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A query may execute yet return incorrect results due to gaps spanning related concepts; misusing NATURAL JOIN in place of an explicit subquery reflects intertwined misunderstandings of JOIN, GROUP BY, and HAVING
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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/list/cs.AI/new
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Artificial Intelligence
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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
The read step processes all chunks in parallel, with each chunk-level read conditioned on the same global memory, thereby avoiding sequential memory overwrites that may discard earlier evidence and reducing inference latency through parallel chunk processing
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/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/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/new
For what to learn, conventional next-token prediction treats adjacency as dependency and may encode spurious transitions across unrelated sessions
https://arxiv.org/list/cs.IR/recent
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Information Retrieval
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Wed, 5 Aug 2026
https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
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https://arxiv.org/html/1802.00048v2
Our results show that all tested agents are vulnerable to several kinds of deception, but that different agents have different weaknesses
https://arxiv.org/html/2607.27188v1
2 Two outputs and two kinds of evidence
3
https://arxiv.org/html/2609.02852v1
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The Implications of Linguistic Illegibility for LLM Security
James Mickens Affiliation: Harvard University Email: mickens@g.harvard.edu
Abstract
LLMs are trained to
https://arxiv.org/html/2607.22511v2
However, such reviewers remain empirically unreliable: they may accept fabricated papers and detect them at rates close to chance [19]
https://arxiv.org/html/2407.10064v4
An Overview of Applications and Insights
1 Introduction
2 Background
2.1 Review of Bridge Operation and maintenance Research
2.2 Overview of LLM-based Agents Research
2.2.1 Larger Language Models
2.2.2 LLM-based Agents
2.3 Why Bridge
https://arxiv.org/html/2407.04503v4
Here, we ask whether multi-turn behaviors conditioned on LLM-generated content cause the appearance of new kinds of biases, undetectable in single-turn behaviors but accumulating across iterated interactions
https://arxiv.org/list/hep-ph/new
A smooth log-linear trend is given a conditional renormalization-group motivation through dimensional transmutation in a hypothetical asymptotically free confining sector; this motivates, but does not microscopically derive, the mass formula