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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
(3) Search latency bites only at very small reader: on a Spark GB10 edge node, memory-search adds a moderate and fixed 8787/77/4848 ms (BM25-RAG/Memobase/MemSearch) that composes a small part of TTFT for most reader-backend combinations
https://arxiv.org/html/2510.03215v2
However, current text-to-text (T2T) interfaces restrict information exchange among LLMs, particularly when conveying rich or diverse semantic interpretations of a shared context
https://arxiv.org/list/cs.AI/new
However, their performance heavily depends on the clarity and completeness of data semantics
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
(3) Search latency bites only at very small reader: on a Spark GB10 edge node, memory-search adds a moderate and fixed 87/7/48 ms (BM25-RAG/Memobase/MemSearch) that composes a small part of TTFT for most reader-backend combinations
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/2608.03233v1
3 Data Pollution
5 Evaluation of Diversity Enhancing Methods in Analogy Making
Probabilistic and Logistic steering
https://arxiv.org/html/2609.02852v1
Report GitHub Issue
Description:
arXiv is now an independent nonprofit
https://arxiv.org/html/2608.03372v1
The check compares the stored text against its source; the question is what the comparison is made of: a free, auditable word list, or an LLM call that must be trusted
https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/html/2508.16279v1
We provide a scalable evaluation module with a visual studio interface, making the development of long-trajectory agentic applications more manageable and easier to trace
https://arxiv.org/html/2607.22511v2
2 A tour of the clusters
Graphs and structural models
https://arxiv.org/html/2608.00028v1
˜Kuznetsov is with the Department of Theoretical and Applied Sciences, eCampus University, Via Isimbardi 10, 22060 Novedrate (CO), Italy; with the SMARTEST Research Center, eCampus University, Via Isimbardi 10, 22060 Novedrate (CO), Italy; and with the Department of Intelligent Software Systems and Technologies
https://arxiv.org/html/2407.17789v2
com/modelscope/agentscope/tree/main/examples/paper_large_scale_simulation to inspire further research and development in large-scale multi-agent simulations
https://arxiv.org/html/2608.01366v2
com Affiliation: Department of Design and Manufacturing, Indian Institute of Science (IISc) , Bangalore , India , Srinidhi Ranjini Girish email: srinidhirgirish@gmail
https://arxiv.org/html/2503.11576v1
Then, the projected embeddings are concatenated with the text embeddings of the user prompt, possibly with interleaving
https://arxiv.org/html/2608.03038v1
Although recent studies have begun to examine the text of LLM-generated explanations, such analyses remain comparatively rare relative to accuracy-based evaluations
https://arxiv.org/abs/2608.03883
The task requires systems to rank candidate Reddit sentences according to their relevance to each of the 18 symptoms in the Adult ADHD Self-Report Scale (ASRS-v1
https://arxiv.org/abs/2104.08663
We leverage a careful selection of 18 publicly available datasets from diverse text retrieval tasks and domains and evaluate 10 state-of-the-art retrieval systems including lexical, sparse, dense, late-interaction and re-ranking architectures on the BEIR benchmark
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/html/2608.03044v1
edu
1 Introduction
Large Language Models (LLMs), trained on large corpora of human text, are well-positioned to act as simulators of human opinion (Argyle et al
https://arxiv.org/html/2607.29442v2
Microstructural Foundations of Rough Noise1footnote 1footnoteFootnotefootnotesFootnotes1footnote 1We are grateful to Carsten Chong for fruitful discussions on rough noise
https://arxiv.org/list/cs.IR/recent
CL) [pdf, html, other]
Title: SciRet: A Compute-Aware Empirical Study of Retrieval and Reranking for Scientific RAG
Kaysarul Anas Apurba, Md
https://arxiv.org/html/2608.03532v1
2 Sentiment of Stereotypical Completions
5
https://arxiv.org/html/2608.02625v1
Latency-window-matched flash-only controls indicate that these gains persist after targeted tuning of block autoregressive decoding
https://arxiv.org/abs/astro-ph/0602280
These are: A: The uniqueness of the universe; B: The large scale of the universe in space and time; C: The unbound energies in the early universe; D: Explaining the universe -- the question of origins; E: The universe as the background for existence; F: The explicit philosophical basis; G: The Anthropic question: fine
https://arxiv.org/list/cs.IR/new
We provide the core implementation of KGD at this https URL
https://arxiv.org/html/2504.19413v1
3 Performance Comparison of Mem0 and Mem0gsuperscriptMem0𝑔\texttt{Mem0}^{\tiny g}Mem0 start_POSTSUPERSCRIPT italic_g end_POSTSUPERSCRIPT Against RAG Approaches and Full-Context Model
4
https://arxiv.org/html/2407.02518v2
com
Abstract
Large language models (LLMs) for code are typically trained to align with natural language instructions to closely follow their intentions and requirements