Search: “engineering”

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution

arXiv:2512.05958v2 Announce Type: replace-cross Abstract: Generative search engines based on large language models (LLMs) are replacing traditional search, fundamentally changing how information provi

20 May 2026

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

The Silent Hyperparameter: Quantifying the Impact of Inference Backends on LLM Reproducibility

DGX agent

arXiv:2605.19537v1 Announce Type: new Abstract: Progress in LLMs is increasingly measured through standardized benchmarks, where state-of-the-art improvements are often separated by fractions of a per

model-releasesarxiv-cs-lg

20 May 2026

A Feature-Driven Framework for Software Fault Prediction

arXiv:2605.17611v1 Announce Type: cross Abstract: Software fault prediction (SFP) is a critical task in software engineering, enabling early identification of faults in modules to improve software qua

Analyzing and Guiding Zero-Shot Posterior Sampling in Diffusion Models

arXiv:2602.07715v2 Announce Type: replace Abstract: Recovering a signal from its degraded measurements is a long standing challenge in science and engineering. Recently, zero-shot diffusion based meth

Evolutionary Extreme Learning Machine of ab-initio Energy Landscapes for Crystal Structure Prediction using Manta Ray Optimization with Levy Flight

arXiv:2605.17148v1 Announce Type: cross Abstract: The Manta Ray Foraging Optimization algorithm (MRFO) has proven to be a powerful heuristic strategy in the optimal solution of a large number of engin

GUIDE-VAE: Advancing Data Generation with User Information and Pattern Dictionaries

arXiv:2411.03936v2 Announce Type: replace Abstract: Generative modelling of multi-user datasets has become prominent in science and engineering. Generating a data point for a given user requires emplo

tutorialsarxiv-cs-lg

Isotonic Survival Regression: Calibrated Survival Distributions from Deep Cox Models

arXiv:2605.16571v1 Announce Type: cross Abstract: Time-to-event data is widespread across the life sciences and engineering, but it is typically encountered together with censoring, which complicates

applicationsarxiv-cs-ai

Offline Contextual Bandits in the Presence of New Actions

arXiv:2605.18509v1 Announce Type: new Abstract: Automated decision-making algorithms drive applications such as recommendation systems and search engines. These algorithms often rely on off-policy con

Online Conformal Prediction for Non-Exchangeable Panel Data

arXiv:2605.17705v1 Announce Type: cross Abstract: Panel data, in which multiple units are repeatedly observed over time, arise throughout science and engineering. Quantifying predictive uncertainty in

Local Ai

Proximal-IMH: Proximal Posterior Proposals for Independent Metropolis-Hastings with Approximate Operators

arXiv:2602.21426v2 Announce Type: replace Abstract: We consider the problem of sampling from a posterior distribution arising in Bayesian inverse problems in science, engineering, and imaging. Our met

local-aiarxiv-cs-lg

Responsible Agentic AI Requires Explicit Provenance

arXiv:2605.17169v1 Announce Type: new Abstract: Agentic AI is rapidly proliferating across diverse real-world domains such as software engineering, yet public trust has not kept pace. The central reas

Scale-Equivariant Generative Forecasting: Weight-Tied Dilated Convolutions, Wavelet Scattering Inputs, and Spectral-Consistency Training for Self-Similar Time Series

arXiv:2605.17582v1 Announce Type: new Abstract: Many natural and engineered time series -- equity returns, climate anomalies, turbulent velocities, neural recordings, packet-level network traffic -- a

Scaling Laws for Code: A More Data-Hungry Regime

model-releasesarxiv-cs-cl

arXiv:2510.08702v2 Announce Type: replace Abstract: Code Large Language Models (LLMs) are revolutionizing software engineering. However, scaling laws that guide the efficient training are predominantl

