Search: “tools”

BenCSSmark: Making the Social Sciences Count in LLM Research

model-releasesarxiv-cs-cl

arXiv:2605.04886v1 Announce Type: new Abstract: This position paper argues that the under-representation of social science tasks in contemporary LLM benchmarks limits advances in both LLM evaluation a

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Safety

Beyond Fixed Thresholds and Domain-Specific Benchmarks for Explainable Multi-Task Classification in Autonomous Vehicles

DGX agent

arXiv:2605.04299v1 Announce Type: new Abstract: Scene understanding is a vital part of autonomous driving systems, which requires the use of deep learning models. Deep learning methods are intrinsical

safetyarxiv-cs-cv

7 May 2026

Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation

arXiv:2605.04366v1 Announce Type: cross Abstract: Safety-critical scenarios are essential for the development of autonomous vehicles (AVs) but are rare in real-world driving data. While simulation off

Connecting online criminal behavior with machine learning: Using authorship attribution to analyze and link potential online traffickers

arXiv:2605.04080v1 Announce Type: new Abstract: This research investigated how online criminal activities can be better understood and connected using data-driven machine learning methods. Many illega

safetyarxiv-cs-cl

Copula-Based Endogeneity Correction for Doubly Robust Estimation of Treatment Effect

arXiv:2605.03278v2 Announce Type: cross Abstract: Doubly Robust (DR) estimation of treatment effect relies on an untestable assumption that is the absence of unobserved confounding. This assumption is

safetyarxiv-cs-ai

Empirical Study of Pop and Jazz Mix Ratios for Genre-Adaptive Chord Generation

model-releasesarxiv-cs-lg

arXiv:2605.04998v1 Announce Type: cross Abstract: Chord progression generation is practically important but understudied. Most large-scale symbolic music systems target melody, multi-track arrangement

Tutorials

Estimating the expected output of wide random MLPs more efficiently than sampling

arXiv:2605.05179v1 Announce Type: new Abstract: By far the most common way to estimate an expected loss in machine learning is to draw samples, compute the loss on each one, and take the empirical ave

tutorialsarxiv-cs-lg

Evaluating Prompting and Execution-Based Methods for Deterministic Computation in LLMs

arXiv:2605.03227v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated strong capabilities in natural language understanding and reasoning. However, their ability to perform ex

researcharxiv-cs-ai

Local Ai

From Intent to Execution: Composing Agentic Workflows with Agent Recommendation

arXiv:2605.03986v1 Announce Type: new Abstract: Multi-Agent Systems (MAS) built using AI agents fulfill a variety of user intents that may be used to design and build a family of related applications.

local-aiarxiv-cs-ai

Hierarchical Visual Agent: Managing Contexts in Joint Image-Text Space for Advanced Chart Reasoning

arXiv:2605.04304v1 Announce Type: cross Abstract: Advanced chart question answering requires both precise perception of small visual elements and multi-step reasoning across several subplots. While ex

agentsarxiv-cs-cl

Applications

Learning Time-Inhomogeneous Markov Dynamics in Financial Time Series via Neural Parameterization

arXiv:2605.04690v1 Announce Type: new Abstract: Modeling the dynamics of non-stationary stochastic systems requires balancing the representational power of deep learning with the mathematical transpar

applicationsarxiv-cs-lg

Neural Discovery of Strichartz Extremizers

arXiv:2605.04918v1 Announce Type: cross Abstract: Strichartz inequalities are a cornerstone of the modern theory of dispersive PDEs, but their extremizers are known explicitly only in a handful of sha

OPENJ: A Conceptual Framework for Open-Source Digital Human Modeling and Ergonomic Assessment in a CAD Environment

arXiv:2605.04270v1 Announce Type: cross Abstract: Industrial workplace challenges range from musculoskeletal disorders -- a leading cause of occupational injury -- to suboptimal workstation layouts, i

researcharxiv-cs-ro

Preference-Based Self-Distillation: Beyond KL Matching via Reward Regularization

arXiv:2605.05040v1 Announce Type: new Abstract: On-policy distillation is an efficient alternative to reinforcement learning, offering dense token-level training signals. However, its reliance on a st

ProgramBench: Can Language Models Rebuild Programs From Scratch?

arXiv:2605.03546v1 Announce Type: cross Abstract: Turning ideas into full software projects from scratch has become a popular use case for language models. Agents are being deployed to seed, maintain,

