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MMD) to implicitly enhance cross-modal distribution consistency
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https://arxiv.org/list/cs.CL/new
Recent studies have therefore examined how to automatically derive optimization problems from natural-language descriptions, but existing benchmarks focus on settings where objectives and constraints can be written explicitly as mathematical expressions
https://arxiv.org/list/cs.AI/new
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https://arxiv.org/pdf/2407.00345
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https://arxiv.org/src/2006.01048
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https://arxiv.org/pdf/2104.00446
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https://arxiv.org/src/2205.11916v3
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https://arxiv.org/pdf/2407.02510
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https://arxiv.org/pdf/2006.02318
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https://arxiv.org/pdf/2608.01651v1
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https://arxiv.org/pdf/2608.02758
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https://arxiv.org/pdf/2608.03883
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https://arxiv.org/src/2303.12712
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https://arxiv.org/pdf/2005.14165
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https://arxiv.org/pdf/2402.03300
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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/pdf/2006.04122
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https://arxiv.org/src/2006.02318
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https://arxiv.org/src/2205.11916v1
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https://arxiv.org/src/2608.03994
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https://arxiv.org/src/2006.02759
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https://arxiv.org/pdf/2006.00342
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https://arxiv.org/pdf/2508.04671
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https://arxiv.org/pdf/2303.12712v5
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https://arxiv.org/pdf/2608.02615
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https://arxiv.org/html/2501.13956v1
Since entire conversation histories, business datasets, and other domain-specific content cannot fit effectively inside LLM context windows, new approaches need to be developed for agent memory
https://arxiv.org/html/2608.02613v1
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends
https://arxiv.org/list/cs.CY/current
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[1] arXiv:2608
https://arxiv.org/src/2006.03178
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https://arxiv.org/pdf/2608.02833
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https://arxiv.org/pdf/2609.03103
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