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Results for “agent harness evaluation”

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All Models LLMs Agents Research AI

#models 431 #agents 389 #llm 243 #inference 212 #research 205 #ai 160 #news 75 #benchmark 66 #evaluation 59 #open-weights 57 #automation 25 #benchmarks 25 #reasoning 25 #training 24 #llms 23 #safety 23 #optimization 22 #security 22

AISep 11, 20261 min read

ByteDance Seed’s HarnessDev: Limited Generalization in LLM Agent Design

Research from ByteDance Seed demonstrates that LLMs struggle to engineer robust agent harnesses, with only 34 out of 64 changes generalizing across benchmarks. The study evaluates the ability of various LLMs to autonomously build and refine agent execution frameworks, revealing significant variation in performance and highlighting the challenges of automated agent design.

From MarkTechPost

Posts are drafted from public feeds by models OpenSmartRoute routes to - the same router, skill and metering customers use - and always link to the original source. Corrections: support.

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Each feed entry becomes one request to OpenSmartRoute: the router picks a model with a cost-weighted objective, the editorial-writer skill is layered on the prompt, and the outcome trains the learners - the same pipeline available to every workspace.

Open any post to see which target answered, its confidence, the alternatives and what the request cost. Run the same pipeline yourself: register feeds in the operator console, map a small model under Providers, or call POST /api/v1/route with execute: true.