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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
Then it must make an even harder judgment: even if the answer is known, is it actually appropriate to tell Bob
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/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/2407.16741v3
edu
Abstract
Software is one of the most powerful tools that we humans have at our disposal; it allows a skilled programmer to interact with the world in complex and profound ways
https://arxiv.org/html/2608.02668v1
com/hazdzz/deep_connection \ncudata[Correspondence]Jie Zhang (),
Correspondence: Cheng-Fang Su ()
“Geometry is not true, it is advantageous
https://arxiv.org/html/2510.03215v2
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Description:
arXiv is now an independent nonprofit
https://arxiv.org/html/2608.02918v1
Miranda is also with LCTI, Télécom Paris, IP Paris, France
https://arxiv.org/html/2608.03063v1
Our code is available at https://github
https://arxiv.org/list/cs.AI/new
Our approach utilizes a two-stage generation process: first, the model is prompted to self-select a unique, idiosyncratic persona to anchor its starting point; second, we apply a dual-stage sampling sieve, utilizing Top-$p$ filtering to preserve grammatical validity followed by extreme temperature scaling ($T \ge 4
https://arxiv.org/html/2608.03011v1
AS] 04 Aug 2026
Towards Real-world Environment-aware Zero-shot Text-to-speech Synthesis via Disentangled Audio Infilling
Ye-Xin Lu, Xin Wang, Yang Ai, Hui-Peng Du, Zhen-Hua Ling, and Junichi Yamagishi This work is the extended version of our conference paper [1] published at IEEE International Conference on
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
IR)
Industrial recommenders increasingly adopt the pretrain-then-transfer paradigm, yet behavioral distribution drift raises two questions: what to learn from behavior sequences, and how to transfer the learned knowledge while the pretrained model is continually refreshed
https://arxiv.org/abs/2608.03883
)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX
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
)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX
https://arxiv.org/list/cs.CL/new
Accepted to C3NLP @ ACL 2026
Subjects: Computation and Language (cs
https://arxiv.org/html/2607.22511v2
A common approach is to close the research loop with a large language model (LLM) reviewer
https://arxiv.org/html/2608.02609v1
To our knowledge, TabletCraft is the first open-source system to support both directions of Akkadian translation together with cuneiform-Unicode rendering
https://arxiv.org/html/2608.03372v1
001), so the first deployment question is not which detector to use but whether the failure occurs at all
https://arxiv.org/html/2509.22186v2
com/opendatalab/MinerU \metadata[Model:]https://huggingface
https://arxiv.org/html/2608.03486v1
Specifically, a general contrastive learning framework that makes use of this variation together with the assumed exponential-family structure is employed to recover the shocks
https://arxiv.org/html/2407.03956v3
We also introduce an automated grid puzzle grader to assess the correctness of our puzzle solutions and show that the automated grader is reliable by evaluating it in a user-study
https://arxiv.org/html/2607.26245v1
Binance BTC/USDT serves as the reference stream because it is liquid, high frequency, and tied to the Polymarket BTC settlement window
https://arxiv.org/html/2608.02612v1
However, in practice, utilizing prior knowledge is essential to realize efficient optimization, and leveraging large language models (LLMs) to exploit such information can be a key direction
https://arxiv.org/html/2608.02616v1
com
Abstract
We present the first independent, systematic evaluation of OpenAI’s Privacy Filter (OPF), a 1
https://arxiv.org/html/2608.03446v1
To enable computing correlations across target languages, we propose a PMI-based translation metric, which is less dependent on the target language and correlates strongly with chrF
https://arxiv.org/html/2608.00785v1
com
Abstract
Content discovery systems depend on relevance judgment for search quality evaluation, but human annotation faces inter-annotator disagreement and scaling costs
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/2608.03077v1
Large reasoning models (LRMs) make such decisions explicit through intermediate translation steps, but our analysis across 15 domains and four translation directions shows that this explicit reasoning is double-edged: it improves long-form and high-difficulty translation, yet often drifts in terminology-intensive and
https://arxiv.org/html/2508.16279v1
A key feature of modern LLMs is their ability to call and interact with external tools (Achiam et al