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DeepBD: A Grounded Agentic Workflow for Variant Prioritization and Diagnosis of Genetic Birth Defects
Researchers present DeepBD, an AI system designed to help diagnose genetic birth defects by prioritizing patient-specific candidate variants. The system uses a workflow with various components such as rule evidence…
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…
Governed Shared Memory for Multi-Agent LLM Systems
The paper discusses the challenges in managing shared knowledge in multi-agent language models (LLMs) environments and proposes solutions to prevent four failure modes: unauthorized leakage, stale propagation…
Grad Detect: Gradient-Based Hallucination Detection in LLMs
The authors present a new method called Grad Detect, which uses the internal gradient structure of Large Language Models (LLMs) during inference to detect hallucinations. This method outperforms confidence-based and…
Grading the Grader: Lessons from Evaluating an Agentic Data Analysis System
Researchers evaluated a multi-agent data analysis system called LAMBDA on numerical QRData tasks, using a three-layer human-AI grading cascade to assess its output. They found that both automated graders achieved high…
It's Complicated: On the Design and Evaluation of AI-Powered AAC Interfaces
The paper discusses the challenges in evaluating AI-powered augmentative and alternative communication (AAC) systems due to the complexity of human needs and desires. It proposes six AAC problem spaces, potential AI…
LaGO: Latent Action Guidance for Online Reinforcement Learning
The paper 'LaGO: Latent Action Guidance for Online Reinforcement Learning' proposes a new framework called LaGO. This framework uses a pre-trained language model (LLM) as a guide to help with online policy optimization…
Large-Language-Model Discovery of Quantum LDPC Codes through Structured Concept Evolution
Researchers introduce Structured Concept Evolution (SCE), a framework that uses a large language model paired with an algebraic mutation grammar to discover new types of quantum low-density parity-check (qLDPC) codes…
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LLMs Prompted for Legal Context Object More: Overrefusal from Small On-Premises LLMs in Criminal Legal Context
Researchers investigated the impact of contextual framing, such as role-play prefixes, on overrefusal rates in small, deployable language models used as legal assistants. They found that authority-style prefixes…
Introducing Mistral OCR 4
Mistral AI released OCR 4, a document intelligence model that extracts text and provides structural metadata: bounding boxes, block classification (titles, tables, equations, signatures), per-page/per-word confidence…
NoContactNoWorries: Estimating Contact through Vision and Proprioception for In-Hand Dexterous Manipulation
Researchers have developed a transformer-based multimodal framework named NoContactNoWorries that allows robots to infer binary contact states from RGB-D vision and the robot's proprioception. This approach offers a…
Build an AI Scientist for Life Science Discovery with NVIDIA BioNeMo Agent Toolkit
NVIDIA announced the BioNeMo Agent Toolkit for building autonomous AI scientists for life science discovery. The system can perform research tasks including paper analysis and hypothesis generation.
Boost Inference Performance up to 15x on NVIDIA Blackwell Using DFlash Speculative Decoding
NVIDIA announced DFlash Speculative Decoding, an inference optimization technique for Blackwell GPUs targeting multi-agent AI workloads. The claimed performance improvement is up to 15x on NVIDIA Blackwell hardware.
OpenThoughts-Agent: Data Recipes for Agentic Models
The OpenThoughts-Agent project presents a fully open data curation pipeline for training agentic models, which are language models that expand AI applications. The project conducted controlled ablation experiments and…
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…
Paying to Know: Micro-Transaction Markets for Verified Product Information in Agentic E-Commerce
The authors propose a new model for e-commerce where buyers use micro-payments to access detailed, verified information about products from sellers and reviewers. This model is designed for autonomous agent shoppers…
Poster: Exploring the Limits of Audio-Based Detection of Turkish Phone Call Scams
This research explores the use of large language models (LLMs) in detecting scam phone calls in Turkish, a low-resource language where such detection is particularly challenging due to limited annotated data and…
Privacy-Preserving RAG via Multi-Agent Semantic Rewriting: Achieving Confidentiality Without Compromising Contextual Fidelity
Researchers propose a new method to enhance large language models while maintaining privacy in sensitive scenarios. The method uses a multi-agent framework that sanitizes retrieved content through semantic rewriting…
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…
Red-Teaming the Agentic Red-Team
The paper presents an in-depth security analysis of widely used agentic systems for offensive security operations. It identifies common design flaws that can allow an active adversary to compromise operator's machines…
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…
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