Tensor Cookbook: Mastering Tensors through Diagrams

arXiv:2605.16610v1 Announce Type: new Abstract: High-dimensional data arise naturally in many areas of science and engineering, including machine learning, signal processing, computational physics, an

tutorialsarxiv-cs-lg

The Impact of AI Search on the Online Content Ecosystem: Evidence from Google and Reddit

arXiv:2605.16428v1 Announce Type: cross Abstract: Search engines traditionally complement online content platforms by directing users seeking information to external websites. The emergence of generat

GenAI-Driven Approach to RISC-V Supply Chain Exploration

arXiv:2605.15223v1 Announce Type: cross Abstract: This paper presents an LLM-empowered workflow for RISC-V supply chain analysis, integrating Vision-Language Models (VLMs) and Model-Driven Engineering

Opponent State Inference Under Partial Observability: An HMM-POMDP Framework for 2026 Formula 1 Energy Strategy

arXiv:2603.01290v3 Announce Type: replace Abstract: The 2026 Formula 1 technical regulations introduce a fundamental change to energy strategy: under a 50/50 internal combustion engine / battery power

Position: Ideas Should be the Center of Machine Learning Research

arXiv:2605.15253v1 Announce Type: new Abstract: Machine learning research increasingly bifurcates into two disconnected modes: benchmark-driven engineering that prioritizes metrics over understanding,

Symplectic Neural Operators for Learning Infinite Dimensional Hamiltonian Systems

arXiv:2605.15881v1 Announce Type: cross Abstract: The modeling and simulation of infinite-dimensional Hamiltonian systems are central problems in mathematical physics and engineering, however they pos

ASH: Agents that Self-Hone via Embodied Learning

arXiv:2605.14211v1 Announce Type: new Abstract: Long-horizon embodied tasks remain a fundamental challenge in AI, as current methods rely on hand-engineered rewards or action-labeled demonstrations, n

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits

arXiv:2605.14553v1 Announce Type: cross Abstract: Prompt engineering has become central to eliciting the capabilities of large language models (LLMs). At its core lies prompt selection -- efficiently

GraphBit: A Graph-based Agentic Framework for Non-Linear Agent Orchestration

arXiv:2605.13848v1 Announce Type: new Abstract: Agentic LLM frameworks that rely on prompted orchestration, where the model itself determines workflow transitions, often suffer from hallucinated routi

Prompt Segmentation and Annotation Optimisation: Controlling LLM Behaviour via Optimised Segment-Level Annotations

arXiv:2605.14561v1 Announce Type: new Abstract: Prompt engineering is crucial for effective interaction with generative artificial intelligence systems, yet existing optimisation methods often operate

tutorialsarxiv-cs-ai

Prompting Policies for Multi-step Reasoning and Tool-Use in Black-box LLMs with Iterative Distillation of Experience

arXiv:2605.14443v1 Announce Type: new Abstract: The shift toward interacting with frozen, 'black-box' Large Language Models (LLMs) has transformed prompt engineering from a heuristic exercise into a c

AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHub

arXiv:2604.03551v2 Announce Type: replace-cross Abstract: Software Engineering 3.0 marks a paradigm shift in software development, in which AI coding agents are no longer just assistive tools but acti

agentsarxiv-cs-ai

AgentLens: Revealing The Lucky Pass Problem in SWE-Agent Evaluation

arXiv:2605.12925v1 Announce Type: cross Abstract: Evaluation of software engineering (SWE) agents is dominated by a binary signal: whether the final patch passes the tests. This outcome-only view trea

agentsarxiv-cs-ai

Local Ai

CANTANTE: Optimizing Agentic Systems via Contrastive Credit Attribution

arXiv:2605.13295v1 Announce Type: cross Abstract: LLM-based multi-agent systems have demonstrated strong performance across complex real-world tasks, such as software engineering, predictive modeling,

local-aiarxiv-cs-ai

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts

arXiv:2502.05157v3 Announce Type: replace Abstract: The perspective of developing trustworthy AI for critical applications in science and engineering requires machine learning techniques that are capa

tutorialsarxiv-cs-lg

Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems Reconstruction

arXiv:2605.12683v1 Announce Type: cross Abstract: Reconstructing nonlinear dynamical systems (DS) from data (DSR) is a fundamental challenge in science and engineering, but it inherently relies on seq

agentsarxiv-cs-ai

Pragmatic Curiosity: A Unified Framework for Hybrid Learning and Optimization via Active Inference

arXiv:2602.06104v2 Announce Type: replace Abstract: Many engineering and scientific workflows rely on expensive black-box evaluations, requiring sequential decisions that must both improve task perfor

