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MMD) to implicitly enhance cross-modal distribution consistency
from the top result below
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/list/cs.AI/new
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Artificial Intelligence
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https://arxiv.org/html/2501.13956v1
2 Knowledge Graph Construction
2.1 Episodes
2.2 Semantic Entities and Facts
2.2.1 Entities
2.2.2 Facts
2.2.3 Temporal Extraction and Edge Invalidation
2.3 Communities
3 Memory Retrieval
3.1 Search
3.2 Reranker
4 Experiments
4.1 Choice
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
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/2407.16741v3
2 OpenHands Architecture
2.1 Agent Definition and Implementation
2.2 Agent Runtime: How Execution of Actions Results in Observations
2.3 Agent Skills: The Extensible Agent-Computer Interface
2.4 Agent Delegation: Cooperative Multi-agent
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
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
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.IR/new
For what to learn, conventional next-token prediction treats adjacency as dependency and may encode spurious transitions across unrelated sessions
https://arxiv.org/list/econ.TH/new
We study the overlapping-generations learning model of Fudenberg and Levine (1993), in which finitely lived Bayesian agents are repeatedly and randomly matched with agents in other player roles, observe only their own matches, and learn from experience
https://arxiv.org/list/cs.SY/new
SY)
In active noise control (ANC) systems, adaptive approaches may suffer from instability or divergence, limiting their practical deployment
https://arxiv.org/list/cs.LG/2022-12
Lee, Claire Tomlin, Sergey Levine, Stephen Tu, Nikolai Matni
Comments: Appeared in L4DC 2023
https://arxiv.org/list/cs.CL/2022-10
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Computation and Language
Authors and titles for October 2022
Total of 1397
https://arxiv.org/list/cs.LG/2022-11
Dennis, Yawen Duan, Viktor Pogrebniak, Sergey Levine, Stuart Russell
Comments: Accepted to ICML 2023, see paper for changelog
Subjects: Machine Learning (cs
https://arxiv.org/list/q-bio/recent
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Quantitative Biology
Authors and titles for recent submissions
Wed, 5 Aug 2026
https://arxiv.org/html/2508.19205v1
1 Introduction
2 Method
2.1 Speech Tokenizers
2.2 VibeVoice
3 Results
3.1 VibeVoice Podcast
3.2 VibeVoice Short Utterance
3.3 Tokenizer Reconstruction
4 Conclusion, Limitations, and Risks
VibeVoice Technical Report
Zhiliang Peng
https://arxiv.org/html/2407.19144v1/
In real-world settings, agents may face unexpected failures, such as a robot’s leg malfunctioning or a teammate’s battery running out
https://arxiv.org/html/2410.05779v3
Second, these systems often lack the contextual awareness needed to maintain coherence across various entities and their interrelations, resulting in responses that may not fully address user queries
https://arxiv.org/html/2407.00632v1
II Related Work
II-A Visual Object Navigation
II-B LLM-Based Cooperative Embodied Agents
II-C Multi-Agent Organizational Structures
III Problem Setup
IV CAMON Approach
IV-A Perception Module
IV-A1 Map Construction
IV-A2 Room Description
https://arxiv.org/html/1802.00048v2
Tandon School of Engineering, New York University, New York, USA John Levine Affiliation: Computer and Information Science Department, University of Strathclyde, Glasgow, UK, E-mail Damien
https://arxiv.org/html/2407.03956v3
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https://arxiv.org/html/2608.03063v1
Merchant risk control at large payment platforms screens tens of millions of merchants per day, where errors are costly in both directions: a false positive may harm a legitimate merchant, while a false negative leaves harmful activity undetected
https://arxiv.org/html/2607.28127v1
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https://arxiv.org/html/2608.00426v1
Warning: This paper includes examples that may be misleading or harmful
https://arxiv.org/html/2608.03099v1
Interpretable or revised linguistic intermediates may be incorrect, go unused, or fail to affect later behavior
https://arxiv.org/html/2608.03545v1
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https://arxiv.org/html/2609.03103v1
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Optimizing Field Geometry for Modular Micropore-Optics Soft X-ray Time-Domain Surveys
Nicholas E. White Affiliation: The George Washington University, Washington, DC, USA
https://arxiv.org/html/2407.02518v2
In these tasks, unexpected damage may likely occur and create systematic impacts on execution environments in practice (Hendrycks et al