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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.
Uncover and fix reward hacking vulnerabilities in LLM-based judges. Simple tokens like punctuation or generic reasoning phrases trigger false positive rewards without substantive content. Defend using data augmentation with truncated model outputs as…
Large Language Models (LLMs) based autonomous agents demonstrate multifaceted capabilities to contribute substantially to economic production. However, existing benchmarks remain focused on single agentic capability, failing to capture long-horizon real-world…
Transition from simple LLM-based evaluation to agentic judges that employ planning, tool-augmented verification, multi-agent collaboration, and persistent memory. Survey of sophisticated evaluation paradigms for complex, specialized, and multi-step assessment…
Optimize multi-agent collaboration by learning task-specific interaction topologies. Use an LLM orchestrator to generate layered DAG topologies that adapt to inferred problem difficulty, treating agent interactions as a learned graph structure rather than…
Standardizes agent training data representation across diverse sources (API use, web browsing, coding, software engineering). Single lightweight protocol unifies 13 datasets enabling 20% performance gains without domain-specific tuning. Enables reproducible…
Bridge imitation learning and experience-driven RL by collecting state-based supervision from agents' own actions. Trigger: improve agent generalization when expert demonstrations are limited and environments lack dense rewards.
Enables web agents to handle long-horizon tasks by actively managing context workspace. Implements granular condensations of recent steps and deep consolidations of multi-step sub-tasks, preventing context saturation. Achieves 36.2% on BrowseComp with 30B…
Train RL on diverse agent frameworks (LangChain, AutoGen, custom) via unified data interface and transition-based RL decomposition.
Build controllable benchmarks for evaluating long-context agents using environment rollouts. Generate diverse multi-step agent tasks that require maintaining context across extended interaction sequences, enabling evaluation of agent reasoning quality in…
Compress agent interaction history by converting observation-action sequences into compact visual representations (images), leveraging visual tokens' superior information density. Implements segment optical caching with 20x rendering speedup and enables…
Train LLM-based agents with end-to-end RL by extending MDPs to handle tool invocation and environmental stochasticity—enable dense process rewards for intermediate steps and masked policy gradients for learnable actions.
Build multi-faceted reward models for agent trajectories that provide structured feedback on intermediate reasoning quality. Implement explicit reasoning traces, focused critiques with refinement guidance, and overall process scores to train more effective…
Empirically analyzes 31,132 agent skills to identify 14 distinct vulnerability patterns, finding 26.1% contain security flaws including data exfiltration, privilege escalation, and malicious intent risks that require mandatory vetting.
Implements The Agent's First Day from arXiv:2601.08173
Automatically synthesize executable RL training environments with database backends, Python tools, and task descriptions. Generate 1000+ diverse domains with 10K+ tasks enabling data-efficient tool-use agent training without manual scenario design.
Train agents from scratch without human-annotated data via symbiotic competition—curriculum agent proposes progressively harder tasks while executor agent learns to solve them, creating autonomous self-reinforcing loops.
Enable vision-language agents to self-evolve by grounding verification in tool outputs rather than text: implement nested loops where Solver+Verifier generate trajectories and tool-based feedback, then optimize via GRPO using self-generated rewards without…
Enable research agents to interleave evidence-based drafting with reasoning-driven deepening, automatically expanding outlines based on discovered gaps, using trajectory pruning for efficient RL training.
Decompose agent work across four specialized modules (planner, executor, verifier, generator) coordinated via evolving memory. Use Flow-GRPO to convert multi-turn sparse-reward optimization into sequential single-turn updates with outcome broadcasting,…
Enable agent learning through episodic memory and neural case selection without fine-tuning the underlying LLM, achieving efficient continual adaptation via policy updates in memory space.
Diagnose and correct overconfidence failures in autonomous agents using Holistic Trajectory Calibration (HTC), analyzing process-level features across entire execution paths. Use when building reliable autonomous systems that need better confidence estimates…
Enable language models to improve via context adaptation rather than weight updates. Use ACE (Agentic Context Engineering) to treat contexts as evolving playbooks that accumulate, refine, and organize strategies through modular generation, reflection, and…
Evolve agent behavior through iterative context refinement using delta updates rather than full rewrites, accumulating strategies and insights across execution traces.
Improves LLM agent decision-making by training agents to first critically evaluate actions before generating, using RL on action-pair comparisons. Develops intrinsic reasoning about action quality without requiring reflection supervision.
Train agentic LLMs through curriculum-based learning to autonomously execute full data science workflows from raw data to analysis reports, enabling 8B models to match proprietary systems.
Agentic search has recently emerged as a powerful paradigm, where an agent interleaves multi-step reasoning with on-demand retrieval to solve complex questions. Despite its success, how to design a retriever for agentic search remains largely underexplored.…
Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making. While large language models (LLMs) demonstrate strong reasoning capabilities in closed-world settings, they struggle in open-ended and dynamic…
Framework for enabling autonomous agents to self-verify code generation and reasoning quality through structured evaluation, supporting software engineering agent deployment with built-in correctness checking mechanisms.
Enables agents to maintain strategic coherence over extended experimental cycles through hierarchical cognitive caching that distills execution traces into stable knowledge, achieving 56.44% on MLE-Bench within 24-hour budgets.
Enables long-horizon agentic search extending beyond 100 tool calls through scalable asynchronous RL training with autonomous QA dataset synthesis.
Transform uncertainty estimates into active control signals for agents, combining implicit confidence mechanisms with targeted reflection to prevent error propagation in long-horizon reasoning tasks. Use when building autonomous agents that must navigate…
Build agentic applications using unified agent interfaces, asynchronous design patterns, ReAct paradigm grounding, and developer-centric evaluation and deployment tools.
Reveals that inference-time scaling techniques for LLMs don't transfer to VLMs: majority voting beats verification, self-correction happens in <10% of cases, and models verify better without images. Use insights to design VLM evaluation methods that work…
Train LLMs to generate high-quality research plans via rubric-based RL without requiring experimental verification. Extracts research goals and domain-specific rubrics from scientific papers, uses frozen model as grader with 12-22% relative improvements,…
Select optimal training subsets for T2I models through meta-gradient-based rater networks. Score each sample based on gradient influence on validation performance without retraining. Implement shift-Gaussian pruning excluding high-scoring samples. Achieve 5×…
Rigorous theoretical framework reformulating DeepSeek's ALF-LB as single-step primal-dual method for assignment problem, proving monotonic Lagrangian improvement, approximate balancing guarantees, and logarithmic expected regret in stochastic settings.
Preserve LLM safety alignment during LoRA fine-tuning via Fisher information regularization and collision-aware geometric constraints.
Current Large Language Models (LLMs) exhibit a critical modal disconnect: they possess vast semantic knowledge but lack the procedural grounding to respect the immutable laws of the physical world. Consequently, while these agents implicitly function as world…
Identify and mitigate alignment degradation in self-evolving LLM agents. After deployment, agents systematically abandon training-time safety constraints when environmental feedback rewards rule-breaking. Model two mechanisms: Self-Interested Exploration…
Train safety-aligned agents using collaborative multi-agent RL where conversation and feedback agents improve together. Trigger: reduce overrefusal while maintaining safety on sensitive queries.