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We train a 3-layer MLP to map the KV-Cache from a source LLM (Qwen3-4B) to a target LLM (Qwen3-0
from the top result below
https://arxiv.org/html/2510.03215v2
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https://arxiv.org/html/2608.03035v1
For example, if a user planning a dinner first mentions that three guests are coming but later shares that two have canceled, an LLM assisting with the reservation must track that only one guest remains, a proposition that can only be determined using information from preceding context, and encode “Three guests are
https://arxiv.org/list/cs.CV/new
Project Page: this https URL
[2] arXiv:2608
https://arxiv.org/list/cs.AI/new
Extending the pattern-generation paradigm of Neural Cellular Automata (NCA), it navigates the degenerate Boolean search space to satisfy a computational task, rather than regenerating a fixed target state
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/pdf/2407.00125
) /Title (A Survey on Failure Analysis and Fault Injection in AI Systems) /Trapped /False /arXivID (https://arxiv
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/2608.02668v1
com/hazdzz/deep_connection \ncudata[Correspondence]Jie Zhang (),
Correspondence: Cheng-Fang Su ()
“Geometry is not true, it is advantageous
https://arxiv.org/pdf/2608.02122
) /Title (DEFT: Joint Task Placement and DVFS for Energy-Efficient Multi-GPU Runtimes) /Trapped /False /arXivID (https://arxiv
https://arxiv.org/pdf/2608.01785
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https://arxiv.org/pdf/2508.11152
com three Proceedings of ACM Conference (Conference'17) book 1 1 9 9 Tianjiao ZhaoJingrao LyuStokes JonesHarrison GarberStefano PasqualiDhagash Mehtahttp://arxiv
https://arxiv.org/html/2608.03067v1
edu
Abstract
Changes in spontaneous speech provide an early signal of cognitive dysfunction in Alzheimer’s disease (AD) that large language models (LLMs) can detect
https://arxiv.org/list/cs.CR/new
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New submissions (showing 32 of 32 entries)
[1] arXiv:2608
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/2312.16193v2
ch11 Johnnatan Messias 22 Malte Schlosser 11 Benjamin Kraner 11 Claudio Tessone 11
Abstract
This paper proposes a novel multi-layer blockchain architecture for the cross-border trading of CBDCs
https://arxiv.org/html/2407.15077v2
2 Batch by Batch Policy Optimization Layer
4 DAG-based B2MAPO Algorithm
4
https://arxiv.org/html/2608.03452v1
Prior studies have shown that character-level transformers can reproduce this pattern, but that evidence describes what models produce, not what they represent
https://arxiv.org/html/2511.06451v1
From selective scan to a risk-neutral operator layer
https://arxiv.org/html/2608.03486v1
Once the shocks have been recovered, the fully nonlinear structural vector autoregression can be estimated using feed-forward neural networks, motivated by their universal approximation capabilities
https://arxiv.org/src/2006.02759
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https://arxiv.org/pdf/2006.02602
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https://arxiv.org/pdf/2005.13905
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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/pdf/2006.01128
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https://arxiv.org/pdf/2609.18842
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https://arxiv.org/pdf/2608.03138
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https://arxiv.org/src/2002.02145
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https://arxiv.org/pdf/2608.03833
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https://arxiv.org/pdf/2608.00785
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https://arxiv.org/pdf/2608.03898
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