交易 bot 证据闸门：把 Jev 放在检索与生成之间
用 Jev 对 RAG 检索到的段落做结构化判断——是否支持论点、是否矛盾、是否同一标的，过滤噪声后再决定下单或等待，并给出三个可搭建的仓库。
this feels like a f*cking cheat code a trading bot can cite 10 sources and still have zero evidence for the trade RAG finds the closest passages. the closest passage can still be last quarter's guidance or the sixth copy of the same headline feed that straight to the LLM and a search match can become your reason to buy you just turned similarity into conviction put Jev between retrieval and generation does this passage actually support the claim? does it contradict the setup? is it only talking about the same ticker? Jev returns structured judgments. code keeps useful evidence, preserves contradictions, and drops the noise a second check asks whether the surviving context is enough to answer if it isn't: more research or WAIT 3 repos to build the stack: -> FinMem: layered memory for an LLM trading agent https://github.com/pipiku915/FinMem-LLM-StockTrading -> jev-reranker: Jev reranking and evidence filtering before generation https://github.com/hotchpotch/jev-reranker -> QuantDinger: paper/live trading with a Jev decision gate before entry orders https://github.com/OpenByteInc/QuantDinger $0.042 per million input tokens. output is free 10,000 total input tokens across the Jev checks = $0.00042 data, embeddings and the writing model have their own bill the catch: an evidence score is not a win rate. keep the rejected candidates and paper trade the full loop. configure failed checks to block new entries while keeping exits available "let RAG find the context. let Jev check the evidence. let code decide whether an order is allowed." bookmark this before your bot mistakes a citation for an edge
这条是引用着另一条写的：x.com/mellon0x/status/2106107298418512123