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If AI gets smart enough to pass as human every time does being human even matter anymore?

one hand Im here because I think the tech is amazing. on the other hand I cant shake the feeling that we are building something that make...

Reddit - Artificial Intelligence · 1 min ·
Machine Learning

Nvidia unveils Ising AI models for quantum error correction and calibration

submitted by /u/tekz [link] [comments]

Reddit - Artificial Intelligence · 1 min ·
UMKC Announces New Master of Science in Artificial Intelligence
Ai Infrastructure

UMKC Announces New Master of Science in Artificial Intelligence

UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...

AI News - General · 4 min ·

All Content

[2602.16570] Steering diffusion models with quadratic rewards: a fine-grained analysis
Machine Learning

[2602.16570] Steering diffusion models with quadratic rewards: a fine-grained analysis

This article presents a detailed analysis of sampling from reward-tilted diffusion models, focusing on quadratic rewards and their comput...

arXiv - Machine Learning · 4 min ·
[2602.15862] Enhancing Action and Ingredient Modeling for Semantically Grounded Recipe Generation
Llms

[2602.15862] Enhancing Action and Ingredient Modeling for Semantically Grounded Recipe Generation

This paper presents a novel framework for improving recipe generation from food images by enhancing action and ingredient modeling, addre...

arXiv - AI · 3 min ·
[2602.15861] CAST: Achieving Stable LLM-based Text Analysis for Data Analytics
Llms

[2602.15861] CAST: Achieving Stable LLM-based Text Analysis for Data Analytics

The paper presents CAST, a framework designed to improve the stability of LLM-based text analysis in data analytics by enhancing output c...

arXiv - AI · 3 min ·
[2602.16507] Small molecule retrieval from tandem mass spectrometry: what are we optimizing for?
Machine Learning

[2602.16507] Small molecule retrieval from tandem mass spectrometry: what are we optimizing for?

This paper explores the optimization of loss functions in deep learning models for small molecule retrieval from tandem mass spectrometry...

arXiv - Machine Learning · 3 min ·
[2602.15858] State Design Matters: How Representations Shape Dynamic Reasoning in Large Language Models
Llms

[2602.15858] State Design Matters: How Representations Shape Dynamic Reasoning in Large Language Models

This paper explores how state representations impact the reasoning capabilities of large language models (LLMs) in dynamic environments, ...

arXiv - AI · 4 min ·
[2602.16498] Fast and Scalable Analytical Diffusion
Machine Learning

[2602.16498] Fast and Scalable Analytical Diffusion

The paper presents GoldDiff, a novel framework for analytical diffusion that enhances scalability and speed in generative modeling by dyn...

arXiv - AI · 4 min ·
[2602.15856] Rethinking Soft Compression in Retrieval-Augmented Generation: A Query-Conditioned Selector Perspective
Llms

[2602.15856] Rethinking Soft Compression in Retrieval-Augmented Generation: A Query-Conditioned Selector Perspective

The paper presents SeleCom, a novel selector-based soft compression framework for Retrieval-Augmented Generation (RAG), addressing limita...

arXiv - AI · 4 min ·
[2602.16456] Beyond SGD, Without SVD: Proximal Subspace Iteration LoRA with Diagonal Fractional K-FAC
Machine Learning

[2602.16456] Beyond SGD, Without SVD: Proximal Subspace Iteration LoRA with Diagonal Fractional K-FAC

This paper presents a novel approach called LoRSum for optimizing Low-Rank Adaptation (LoRA) in machine learning, enhancing efficiency in...

arXiv - Machine Learning · 4 min ·
[2602.15852] Building Safe and Deployable Clinical Natural Language Processing under Temporal Leakage Constraints
Machine Learning

[2602.15852] Building Safe and Deployable Clinical Natural Language Processing under Temporal Leakage Constraints

This article discusses the development of clinical NLP models that mitigate risks associated with temporal leakage, emphasizing the impor...

arXiv - AI · 4 min ·
[2602.16442] Hardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGA
Machine Learning

