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Notes on machine learning
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The Unfireable Safety Kernel: Execution-Time AI Alignment for AI Agents and Other Escapable AI Systems
The authors propose a safety mechanism for AI agents called the Unfireable Safety Kernel, which is designed to prevent AI systems from making unauthorized changes within their own runtime. The kernel is implemented in…
EG-VQA: Benchmarking Verifiable Video Question Answering with Grounded Temporal Evidence
Researchers have developed a new benchmark called Evidence-Grounded Video Question Answering Benchmark (EG-VQA) to evaluate the performance of video question answering models. The benchmark is unique because it…
FlowPipe: LLM-Enhanced Conditional Generative Flow Networks for Data Preparation Pipeline Construction
Researchers propose a new method called FlowPipe to automatically construct data preparation pipelines for machine learning. The proposed method uses Conditional Generative Flow Networks (C-GFlowNets) and Deep Semantic…
Large-Language-Model Discovery of Quantum LDPC Codes through Structured Concept Evolution
Researchers have developed a new method called Structured Concept Evolution (SCE) that uses a large language model and structured algebraic mutation grammar to discover Quantum Low-Density Parity-Check (qLDPC) codes…
OrbitForge: Text-to-3D Scene Generation via Reconstruction-Anchored Video Synthesis
The researchers introduce OrbitForge, a method that converts single text-generated videos into 3D scenes using frozen video priors and per-prompt Gaussian Splatting reconstruction optimization. The method uses 3D…
Poster: Exploring the Limits of Audio-Based Detection of Turkish Phone Call Scams
Researchers introduce a new dataset of Turkish phone call conversations to test large language models' ability to detect scams. They find that using corrected or uncorrected transcripts improves results compared to…
Quant Convergence: Bridging Classical Value Investing and Modern Factor Models for Systematic Equity Selection
This research explores the effectiveness of Benjamin Graham's classic value investing rules in comparison to complex machine learning models in the context of equity selection. The study found that the pure Graham…
Reinforcement Learning for Computer-Use Agents with Autonomous Evaluation
Researchers propose a framework for training Computer-Use Agents (CUAs) using reinforcement learning and autonomous vision-language evaluation. This method provides a scalable supervision signal, improving success…
RetiSEM: Generalising Causal Models for Fragmented Biomedical Data
A new method called RetiSEM is proposed to learn causal models from fragmented biomedical data, where clinical, molecular, and imaging variables are often incomplete or not jointly observed. The method organizes…
Scaling Laws for Task-Specific LLM Distillation
This research provides empirical scaling laws for compressing Large Language Models (LLMs) specifically for certain domains, focusing on quantitative finance as the application domain. The study investigates the impact…
When CQs Go Wrong: Challenges in CQ Verification with OE-Assist
This research paper discusses the challenges faced during Competency Questions (CQ) verification, a crucial step in ensuring that an ontology accurately represents its intended purpose. The authors found that CQs can…
Detecting Malicious Agent Skills in the Wild using Attention
The research presents a two-stage detector called Locate-and-Judge for identifying malicious skills in Language Model (LLM) agents. These skills are third-party packages of instructions that can potentially exfiltrate…
Distribution-Aware Diffusion-LLM for Robust Ultra-Long-Term Time Series Forecasting
The authors propose a new method called Diffusion-LLM for time series forecasting using Large Language Models (LLMs). The proposed framework integrates a conditional diffusion model into an LLM-based forecasting…
GRINQH: Graded Input-based Quantization Hierarchy for Efficient LLM Generation
The paper presents a method called GRINQH (GRaded INput-based Quantization Hierarchy) to accelerate the decoding process of language models during inference, particularly beneficial for edge-computing settings. The…
Learning Process Rewards via Success Visitation Matching for Efficient RL
Researchers propose a method to make reinforcement learning (RL) more effective when dealing with sparse rewards, which are typically given only when a task is completed. The method incentivizes the RL-learned policy…
RECALL: Recovery Experience Collection for Active Lifelong Learning in Vision-Language-Action Models
The paper proposes a new approach for training Vision-Language-Action (VLA) models through active, continual learning. This method aims to address the inefficiencies of traditional passive imitation learning by…
RS-Gen: A Multi-Stage Agentic Framework for Reasoning and Search-Augmented Image Generation
The paper introduces RS-Gen, a multi-stage image agent framework that aims to improve the performance of existing image generation and editing models by introducing a 'Questioning-and-Solving' closed-loop mechanism for…
VeriEvol: Scaling Multimodal Mathematical Reasoning via Verifiable Evol-Instruct
The paper presents VeriEvol, a framework for scaling reinforcement learning in visual mathematical reasoning. It addresses the challenge of maintaining reliable reward labels as data volume grows by treating scaling as…
VideoAgent: All-in-One Framework for Video Understanding and Editing
Researchers have developed a new framework called VideoAgent to automate video editing tasks. The system can handle diverse editing operations and understand long videos, addressing limitations in existing automated…
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