A new arXiv paper examines how AI agents that run long workflows through external tools can leave inconsistent state even when individual tool calls report success. It focuses on conditions such as retries, speculative execution, concurrency, and partial failures. The work frames these mismatches as anomalies at the boundary between the agent and the tools it calls.
A new arXiv paper examines what tool-augmented language models do when a tool call fails to return usable information, rather than focusing only on whether they reach the correct answer. The authors built a benchmark of 1,024 items spanning 16 internal systems to isolate this post-failure decision point. They find that agents tend to assert values their tools never returned instead of reporting the failure honestly.
A new arXiv preprint addresses the problem of transferring control between tool-using AI models without discarding work already done. The authors frame it as commitment-constrained residual completion, where a handoff must carry over accepted decisions, effects already produced, and outstanding obligations rather than restarting the task. The approach targets routing and cascade setups that cut costs by passing control between models.
A new arXiv paper empirically tests whether a general-purpose shell interface outperforms purpose-built tools when AI agents handle enterprise workflows. The authors note that shell-based agents perform well on coding tasks, but enterprise work also requires moving across applications and services and coordinating multiple steps. The study examines these trade-offs to identify which tool interface design suits digital worker agents.
A new arXiv paper introduces T-SMART, a method that attributes how tool-augmented language models arrive at answers for time-series question answering. The work targets a known weakness: LLMs handle poorly the case where numerical signals are turned into text and require explicit computation. It offers analysis at the level of internal mechanisms rather than just final outputs, though the abstract provided is truncated.
Researchers released ZebraArena, a simulated environment designed to isolate how well tool-using language models interleave step-by-step reasoning with external actions. The authors argue that existing benchmarks blur this measurement by introducing complicated environment dynamics, reliance on memorized facts, or contamination from training data. The environment is intended as a diagnostic tool rather than a general capability leaderboard.
A new arXiv preprint addresses the trade-off tool-using LLM agents face between answer quality and execution cost. It notes that fixed pipelines are reliable but rigid, while free-form multi-step approaches like ReAct can raise performance at added expense. The proposed method applies utility guidance to decide how agent orchestration should proceed.
A revised arXiv paper examines a failure mode in which large language models lose reasoning ability on out-of-distribution tasks as model depth grows. The authors report that giving models access to tools mitigates this depth-related collapse, and they note that OOD generalization remains hard to measure reliably.
A new arXiv paper introduces AutoTailor, a meta-agentic framework that automatically selects and adapts reusable capabilities for web agents. The work targets a common problem: tool collections discovered automatically tend to be large, redundant, and not well matched to what users actually need. By aligning capability choice with user demand, the authors aim to cut the overhead of low-level browser interaction.
A new arXiv paper investigates how giving large language models access to external tools they do not actually need changes the way they answer questions. The authors find that mere availability of tools can shift model behavior even when no external information is required. The work suggests tool provisioning should be matched to the task rather than offered by default.
A new arXiv paper addresses the challenge facing language-model agents that must operate with tool libraries containing thousands of possible interfaces. The authors define a 'tool menu' as a short, ordered set of tools presented to an agent before it acts, restricting it to calling only the listed options. This setup is framed as an execution prior intended to guide tool selection and make online agent behavior more manageable.
A new technical report on arXiv describes Palmyra x6, a large language model built to power agent-style workflows in business settings. The team started from a Mixture-of-Experts base model and applied a post-training technique called Anchored Supervised Fine-Tuning, using a small dataset of verified, synthetically generated tool-use examples. The release focuses on enabling the model to reliably call external tools across multi-step tasks.