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Headlines · 3

  1. Xuanjing Combines Feature-Based Query Expansion With Retrieval Filters

    A technical report describes how Xuanjing improves RAG retrieval for small, specialized fortune-telling corpora by combining feature-based query expansion, module-level metadata filtering and similarity thresholds. Extracted features are appended to the user query before Chroma semantic retrieval, with core features repeated to emphasize them. The report also corrects an ordering bug in which later deduplication removed the intended repetition; the revised example is presented as a possible pull request and was not applied directly to the repository. The comparison table is illustrative, and the report says an offline comparison found combining the original query with the feature query more stable than using features alone, while a small-traffic online A/B test remains pending.

    掘金 · 人工智能 · 🔥 3
  2. Agent-written RAG summaries can drop access-control metadata

    A developer using Chroma and LlamaIndex reports that background agents can save derived summaries without upstream allowed_groups or classification tags, making private source data retrievable through shared memory. The author’s offline sentinel-eval harness found no leaks in direct RAG queries, while two of three agent-memory synthesis tasks exposed six of 18 restricted canaries; the results are self-reported.

    r/LocalLLaMA · 🔥 2
  3. Million-Token Context Windows: Effective Limits Still Differ

    A Juejin AI analysis says Gemini 3.1 Pro, GPT-5.5, Claude Opus 4.7 Adaptive, and DeepSeek V4 Pro each advertise roughly 1 million tokens of context, while their output ceilings range from 64,000 to 384,000 tokens. BenchLM data cited by the report places Gemini 3.1 Pro strongest for effective context between 500,000 and 1 million tokens, although GPT-5.5 leads the LongBench v2 and MRCRv2 scores. Community reports cited in the article also describe reliability declining as inputs grow, while Codex reportedly limits GPT-5.6 to a default 272,000-token window.

    掘金 · 人工智能 · 🔥 1

Experience and discussion from the community

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mempalace• Pinned

mempalace: A local-first AI memory system that stores conversation history verbatim and retrieves it with semantic search.

What problem it solves AI coding sessions and project context can be difficult to retain and recover across tools and sessions. MemPalace addresses this by storing source content locally without summarizing or paraphrasing it, then making that content searchable through a structured, scope-aware retrieval system. Key features Verbatim semantic memory — Conversation history and project content are …

github.comhttps://github.com/MemPalace/mempalace
claude-mem• Pinned

claude-mem: Persistent memory compression system built for Claude Code.

What problem it solves Agent sessions often end without preserving the observations and project knowledge needed for continuity. Claude-Mem addresses this by capturing session activity, generating semantic summaries, and making relevant context available in later sessions. Key features Automatic session capture — Claude-Mem records tool usage and other session observations through lifecycle hooks …

github.comhttps://github.com/thedotmack/claude-mem