The paper introduces FRESH, a Failure-aware Retrieval framework that uses Experience-Structured Heterogeneous graphs to transform past successes and failures into structured external memory for tool‑using agents. By explicitly modeling dependencies among tasks, actions, errors, repairs, and execution conditions, FRESH enables frozen language models to reuse reliable strategies, avoid recurring failures, and make safer decisions in stateful tool interactions. Experiments on τ‑Bench and AppWorld with multiple open‑source models demonstrate that FRESH consistently improves task success and tool‑use reliability compared to no‑memory agents and other memory‑based baselines.
arXiv:2608. 02650v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external tools to complete complex real-world tasks.
The paper surveys how graphs are increasingly used in large language model systems to support reasoning, knowledge grounding, retrieval, memory, execution, and multi‑agent coordination. It introduces an operation‑level taxonomy that distinguishes the function of each graph operation, the component that computes it, and how its output is subsequently used. The authors compare graph observations with external computations, analyze memory updates, and examine execution dependencies, then propose an evaluation framework that organizes benchmarks, diagnostics, and system costs, concluding with open challenges in adaptive computation, memory maintenance, and dependency‑aware recovery.
arXiv:2608. 11248v1 Announce Type: new Abstract: Long-term memory is essential for language agents operating across extended interactions and evolving tasks.
AnyAct introduces a universal action layer that consolidates diverse tool capabilities into a self‑evolving action space for AI agents operating in open‑world environments. It tackles the scale dilemma, tool non‑stationarity, and heterogeneous feedback by using hierarchical progressive retrieval and test‑time reliability evolution, while a heterogeneous observation grounding module unifies multi‑modal feedback. Evaluations on LiveMCPBench and the newly created OSMCP benchmark show state‑of‑the‑art performance, with significant gains in task success rate and reduced execution steps, especially for models with limited native capabilities.
arXiv:2607. 13884v1 Announce Type: new Abstract: Large Language Model (LLM) agents have shown remarkable capabilities in autonomous decision-making by generating sequential trajectories of states, actions, and observations.
The paper investigates the effectiveness of tool graph memory—a component that records past tool transitions to guide future tool selection—in large language model agents performing multi‑turn tasks. Across nine models and two benchmarks, the authors find that tool graph memory rarely yields significant performance gains, often incurs more than twice the number of LLM calls, and its guidance is frequently incorrect. They observe that random or no guidance can achieve similar improvements, suggesting the benefits arise from additional computation rather than the memory itself, and that storing experiences as textual insights outperforms the graph approach with minimal extra calls.
Bench2Robust is a framework that augments existing tool‑using LLM benchmarks with scenario‑controlled solvability, adding episodes that require retrying the original path, switching to a fallback tool, or recognizing that no viable path remains. It separates recovery‑policy quality from randomness in failure persistence, and across 7 models and 10 task slices, 69 of 70 model‑subset pairs degrade under tool failures by up to 46.7 percentage points. The authors introduce Bayesian Tool Memory (BTM) for structured runtime recovery context and use reinforcement learning (RL) for learned recovery behavior, achieving up to a 20.7‑point improvement over the base model without sacrificing clean performance.
The paper introduces Experience Memory Graph (EMG), a framework that transforms agent failure recovery into a graph matching problem. By converting failed and successful trajectories into directed action decision graphs, EMG extracts common subgraphs and edit paths that reveal explicit correction strategies, storing them in a memory graph with intra‑task nodes and cross‑task edges. During testing, EMG retrieves relevant insights to guide the agent in a single attempt, improving performance on ALFWorld and ScienceWorld without extra reflection rounds.
arXiv:2607. 11126v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external tools served by shared providers and accessed by heterogeneous downstream agents.
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.
arXiv:2608. 10039v1 Announce Type: new Abstract: Agentic workflows have become an important abstraction for building reliable LLM-based automation systems by organizing large language models (LLMs), tools, and control logic into explicit execution structures.
The paper introduces RUPA, a trajectory‑level uncertainty quantification framework for large language model agents. RUPA models an agent’s execution as a directed graph of reasoning states, tool interactions, and environment feedback, then propagates uncertainty across this graph to capture long‑range dependencies. Experiments on benchmarks such as τ‑2, Terminal‑Bench‑2, and GAIA show that RUPA outperforms existing methods, enabling earlier failure detection and more reliable agent execution.