Solving Context Window Overflow in AI Agents

• URL: http://arxiv.org/abs/2511.22729v1
• Date: Thu, 27 Nov 2025 19:22:20 GMT
• Title: Solving Context Window Overflow in AI Agents
• Authors: Anton Bulle Labate, Valesca Moura de Sousa, Sandro Rama Fiorini, Leonardo Guerreiro Azevedo, Raphael Melo Thiago, Viviane Torres da Silva,
• Abstract summary: Large Language Models (LLMs) have become increasingly capable of interacting with external tools, granting access to specialized knowledge beyond their training data.<n>Existing solutions such as truncation or summarization fail to preserve complete outputs, making them unsuitable for work requiring the full data.<n>This paper introduces a method that enables LLMs to process and utilize tool responses of arbitrary length without loss of information.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have become increasingly capable of interacting with external tools, granting access to specialized knowledge beyond their training data - critical in dynamic, knowledge-intensive domains such as Chemistry and Materials Science. However, large tool outputs can overflow the LLMs' context window, preventing task completion. Existing solutions such as truncation or summarization fail to preserve complete outputs, making them unsuitable for workflows requiring the full data. This work introduces a method that enables LLMs to process and utilize tool responses of arbitrary length without loss of information. By shifting the model's interaction from raw data to memory pointers, the method preserves tool functionality, allows seamless integration into agentic workflows, and reduces token usage and execution time. The proposed method is validated on a real-world Materials Science application that cannot be executed with conventional workflows, and its effectiveness is demonstrated via a comparative analysis where both methods succeed. In this experiment, the proposed approach consumed approximately seven times fewer tokens than the traditional workflow.

Related papers

• Ontology-aligned structuring and reuse of multimodal materials data and workflows towards automatic reproduction [1.4658400971135652]
  Existing text-mining approaches are insufficient to extract complete computational with associated parameters.<n>A large language model (LLM)-assisted framework is introduced for the automated extraction and structuring of computational density from the literature.<n>The framework provides a foundation for organizing and contextualizing published results in a semantically interoperable form, thereby improving transparency and reusability of computational materials data.
  arXiv  Detail & Related papers  (2026-01-18T20:51:23Z)
• LLM Flow Processes for Text-Conditioned Regression [4.196805115026664]
  Large Language Models (LLMs) are trained on giant corpora including varied real-world regression datasets alongside descriptions and metadata.<n>Recent work has extended this to regression tasks and is able to leverage such prior knowledge and metadata, achieving surprisingly good performance.<n>Here we introduce a general method for sampling from a product-of-experts of a diffusion or flow matching model and an expert' with binned probability density.
  arXiv  Detail & Related papers  (2026-01-05T21:20:38Z)
• LLM Agents for Interactive Workflow Provenance: Reference Architecture and Evaluation Methodology [3.470217255779291]
  We introduce an evaluation methodology, reference architecture, and open-source implementation that leverages interactive Large Language Model (LLM) agents for runtime data analysis.<n>Our approach uses a lightweight, metadata-driven design that translates natural language into structured provenance queries.<n> Evaluations across LLaMA, GPT, Gemini, and Claude, covering diverse query classes and a real-world chemistry workflow, show that modular design, prompt tuning, and Retrieval-Augmented Generation (RAG) enable accurate and insightful agent responses.
  arXiv  Detail & Related papers  (2025-09-17T13:51:29Z)
• Advancing SLM Tool-Use Capability using Reinforcement Learning [0.0]
  The ability to use tools effectively has become a defining feature of Large Language Models (LLMs), allowing them to access external data and internal resources.<n>Small Language Models (SLMs) face challenges in accurately integrating tool use, especially in resource-constrained settings.<n>This study investigates how Reinforcement Learning, specifically Group Relative Policy Optimization, can enhance the tool-use of SLMs.
  arXiv  Detail & Related papers  (2025-09-03T07:41:14Z)
• DWIM: Towards Tool-aware Visual Reasoning via Discrepancy-aware Workflow Generation & Instruct-Masking Tuning [57.285435980459205]
  compositional visual reasoning approaches have shown promise as more effective strategies than end-to-end VR methods.<n>We propose DWIM: Discrepancy-aware training generation, which assesses tool usage and extracts more viable for training.<n>Instruct-Masking fine-tuning, which guides the model to only clone effective actions, enabling the generation of more practical solutions.
  arXiv  Detail & Related papers  (2025-03-25T01:57:59Z)
• Learning Task Representations from In-Context Learning [67.66042137487287]
  Large language models (LLMs) have demonstrated remarkable proficiency in in-context learning (ICL)<n>We introduce an automated formulation for encoding task information in ICL prompts as a function of attention heads.<n>The proposed method successfully extracts task-specific information from in-context demonstrations and excels in both text and regression tasks.
  arXiv  Detail & Related papers  (2025-02-08T00:16:44Z)
• TL-Training: A Task-Feature-Based Framework for Training Large Language Models in Tool Use [72.32614703504122]
  Large language models (LLMs) achieve remarkable advancements by leveraging tools to interact with environments.<n>Standard supervised fine-tuning approach, which relies on large-scale datasets, often overlooks task-specific characteristics in tool use.<n>We propose TL-Training, a task-feature-based framework that mitigates the effects of suboptimal training data.
  arXiv  Detail & Related papers  (2024-12-20T02:21:36Z)
• AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning [93.96463520716759]
  Large language model (LLM) agents have demonstrated impressive capabilities in utilizing external tools and knowledge to boost accuracy and hallucinations. Here, we introduce AvaTaR, a novel and automated framework that optimize an LLM agent to effectively leverage provided tools, improving performance on a given task.
  arXiv  Detail & Related papers  (2024-06-17T04:20:02Z)
• Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents [56.822238860147024]
  Augmenting large language models with external tools has emerged as a promising approach to extend their utility.<n>Previous methods manually parse tool documentation and create in-context demonstrations, transforming tools into structured formats for LLMs to use in their step-by-step reasoning.<n>We propose AutoTools, a framework that enables LLMs to automate the tool-use workflow.
  arXiv  Detail & Related papers  (2024-05-26T11:40:58Z)
• Towards Completeness-Oriented Tool Retrieval for Large Language Models [60.733557487886635]
  Real-world systems often incorporate a wide array of tools, making it impractical to input all tools into Large Language Models. Existing tool retrieval methods primarily focus on semantic matching between user queries and tool descriptions. We propose a novel modelagnostic COllaborative Learning-based Tool Retrieval approach, COLT, which captures not only the semantic similarities between user queries and tool descriptions but also takes into account the collaborative information of tools.
  arXiv  Detail & Related papers  (2024-05-25T06:41:23Z)
• Efficient Tool Use with Chain-of-Abstraction Reasoning [63.08202389132155]
  Large language models (LLMs) need to ground their reasoning to real-world knowledge.<n>There remains challenges for fine-tuning LLM agents to invoke tools in multi-step reasoning problems.<n>We propose a new method for LLMs to better leverage tools in multi-step reasoning.
  arXiv  Detail & Related papers  (2024-01-30T21:53:30Z)
• O3D: Offline Data-driven Discovery and Distillation for Sequential Decision-Making with Large Language Models [16.91329676173649]
  Offline Data-driven Discovery and Distillation (O3D) is proposed to improve large language models (LLMs) O3D automatically discovers reusable skills and distills generalizable knowledge across multiple tasks based on offline interaction data. Empirical results under two interactive decision-making benchmarks (ALFWorld and WebShop) verify that O3D can notably enhance the decision-making capabilities of LLMs.
  arXiv  Detail & Related papers  (2023-10-22T20:28:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.