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30 result(s) for “okapi bm25 site hotchips pp site github com site does cercos de site de to site:arxiv.org” of 37 matched · 561 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
A million-token cloud context window does not help here, as it requires off-device upload, which privacy-sensitive personal memory management is meant to avoid
https://arxiv.org/html/2608.02668v1
Both prove to be members of a one-parameter family of angular retractions, pp-SpheretNorm, whose rotation angle saturates rather than growing without bound
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/2608.03063v1
Such expert review is accurate but slow, and does not scale to tens of millions of merchants
https://arxiv.org/list/cs.AI/new
To bridge this gap, we introduce a novel and comprehensive Interactive SEmantic Enrichment system (ISEE)
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/2608.02918v1
Biscainho are with SMT, DEL/Poli & PEE/COPPE, Federal University of Rio de Janeiro, Brazil (corresponding e-mail: wallace
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/list/cs.CL/new
Accepted to C3NLP @ ACL 2026
Subjects: Computation and Language (cs
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/2608.03372v1
3 A word list cannot be finished
6 Where the model earns its cost
The witness perceives; it does not judge
https://arxiv.org/html/2607.26245v1
1 What This Enables That Prior Work Does Not
12
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/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/2608.02616v1
2 Related Work
De-identification and text anonymization
https://arxiv.org/html/2210.00131v4
spanning known architectures that are encoder-only, encoder-decoder and decoder-only, with a range of pre-training tasks: 1) masked language modeling (MLM)333This paper does not address the next sentence prediction pre-training objective used in BERT and subsequently dropped in RoBERTa due to limited effectiveness
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/2509.22186v2
3 Domain-Specific Vision Language Models
End-to-End Approaches
https://arxiv.org/html/2407.04503v4
team, Université de Bordeaux, France
Abstract
As large language models (LLMs) start interacting with each other and generating an increasing amount of text online, it becomes crucial to better understand how information is transformed as it passes from one LLM to the next
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/html/2608.02609v1
TabletCraft: Bridging a 4,000-Year Cultural Gap with Bidirectional Akkadian NMT and Cuneiform RenderingAccepted to the 4th Workshop on Cross-Cultural Considerations in NLP (C3NLP) at ACL 2026
https://arxiv.org/html/2608.02612v1
2 Behavioral Analysis of LLMs as Meta-Solvers
Sensitivity to Problem Domains and Difficulties
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/2608.03446v1
cz
Abstract
Multilingual large language models (LLMs) have been shown to perform better on non-English classification tasks when the representations of the given language are more aligned to English within the model
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
1 From Tasks, Solutions and Metrics to Benchmark
4
https://arxiv.org/html/2508.19205v1
Abstract
This report presents VibeVoice, a novel model designed to synthesize long-form speech with multiple speakers by employing next-token diffusion SBW+ (24), which is a unified method for modeling continuous data by autoregressively generating latent vectors via diffusion
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/list/cs.IR/recent
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Information Retrieval
Authors and titles for recent submissions
Wed, 5 Aug 2026
https://arxiv.org/abs/2608.03883
Abstract:This paper describes our submissions to eRisk 2026 Task 3, ADHD Symptom Sentence Ranking