[2602.16442] Hardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGA

The paper presents a hardware-accelerated graph neural network approach for neuromorphic event-based audio classification and keyword spo...

arXiv - AI · 4 min ·
[2602.16400] Easy Data Unlearning Bench
Machine Learning

[2602.16400] Easy Data Unlearning Bench

The paper introduces the Easy Data Unlearning Bench, a unified benchmarking suite aimed at simplifying the evaluation of machine unlearni...

arXiv - Machine Learning · 3 min ·
[2602.16363] Improved Bounds for Reward-Agnostic and Reward-Free Exploration
Ai Infrastructure

[2602.16363] Improved Bounds for Reward-Agnostic and Reward-Free Exploration

This paper presents improved algorithms for reward-free and reward-agnostic exploration in Markov decision processes, enhancing the abili...

arXiv - Machine Learning · 3 min ·
[2602.15836] EdgeNav-QE: QLoRA Quantization and Dynamic Early Exit for LAM-based Navigation on Edge Devices
Machine Learning

[2602.15836] EdgeNav-QE: QLoRA Quantization and Dynamic Early Exit for LAM-based Navigation on Edge Devices

The paper presents EdgeNav-QE, a framework that combines QLoRA quantization and dynamic early exit mechanisms to enhance LAM-based naviga...

arXiv - AI · 3 min ·
[2602.16340] The Implicit Bias of Adam and Muon on Smooth Homogeneous Neural Networks
Machine Learning

[2602.16340] The Implicit Bias of Adam and Muon on Smooth Homogeneous Neural Networks

This paper investigates the implicit bias of momentum-based optimizers like Adam and Muon in smooth homogeneous neural networks, extendin...

arXiv - Machine Learning · 3 min ·
[2602.16336] HAWX: A Hardware-Aware FrameWork for Fast and Scalable ApproXimation of DNNs
Machine Learning

[2602.16336] HAWX: A Hardware-Aware FrameWork for Fast and Scalable ApproXimation of DNNs

HAWX introduces a hardware-aware framework for efficiently approximating deep neural networks (DNNs), achieving significant speedups whil...

arXiv - AI · 3 min ·
[2602.16284] Fast KV Compaction via Attention Matching
Llms

[2602.16284] Fast KV Compaction via Attention Matching

The paper presents a novel approach for fast key-value (KV) compaction via Attention Matching, addressing the challenges of scaling langu...

arXiv - Machine Learning · 3 min ·
[2602.16301] Multi-agent cooperation through in-context co-player inference
Machine Learning

[2602.16301] Multi-agent cooperation through in-context co-player inference

This paper explores multi-agent cooperation in reinforcement learning through in-context learning, demonstrating how sequence models can ...

arXiv - AI · 4 min ·
[2602.16192] Revolutionizing Long-Term Memory in AI: New Horizons with High-Capacity and High-Speed Storage
Nlp

[2602.16192] Revolutionizing Long-Term Memory in AI: New Horizons with High-Capacity and High-Speed Storage

This article discusses innovative approaches to long-term memory in AI, emphasizing the importance of retaining raw experiences for bette...

arXiv - Machine Learning · 4 min ·
[2602.16179] EnterpriseGym Corecraft: Training Generalizable Agents on High-Fidelity RL Environments
Machine Learning

[2602.16179] EnterpriseGym Corecraft: Training Generalizable Agents on High-Fidelity RL Environments

The paper presents EnterpriseGym Corecraft, a novel high-fidelity reinforcement learning environment designed to train AI agents for gene...

arXiv - Machine Learning · 4 min ·
[2602.16039] How Uncertain Is the Grade? A Benchmark of Uncertainty Metrics for LLM-Based Automatic Assessment
Llms

[2602.16039] How Uncertain Is the Grade? A Benchmark of Uncertainty Metrics for LLM-Based Automatic Assessment

This article benchmarks various uncertainty metrics for LLM-based automatic assessment, highlighting the challenges of output uncertainty...

arXiv - AI · 4 min ·
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