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Summary
Wrap the query router (#04) in a conversational, multi-turn agent. Today the engine answers a single POST /ask. Phase 2
makes it a chatbot: it keeps conversation state, can decompose a compound ...
enhancement
phase-2
NikolaiSachok
Summary
The core design insight is that questions over the corpus split into two classes: semantic retrieval (embed → top-k) and
structured aggregation/intersection (GROUP BY / COUNT / set-intersection ...
enhancement
phase-2
Sub-issue #5a — utils/taskEvents.js salah penempatan
Status: pending Commit: refactor(server): #5a move taskEvents from utils/ to services/
Masalah
File utils/taskEvents.js berada di direktori utils/ ...
ziadbwdn
Summary
The repo s leak gates were built for text. Adding images (#01, #02) opens leak surfaces a text scan cannot see:
- EXIF / embedded metadata — GPS coordinates, device serials, author names, software ...
phase-1.5
security
Hotfix bug report from owner.
Symptom
After dragging a variadic block such as DataRouter onto the canvas, clicking the canvas immediately can show:
Workflow validation failed: Node datarouter_block-... ...
zjzcpj
Summary
Icons and other named visual assets are often referenced from a project s manifest / descriptor rather than reliably
placed at a fixed path. A naive directory walk both misses icons that live ...
enhancement
phase-1.5
Summary
The engine currently indexes only text. A heterogeneous, multi-format corpus also carries images (rendered screens,
illustrations, icons). These are invisible to a text-only retriever, so any ...
enhancement
phase-1.5
Context
evaluate_response (reflection.py) resolves the judge model as reflection.model → else config.default_model → else legacy
resolved. In practice reflection.model is unset, so the reflection judge ...
core
enhancement
lmorchard
Summary
/api/chat is a public, unauthenticated proxy to Anthropic. If ANTHROPIC_API_KEY is configured as the README instructs
for production AI support, any caller can send arbitrary chat payloads through ...
tg12
テーマ: 日本株市場におけるコーディング・金融数理を用いた分析
日付: 2026-06-18
未解決の課題
- 深層学習クロスセクション予測モデル（DNN・QTCNN等）のアウトオブサンプル性能を、東証固有の呼値制約・市場インパクト・取引コストを明示的に組み込んだ「実装可能アルファ」ベースで評価した再現可能ベンチマークが日本市場では未整備であり、予測精度と実現収益のギャップが未解明のまま残る。 ...
auto-research
domain:finance
related-work
github-actions[bot]
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