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SkillsMP has collected 1,228 skills from ADu2021/skillXiv. Open a skill to review its source and details.
ADu2021/skillXivShowing 40 of 1,228 collected skills.
Enable LLMs to scale reasoning length from O(n²) to O(n) by structuring thinking into fixed-size chunks with learnable cross-chunk summaries. Trigger: train reasoning models with unbounded or expensive chain-of-thought sequences.
Train multi-agent reasoning systems with decoupled reward signals and pipeline parallelism—enable specialized Solver/Verifier/Corrector agents to iteratively refine solutions without waiting for full trajectories, handling extended reasoning up to 320K tokens.
Improves tool-integrated reasoning by using bipartite matching to assign dense turn-level rewards, enabling credit assignment for individual tool interactions in multi-turn tasks where 4B models outperform 8B competitors.
Understand why math reasoning improvements don't always transfer to general capabilities. Use RL-based training instead of SFT to preserve representation structure and enable broader generalization.
Improve multimodal mathematical reasoning through iterative reflection cycles where an outcome reward model provides feedback on reasoning quality, and correct solutions are incorporated back into training—enabling continuous model adaptation beyond static…
Enables LLM-based agent teams to improve reasoning accuracy at inference time through collaborative deliberation and structured experience retrieval, achieving 3-8% accuracy gains without expensive multi-agent training.
Evaluate LLM agents on realistic tool-use tasks via 28 live MCP servers with 250 tools, assessing fuzzy tool discovery, multi-step planning, and cross-domain workflow coordination
Evaluate LLM agents through realistic multi-turn tool-use workflows across 127 complex MCP tasks spanning CRUD operations, state management, and error handling. Use when assessing agent capabilities on real-world tool orchestration beyond shallow read-only…
Generative approach for creating complex dynamic scenes and content, supporting agent capabilities in understanding and reasoning about multi-agent environments.
Trains mean-velocity models on rectified couplings from pretrained flow models to dramatically smooth loss landscape, enabling faster convergence and superior one-step generation quality without additional training data.
Implement techniques from Mecellem Models: Turkish Models Trained from Scratch and Continually Pre-trained for the Legal Domain. This paper presents Mecellem models, a framework for developing specialized language models for the Turkish legal domain through…
Analysis framework for understanding how large language models perform complex cognitive tasks, revealing internal reasoning mechanisms that inform agent architecture and capability assessment.
Build efficient medical vision-language models that reason about medical images and text simultaneously, achieving competitive performance with much larger models while maintaining 500× lower computational cost.
Promptable segmentation foundation models such as SAM3 have demonstrated strong generalization capabilities through interactive and concept-based prompting. However, their direct applicability to medical image segmentation remains limited by severe domain…
Generate accurate, high-quality medical videos for clinical education and documentation by leveraging large-scale annotated medical datasets with domain-specific fine-tuning on video diffusion models.
Implement techniques from MeepleLM: A Virtual Playtester Simulating Diverse Subjective Experiences. Recent advancements have expanded the role of Large Language Models in board games from playing agents to creative co-designers
Enable agents to autonomously design and refine task-specific agents by evolving externalised behavioral skills and prompts without modifying base LLM parameters.
Optimize agent long-term memory by treating it as an information bottleneck problem. Dynamically compress redundant information while preserving task-relevant content through semantic-symbolic-topological hybrid retrieval.
Reduces optical flow GPU memory 3.9× while maintaining state-of-the-art accuracy through correlation volume downsampling and dimension compensation. Enables native FullHD training with 2.09GB inference memory. Use for motion estimation in memory-constrained…
MemGovern: Enhancing Code Agents through Learning from Governed Human Experiences. From arXiv:2601.06789
Implements MemoBrain from arXiv:2601.08079
Evaluate memorization in 3D generative models through controlled experiments discovering factors like dataset diversity and guidance scale. Provide simple yet effective strategies like rotation augmentation to reduce memorization without degrading generation…
Evaluate and improve memory capabilities in LLM agents across four competencies: accurate retrieval, test-time learning, long-range understanding, and selective forgetting. Identifies critical gaps in how agents store, update, and revise information.
Treat memory management as learnable RL policy actions (delete/insert) rather than fixed mechanisms. Enable models to autonomously decide what to keep, remove, or add to context, reducing average context length by 51% while matching larger models.
Enable long-horizon agents to manage finite context by separating working memory from persistent storage. Use indexed summaries with pointers to archived evidence, treating memory operations as first-class agent actions learned via RL.
Decouple feed-forward networks from self-attention by training FFNs on context-free token embeddings instead of residual streams. Enables pre-computation of FFN outputs as static lookup tables for inference efficiency and improved interpretability.
Treat memory as a manageable system resource for LLMs through unified management of plaintext, activation, and parameter-level memories with dynamic scheduling and lifecycle governance.
Enable agents to accurately identify temporally relevant information in long multi-session dialogues through RL-based memory retrieval. Combines coarse-to-fine candidate selection with multi-level temporal consistency rewards—providing dense supervision that…
Existing works increasingly adopt memory-centric mechanisms to process long contexts in a segment manner, and effective memory management is one of the key capabilities that enables large language models to effectively propagate information across the entire…
Build agents that evolve their own memory operations by learning a skill bank of memory transformations and periodically discovering new skills from challenging cases, enabling adaptive memory management that improves with scale.
Improve reasoning models by aligning their meta-predictions with actual rollouts through self-generated training signals. Trigger: accelerate reasoning model training while maintaining performance through better meta-cognitive awareness.
Enable LLM agents to evolve behavioral skills and policies online through skill synthesis from failures and opportunistic gradient-based refinement, without service interruption.
Train LLMs to faithfully express uncertainty through natural language that accurately reflects their actual confidence, improving trustworthiness and reducing overconfidence.
Research contribution advancing agent and reasoning capabilities through novel approaches to model development, training, and evaluation.
Build state-of-the-art 7B multimodal models by combining four-stage vision-language pretraining with mixed on-policy RL integrating verifiable and human feedback rewards.
System enabling intelligent tool integration and selection for enhanced multimodal reasoning, allowing agents to leverage heterogeneous capabilities for complex problem-solving across domains.
Combine sparse attention (25% of layers) and linear attention (75% of layers) via strategic layer placement to handle 1M-token contexts with 75% training cost reduction. Hybrid positional encoding preserves long-range information while maintaining position…
Build ultra-efficient language models for edge devices using sparse attention, high-quality data filtering, and ternary quantization, achieving Qwen3-8B performance with 22% of training tokens.
Hybrid-attention MoE reasoning model supporting 1M token context and 80K token generation, combining lightning attention with CISPO RL algorithm for efficient scaling.
Integrates fine-grained visual tokens into mathematical reasoning via Interleave Tokens that dynamically select relevant image regions for each reasoning step.