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Neuromem: A Granular Decomposition of the Streaming Lifecycle in External Memory for LLMs

Ruicheng Zhang, Xinyi Li, Tianyi Xu, Shuhao Zhang, Xiaofei Liao, Hai Jin · Feb 15, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 83% Moderate protocol signal Freshness: Warm Status: Ready
Automatic Metrics General
  • We present Neuromem, a scalable testbed that benchmarks External Memory Modules under an interleaved insertion-and-retrieval protocol and decomposes its lifecycle into five dimensions including memory data structure, normalization strategy,…
  • Using three representative datasets LOCOMO, LONGMEMEVAL, and MEMORYAGENTBENCH, Neuromem evaluates interchangeable variants within a shared serving stack, reporting token-level F1 and insertion/retrieval latency.
Open paper
Benchmark Test-Time Scaling of General LLM Agents

Xiaochuan Li, Ryan Ming, Pranav Setlur, Abhijay Paladugu, Andy Tang, Hao Kang · Feb 22, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 77% Sparse protocol signal Freshness: Warm Status: Ready
Coding
  • LLM agents are increasingly expected to function as general-purpose systems capable of resolving open-ended user requests.
  • We introduce General AgentBench, a benchmark that provides such a unified framework for evaluating general LLM agents across search, coding, reasoning, and tool-use domains.
Open paper
Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions

Yuanzhe Hu, Yu Wang, Julian McAuley · Jul 7, 2025

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 71% Sparse protocol signal Freshness: Cold Status: Ready
General
  • We introduce MemoryAgentBench, a new benchmark specifically designed for memory agents.
  • We evaluate a diverse set of memory agents, ranging from simple context-based and retrieval-augmented generation (RAG) systems to advanced agents with external memory modules and tool integration.
Open paper
PRISM: Prompt-Refined In-Context System Modelling for Financial Retrieval

Chun Chet Ng, Jia Yu Lim, Wei Zeng Low · Nov 18, 2025

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 78% High protocol signal Freshness: Cold Status: Fallback
Automatic Metrics Multi Agent Coding
  • We present PRISM, a training-free framework that integrates refined system prompting, in-context learning (ICL), and lightweight multi-agent coordination for document and chunk ranking tasks.
  • Our primary contribution is a systematic empirical study of when each component provides value: prompt engineering delivers consistent performance with minimal overhead, ICL enhances reasoning for complex queries when applied selectively,…
Open paper

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