arXiv Computation and Language
MemDefrag: Latent Memory Defragmentation for Large Language Models
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Recall What Is Missing: Composing Evidence from Long-Term Agent Memory
arXiv:2610.15642v1 Announce Type: new Abstract: Under a limited context budget, a long-term agent must recall not merely memories that are individually relevant, but evidence that is collectively suf...
arXiv Computation and Language
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Titan-Llama: Retrofitting Pre-trained LLMs with External Neural Memory Modules
arXiv:2610.19210v1 Announce Type: new Abstract: Transformers are efficient to pre-train at scale because attention permits parallel computation across sequence positions, but long-context inference r...
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MemTrapBench: Benchmarking Cognitive Traps in LLM Memory Use
arXiv:2608. 20202v1 Announce Type: new Abstract: Memory has become a key component of large language models, enabling them to retain information and learn from long-term interactions.
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Multi-Head Recurrent Memory Agents
arXiv:2607. 01523v1 Announce Type: cross Abstract: Recurrent memory agents extend LLMs to arbitrarily long contexts by iteratively consolidating input into a fixed-size memory window.
arXiv Computation and Language
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From Long Context to Selective Memory:A Survey of Memory-Centric Compression for Large Language Models
The paper surveys memory‑centric compression techniques for large language models, framing long‑context handling as a memory‑management problem. It distinguishes compute‑active memory from compute‑inactive memory, and models operations such as writing, offloading, recall, and forgetting as state transitions within an information lifecycle. The authors propose a unified cost model and argue that future research should focus on dynamic, lifecycle‑aware memory management rather than static compression.
arXiv AI
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MemDecay: Region-Aware KV Cache Eviction for Efficient LLM Agent Inference
arXiv:2607. 10582v1 Announce Type: cross Abstract: Large language model (LLM) agents accumulate heterogeneous context, including system instructions, plans, user turns, retrieved documents, tool outputs, and intermediate reasoning, whose key-value (KV) cache can become a major memory bottleneck.