial tasks by augmenting LLMs with map tools and parallel test-time exploration. Framework uses reinforcement learning to train agents to iteratively refine hypotheses using map feedback. Parallel exploration enables agents to…
原文の言語: 英語
Demonstrates that chain-of-thought reasoning improves LLM factual retrieval through computational buffering and self-priming. Improves single-hop factual accuracy by enabling models to generate contextual bridge facts before recalling answers.
原文の言語: 英語
Leverage video generation models as unified multimodal reasoning engines that bridge text and vision by embedding reasoning as dynamic visual processes, enabling models to tackle both spatial puzzles and mathematical problems within a single temporal…
原文の言語: 英語
Train unified models to generate interleaved reasoning steps combining text and image thoughts as complementary modalities, enabling adaptive behavior like autonomous mode-switching and superior test-time scaling for vision-centric tasks without requiring…
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 reasoning models to be safe without external supervision by prepending refusal instructions to harmful queries and training on self-generated safe responses. Unlocks latent safety knowledge within the model's native distribution.
原文の言語: 英語
Generates high-quality audio through three-stage CoT reasoning pipeline: foundational foley synthesis, object-focused refinement, and instruction-guided editing. Uses fine-tuned VideoLLaMA for reasoning and flow-matching audio foundation model. Apply for…
原文の言語: 英語
Train LLMs for tool-integrated mathematical reasoning via hierarchical RL combining episode-level problem correctness with step-level code execution quality. Addresses sparse rewards in reasoning chains through TIRGen data construction and self-correcting…
原文の言語: 英語
Enable parallel reasoning threads on standard autoregressive inference engines without modifications to position embeddings or KV cache. ThreadWeaver achieves 1.53× speedup while maintaining chain-of-thought quality—ideal when you need faster reasoning…
原文の言語: 英語
Combine diffusion-based parallel drafting with autoregressive sampling in a single forward pass using structured attention masks—achieving 5x+ token throughput while maintaining autoregressive-level output quality through hybrid generation.
原文の言語: 英語
Guarantee LLM inference completes within time budgets via response length prediction, execution time estimation, and adaptive KV cache eviction. Three-stage pipeline predicts response length, estimates end-to-end time with 1.22% accuracy, adjusts cache…
原文の言語: 英語
Automate univariate time series forecasting through a four-agent system orchestrating preprocessing, model selection, validation, and reporting. Use when reducing manual effort in forecasting pipelines and improving reproducibility.
原文の言語: 英語
Evaluate multimodal models on agentic visual reasoning through 13 diverse tasks requiring novel tool use for image processing and manipulation in chain-of-thought, revealing that strong performance requires genuine thinking-with-images capabilities.
原文の言語: 英語
Compress reasoning models by dynamically re-weighting short-CoT (System-1) and long-CoT (System-2) training data, achieving 40% token reduction while maintaining accuracy.
原文の言語: 英語
Autonomously synthesize task-specific agent planning architectures by optimizing for correctness, stability, and efficiency via impedance-guided preference optimization.
原文の言語: 英語
Learn to compress entire scenes into a single bottleneck token that captures temporal dynamics. Enables efficient visual tracking and robotic manipulation by forcing reconstruction from minimal target hints, achieving superior performance with training costs…
原文の言語: 英語
Improve LM performance with token order prediction (TOP) auxiliary loss using learning-to-rank instead of exact multi-token prediction, achieving gains across math, code, and NLP tasks
原文の言語: 英語
Dynamically select important tokens at the attention head level, performing dense attention only on selected tokens and scattering results back. Achieves 3.23x attention speedup at 128K context with 1% accuracy loss through layer-wise representation stability…
原文の言語: 英語
The Confidence Dichotomy: Analyzing and Mitigating Miscalibration in Tool-Use Agents. From arXiv:2601.07264
原文の言語: 英語
Implements ET-Agent from arXiv:2601.06860
原文の言語: 英語
Two-stage post-training framework combining rejection-sampled SFT and RL for LLM-guided repository code search and issue localization.
原文の言語: 英語
Prove that tool-augmented learning unboundedly scales factual knowledge recall compared to parameter-constrained memorization, enabling efficient knowledge retrieval via external tools
原文の言語: 英語
8B parameter orchestrator trained with end-to-end RL balancing outcome, efficiency, and user preference rewards to strategically coordinate diverse tools and models. Generate realistic tool-use data via ToolScale synthetic pipeline for verifiable multi-turn…
原文の言語: 英語
Tool-R0 trains tool-use agents through self-play between Generator and Solver, creating curriculum-like progression without manual task curation.
原文の言語: 英語
Enable non-English language models to reliably generate function calls and tool invocations through continued training on bilingual function-calling datasets. Achieves 28% improvement in parsing accuracy while preserving language understanding.
原文の言語: 英語
Tool Verification stabilizes self-improving reasoning models by using external tool execution as ground-truth evidence to prevent spurious consensus from becoming reinforced training signals.
原文の言語: 英語
Reward-guided search methods have demonstrated strong potential in enhancing tool-using agents by effectively guiding sampling and exploration over complex action spaces. As a core design, those search methods utilize process reward models (PRMs) to provide…
原文の言語: 英語
Specialized outcome reward models (1.7B-14B) for evaluating tool-calling performance in LLMs. Addresses the critical gap in reward modeling where general-purpose models miss key signals of effective tool use. Enables better Best-of-N sampling, data filtering,…
原文の言語: 英語
Detects and prevents unsafe tool invocations in LLM agents through proactive step-level guardrails, reducing harmful tool calls by 65% while improving task success rates by 10% against prompt injection attacks.
原文の言語: 英語
Generate 1.5M realistic agent training trajectories from 495 real-world MCP servers without human annotation. Use to build tool-agentic training datasets with authentic tool execution, multi-turn conversations, and error handling.
原文の言語: 英語
Synthesize large-scale training data for LLM agents by generating diverse tool-use trajectories from real Model Context Protocol (MCP) environments. A 1.5M-example dataset created via multi-stage pipeline: query generation, quality filtering, trajectory…
原文の言語: 英語
Implements TourPlanner from arXiv:2601.04698
原文の言語: 英語
Recent years have witnessed increasing interest in extending large language models into agentic systems. While the effectiveness of agents has continued to improve, efficiency, which is crucial for real-world deployment, has often been overlooked. This paper…
原文の言語: 英語
Linguistic expressions of emotions such as depression, anxiety, and trauma-related states are pervasive in clinical notes, counseling dialogues, and online mental health communities, and accurate recognition of these emotions is essential for clinical triage,…
原文の言語: 英語
Implement techniques from Towards Pixel-Level VLM Perception via Simple Points Prediction. We present SimpleSeg, a strikingly simple yet highly effective approach to endow Multimodal Large Language Models (MLLMs) with native pixel-level perception
原文の言語: 英語
Evaluate LLM agent capabilities using tower defense game environment with multimodal observations (pixel, text, structured state). Benchmark reveals critical agent limitations: inadequate planning validation, inflexible decision-making, and inefficient action…
原文の言語: 英語
Build multilingual LLMs that excel at machine translation while preserving general-purpose capabilities through a staged training pipeline combining continued pretraining, SFT, preference optimization, and RL with verifiable rewards.
原文の言語: 英語
Trace unsafe LLM outputs back to conflicting beliefs in training data using suffix-array matching and Belief Conflict Index.
原文の言語: 英語