Contract2Tool learns normalized symbolic contracts from tool metadata and traces to support causal filtering in LLM agents, reaching 0.980 downstream success versus 0.990 with gold contracts.
ToolScope: Enhancing LLM Agent Tool Use through Tool Merging and Context-Aware Filtering
5 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Large language model (LLM) agents rely on external tools to solve complex tasks, but real-world toolsets often contain redundant tools with overlapping names and descriptions, introducing ambiguity and reducing selection accuracy. LLMs also face strict input context limits, preventing efficient consideration of large toolsets. To address these challenges, we propose ToolScope, which includes: (1) ToolScopeMerger with Auto-Correction to automatically audit and fix tool merges, reducing redundancy, and (2) ToolScopeRetriever to rank and select only the most relevant tools for each query, compressing toolsets to fit within context limits without sacrificing accuracy. Evaluations on three state-of-the-art LLMs and three open-source tool-use benchmarks show gains of 8.38% to 38.6% in tool selection accuracy, demonstrating ToolScope's effectiveness in enhancing LLM tool use.
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2026 5roles
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background 1representative citing papers
CMTF is a causal filtering method that reduces tool exposure to one per step while matching baseline success rates and cutting token use by ~90% in 102-task benchmarks with 100 tools.
EvoSOP enables LLM agents to self-evolve by iteratively synthesizing atomic tool actions into reusable Standard Operating Procedures, improving success rates and reducing reasoning rounds on ACEBench and Tau2Bench.
LLM agent progress depends on externalizing cognitive functions into memory, skills, protocols, and harness engineering that coordinates them reliably.
The Workload-Router-Pool architecture is a 3D framework for LLM inference optimization that synthesizes prior vLLM work into a 3x3 interaction matrix and proposes 21 research directions at the intersections.
citing papers explorer
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Contract2Tool: Learning Preconditions and Effects for Reliable Tool-Augmented LLM Agents
Contract2Tool learns normalized symbolic contracts from tool metadata and traces to support causal filtering in LLM agents, reaching 0.980 downstream success versus 0.990 with gold contracts.
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ToolChoiceConfusion: Causal Minimal Tool Filtering for Reliable LLM Agents
CMTF is a causal filtering method that reduces tool exposure to one per step while matching baseline success rates and cutting token use by ~90% in 102-task benchmarks with 100 tools.
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From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents
EvoSOP enables LLM agents to self-evolve by iteratively synthesizing atomic tool actions into reusable Standard Operating Procedures, improving success rates and reducing reasoning rounds on ACEBench and Tau2Bench.
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Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering
LLM agent progress depends on externalizing cognitive functions into memory, skills, protocols, and harness engineering that coordinates them reliably.
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The Workload-Router-Pool Architecture for LLM Inference Optimization: A Vision Paper from the vLLM Semantic Router Project
The Workload-Router-Pool architecture is a 3D framework for LLM inference optimization that synthesizes prior vLLM work into a 3x3 interaction matrix and proposes 21 research directions at the intersections.