Revisiting the Travel Planning Capabilities of Large Language Models

arXiv:2605.03308v1 Announce Type: new Abstract: Travel planning serves as a critical task for long-horizon reasoning, exposing significant deficits in LLMs. However, existing benchmarks and evaluation

ScriptHOI: Learning Scripted State Transitions for Open-Vocabulary Human-Object Interaction Detection

arXiv:2605.05057v1 Announce Type: new Abstract: Open-vocabulary human-object interaction (HOI) detection requires recognizing interaction phrases that may not appear as annotated categories during tra

researcharxiv-cs-cv

StableI2I: Spotting Unintended Changes in Image-to-Image Transition

model-releasesarxiv-cs-cv

arXiv:2605.04453v1 Announce Type: new Abstract: In most real-world image-to-image (I2I) scenarios, existing evaluations primarily focus on instruction following and the perceptual quality or aesthetic

Superposition Is Not Necessary: A Mechanistic Interpretability Analysis of Transformer Representations for Time Series Forecasting

arXiv:2605.05151v1 Announce Type: new Abstract: Transformer architectures have been widely adopted for time series forecasting, yet whether the representational mechanisms that make them powerful in N

Local Ai

The Hive Mind is a Single Reinforcement Learning Agent

arXiv:2410.17517v5 Announce Type: replace-cross Abstract: Decision-making is an essential attribute of any intelligent agent or group. Natural systems are known to converge to effective strategies thr

local-aiarxiv-cs-ai

Applications

When LLMs get significantly worse: A statistical approach to detect model degradations

arXiv:2602.10144v2 Announce Type: replace-cross Abstract: Minimizing the inference cost and latency of foundation models has become a crucial area of research. Optimization approaches include theoreti

applicationsarxiv-cs-lg

Local Ai

A Cellular Doctrine of Morality: Intrinsic Active Precision and the Mind-Reality Overload Dilemma

arXiv:2605.01376v1 Announce Type: new Abstract: Current AI systems, grounded in oversimplified neuroscience, risk eroding the distinction between truth and falsehood. They maximize reward by amplifyin

local-aiarxiv-cs-ai

Agentic Multi-Source Grounding for Enhanced Query Intent Understanding: A DoorDash Case Study

arXiv:2603.01486v2 Announce Type: replace Abstract: Accurately mapping user queries to business categories is a fundamental Information Retrieval challenge for multi-category marketplaces, where conte

Aligning Inductive Bias for Data-Efficient Generalization in State Space Models

arXiv:2509.20789v4 Announce Type: replace Abstract: The remarkable success of modern AI has been closely tied to scaling laws, yet the finite supply of high-quality data makes data efficiency--learnin

Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations

model-releasesarxiv-cs-lg

arXiv:2512.13913v3 Announce Type: replace Abstract: Out-of-equilibrium quantum many-body systems exhibit rapid correlation buildup that underlies many emerging phenomena. Exact wave-function methods t

Causal Software Engineering: A Vision and Roadmap

arXiv:2605.02454v1 Announce Type: cross Abstract: Software engineering increasingly involves making high-stakes decisions under uncertainty, using signals from code, field data, and socio-technical pr

ClinicBot: A Guideline-Grounded Clinical Chatbot with Prioritized Evidence RAG and Verifiable Citations

arXiv:2605.00846v1 Announce Type: new Abstract: Clinical diagnosis requires answers that are accurate, verifiable, and explicitly grounded in official guidelines. While large language models excel at

ContextCov: Deriving and Enforcing Executable Constraints from Agent Instruction Files

arXiv:2603.00822v2 Announce Type: replace-cross Abstract: As Large Language Model (LLM) agents increasingly execute complex, autonomous software engineering tasks, developers rely on natural language

Conventional Commit Classification using Large Language Models and Prompt Engineering

arXiv:2605.02033v1 Announce Type: cross Abstract: Conventional commits provide a structured format for writing commit messages, which improves readability, software maintenance, and enables automation

Design-OS: A Specification-Driven Framework for Engineering System Design with a Control-Systems Design Case

arXiv:2603.20151v2 Announce Type: replace-cross Abstract: Engineering system design -- whether mechatronic, control, or embedded -- often proceeds in an ad hoc manner, with requirements left implicit

DocSync: Agentic Documentation Maintenance via Critic-Guided Reflexion

arXiv:2605.02163v1 Announce Type: cross Abstract: Software documentation frequently drifts from executable logic as codebases evolve, creating technical debt that degrades maintainability and causes d

