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Table 9), briefly summarized here: (1) Alternative retrievers (dense E5, dense BGE-M3, hybrid BM25++rerank, and temporal across 55 readers ×\times 33 seeds) all stay ≥61\geq 61 pp below the Qwen3-3232B Oracle cross-session reasoning ceiling of 93
from the top result below
https://arxiv.org/html/2608.02613v1
2 pp, exceeding MemSearch reader scaling (+10
https://arxiv.org/html/2608.02668v1
Sphere pp-Angular Retraction Normalization
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/abs/2608.03883
Our systems combine sparse BM25 retrieval, evidence-aware rescoring for self-referential symptom reports, embedding-based reranking, query-prototype expansion, and LLM-based reranking
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/list/cs.CL/new
2 pp, exceeding MemSearch reader scaling (+10
https://arxiv.org/abs/2608.03860
The pipeline combines sentence-window chunking, BM25, BGE-M3 dense retrieval, reciprocal rank fusion, optional cross-encoder reranking, and grounded answer generation
https://arxiv.org/list/cs.IR/recent
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Information Retrieval
Authors and titles for recent submissions
Wed, 5 Aug 2026
https://arxiv.org/list/cs.IR/new
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Information Retrieval
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https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/html/2608.00028v1
Index Terms:
Multi-agent systems, scalability, minimum-variance control, internal model principle, disturbance rejection, time delay, adaptive control, resource trade-offs, large-language-model agents
https://arxiv.org/html/2608.02851v1
The design relies on two affine Volterra transformations involving both Fredholm- and Volterra-type integral terms, which results in a four-PIDE kernel equations
https://arxiv.org/list/cs.AI/new
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Artificial Intelligence
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https://arxiv.org/html/2407.05763v1
fr Siyuan Wang siyuanwang0603@buaa
https://arxiv.org/html/2608.03573v1
Multi-Task Learning Paradigms for LLMs
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