Sculptor: Empowering LLMs with Cognitive Agency via Active Context Management

• URL: http://arxiv.org/abs/2508.04664v2
• Date: Sat, 27 Sep 2025 04:36:52 GMT
• Title: Sculptor: Empowering LLMs with Cognitive Agency via Active Context Management
• Authors: Mo Li, L. H. Xu, Qitai Tan, Long Ma, Ting Cao, Yunxin Liu,
• Abstract summary: Large Language Models (LLMs) suffer from significant performance degradation when processing long contexts due to proactive interference.<n>We introduce Sculptor, a framework that equips LLMs with three categories of tools: context fragmentation, summary, hide, and restore, and precise search.<n> Experimental evaluation on diverse long-context benchmarks demonstrates that Sculptor significantly improves performance even without specific training.
• Score: 15.059686456324853
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) suffer from significant performance degradation when processing long contexts due to proactive interference, where irrelevant information in earlier parts of the context disrupts reasoning and memory recall. While most research focuses on external memory systems to augment LLMs' capabilities, we propose a complementary approach: empowering LLMs with Active Context Management (ACM) tools to actively sculpt their internal working memory. We introduce Sculptor, a framework that equips LLMs with three categories of tools: (1) context fragmentation, (2) summary, hide, and restore, and (3) precise search. Our approach enables LLMs to proactively manage their attention and working memory, analogous to how humans selectively focus on relevant information while filtering out distractions. Experimental evaluation on diverse long-context benchmarks demonstrates that Sculptor significantly improves performance even without specific training, leveraging LLMs' inherent tool-calling and instruction-following capabilities. To further optimize these strategies, we introduce a novel dynamic context-aware reinforcement learning (RL) approach, advancing the training of an agent that actively modifies its own conversational history. By enabling Active Context Management, Sculptor not only mitigates proactive interference but also provides a cognitive foundation for more reliable reasoning across diverse long-context tasks-highlighting that explicit context-control strategies, rather than merely larger token windows, are key to robustness at scale.

