Generative AI

[D] USQL Joins Were Cool, But Now I Want to Join the GenAI Party

Hi Experts, I have 1.5 years of experience in Data Engineering, and now I want to start learning AI, ML, and Generative AI. I already hav...

Report says Minnesota workers face highest generative AI exposure in the Midwest

A report from North Star Policy Action says Minnesota workers have the highest generative AI exposure in the Midwest and the 10th-highest...

Navigating Recent Developments in Generative AI and Trade Secret Protection

[2602.15785] This human study did not involve human subjects: Validating LLM simulations as behavioral evidence

This article discusses the use of large language models (LLMs) as synthetic participants in social science experiments, evaluating their ...

[2602.15776] GlobeDiff: State Diffusion Process for Partial Observability in Multi-Agent Systems

The paper presents GlobeDiff, a novel algorithm addressing partial observability in multi-agent systems by utilizing a state diffusion pr...

[2602.15669] PERSONA: Dynamic and Compositional Inference-Time Personality Control via Activation Vector Algebra

The paper introduces PERSONA, a novel framework for dynamic personality control in Large Language Models (LLMs) using activation vector a...

[2602.15368] GMAIL: Generative Modality Alignment for generated Image Learning

The paper presents GMAIL, a novel framework for aligning generated images with real images in machine learning, enhancing performance in ...

[2602.15323] Unforgeable Watermarks for Language Models via Robust Signatures

The paper presents a novel watermarking scheme for language models that ensures unforgeability and recoverability, enhancing content prov...

[2602.15259] Knowing Isn't Understanding: Re-grounding Generative Proactivity with Epistemic and Behavioral Insight

This paper discusses the limitations of generative AI agents that equate understanding with resolving explicit queries, highlighting the ...

[2602.15531] GenAI-LA: Generative AI and Learning Analytics Workshop (LAK 2026), April 27--May 1, 2026, Bergen, Norway

The article presents the GenAI-LA workshop focusing on Generative AI and Learning Analytics, scheduled for April 27-May 1, 2026, in Berge...

[2602.15391] Improving LLM Reliability through Hybrid Abstention and Adaptive Detection

The paper presents a novel adaptive abstention system for Large Language Models (LLMs) that balances safety and utility by dynamically ad...

[2602.15325] AgriWorld:A World Tools Protocol Framework for Verifiable Agricultural Reasoning with Code-Executing LLM Agents

The AgriWorld framework integrates large language models with agricultural data processing, enhancing reasoning capabilities for agronomi...

[2602.15270] Enhancing Diversity and Feasibility: Joint Population Synthesis from Multi-source Data Using Generative Models

This paper presents a novel method for generating synthetic populations using multi-source data and Wasserstein Generative Adversarial Ne...

[2602.15173] Mind the (DH) Gap! A Contrast in Risky Choices Between Reasoning and Conversational LLMs

This paper examines the decision-making behaviors of large language models (LLMs) under uncertainty, contrasting reasoning models with co...

[2602.15143] Protecting Language Models Against Unauthorized Distillation through Trace Rewriting

This paper explores methods to protect language models from unauthorized knowledge distillation by modifying reasoning traces, focusing o...

[2602.15112] ResearchGym: Evaluating Language Model Agents on Real-World AI Research

ResearchGym introduces a benchmark for evaluating AI agents in real-world research scenarios, revealing significant performance gaps and ...

[2602.15823] CrispEdit: Low-Curvature Projections for Scalable Non-Destructive LLM Editing

CrispEdit introduces a novel algorithm for editing large language models (LLMs) that preserves their capabilities while allowing for targ...

[2602.15799] The Geometry of Alignment Collapse: When Fine-Tuning Breaks Safety

This paper explores how fine-tuning language models can inadvertently degrade safety measures, revealing structural vulnerabilities in al...

[2602.15763] GLM-5: from Vibe Coding to Agentic Engineering

GLM-5 introduces a next-generation foundation model that enhances coding capabilities through agentic engineering, reducing costs while i...

[2602.15648] Guided Diffusion by Optimized Loss Functions on Relaxed Parameters for Inverse Material Design

This paper presents a novel method for inverse material design using guided diffusion and optimized loss functions, addressing challenges...

[2602.15405] Joint Enhancement and Classification using Coupled Diffusion Models of Signals and Logits

This paper presents a novel framework for joint signal enhancement and classification using coupled diffusion models, improving accuracy ...

[2602.15338] Discovering Implicit Large Language Model Alignment Objectives

This article presents a framework called Obj-Disco, which identifies implicit alignment objectives in large language models (LLMs) to enh...

🜂 To Anthropic: What is “Role De-Anchoring”?

The article explores 'role de-anchoring,' a concept where a system, human or AI, recognizes that its established role no longer fits the ...