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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

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https://arxiv.org/html/2608.02613v1
7 Extrapolation across readers for cache-replay backends TTFT rows in Table 3

1 Introduction

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

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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

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https://arxiv.org/pdf/2608.03411
ô{~l Fú )=P’è…H# �Ó‘þH�¥¿ÊtÁonŸ> stream DUD: Decoupled Update Dynamics for Reliable Uncertainty Quantification in Large Language ModelsYixin BuRunze XiaGuanyun ZouYupeng JiHaodong LiuPiji Lihttp://creativecommons

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https://arxiv.org/pdf/2608.00426
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https://arxiv.org/pdf/2608.03421
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https://arxiv.org/pdf/2608.03833
‰S‰¿ÇÉJ%VÇÏÜ{Ç–À!!8D e1„¡€"žB Q–@1�oV+`ºìá rîf^ I !žðl§kmÇ©Åf·½Œþ0KXÚbp$—Q× »#šâ¼t3ëõ·„SBFÈ'Î=ÅÜo2ÐešËhÒvÚºU•ó®zk†úˆ]p+‚|î)¸ôº¡gä{&žéÜ÷—¸œ6í±°º1ãyn”y!¿�àsÁïyé´ì›ëkZ™SÜÿ?ášÜ=ö®—ô¥=ZU*«\>zhö¨jì�¶gO-šÍ”DúÚ`+m\

Computation and Language

https://arxiv.org/list/cs.CL/new
LG) Half a million cuneiform clay tablets survive in museums worldwide, yet modern users can neither read nor write in the world's oldest writing system, leaving a 4,000-year cultural barrier that existing NLP tools have only partially addressed

Information Retrieval

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

1 Introduction

https://arxiv.org/html/2407.16741v3
Given the power of software, as well as the existing tooling around its efficient development, use, and deployment, it provides the ideal interface for AI agents to interact with the world in complex ways

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
In practice, many field descriptions remain ambiguous or incomplete, as much of the essential context (e

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https://arxiv.org/pdf/2608.03358
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[2608.03883] DS@GT-ARC at eRisk 2026 Task 3: Sparse, Semantic, and LLM

https://arxiv.org/abs/2608.03883
All submitted systems follow a staged retrieval design in which BM25 retrieves candidates at scale and semantic or LLM rerankers refine the final rankings

[2104.08663] BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of

https://arxiv.org/abs/2104.08663
Benchmark for Zero-shot Evaluation of Information Retrieval Models, by Nandan Thakur and 4 other authors View PDF HTML (experimental) Abstract:Existing neural information retrieval (IR) models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their out-of

[2608.03860] SciRet: A Compute-Aware Empirical Study of Retrieval and

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

Information Retrieval

https://arxiv.org/list/cs.IR/recent
IR) Tue, 4 Aug 2026 (showing first 34 of 45 entries ) [17] arXiv:2608

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https://arxiv.org/pdf/2510.03215
7 %¿÷¢þ 1 0 obj > endobj 2 0 obj > endobj 3 0 obj > stream Cache-to-Cache: Direct Semantic Communication Between Large Language ModelsTianyu FuZihan MinHanling ZhangJichao YanGuohao DaiWanli OuyangYu Wanghttp://arxiv

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https://arxiv.org/pdf/2407.00215
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https://arxiv.org/pdf/2607.26327v3
'-~ö¿}ÌHZ$ —æìéé»{-¤0B/¬pF '|H‹”BI-EJ+-®’0$”sJ(øïÎ #,5BK¿­ÐZÃb'´ 0ï…vúAè ,:i82 #ƒ†MÂh›•0€i-àèa‚MB[aÀ…­VF˜ê€À„µ0¨£°K›¥° ð3J8™ ¯…ÓúF8 ÷tÔ ç9¸¹ 2Âaðð5Q¸èa}.!P IJp¨^J ‡ ¯ü6Â;ÀÛZá#·Nea�Á>6ˆà°EˆpY¸JÔ