Related papers

• From Experience to Strategy: Empowering LLM Agents with Trainable Graph Memory [48.22750809620306]
  Large Language Models (LLMs) based agents have demonstrated remarkable potential in autonomous task-solving.<n>In this paper, we introduce a novel agent-centric, trainable, multi-layered graph memory framework.<n>We show how context memory enhances the ability of LLMs to utilize information.
  arXiv  Detail & Related papers  (2025-11-11T03:36:33Z)
• Beyond RAG vs. Long-Context: Learning Distraction-Aware Retrieval for Efficient Knowledge Grounding [5.353135097018941]
  Retrieval-Augmented Generation (RAG) is a framework for grounding Large Language Models (LLMs) in external, up-to-date information.<n>We propose LDAR (Learning Distraction-Aware Retrieval), an adaptive retriever that learns to retrieve contexts in a way that mitigates interference from distracting passages.
  arXiv  Detail & Related papers  (2025-09-26T04:40:42Z)
• Feature Engineering for Agents: An Adaptive Cognitive Architecture for Interpretable ML Monitoring [2.1205272468688574]
  We propose a cognitive architecture for ML monitoring that applies feature engineering principles to agents based on Large Language Models.<n>Decision Procedure module simulates feature engineering through three key steps: Refactor, Break Down, and Compile.<n> Experiments using multiple LLMs demonstrate the efficacy of our approach, achieving significantly higher accuracy compared to various baselines.
  arXiv  Detail & Related papers  (2025-06-11T13:48:25Z)
• Active-O3: Empowering Multimodal Large Language Models with Active Perception via GRPO [63.140883026848286]
  Active vision refers to the process of actively selecting where and how to look in order to gather task-relevant information.<n>Recently, the use of Multimodal Large Language Models (MLLMs) as central planning and decision-making modules in robotic systems has gained extensive attention.
  arXiv  Detail & Related papers  (2025-05-27T17:29:31Z)
• Planning without Search: Refining Frontier LLMs with Offline Goal-Conditioned RL [62.984693936073974]
  Large language models (LLMs) excel in tasks like question answering and dialogue.<n>Complex tasks requiring interaction, such as negotiation and persuasion, require additional long-horizon reasoning and planning.<n>We propose a novel approach that uses goal-conditioned value functions to guide the reasoning of LLM agents.
  arXiv  Detail & Related papers  (2025-05-23T16:51:54Z)
• How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective [64.00022624183781]
  Large language models (LLMs) can assess relevance and support information retrieval (IR) tasks.<n>We investigate how different LLM modules contribute to relevance judgment through the lens of mechanistic interpretability.
  arXiv  Detail & Related papers  (2025-04-10T16:14:55Z)
• Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger [49.81945268343162]
  We propose MeCo, an adaptive decision-making strategy for external tool use.<n>MeCo quantifies metacognitive scores by capturing high-level cognitive signals in the representation space.<n>MeCo is fine-tuning-free and incurs minimal cost.
  arXiv  Detail & Related papers  (2025-02-18T15:45:01Z)
• Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search [57.28671084993782]
  Large language models (LLMs) have demonstrated remarkable reasoning capabilities across diverse domains.<n>Recent studies have shown that increasing test-time computation enhances LLMs' reasoning capabilities.<n>We propose a two-stage training paradigm: 1) a small-scale format tuning stage to internalize the COAT reasoning format and 2) a large-scale self-improvement stage leveraging reinforcement learning.
  arXiv  Detail & Related papers  (2025-02-04T17:26:58Z)
• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making [51.737762570776006]
  LLM-ACTR is a novel neuro-symbolic architecture that provides human-aligned and versatile decision-making. Our framework extracts and embeds knowledge of ACT-R's internal decision-making process as latent neural representations. Our experiments on novel Design for Manufacturing tasks show both improved task performance as well as improved grounded decision-making capability.
  arXiv  Detail & Related papers  (2024-08-17T11:49:53Z)
• Retrieval Meets Reasoning: Dynamic In-Context Editing for Long-Text Understanding [11.5386284281652]
  We introduce a novel approach that re-imagines information retrieval through dynamic in-context editing. By treating lengthy contexts as malleable external knowledge, our method interactively gathers and integrates relevant information. Experimental results demonstrate that our method effectively empowers context-limited LLMs to engage in multi-hop reasoning with improved performance.
  arXiv  Detail & Related papers  (2024-06-18T06:54:28Z)
• How Does the Textual Information Affect the Retrieval of Multimodal In-Context Learning? [11.374310255084753]
  We introduce a novel supervised MLLM-retriever MSIER that employs a neural network to select examples that enhance multimodal in-context learning efficiency. This approach is validated through extensive testing across three distinct tasks, demonstrating the method's effectiveness. This exploration paves the way for future advancements, highlighting the potential for refined in-context learning in MLLMs through the strategic use of multimodal data.
  arXiv  Detail & Related papers  (2024-04-19T13:05:37Z)
• LLM In-Context Recall is Prompt Dependent [0.0]
  A model's ability to do this significantly influences its practical efficacy and dependability in real-world applications. This study demonstrates that an LLM's recall capability is not only contingent upon the prompt's content but also may be compromised by biases in its training data.
  arXiv  Detail & Related papers  (2024-04-13T01:13:59Z)
• LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models [56.25156596019168]
  This paper introduces the LMRL-Gym benchmark for evaluating multi-turn RL for large language models (LLMs) Our benchmark consists of 8 different language tasks, which require multiple rounds of language interaction and cover a range of tasks in open-ended dialogue and text games.
  arXiv  Detail & Related papers  (2023-11-30T03:59:31Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)
• Pay Attention to What You Need [8.369701050186867]
  Large language models (LLMs) struggle with long-context comprehension.<n>We propose a method called Scaled ReAttention (SRA) to strengthen LLMs' ability to interpret and retrieve information.
  arXiv  Detail & Related papers  (2023-07-25T09:34:42Z)

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.