arXiv:2607. 05441v1 Announce Type: cross Abstract: Integrating external tools with Large Language Models (LLMs) has emerged as a promising paradigm for accomplishing complex tasks.
arXiv:2606. 12451v1 Announce Type: new Abstract: Large language models deployed as agents over large tool catalogs face a critical tool-retrieval bottleneck.
The pro-team at LLMs4OL 2026 presented a system for ontology learning that tackles both the End-to-End Flagship Task (Task A) and the Ontology Extension Reuse Task (Task B). Their approach uses an offline retrieval‑augmented few‑shot prompting pipeline with Qwen2.5‑14B‑Instruct and MiniLM‑L6‑v2 for retrieval, selecting top‑5 examples for Task A and top‑2 for Task B, and applies a left‑truncated context‑windowing strategy to keep task instructions in long prompts. For Task B, generated triples are filtered deterministically by a vocabulary constraint, keeping triples that involve at least one term from the closed vocabulary and removing duplicates of the initial ontology, achieving high scores in Semantic Graph Similarity, Term‑Typing F1, and Taxonomy Discovery F1, though no non‑taxonomic relations were extracted.
LIBRA is a framework that transforms a flat catalog of tools into an executable library by having a large language model propose contracts for tool invocations, reusable outputs, state changes, and success conditions. These proposed compositions are then tested in a sandbox, and only the relations that succeed at runtime are retained, eliminating the need for dependency annotations or task‑specific training. The approach improves one‑shot recall and NDCG on several benchmarks, and further enhances retrieval performance after multiple iterations.
The paper introduces DRAG, a query‑adaptive framework that jointly selects retriever and generator configurations for Retrieval‑Augmented Generation (RAG) systems. Two variants are presented: DRAG_QPP, a training‑free routing method using Query Performance Prediction and perplexity signals, and DRAG_SFT, a supervised approach that fine‑tunes an LLM to predict configurations. Experiments on three LLM families and four QA benchmarks show that DRAG_QPP matches strong static baselines while cutting inference latency, and DRAG_SFT consistently outperforms both static and training‑free adaptive baselines, demonstrating a better effectiveness‑efficiency trade‑off.
arXiv:2606. 03657v1 Announce Type: new Abstract: Large language models for code generation often need to use APIs that are absent from their pretraining data.
arXiv:2604.05087v4 Announce Type: replace Abstract: Generative large language models (LLMs) are increasingly used as inference-time components in retrieval pipelines, for tasks such as query rewritin...
UniToolCall introduces a unified framework for tool-use in large language model agents, standardizing toolset construction, dataset generation, and evaluation. The framework aggregates over 22,000 tools and creates a hybrid training corpus of more than 390,000 instances by combining ten public datasets with synthetically generated, structurally controlled trajectories. It models diverse interaction patterns—single‑hop vs. multi‑hop, single‑turn vs. multi‑turn, serial vs. parallel execution—and adds an Anchor Linkage mechanism to enforce cross‑turn dependencies, while converting seven public benchmarks into a common Query–Action–Observation–Answer format for fine‑grained evaluation.
arXiv:2606. 06566v1 Announce Type: cross Abstract: Agentic tool-calling language models depend on large registries of callable APIs, functions, and local actions.
The paper introduces CHAP, a personalized generative retrieval framework that aligns query semantics with item representations through a hierarchical semantic alignment module and models user behavior using both discrete Semantic IDs and continuous representations. It also proposes a Residual Cascading Generation mechanism to reduce inference latency by limiting the Transformer decoder to a single pass. Experiments on multiple datasets and online A/B tests show that CHAP outperforms existing methods, demonstrating its practical value.
The paper introduces a method to improve large language model agents that invoke enterprise APIs by embedding process and schema constraints into a typed property graph. By integrating OData metadata and BPMN reference processes, a hybrid retriever can identify correct endpoints and follow process edges, boosting Recall@3 for low lexical overlap queries and enabling more reliable multi-step service invocation. Experiments on 42 enterprise services and 81 multi-step queries demonstrate significant gains over baseline approaches, with a marked reduction in invalid API calls.
RATED (Reliability‑Aware Tool Retrieval via Execution Dynamics) is a retrieval framework that blends robust semantic matching with dynamic reliability adaptation for AI agents using external tools. It uses a frozen dual‑encoder with contrastive fine‑tuning for semantic relevance and a lightweight contextual bandit module that re‑ranks tool candidates based on real‑time execution feedback, allowing rapid adaptation to runtime failures without costly retraining. The EcoAgent benchmark, comprising 5,172 real‑world tools and over 210,000 query‑tool pairs, shows that RATED improves mean reciprocal rank by 52.2% over the ToolLLM API retriever and cuts fault selection from 86.2% to 2.9% during severe API outages, with negligible latency overhead.