MemRiskBench: Trace-Aware Risk-Preserving Evaluation for Long-Horizon LLM Agents

arXiv:2609.14976 · cs.AI · Submitted 2026-09-14 · Read on arXiv

cs.AI

Submitted: 2026-09-14

Updated: 2026-09-14

Comments: 11 pages, 4 figures,

Code: https://github.com/fuxue-mingzhu/MemRiskBench

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Trace — 40× in this paper · explained in 2 episodes
• Benchmark — 36× in this paper · explained in 2 episodes
• Clustering — 6× in this paper · explained in 2 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Quantization — 5× in this paper · explained in 12 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Ablation — 3× in this paper · explained in 1 episode
• Failure Modes — 3× in this paper · explained in 3 episodes
• Generative AI — 3× in this paper · explained in 2 episodes
• LLM judge — 3× in this paper · explained in 2 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Active Learning — 1× in this paper · explained in 6 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Transactions on Machine Learning Research — 1× in this paper

Sources

• Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions
• Efficient Benchmarking of AI Agents
• Beyond a Million Tokens: Benchmarking and Enhancing Long-Term Memory in LLMs
• LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory

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