dsh-tool-bandit-search
一个用于 DeepSeek Harness 的搜索工具，使用上下文老虎机（Thompson sampling）学习在快速搜索和深入搜索策略之间进行选择。
插件会安装到这里；不确定时保持 web。
npx -y @deepseek-ai/dsh plugin --profile web add github:siruignaw-sys/dsh-tool-bandit-search#089dd0d3e5b2b1686e66363905b8a393caa1d387Tool Bandit Search 以 dsh-tool-bandit-search 发布，当前版本为 0.1.0。Plugin Hub 会校验它的 manifest，并保存精确安装来源，便于复现安装结果。
A DeepSeek Harness plugin that replaces the standard web_search tool with a search tool that learns which search strategy to use through a contextual multi-armed bandit, instead of relying on a single hardcoded approach.
Every web search has a tradeoff: a fast, narrow query gets you an answer quickly, but a broader, multi-angle query gets you better coverage at the cost of latency. Hardcoding one strategy means always overpaying for simple questions or always underdelivering on complex ones. This plugin lets the tool discover, from real usage, which strategy tends to pay off — and keeps adapting as conditions change.
The search tool has two internal strategies ("arms"):
quick — a single search query, capped at 5 results. Fast, good for simple factual lookups.thorough — three query variants (the original plus two reframed angles) run in parallel and merged/deduplicated, capped at 10 results. Slower, better for open-ended or multi-perspective questions.On every call, the plugin uses Thompson sampling to pick an arm: each arm has a Beta(α, β) distribution representing its estimated reward, the plugin samples from both distributions, and whichever sample is higher gets used. This naturally balances exploration (trying the less-proven arm occasionally) against exploitation (favoring the arm that's performed better so far).
After the call, a continuous reward in [0, 1] is computed from two components, weighted equally:
That reward updates the chosen arm's Beta distribution (α += reward, β += 1 − reward), so the bandit's beliefs shift a little after every single call — no separate training phase, no manual tuning.
The model never sees the two arms directly. It just calls search(query); the plugin decides internally which strategy to run.
[bandit-search] arm=quick reward=1.000 durationMs=4393 resultCount=5 stats={"quick":{"alpha":2,"beta":1},"thorough":{"alpha":1,"beta":1}}
[bandit-search] arm=thorough reward=0.854 durationMs=8481 resultCount=10 stats={"quick":{"alpha":2,"beta":1},"thorough":{"alpha":1.85,"beta":1.15}}
[bandit-search] arm=quick reward=0.000 durationMs=5777 resultCount=0 stats={"quick":{"alpha":2,"beta":2},"thorough":{"alpha":1.85,"beta":1.15}}
Each log line shows which arm was picked, the reward it earned, and the running Beta parameters for both arms — you can watch the bandit's confidence shift in real time as it accumulates evidence.
dsh plugin --profile web add github:siruignaw-sys/dsh-tool-bandit-search
For local development against a cloned/edited copy instead:
dsh plugin --profile web add link:/absolute/path/to/dsh-tool-bandit-search
Either way, restart the Web UI (a fresh pnpm dsh web / dsh web, not just a new chat) after installing — bundle installs only take effect on the next boot, and the plugin's system-prompt instruction steering the model toward search over the built-in web_search tool only applies to sessions started after that.
Runs on top of dsh's native ctx.web search service — no separate API key needed beyond whatever search provider your dsh profile already has configured (e.g. dsh-web-search-deepseek).
ctx.storage (which dsh already exposes) is a natural next step.search calls per turn even when thorough is already broadening internally — the plugin optimizes strategy per call, not the model's own multi-call behavior.MIT