THU-Agent/Coca-cola: Large language model (LLM) agents typically append raw tool outputs to their context. Over multi-step interactions, long and noisy observations incur substantial context overhead and dilute the agent's attention to task-relevant information. Existing token pruning and summariz...
Pillar = mean of 3 scaled values = 2.5.
Awaiting first reading — these signals apply to this agent and will be ingested on the next tier tick: Product Hunt upvotes, Docker Hub pulls, Crates.io downloads (90d), Tech-news mentions (30d)
Not applicable — this agent doesn't have the prerequisite (no GitHub repo, no HF mirror, etc.) for these signals to ever apply: HF downloads (30d), npm weekly installs, PyPI monthly installs
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