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30 result(s) for “okapi bm25 site hotchips pp site github com site text of de site www context de site:arxiv.org” of 61 matched · 714 ms
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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
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends
https://arxiv.org/html/2608.03048v1
Yet model performance often degrades as context length grows, highlighting the persistent difficulty of identifying, retaining, and effectively utilizing relevant information across distant parts of the context (Hsieh et al
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
In practice, many field descriptions remain ambiguous or incomplete, as much of the essential context (e
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
2 Agent Runtime: How Execution of Actions Results in Observations
2
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/html/2608.03138v1
Experiments show that StructPO improves semantic alignment, structural rationality and inference efficiency over workflow-based baselines, generalizes to out-of-domain settings, and remains competitive with GPT-5
https://arxiv.org/html/2608.02918v1
Biscainho are with SMT, DEL/Poli & PEE/COPPE, Federal University of Rio de Janeiro, Brazil (corresponding e-mail: wallace
https://arxiv.org/list/cs.IR/new
We provide the core implementation of KGD at this https URL
https://arxiv.org/html/2608.00458v1
This exchange consumes network capacity, expands the input context of the receiver, and increases model-serving computation
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/2608.03141v1
Transformer architectures represent these inputs as sequences of high-dimensional embedding tokens [14], each corresponding to a modality-specific semantic unit, such as an image patch, a text subword, or an audio segment
https://arxiv.org/html/2608.03411v1
com
Abstract
Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs), yet traditional probability-based metrics often fail to capture the model’s true epistemic state
https://arxiv.org/html/2608.02673v1
2 Task-Specific Editing Pipelines
Text editing
https://arxiv.org/html/2608.01085v1
Diagnostics
G Conditional View of LATTE
Assumption 1 (Clean transition stability)
https://arxiv.org/html/2608.03154v1
4 Practicality of Offline Error Pattern Learning
5
https://arxiv.org/html/2608.02971v1
of Computer Science, The University of Texas at Austin, Austin, TX, USA
4Department of Civil and Transportation Engineering, South China University of Technology, Guangzhou, China
†\daggerEqual contribution
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/2608.03446v1
Predicting Multilingual Classification and Translation Performance of LLMs with Cross-Lingual Alignment – Is English Enough
https://arxiv.org/html/2608.00648v1
MA] 01 Aug 2026
MDGAM-Based Cooperative Task Scheduling for Communication-Constrained Distributed Multi-Agent Systems
Licheng Wang Mingtao Huang Yuan Shen Thanks: The authors are with the Department of Electronic Engineering, and Beijing National Research Center for Information Science and Technology
https://arxiv.org/html/2410.05779v3
By breaking down a large external text corpus into smaller, more manageable segments, chunking significantly enhances the accuracy of information retrieval
https://arxiv.org/html/2608.03655v1
2 Effect of Sentence Budget and Output Length
D
https://arxiv.org/html/2607.26245v1
The attempt did not produce a tradable edge: out-of-sample, a walk-forward logistic model over 43 microstructure features does not beat, and slightly underperforms, the probability already implied by Polymarket’s own order book, and simulated trading nets −0
https://arxiv.org/html/2407.02518v2
For instance, in a study of Github’s Copilot, Pearce et al
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/2608.00426v1
2 Attack Potency of Memory-Link Poisoning
Obs1
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/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/2503.11576v1
Then, the projected embeddings are concatenated with the text embeddings of the user prompt, possibly with interleaving