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Microsoft Research introduces EvoLib, a framework that enables LLMs to continually learn from their own experience during inference by extracting reusable skills and insights, without model updates or external labels.
ThetaEvolve is an open-source framework that extends AlphaEvolve to enable small LLMs like DeepSeek-R1-0528-Qwen3-8B to achieve new best-known bounds on open problems through test-time reinforcement learning, accelerating AI self-evolution.
Continual Harness is a reset-free, self-improving agentic harness that achieves 20.54% on ARC-AGI-3 at a cost of $774 by storing memories, reusing skills, and refining its prompt, outperforming prior baselines like Hermes and OpenClaw with greater efficiency.
The author shares excitement about three continual learning methods: SEAL models that self-adapt, test-time learning, and lifelong model editing, predicting true continual learning by 2027–2028 that will create a feedback loop toward artificial superintelligence.
This paper investigates many-shot chain-of-thought in-context learning for reasoning tasks, revealing that standard scaling rules do not transfer and proposing Curvilinear Demonstration Selection (CDS) for improved ordering, achieving up to 5.42 percentage-point gain.
The paper introduces PACEvolve++, a reinforcement learning framework that improves test-time policy adaptation for evolutionary search agents by decoupling hypothesis generation from execution.
EvoTest introduces J-TTL, a benchmark for measuring agent test-time learning capabilities, and proposes an evolutionary framework where an Actor Agent plays games while an Evolver Agent iteratively improves the system's prompts, memory, and hyperparameters without fine-tuning. The method demonstrates superior performance compared to reflection and memory-based baselines on complex text-based games.