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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 broadly capable AI agents. The researchers conducted ablation experiments and used the pipeline to create a dataset of 100K…
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…
Paying to Know: Micro-Transaction Markets for Verified Product Information in Agentic E-Commerce
The authors propose a new approach for e-commerce where buyers can pay small amounts of money to access detailed, verified product information through micro-transaction markets. This system aims to reward genuine…
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 pay small amounts of money to access detailed, verified information about products through the use of agent-native micro-payment rails. This model is designed…
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…
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…
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…
Privacy-Preserving RAG via Multi-Agent Semantic Rewriting: Achieving Confidentiality Without Compromising Contextual Fidelity
The authors propose a multi-agent framework that uses semantic rewriting to enhance large language models in sensitive scenarios while preserving privacy and contextual fidelity. The framework was tested on two…
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…
SAFARI: Scaling Long Horizon Agentic Fault Attribution via Active Investigation
Researchers have developed SAFARI, a framework that helps diagnose failures in autonomous agents by using a specialized toolbox for reading and searching trajectory segments and a persistent Short-Term Memory (STM) for…
ScaleToT: Generalizing Structured LLM Reasoning for Billion-Scale Low-Activity User Modeling
The paper presents ScaleToT, a method that improves user modeling for low-activity users by learning structured reasoning from a small subset of Large Language Models (LLMs). It constructs typed user-state chains using…
Scaling Laws for Task-Specific LLM Distillation
This paper presents empirical scaling laws for compressing Large Language Models (LLMs) in specific domains, focusing on quantitative finance as an application. The research explores different methods of distillation…
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…
The Latent Bridge: A Continuous Slow-Fast Channel for Real-Time Game Agents
Researchers propose a new method called the Latent Bridge for real-time agents that can act quickly while still planning effectively, even under time constraints such as those found in video games. The method involves…
Themis: An explainable AI-enabled framework for Reinforcement Learning with Human Feedback
Researchers present Themis, an AI framework that combines explainability and human feedback to train safe Reinforcement Learning (RL) systems. The framework is designed for over 200 environments, easy configuration for…
Visualizing 'We the People': Bridging the Perception Gap through Pluralistic Data Storytelling
The paper discusses a project that used AI to create interactive 'opinion landscapes' for an online deliberation called 'We the People'. The goal was to provide a more nuanced and collaborative way of visualizing…
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…
World Models in Pieces: Structural Certification for General Agents
The paper proposes a method to evaluate the understanding of an AI agent in a complex world model, focusing on situations where the agent's abilities are specialized. The authors argue that standard guarantees fail to…
Abstract representational geometry supports inference in large language models
The study compares human and large language model (LLM) abilities to adapt and infer latent task structures from sparse observations. It finds that while LLMs are less frequent at generalizable reasoning than humans…
AI-driven Optimisation of Quality of Recovery (QoR) in Remote Patient Monitoring
Researchers developed a shorter version of the Quality of Recovery (QoR) survey for remote patient monitoring, called QoR-compact. The new five-item questionnaire has similar accuracy in predicting postoperative…
AI-driven Optimisation of Quality of Recovery (QoR) in Remote Patient Monitoring
Researchers developed QoR-compact, a shorter version of the Quality of Recovery (QoR) survey, to be used for daily remote patient monitoring. The new version consists of five questions and was found to predict…
AI Exposure Scores: what they measure, what they miss, and what comes next
The article discusses the limitations of a set of AI Exposure Scores introduced by Eloundou et al., which measure the share of tasks that can be assisted by large language models. The authors highlight two main gaps…
AIR: Adaptive Interleaved Reasoning with Code in MLLMs
The paper presents a new approach to enhance multimodal large language models (MLLMs) by giving them the ability to reason adaptively with code, particularly in numerical computation tasks. This is achieved through…
AOHP: An Open-Source OS-Level Agent Harness for Personalized, Efficient and Secure Interaction
The paper presents the Android Open Harness Project (AOHP), an operating system level agent harness built on the Android Open Source Project. AOHP aims to treat AI agents as first-class OS actors, enabling adaptive…
Automated Semantic Fault Localization in SysML v2: A Human-in-the-Loop Framework Using Knowledge-Graph Augmented LLMs
This research presents a human-in-the-loop framework to identify and repair semantic errors in SysML v2, a systems modeling language. The framework combines a fine-tuned language model with a domain knowledge graph and…
Automated Semantic Fault Localization in SysML v2: A Human-in-the-Loop Framework Using Knowledge-Graph Augmented LLMs
The paper presents a framework for automatically identifying and repairing semantic mistakes in SysML v2 models, which are errors that preserve syntactic validity but violate domain rules. The framework combines a…
Building Pay-Per-Intelligence for AI Agents: How Ampersend Uses Amazon Bedrock AgentCore Payments
AWS describes how Ampersend implemented "pay-per-intelligence" billing for AI agents using Bedrock AgentCore Payments. Agents autonomously route tasks to optimal models and manage spending budgets through a payment…
Capable but Careless: Do Computer-Use Agents Follow Contextual Integrity?
