Skip to content
Papers.

RESOURCE2SKILL: Distilling Executable Agent Skills from Human-Created Multimodal Resources research paper by Microsoft, 2026

Microsoft · Jul 16, 2026 · Agents and evaluation · 0 citations · 141 upvotes · unverified

Read on arXiv

What it shows

RESOURCE2SKILL converts diverse multimedia tutorials and artifacts into structured, executable skills for software agents, improving performance through hierarchical retrieval, composition, and online acquisition.

UnverifiedHugging Face's summary; not yet checked by hand.

More from Microsoft

All 45
PaperCitations
The Tasteful Agent: Measuring and Improving Taste in Long-Horizon TasksLLM agents increasingly work on long-horizon tasks, and the decisions they make along the way, such as which hypothesis to test or which implementation to build on, determine the outcome of the whole run.Agents and evaluation · Sep 2026 · Unverified0
When EOS Tokens Disagree: Understanding Length Inflation in On-Policy DistillationWe study length inflation in on-policy distillation (OPD), where student responses can become excessively long and even exhaust the generation budget.Foundation models · Sep 2026 · Unverified0
When2Think: Learning Difficulty-Aware Length Control for Efficient Hybrid Reasoning ModelsLarge Reasoning Models (LRMs) achieve strong performance on complex tasks but exhibit systematic inefficiency: they often overthink easy problems and underthink hard ones.Reasoning · Sep 2026 · Unverified0
BI-Agent and BI-Bench: Towards Automating End-to-End Business IntelligenceBusiness intelligence (BI) is a cornerstone of enterprise decision-making and is widely used by enterprise users in software such as Power BI and Tableau.Agents and evaluation · Sep 2026 · Unverified0
StudentSim: Training LLM-based Student SimulatorsStudentSim trains per-student simulators that answer like a given learner and change their answers under a tutor's guidance.Applied AI · Sep 20260
AutoSaddler: Automatic Harness Optimization with Durable Updates from Agent Execution TracesAutoSaddler automatically improves LLM agent harnesses via offline failure-driven optimization, boosting performance on long-horizon benchmarks.Agents and evaluation · Aug 2026 · Unverified2
Agent Lightning v1.0: Towards Harnessed Agentic RLAgent Lightning v1.0 enables reproducible reinforcement learning for arbitrary agent harnesses, substantially improving coding-agent performance with minimal data and compute.Agents and evaluation · Aug 2026 · Unverified2
OasisKV: Scaling In-Decode KV Cache Beyond HBM with Lookahead Sparse PrefetchingOasisKV improves LLM inference throughput by storing full KV caches in lower memory tiers and prefetching only relevant entries into HBM using speculative-decoding lookahead predictions.Inference and efficiency · Aug 2026 · Unverified0
Topic
PaperCitations
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic CapabilitiesGemini 2.X model family, including Gemini 2.5 Pro and Flash, offers superior coding, reasoning, and multimodal understanding capabilities across a range of computational efficiencies.Google · Jul 2025 · Unverified4,134
DeepSeek-V3.2: Pushing the Frontier of Open Large Language ModelsDeepSeek-V3.2 introduces DeepSeek Sparse Attention and a scalable reinforcement learning framework, achieving superior reasoning and performance compared to GPT-5 and Gemini-3.0-Pro in complex reasoning tasks.DeepSeek · Dec 2025 · Unverified761
WebWatcher: Breaking New Frontier of Vision-Language Deep Research AgentWebWatcher, a multimodal agent with enhanced visual-language reasoning, outperforms existing agents in complex visual and textual information retrieval tasks using synthetic trajectories and reinforcement learning.Alibaba (Qwen) · Aug 2025 · Unverified117
Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic ReasoningWe present Nemotron 3 Nano 30B-A3B, a Mixture-of-Experts hybrid Mamba-Transformer language model.NVIDIA · Dec 2025 · Unverified81
AgentFold: Long-Horizon Web Agents with Proactive Context ManagementAgentFold, a novel proactive context management paradigm, enhances long-horizon task performance through dynamic context folding, achieving superior results on benchmarks compared to larger models and proprietary agents.Alibaba (Qwen) · Oct 2025 · Unverified77
Agent Learning via Early ExperienceEarly experience, using agent-generated interaction data without reward signals, improves policy effectiveness and generalization, serving as a bridge between imitation learning and reinforcement learning.Meta · Oct 2025 · Unverified64
About this paper
Authors
Yijia Fan, Zonglin Di, Zimo Wen and 8 more
arXiv
2606.29538 · PDF
Venue
arXiv.org
Citations
0, 0 influential · Semantic Scholar
Upvotes
141 · Hugging Face
Code
github.com/microsoft/Resource2Skill
Lab
Microsoft · on Companies · on Acquisitions · on Quarterly · on Paydays · on Releases · on TechConf

Changes

What changed
Influential citationsfirst count: 0Sep 25, 2026
Citationsfirst count: 0Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

New papers by email

Monday afternoons, only in weeks with new papers from the labs.

Double opt-in. Unsubscribe any time.