Jev-Mem: System-One-Controlled Agentic Memory for Efficient AI Agents
A System-One control plane, a structured multi-relational memory plane and a System-Two reasoning plane split fast control from slow deliberation.02What the controller does. It assigns memory types and relations during construction and handles query routing, retrieval budget, graph traversal, candidate scoring and adaptive stopping during retrieval.03LLM off the critical path. System Two runs only for complex reasoning and answer synthesis, so memory operations no longer wait on autoregressive generation.04Accuracy.
- ▪A System-One control plane, a structured multi-relational memory plane and a System-Two reasoning plane split fast control from slow deliberation.02What the controller does.
- ▪It assigns memory types and relations during construction and handles query routing, retrieval budget, graph traversal, candidate scoring and adaptive stopping during retrieval.03LLM off the critical path.
- ▪System Two runs only for complex reasoning and answer synthesis, so memory operations no longer wait on autoregressive generation.04Accuracy.
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| Original publisher | Dair |
| Canonical URL | https://academy.dair.ai/papers/jev-mem-system-one-controlled-agentic-memory-for-efficient-ai-agents-2609.23986 |
| Publication time | Fri, 25 Sep 2026 22:17:12 +0000 |
| Retrieval time | 2026-09-25T22:20:22.956Z |
| Last seen | 2026-09-25T22:20:22.956Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | efl_DPMPKykE · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Agents · Memory · ReasoningJev-Mem: System-One-Controlled Agentic Memory for Efficient AI AgentsDongming Jiang, Yi Li, Bingzhe LiChat with PaperFirst pageThe curator’s takeDongming Jiang, Yi Li and Bingzhe Li (UT Dallas) build Jev-Mem, an agent memory system that hands memory organization and retrieval control to a lightweight System-One controller and calls an LLM only for final reasoning.Ask this paperQuestion about this paperAsk in Paper ChatKey points01Three planes. A System-One control plane, a structured multi-relational memory plane and a System-Two reasoning plane split fast control from slow deliberation.02What the controller does.
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