VASO is a verification-guided self-evolution framework for LLM robot skill contracts that reaches 97.2% formal-specification compliance on Jackal and quadcopter tasks using under 100 samples.
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A two-level evolution framework automates the design of task-specific harnesses for AI agents by optimizing both per-task performance and a reusable meta-blueprint that enables adaptation to new domains without human engineering.
LLM agents trained with a task-success reward on self-generated knowledge can spontaneously explore and adapt to new environments without any rewards or instructions at inference, yielding 20% gains on web tasks and allowing a 14B model to beat Gemini-2.5-Flash.
A feedforward graph of heterogeneous frozen LLMs linked by linear projections in a shared latent space outperforms single models on ARC-Challenge, OpenBookQA, and MMLU using just 17.6M trainable parameters.
A new harness and benchmark, VeRO, lets coding agents be scored on how much they improve target agents under a fixed evaluation budget.
citing papers explorer
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VASO: Formally Verifiable Self-Evolving Skills for Physical AI Agents
VASO is a verification-guided self-evolution framework for LLM robot skill contracts that reaches 97.2% formal-specification compliance on Jackal and quadcopter tasks using under 100 samples.
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The Last Harness You'll Ever Build
A two-level evolution framework automates the design of task-specific harnesses for AI agents by optimizing both per-task performance and a reusable meta-blueprint that enables adaptation to new domains without human engineering.
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Training LLM Agents for Spontaneous, Reward-Free Self-Evolution via World Knowledge Exploration
LLM agents trained with a task-success reward on self-generated knowledge can spontaneously explore and adapt to new environments without any rewards or instructions at inference, yielding 20% gains on web tasks and allowing a 14B model to beat Gemini-2.5-Flash.
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Dead Weights, Live Signals: Feedforward Graphs of Frozen Language Models
A feedforward graph of heterogeneous frozen LLMs linked by linear projections in a shared latent space outperforms single models on ARC-Challenge, OpenBookQA, and MMLU using just 17.6M trainable parameters.
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VeRO: A Harness for Agents to Optimize Agents
A new harness and benchmark, VeRO, lets coding agents be scored on how much they improve target agents under a fixed evaluation budget.