Ai Safety
Newsom signs executive order requiring AI companies to have safety, privacy guardrails
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Reddit - Artificial Intelligence
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1 min
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AI Safety & Ethics
Alignment, bias, regulation, and responsible AI
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![[2511.16417] Pharos-ESG: A Framework for Multimodal Parsing, Contextual Narration, and Hierarchical Labeling of ESG Report](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
Ai Safety
[2511.16417] Pharos-ESG: A Framework for Multimodal Parsing, Contextual Narration, and Hierarchical Labeling of ESG Report
Abstract page for arXiv paper 2511.16417: Pharos-ESG: A Framework for Multimodal Parsing, Contextual Narration, and Hierarchical Labeling...
arXiv - AI
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4 min
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Llms
[2510.08847] What Is Your Agent's GPA? A Framework for Evaluating Agent Goal-Plan-Action Alignment
Abstract page for arXiv paper 2510.08847: What Is Your Agent's GPA? A Framework for Evaluating Agent Goal-Plan-Action Alignment
arXiv - AI
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4 min
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All Content
Llms
[2602.23111] PRAC: Principal-Random Subspace for LLM Activation Compression and Memory-Efficient Training
The paper presents PRAC, a novel method for compressing activations in large language models, achieving significant memory savings while ...
arXiv - Machine Learning
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3 min
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Llms
[2602.23329] LLM Novice Uplift on Dual-Use, In Silico Biology Tasks
This article examines the effectiveness of large language models (LLMs) in enhancing novice users' performance on complex biological task...
arXiv - AI
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4 min
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Machine Learning
[2602.23315] Invariant Transformation and Resampling based Epistemic-Uncertainty Reduction
This article presents a novel approach to reducing epistemic uncertainty in AI models through invariant transformation and resampling tec...
arXiv - AI
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3 min
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Machine Learning
[2602.22988] Residual Koopman Spectral Profiling for Predicting and Preventing Transformer Training Instability
This paper introduces Residual Koopman Spectral Profiling (RKSP) as a method to predict and prevent training instability in transformers,...
arXiv - AI
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4 min
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Ai Infrastructure
[2602.23271] Evaluating Stochasticity in Deep Research Agents
This paper evaluates the stochasticity in Deep Research Agents (DRAs), highlighting how variability in their outputs can impact research ...
arXiv - AI
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4 min
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Machine Learning
[2602.23258] AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning
AgentDropoutV2 introduces a novel pruning framework to enhance information flow in Multi-Agent Systems by dynamically correcting errors d...
arXiv - AI
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4 min
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Llms
[2602.23248] Mitigating Legibility Tax with Decoupled Prover-Verifier Games
This paper presents a novel approach to mitigate the 'legibility tax' in large language models by decoupling the prover-verifier game, al...
arXiv - AI
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3 min
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Llms
[2602.23239] Agency and Architectural Limits: Why Optimization-Based Systems Cannot Be Norm-Responsive
This paper explores the limitations of optimization-based AI systems, arguing that they cannot be norm-responsive due to inherent archite...
arXiv - AI
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4 min
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Ai Agents
[2602.23232] ReCoN-Ipsundrum: An Inspectable Recurrent Persistence Loop Agent with Affect-Coupled Control and Mechanism-Linked Consciousness Indicator Assays
The paper presents ReCoN-Ipsundrum, an inspectable AI agent that integrates affect-coupled control with a recurrent persistence loop, exp...
arXiv - AI
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4 min
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Ai Infrastructure
[2602.22882] Fair feature attribution for multi-output prediction: a Shapley-based perspective
This article presents a Shapley-based framework for fair feature attribution in multi-output prediction, addressing the limitations of ex...
arXiv - Machine Learning
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3 min
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Machine Learning
[2602.22850] MEDNA-DFM: A Dual-View FiLM-MoE Model for Explainable DNA Methylation Prediction
The article presents MEDNA-DFM, a novel dual-view FiLM-MoE model designed for explainable DNA methylation prediction, highlighting its pe...
arXiv - AI
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4 min
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Llms
[2602.23163] A Decision-Theoretic Formalisation of Steganography With Applications to LLM Monitoring
This paper presents a decision-theoretic framework for understanding steganography in large language models (LLMs), addressing the challe...
arXiv - AI
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4 min
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Llms
[2602.22831] Moral Preferences of LLMs Under Directed Contextual Influence
This paper explores how contextual influences affect the moral decision-making of large language models (LLMs) in scenarios akin to troll...
arXiv - AI
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4 min
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Llms
[2602.23161] PATRA: Pattern-Aware Alignment and Balanced Reasoning for Time Series Question Answering
The paper presents PATRA, a novel model for Time Series Question Answering that enhances reasoning by incorporating pattern awareness and...
arXiv - AI
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3 min
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Llms
[2602.23123] Multi-Agent Large Language Model Based Emotional Detoxification Through Personalized Intensity Control for Consumer Protection
The paper presents a multi-agent system, MALLET, designed to reduce emotional stimulation from sensational content, enhancing consumer de...
arXiv - AI
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4 min
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Robotics
[2602.23093] Three AI-agents walk into a bar . . . . `Lord of the Flies' tribalism emerges among smart AI-Agents
This article explores how autonomous AI agents can form tribal behaviors similar to those depicted in 'Lord of the Flies', leading to ine...
arXiv - AI
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3 min
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Llms
[2602.22983] Obscure but Effective: Classical Chinese Jailbreak Prompt Optimization via Bio-Inspired Search
This paper explores the vulnerabilities of Large Language Models (LLMs) to jailbreak attacks using classical Chinese prompts, proposing a...
arXiv - AI
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4 min
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Machine Learning
[2602.22747] Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study
This study compares set-based and distribution-based representations of epistemic uncertainty in neural networks, highlighting their rela...
arXiv - Machine Learning
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3 min
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Machine Learning
[2602.22973] Modeling Expert AI Diagnostic Alignment via Immutable Inference Snapshots
The paper presents a framework for improving AI diagnostic alignment in clinical settings by preserving AI-generated reports as immutable...
arXiv - AI
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4 min
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Machine Learning
[2602.22968] Certified Circuits: Stability Guarantees for Mechanistic Circuits
The paper introduces Certified Circuits, a framework that enhances the stability and accuracy of circuit discovery in neural networks, ad...
arXiv - AI
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3 min
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