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    Adding capabilities can make AI agents worse at familiar tasks, EvoHarnessBench announcement says

    The authors introducing EvoHarnessBench say even today’s self-evolving methods cannot reliably adapt to new capabilities while retaining earlier competence.

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    TLDR

    Adding tools, skills and specialist agents can hurt an AI agent’s performance on tasks it could already solve, according to the authors introducing EvoHarnessBench. They say their results show that expanding this surrounding “harness” can undermine existing abilities—and that today’s self-evolving methods cannot reliably adapt to new capabilities while retaining earlier competence.

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    Combined views

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    4 Sources, first seen 19d ago

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    4 comments
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    34 reposts

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    4 Sources

    @JotyShafiq1/ We keep adding tools, skills, and specialist agents to powerful agentic systems whenever they seem useful. Each addition feels like an upgrade. But what if an evolving harness quietly makes the agent worse at tasks it could already solve? Our results show that it can. More surprisingly, even today’s self-evolving methods cannot reliably adapt to new capabilities while retaining earlier competence. Introducing 🎉EvoHarnessBench: Can Your Agents Keep Pace with an Evolving Harness?
    @mohitban47RT @JotyShafiq: 1/ We keep adding tools, skills, and specialist agents to powerful agentic systems whenever they seem useful. Each addition…
    @vaidehi_patil_Excited to share our co-led work on EvoHarnessBench, a benchmark for self-evolving agents! What happens when an agent’s environment keeps changing—not just because the tasks change, but because its capabilities do? We study this overlooked form of continual adaptation, where tools 🔧, skills 📚, and specialist agents 🤖 are progressively added to the harness while previously solved tasks remain in evaluation. We find that expanding the harness itself can induce forgetting, and that current self-evolving agents do not reliably recover from it.

    4 Sources

    @JotyShafiq1/ We keep adding tools, skills, and specialist agents to powerful agentic systems whenever they seem useful. Each addition feels like an upgrade. But what if an evolving harness quietly makes the agent worse at tasks it could already solve? Our results show that it can. More surprisingly, even today’s self-evolving methods cannot reliably adapt to new capabilities while retaining earlier competence. Introducing 🎉EvoHarnessBench: Can Your Agents Keep Pace with an Evolving Harness?
    @mohitban47RT @JotyShafiq: 1/ We keep adding tools, skills, and specialist agents to powerful agentic systems whenever they seem useful. Each addition…
    @vaidehi_patil_Excited to share our co-led work on EvoHarnessBench, a benchmark for self-evolving agents! What happens when an agent’s environment keeps changing—not just because the tasks change, but because its capabilities do? We study this overlooked form of continual adaptation, where tools 🔧, skills 📚, and specialist agents 🤖 are progressively added to the harness while previously solved tasks remain in evaluation. We find that expanding the harness itself can induce forgetting, and that current self-evolving agents do not reliably recover from it.