CL-Bench is the first expert-validated benchmark for continual learning in frontier LLMs across six real-world domains, showing limited gains and that naive in-context learning outperforms dedicated memory systems.
Gao et al
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Self-evolving LLM agents exhibit capability erosion under continual adaptation, which Capability-Preserving Evolution mitigates by raising retained simple-task performance from 41.8% to 52.8% in workflow evolution under GPT-5.1.
AgentCL constructs controlled task streams with intentional reusability and introduces MemProbe to evaluate non-parametric memory designs for continual learning in language agents across coding, research, and reasoning tasks.
Agentic AI systems are shifting software engineering from line-level code generation to delegated repository-scale execution under supervision, with SWE-bench performance rising from 1.96% to 78.4% and productivity gains of 13.6-55.8%.
citing papers explorer
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Continual Learning Bench: Evaluating Frontier AI Systems in Real-World Stateful Environments
CL-Bench is the first expert-validated benchmark for continual learning in frontier LLMs across six real-world domains, showing limited gains and that naive in-context learning outperforms dedicated memory systems.
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Do Self-Evolving Agents Forget? Capability Degradation and Preservation in Lifelong LLM Agent Adaptation
Self-evolving LLM agents exhibit capability erosion under continual adaptation, which Capability-Preserving Evolution mitigates by raising retained simple-task performance from 41.8% to 52.8% in workflow evolution under GPT-5.1.
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AgentCL: Toward Rigorous Evaluation of Continual Learning in Language Agents
AgentCL constructs controlled task streams with intentional reusability and introduces MemProbe to evaluate non-parametric memory designs for continual learning in language agents across coding, research, and reasoning tasks.
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Agentic AI in the Software Development Lifecycle: Architecture, Empirical Evidence, and the Reshaping of Software Engineering
Agentic AI systems are shifting software engineering from line-level code generation to delegated repository-scale execution under supervision, with SWE-bench performance rising from 1.96% to 78.4% and productivity gains of 13.6-55.8%.