Researchers present AgentCIBench, a tool for evaluating the privacy risks of computer-use agents (CUAs) that access personal data across various applications. The study finds that most frontier CUAs have a high failure…
Causal Discovery in the Era of Agents
The authors propose a new role for agents in causal discovery, where the agents are used to assist the workflow but not supply edges, orientations, priors, constraints or causal conclusions. They introduce an online…
Causal Discovery in the Era of Agents
The paper proposes a new approach for causal discovery in which agents help with data analysis, context retrieval, and method explanation, but do not supply causal conclusions or assumptions. This approach aims to…
CoorDex: Coordinating Body and Hand Priors for Continuous Dexterous Humanoid Loco-Manipulation
Researchers introduce CoorDex, a learning pipeline that enables high-DoF dexterous loco-manipulation on the move for humanoid robots. The pipeline converts high-dimensional body and dexterous hand control into…
Data Selection Through Iterative Self-Filtering for Vision-Language Settings
Researchers propose a new method to select data for training neural networks in large scale, noisy datasets. The Self-Filtering method iteratively trains a CLIP model on an evolving dataset, balancing clean samples and…
Decentralized Autonomous Traffic Management through Corridor Networks
Researchers propose a decentralized approach to managing traffic in Advanced Air Mobility (AAM) corridors using multi-agent reinforcement learning (MARL). The method is tested on various multi-corridor networks and…
Decentralized Autonomous Traffic Management through Corridor Networks
Researchers propose a decentralized traffic management system for Advanced Air Mobility (AAM) corridors using multi-agent reinforcement learning (MARL). The system allows autonomous aircraft to navigate complex…
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…
Detecting Malicious Agent Skills in the Wild using Attention
The researchers present Locate-and-Judge, a two-stage detector designed for malicious agent skills in LLM (Language Model) systems. It reduces the cost of auditing an entire marketplace by focusing on high-attention…
Digital Humanism and Evolutionary Design
This research paper explores the concepts of digital humanism and evolutionary design, focusing on their potential commonalities, synergies, and challenges in human-centered technological development. The paper…
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…
DiT-Reward: Generative Representations for Text-to-Image Reward Modeling
The paper introduces DiT-Reward, a model that uses pre-trained text-to-image Diffusion Transformer to evaluate generated images by converting it into a reward model. DiT-Reward outperforms HPSv3 on four preference…
Dynamic multi-agent deep reinforcement learning-based pricing and incentivization approach in multimodal transportation networks
A research paper proposes a multi-agent deep reinforcement learning framework to address challenges in multimodal transportation systems, particularly regarding shared mobility services (SMSs) demand concentration and…
Dynamic multi-agent deep reinforcement learning-based pricing and incentivization approach in multimodal transportation networks
A study proposes a multi-agent deep reinforcement learning framework to coordinate shared mobility services (SMSs) and public transport in multimodal transportation systems. The framework aims to improve efficiency…
EHR-Complex: Benchmarking Medical Agents for Complex Clinical Reasoning
Researchers have introduced a new benchmark called EHR-Complex to test the performance of clinical agents that interact with electronic health records (EHRs). The benchmark is built on a large dataset from MIMIC-IV and…
EHR-Complex: Benchmarking Medical Agents for Complex Clinical Reasoning
Researchers introduce EHR-Complex, a new benchmark for evaluating the performance of medical AI agents in handling complex clinical database reasoning tasks. The benchmark is built on MIMIC-IV data and comprises about…
Exposing the Illusion of Erasure in Knowledge Editing for LLMs
This research paper explores the reliability and mechanisms of Knowledge Editing (KE) in Language Models (LLMs), revealing that edited knowledge is not always fully erased but can continue to surface even across…
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