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Reduce agent execution steps by 35% and latency by parallelizing sequential tool calls through task dependency graphs (DAGs). Use when deploying information-retrieval agents where tool execution ordering is flexible.
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Convert arXiv papers into ready-to-use agent skills using category-aware extraction. First classifies the paper into one or more of 11 research categories, then applies a specialized extraction pipeline for each category — because different types of papers produce different types of usable knowledge. A single paper can yield multiple skills if it spans categories. Use this skill whenever the user wants to turn a paper into a skill, extract practical techniques from research, build a skill library from papers, convert arXiv papers into reusable agent instructions, or batch-process multiple papers into skills. Also trigger when someone asks about extracting actionable knowledge from papers, making research practical for LLM agents, or systematically converting academic contributions into structured agent capabilities.
Establish regret bounds for behavior cloning with discretized actions combining statistical error and quantization error terms. Prove smoothness requirements for safe quantizer design, show that learning-based quantizers fail these requirements, and propose model-based augmentation to reduce error dependence from H² to H.
Dynamically allocate LoRA ranks per-layer during fine-tuning instead of using fixed uniform ranks. Learn optimal rank for each layer and subject via variational framework with discretized exponential distribution, reducing memory footprint while maintaining fidelity and text-alignment.
| name | flash-searcher-dag-parallel-agents |
| title | Flash-Searcher: DAG-Based Parallel Execution for LLM Web Agents |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2509.25301 |
| keywords | ["agent-orchestration","parallel-execution","DAG","web-agents","efficiency"] |
| description | Reduce agent execution steps by 35% and latency by parallelizing sequential tool calls through task dependency graphs (DAGs). Use when deploying information-retrieval agents where tool execution ordering is flexible. |
Flash-Searcher replaces sequential agent reasoning with concurrent subtask execution via dependency-aware directed acyclic graphs (DAGs). This framework decomposes complex agent tasks into independent execution paths while maintaining logical coherence, reducing steps by 35% and improving overall latency.
Setup DAG-based agent execution framework:
# Initialize DAG-based parallel agent executor
from flash_searcher import DAGAgentExecutor, DependencyAnalyzer
executor = DAGAgentExecutor(
model=your_llm,
max_parallel_tasks=4,
task_timeout=30,
execution_strategy="dynamic"
)
# Create dependency analyzer for task decomposition
analyzer = DependencyAnalyzer(
llm=your_llm,
tool_catalog=web_tools
)
Execute task decomposition and parallel execution:
# Decompose task into DAG structure
task = "Find the top 3 restaurants in NYC with highest ratings, then check their hours"
dag = analyzer.decompose(
task=task,
tools=["search", "get_info", "check_hours"]
)
# DAG structure shows parallelizable stages:
# Stage 1: search(restaurant_query) [parallel with start]
# Stage 2: get_info(restaurant_results) [parallel with start]
# Stage 3: check_hours(hours_info) [depends on Stage 2]
# Execute with parallelization
results = executor.execute(
dag=dag,
max_parallel=4,
dynamic_optimization=True
)
# Return integrated results (67.7% success rate on BrowseComp)
()
When to use Flash-Searcher:
When NOT to use:
Hyperparameters:
The paper provides 3,354 curated task decomposition examples enabling:
The framework deliberately omits code execution tools due to sequentiality requirements. Mathematical reasoning performance could improve with appropriate computational tools but accepts latency tradeoff.
DAG decomposition enables two complementary benefits:
Extends prior work on parallel decoding and multi-agent orchestration systems.