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Inducing Task Models from Computer-Use Traces
Researchers present Task Model Induction (TMI), a method that extracts structured task models from naturalistic computer-use traces. These traces are screenshots and mouse or keyboard actions recorded during work. TMI…
Beyond Teacher Likelihood: Group-Calibrated On-Policy Distillation for Long-Context Reasoning
Researchers introduce Group-Calibrated On-Policy Distillation (GC-OPD), a method for long-context reasoning tasks that aims to address the mismatch between on-policy distillation and task-specific verifiers. The new…
Part of the npj Series
An article titled 'MultiMed-RAG: Leveraging Multi-Source Knowledge and Agent Collaboration for Medical Tasks' published in the npj Series, was written by Yuxing Lu, Sin Yee Goi, and Jinzhuo Wang in 2026. The article…
Diagram-MMU: A Multi-Modal Benchmark for Scientific Diagrams
The paper discusses Diagram-MMU, a new benchmark designed to evaluate the ability of Multimodal Large Language Models (MLLMs) in parsing and understanding scientific diagrams. The benchmark includes 3.7k diagrams and…
Preference Tree Optimization: Enhancing Goal-Oriented Dialogue with Look-Ahead Simulations
Researchers propose Preference Tree Optimization (PTO), a framework that enhances dialogue systems in specialized domains like Motivational Interviewing by using preference data generated through Look-Ahead Simulations…
VICBench: A Multi-Language Benchmark for Code Vulnerability Detection
The paper introduces VICBench, a multi-language benchmark for code vulnerability detection. It was created by human experts and an agentic workflow, featuring 100 verified vulnerabilities across 88 projects in Python…
Two-stage Odd Residual Flows for Mean-Preserving Probabilistic Time Series Forecasting
Researchers propose a new method called Two-stage Odd Residual Flows (TORF) for probabilistic time series forecasting. The method aims to address the trade-off between distributional flexibility and accurate mean…
Defining Decentralization: An Ontological Perspective
This research paper addresses the lack of a universal definition for decentralization in computer science and its related fields like security, AI, cloud infrastructure, IoT, etc. The authors propose a graph-based…
Defining Decentralization: An Ontological Perspective
The authors address the lack of a universally accepted definition of decentralization in computer science, particularly in emerging fields like decentralized AI and machine learning. They propose a graph-based ontology…
From Values to Benchmarks: Evaluating Large Language Models for Governmental Use in Dutch
Researchers present a new evaluation framework called 'Grip on LLMs' for Dutch governmental use of large language models. The framework focuses on six dimensions: factuality, honesty, social bias, energy consumption…
Multi-Agent AI Safety as an Institutional Design Problem
In this research paper, Abdullah X discusses the role of institutions in ensuring safety for AI agents within multi-agent systems. The study includes a 5280-episode study suite with various experiments testing…
Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations
The paper proposes a new method for document search named Reliable Embedding-free Agentic Document-search (READ), which aims to improve the accuracy of retrieval-augmented generation on specific types of documents such…
Evaluating Investment Logic in Large Language Models: A Real-World Benchmark Towards Personalzied Financial Agents
The paper introduces InvestLogicBench, a new benchmark to evaluate the investment logic of large language models (LLMs). The benchmark focuses on personalized financial decision-making and uses real-world data from 151…
FinEvo-Bench: A Longitudinal Benchmark for Self-Evolving Agents in Professional Financial Workflows
Researchers introduce a new benchmark called FinEvo-Bench for testing self-evolving agents in various financial workflows. The benchmark consists of 120 real-world tasks across six domains, with each task having a…
Poli-Bias: Understanding and Measuring Large Language Model Biases in International Political Conflicts
Researchers introduce Poli-Bias, a method to measure political bias in large language models (LLMs) by comparing responses to paired prompts where country identities are swapped across various geopolitical…
What Current AI Benchmarks Leave Unmeasured: Modality, Search, Citations, and Implications (for Safety Evaluations)
The study compares two access modalities of a large language model (LLM), ChatGPT's chat UI and OpenAI's API, with and without web search enabled. The research shows that the accuracy of responses varies between the…
A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination
Researchers have developed a new framework called A-SR for symbolic regression. This framework uses a self-evolving agentic system that shifts control from expression edits to role-conditioned evidence views. It…
RepoProbe: Benchmarking Architecture-Aware Repository Comprehension with Checklists
The text discusses a new benchmark called RepoProbe for evaluating the ability of Large Language Models (LLMs) to understand repository-scale code. The benchmark uses open-ended questions from GitHub Discussions and…
A foundation model of numerical intelligence with cross-disciplinary generalization
Researchers introduce UNICON, a foundation model that exhibits numerical intelligence across various disciplines. This model can infer relationships from graph-based examples within a system and apply them to queries…
MANTA: Multi-Agent Network Topology Adaptation for Self-Evolving Multi-Agent Systems
The paper introduces MANTA, a framework for Multi-Agent Network Topology Adaptation that allows communication structures in large language model-based multi-agent systems to evolve during inference time. This…
ORCA-bench: How Ready Are Language Model Agents for Oncall?
Researchers introduce ORCA-bench, a new benchmark that tests the ability of large language models to perform root cause analysis (RCA) in a production-like setting. The benchmark includes a microservice system with six…
PAIChecker: Uncovering and Checking PR-Issue Misalignment in SWE-Bench-Like Benchmarks
The paper proposes a system called PAIChecker to check for misalignment between Pull Requests (PR) and Issues in SWE-bench-like benchmarks used for evaluating Large Language Models' issue resolution capability. The…
Do Latent Channels Actually Communicate? A Causal Audit of Latent Multi-Agent LLM
Researchers introduce a method to study how latent channels in multi-agent systems, which transmit information without using text, impact the receiver's performance. They apply this method to Qwen3-4B and Qwen3-8B on…
MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair
MemSecBench is a new benchmark for testing the lifecycle security of agent memory systems. The benchmark contains 310 cases drawn from various contexts, and it follows a controlled protocol to test the persistence…
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