Erase Persona, Forget Lore: Benchmarking Multimodal Copyright Unlearning in Large Vision Language Models

model-releasesarxiv-cs-cv

arXiv:2605.03547v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs), trained on web-scale data, risk memorizing and regenerating copyrighted visual content such as characters and logo

Evaluating Agentic AI in the Wild: Failure Modes, Drift Patterns, and a Production Evaluation Framework

arXiv:2605.01604v1 Announce Type: new Abstract: Existing evaluation frameworks for large language models -- including HELM, MT-Bench, AgentBench, and BIG-bench -- are designed for controlled, single-s

Explainable AI for Blind and Low-Vision Users: Navigating Trust, Modality, and Interpretability in the Agentic Era

arXiv:2604.00187v2 Announce Type: replace-cross Abstract: Explainable Artificial Intelligence (XAI) is critical for ensuring trust and accountability, yet its development remains predominantly visual.

Human-in-the-Loop Uncertainty Analysis in Self-Adaptive Robots Using LLMs

arXiv:2605.02983v1 Announce Type: new Abstract: Self-adaptive robots operate in dynamic, unpredictable environments where unaddressed uncertainties can lead to safety violations and operational failur

safetyarxiv-cs-ro

Model Spec Midtraining: Improving How Alignment Training Generalizes

arXiv:2605.02087v1 Announce Type: new Abstract: Some frontier AI developers aim to align language models to a Model Spec or Constitution that describes the intended model behavior. However, standard a

safetyarxiv-cs-ai

New Bounds for Zarankiewicz Numbers via Reinforced LLM Evolutionary Search

arXiv:2605.01120v1 Announce Type: new Abstract: The Zarankiewicz number extbf{Z}(m, n, s, t) is the maximum number of edges in a bipartite graph G_{m, n} such that there is no complete K_{s, t} bipart

OpenSeeker-v2: Pushing the Limits of Search Agents with Informative and High-Difficulty Trajectories

arXiv:2605.04036v1 Announce Type: cross Abstract: Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet their development remains dominat

agentsarxiv-cs-cl

Optimal Posterior Sampling for Policy Identification in Tabular Markov Decision Processes

arXiv:2605.03921v1 Announce Type: new Abstract: We study the (arepsilon, elta)-PAC policy identification problem in finite-horizon episodic Markov Decision Processes. Existing approaches provide finit

Partial Effective Information Decomposition for Synergistic Causality

arXiv:2605.03267v1 Announce Type: cross Abstract: Causality is a central topic in scientific inquiry, yet for complex systems, the identification and analysis of synergistic causation remain a challen

PhysicianBench: Evaluating LLM Agents in Real-World EHR Environments

arXiv:2605.02240v1 Announce Type: new Abstract: We introduce PhysicianBench, a benchmark for evaluating LLM agents on physician tasks grounded in real clinical setting within electronic health record

Predicting missing values: A good idea?

arXiv:2605.03733v1 Announce Type: cross Abstract: Minimizing the Mean Squared Error (MSE) is a key objective in machine learning and is commonly used for imputing missing values. While this approach p

The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence

arXiv:2408.12622v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is reshaping society, from video generation to medical diagnosis, coding agents to autonomous vehicles. Yet resea

The Design and Composition of Structural Causal Decision Processes

arXiv:2605.02681v1 Announce Type: cross Abstract: We present two new classes of causal models of decision-making agents. Our approach is motivated by the needs of modeling the economics of computing s

safetyarxiv-cs-ai

Using LLMs in Software Design: An Empirical Study of GitHub and A Practitioner Survey

arXiv:2605.01392v1 Announce Type: cross Abstract: Recent advancements in Large Language Models (LLMs) have demonstrated significant potential across a wide range of software engineering tasks, includi

researcharxiv-cs-ai

Valley3: Scaling Omni Foundation Models for E-commerce

arXiv:2605.01278v1 Announce Type: new Abstract: In this work, we present Valley3, an omni multimodal large language model (MLLM) developed for diverse global e-commerce tasks, with unified understandi

Will the Carbon Border Adjustment Mechanism Impact European Electricity Prices? A GNN-Based Network Analysis

arXiv:2605.03304v1 Announce Type: new Abstract: The European Union's Carbon Border Adjustment Mechanism (CBAM) creates a complex challenge for the interconnected European electricity market. Tradition

2D-ThermAl: Physics-Informed Framework for Thermal Analysis of Circuits using Generative AI

arXiv:2512.01163v2 Announce Type: replace Abstract: Thermal analysis is increasingly critical in modern integrated circuits, where non-uniform power dissipation and high transistor densities can cause