LOCA-bench: Benchmarking Language Agents Under Controllable and Extreme Context Growth
#LOCA-bench #Large Language Models #context rot #AI agents #benchmarking #arXiv #context window
📌 Key Takeaways
- LOCA-bench introduces a methodology for evaluating AI agents as context grows uncontrollably.
- The study highlights the problem of 'context rot,' where LLM performance drops over time.
- Unlike traditional benchmarks, this focuses on multi-step exploration rather than simple information retrieval.
- The framework aims to bridge the gap between static testing and real-world AI agent deployments.
📖 Full Retelling
🏷️ Themes
Artificial Intelligence, Machine Learning, Technology
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
AI agent
Systems that perform tasks without human intervention
In the context of generative artificial intelligence, AI agents (also referred to as compound AI systems or agentic AI) are a class of intelligent agents distinguished by their ability to operate autonomously in complex environments. Agentic AI tools prioritize decision-making over content creation ...
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Connections for Large language model:
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- 🌐 Generative artificial intelligence (2 shared articles)
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- 🌐 Robustness (1 shared articles)
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- 👤 Clinical Practice (1 shared articles)
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📄 Original Source Content
arXiv:2602.07962v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly capable of carrying out long-running, real-world tasks. However, as the amount of context grows, their reliability often deteriorates, a phenomenon known as "context rot". Existing long-context benchmarks primarily focus on single-step settings that evaluate a model's ability to retrieve information from a long snippet. In realistic scenarios, however, LLMs often need to act as agents that explore envi