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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
1K text-only ego-observed tokens/agent/day)
https://arxiv.org/list/cs.AI/new
Lean 4 checks kernel analogues for positions, evidence, clocks, monitors, atomicity, and branch source retention; 88 tests, 3,534 bounded checks, and 32 Lean/Python runtime-kernel cases bound the implementation claim to the checked cases
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/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/list/cs.CL/new
1K text-only ego-observed tokens/agent/day)
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/2609.02852v1
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arXiv is now an independent nonprofit
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
https://arxiv.org/html/2508.16279v1
org perpetual non-exclusive license
arXiv:2508
https://arxiv.org/html/2407.17789v2
org perpetual non-exclusive license
arXiv:2407
https://arxiv.org/html/2402.03300v3
com/deepseek-ai/DeepSeek-Math
Abstract
Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature
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/list/cs.SE/new
Through an analysis of 1,966 open source projects, 1,355 Github issues, 444 CVE reports, and 361 StackOverflow posts, we systematically map the feature support and real-world usage of globs across six common ecosystems that utilize globs
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
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/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.SE/2024-07
Dearing, Yiheng Tao, Xingfu Wu, Zhiling Lan, Valerie Taylor
Comments: 8 pages, 1 figure, 7 tables
Journal-ref: 2024 IEEE International Conference on Cluster Computing Workshops (CLUSTER Workshops), pp
https://arxiv.org/list/cs.IR/new
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https://arxiv.org/html/2608.03388v1
de
1 Introduction
One fact leads to another—so we continue
https://arxiv.org/html/2608.02620v1
1 Introduction
Evaluating instruction-tuning for Large Language Models (LLMs) is challenging because generative models output free-form text in response to questions that are often open or ambiguous
https://arxiv.org/abs/2108.07732
The MathQA-Python dataset, a Python version of the MathQA benchmark, contains 23914 problems that evaluate the ability of the models to synthesize code from more complex text
https://arxiv.org/html/2609.03371v1
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https://arxiv.org/html/2407.14218v1
com , Iraklis Varlamis Department of Informatics and TelematicsHarokopio University of AthensAthensGreece varlamis@hua
https://arxiv.org/list/cond-mat/new
The mixed anomaly between these is accommodated by the not-on-site action of the symmetries
https://arxiv.org/list/quant-ph/new
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Quantum Physics
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