#context-efficiency News & Analysis

6 articles tagged with #context-efficiency. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles

AIBullisharXiv – CS AI · Jun 107/10

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ActiveMem: Distributed Active Memory for Long-Horizon LLM Reasoning

Researchers introduce ActiveMem, a distributed memory framework that decouples storage from reasoning in large language models, enabling agents to handle longer tasks without context overload. The system separates executive planning from memory management—inspired by human brain architecture—and demonstrates state-of-the-art performance on complex reasoning benchmarks while reducing computational overhead.

AIBullisharXiv – CS AI · Jun 87/10

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NTILC: Neural Tool Invocation via Learned Compression

Researchers introduce NTILC, a neural framework that replaces in-context tool registry lookups with learned latent retrieval for language model agents. The approach reduces context token consumption by over 95% and inference latency by up to 74% while maintaining selection accuracy through signature-aware optimization.

AIBullisharXiv – CS AI · Jun 57/10

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Beyond Semantic Organization: Memory as Execution State Management for Long-Horizon Agents

Researchers introduce MAGE, a novel memory management system for LLM-based agents that organizes task histories as hierarchical state trees rather than semantic similarity clusters. The approach achieves 7.8-20.4 percentage point improvements in task success rates while reducing token consumption by 55.1% on long-horizon tasks with interdependent decisions.

AIBullisharXiv – CS AI · May 97/10

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From History to State: Constant-Context Skill Learning for LLM Agents

Researchers propose constant-context skill learning, a framework enabling LLM agents to learn reusable task procedures as lightweight modules rather than storing long prompts in memory. The approach reduces token usage per inference by 2-7x while maintaining or improving performance across multiple benchmark environments, addressing the privacy-capability tradeoff in agent deployment.

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AIBullisharXiv – CS AI · Jun 236/10

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AdaMem: Learning What to Remember for Personalized Long-Horizon LLM Agents

Researchers introduce AdaMem, an adaptive memory system for LLM agents that learns what information to retain based on individual user preferences rather than storing everything. The method achieves up to 9% QA accuracy improvement while reducing memory bloat, addressing practical constraints of inference costs and finite context windows in production systems.

AINeutralarXiv – CS AI · Apr 206/10

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Experience Compression Spectrum: Unifying Memory, Skills, and Rules in LLM Agents

Researchers propose the Experience Compression Spectrum, a unifying framework that reconciles two separate research communities studying LLM agent memory and skill discovery by positioning them along a single compression axis. The framework identifies a critical gap—no existing system supports adaptive cross-level compression—and reveals that memory systems and skill discovery communities operate in isolation despite solving overlapping problems.