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ARXIV:2603.12572 · MEMORY RETRIEVAL · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.12572MEMORY RETRIEVALSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
LMEB is a benchmark framework designed to evaluate memory embeddings for complex long-horizon retrieval tasks.
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Pain LMEB is a benchmark framework designed to evaluate memory embeddings for complex long-horizon retrieval tasks.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
LMEB is a benchmark framework designed to evaluate memory embeddings for complex long-horizon retrieval tasks. To address this, we introduce the Long-horizon Memory Embedding Benchmark (LMEB), a comprehensive framework that evaluates embedding models' capabilities…
Memory embeddings are crucial for memory-augmented systems, such as OpenClaw, but their evaluation is underexplored in current text embedding benchmarks, which narrowly focus on traditional passage retrieval and fail to assess models' ability to…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The results reveal that (1) LMEB provides a reasonable level of difficulty; (2) Larger models do not always perform better; (3) LMEB and MTEB…
Memory Retrieval moved forward this cycle; last verified April 2026. Public score 7.0/10.
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LMEB is a benchmark framework designed to evaluate memory embeddings for complex long-horizon retrieval tasks.
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10.48550/arXiv.2603.12572LMEB is a benchmark framework designed to evaluate memory embeddings for complex long-horizon retrieval tasks.
Abstract
Memory embeddings are crucial for memory-augmented systems, such as OpenClaw, but their evaluation is underexplored in current text embedding benchmarks, which narrowly focus on traditional passage retrieval and fail to assess models' ability to handle long-horizon memory retrieval tasks involving fragmented, context-dependent, and temporally distant information. To address this, we introduce the Long-horizon Memory Embedding Benchmark (LMEB), a comprehensive framework that evaluates embedding models' capabilities in handling complex, long-horizon memory retrieval tasks. LMEB spans 22 datasets and 193 zero-shot retrieval tasks across 4 memory types: episodic, dialogue, semantic, and procedural, with both AI-generated and human-annotated data. These memory types differ in terms of level of abstraction and temporal dependency, capturing distinct aspects of memory retrieval that reflect the diverse challenges of the real world. We evaluate 15 widely used embedding models, ranging from hundreds of millions to ten billion parameters. The results reveal that (1) LMEB provides a reasonable level of difficulty; (2) Larger models do not always perform better; (3) LMEB and MTEB exhibit orthogonality. This suggests that the field has yet to converge on a universal model capable of excelling across all memory retrieval tasks, and that performance in traditional passage retrieval may not generalize to long-horizon memory retrieval. In summary, by providing a standardized and reproducible evaluation framework, LMEB fills a crucial gap in memory embedding evaluation, driving further advancements in text embedding for handling long-term, context-dependent memory retrieval. LMEB is available at https://github.com/KaLM-Embedding/LMEB.
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PROBLEM
LMEB is a benchmark framework designed to evaluate memory embeddings for complex long-horizon retrieval tasks. To address this, we introduce the Long-horizon Memory Embedding Benchmark (LMEB), a comprehensive framework that evaluates embedding models' capabilities in handling co...
METHOD
Memory embeddings are crucial for memory-augmented systems, such as OpenClaw, but their evaluation is underexplored in current text embedding benchmarks, which narrowly focus on traditional passage retrieval and fail to assess models' ability to handle long-horizon memory retrie...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The results reveal that (1) LMEB provides a reasonable level of difficulty; (2) Larger models do not always perform better; (3) LMEB and MTEB exhibit orthogonality.
WHY NOW
Memory Retrieval moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
LMEB is a benchmark framework designed to evaluate memory embeddings for complex long-horizon retrieval tasks. To address this, we introduce the Long-horizon Memory Embedding Benchmark (LMEB), a comprehensive framework that evaluates embedding models' capabilities in handling complex, long-horizon memory retrieval tasks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Memory embeddings are crucial for memory-augmented systems, such as OpenClaw, but their evaluation is underexplored in current text embedding benchmarks, which narrowly focus on traditional passage retrieval and fail to assess models' ability to handle long-horizon memory retrieval tasks involving fragmented, context-dependent, and temporally distant information. To address this, we introduce the Long-horizon Memory Embedding Benchmark (LMEB), a comprehensive framework that evaluates embedding models' capabilities in handling complex, long-horizon memory retrieval tasks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The results reveal that (1) LMEB provides a reasonable level of difficulty; (2) Larger models do not always perform better; (3) LMEB and MTEB exhibit orthogonality.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Memory Retrieval moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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LMEB is a benchmark framework designed to evaluate memory embeddings for complex long-horizon retrieval tasks.
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