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We propose four training-time mitigation strategies and evaluate them individually and in combinations, finding that log-scaled distances yield the most consistent improvements in passkey retrieval
from the top result below
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
The implemented contract fixes evidence non-overwrite, branch isolation, grounded multi-subject timers, guarded state change, and declaration-ranked event ordering over time and space; an external runner still decides whether evidence becomes an authoritative move
https://arxiv.org/html/2608.02613v1
5 Composition: reader-bound LLM time + reader-free search
C
https://arxiv.org/html/2501.13956v1
2 Knowledge Graph Construction
2.1 Episodes
2.2 Semantic Entities and Facts
2.2.1 Entities
2.2.2 Facts
2.2.3 Temporal Extraction and Edge Invalidation
2.3 Communities
3 Memory Retrieval
3.1 Search
3.2 Reranker
4 Experiments
4.1 Choice
https://arxiv.org/html/2608.03048v1
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https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/html/2607.22511v2
[19] show that a calibrated board of LLM reviewers accepts deliberately fabricated papers up to 82%82\% of the time while detecting the fabrication at near-chance rates
https://arxiv.org/pdf/2608.02758
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https://arxiv.org/pdf/2402.03300
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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/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/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/1411.2842
2842] Logical Limitations to Machine Ethics with Consequences to Lethal Autonomous Weapons
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https://arxiv.org/list/cs.SI/new
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New submissions (showing 4 of 4 entries)
[1] arXiv:2608
https://arxiv.org/abs/2607.29380
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https://arxiv.org/abs/2510.01395
01395] Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence
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https://arxiv.org/list/cs.LG/2024-12
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[1] arXiv:2412
https://arxiv.org/pdf/2609.03103
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https://arxiv.org/pdf/2607.26327v3
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https://arxiv.org/pdf/2407.00345
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https://arxiv.org/pdf/2608.02609
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https://arxiv.org/pdf/2608.02943
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https://arxiv.org/src/2205.11916v1
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https://arxiv.org/html/2508.08980v3
org perpetual non-exclusive license
arXiv:2508
https://arxiv.org/pdf/2608.02244
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https://arxiv.org/pdf/2508.02758
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https://arxiv.org/pdf/2608.01366
) /Title (Asking Questions the Right Way: A Multi-Agent Conversational System for Prompt Formulation in Complex Task Resolution) /Trapped /False /arXivID (https://arxiv
https://arxiv.org/src/2403.08299
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https://arxiv.org/src/2006.02318
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https://arxiv.org/pdf/2609.18842
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