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The query asks the model to recall the topic of an earlier conversation about Tesla; the gold answer is anchored in one specific session about Pooja’s dig site
from the top result below
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/html/2501.13956v1
com
Abstract
We introduce Zep, a novel memory layer service for AI agents that outperforms the current state-of-the-art system, MemGPT, in the Deep Memory Retrieval (DMR) benchmark
https://arxiv.org/html/2608.03048v1
com/JetAstra/PI-Mem
Introduction
The ability to reason over long contexts is increasingly important for large language models (LLMs), supporting applications such as long-document question answering (Gemini Team 2025; Yang et al
https://arxiv.org/list/cs.CL/new
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends
https://arxiv.org/html/2510.03215v2
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https://arxiv.org/list/cs.AI/new
Biological systems, in contrast, exhibit adaptive plasticity, maintaining function through dynamic re-organisation around damage
https://arxiv.org/abs/2104.08663
Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs
https://arxiv.org/html/2407.16741v3
Released under the permissive MIT license, OpenHands is a community project spanning academia and industry with more than 2
https://arxiv.org/abs/2608.03883
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https://arxiv.org/abs/2608.03860
Have an idea for a project that will add value for arXiv's community
https://arxiv.org/list/cs.IR/recent
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Tue, 4 Aug 2026 (showing first 34 of 45 entries )
[17] arXiv:2608
https://arxiv.org/html/2608.02625v1
In recent years, diffusion language models (DLMs) [20, 23, 4] have garnered increasing interest within the language modeling community
https://arxiv.org/html/2608.02612v1
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arXiv:2608
https://arxiv.org/html/2506.01844v1
2 Pretraining data collected by the community
Task annotation with VLM
https://arxiv.org/list/cs.IR/new
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Information Retrieval
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https://arxiv.org/html/2410.15608v2
2 Issues with fixed sequence length encoder
3 Approach
3.1 Model architecture
3.2 Training data collection & preprocessing
3.3 Training
4 Evaluations
4.1 Accuracy vs. Input Length
4.2 Robustness to Input Speech Signal Level
4.3 Robustness
https://arxiv.org/list/cs.NA/new
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[1] arXiv:2608
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[2] arXiv:2608
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[1] arXiv:2608