SHM-Agents: A Generalist-Specialist Integrated Agent System for Structural Health Monitoring

arXiv:2605.12916v1 Announce Type: cross Abstract: Artificial intelligence is increasingly used to simplify complex tasks. In engineering applications of structural health monitoring (SHM), existing sp

agentsarxiv-cs-lg

Continuous Discovery of Vulnerabilities in LLM Serving Systems with Fuzzing

arXiv:2605.11202v1 Announce Type: cross Abstract: LLM inference and serving systems have become security-critical infrastructure; however, many of their most concerning failures arise from the serving

safetyarxiv-cs-lg

13 May 2026

LychSim: A Controllable and Interactive Simulation Framework for Vision Research

arXiv:2605.12449v1 Announce Type: new Abstract: While self-supervised pretraining has reduced vision systems' reliance on synthetic data, simulation remains an indispensable tool for closed-loop optim

agentsarxiv-cs-cv

13 May 2026

Adaptive digital twins for predictive decision-making: Online Bayesian learning of transition dynamics

arXiv:2512.13919v2 Announce Type: replace Abstract: This work shows how adaptivity can enhance value realization of digital twins in civil engineering. We focus on adapting the state transition models

applicationsarxiv-cs-lg

Agentic MIP Research: Accelerated Constraint Handler Generation

arXiv:2605.09186v1 Announce Type: new Abstract: Mixed-integer programming (MIP) research is both mathematically sophisticated and engineering-intensive: testing an algorithmic hypothesis within a bran

Beyond Accuracy: Evaluating Posterior Fidelity of Diffusion Inverse Solvers

arXiv:2602.04189v2 Announce Type: replace Abstract: Uncertainty evaluation is critical in scientific and engineering inverse problems. However, existing benchmarks on Diffusion Inverse Solvers (DIS) p

applicationsarxiv-cs-lg

DiagnosticIQ: A Benchmark for LLM-Based Industrial Maintenance Action Recommendation from Symbolic Rules

arXiv:2605.08614v1 Announce Type: new Abstract: Monitoring complex industrial assets relies on engineer-authored symbolic rules that trigger based on sensor conditions and prompt technicians to perfor

Dissecting Jet-Tagger Through Mechanistic Interpretability

arXiv:2605.09881v1 Announce Type: cross Abstract: Mechanistic interpretability seeks to reverse engineer a trained neural network by identifying the minimal subset of internal components. We perform a

From Mechanistic to Compositional Interpretability

arXiv:2605.08934v1 Announce Type: new Abstract: Mechanistic interpretability aims to explain neural model behaviour by reverse-engineering learned computational structure into human-understandable com

Learning stochastic multiscale models through normalizing flows

arXiv:2605.09718v1 Announce Type: cross Abstract: Many systems in physics, engineering, and biology exhibit multiscale stochastic dynamics, where low-dimensional slow variables evolve under the influe

On Observation Time for Recovering Latent Hawkes Networks

arXiv:2605.08400v1 Announce Type: cross Abstract: Dynamics of interacting systems in engineering, society, and nature often evolve over latent networks that govern which entities can interact. We stud

applicationsarxiv-cs-lg

PAINET: A Principled Efficient Transformer for 3D Dynamics Modeling

arXiv:2510.04233v2 Announce Type: replace-cross Abstract: Modeling 3D dynamics is a fundamental problem in multi-body systems across scientific and engineering domains and has important practical impl

applicationsarxiv-cs-ai

PromptDx: Differentiable Prompt Tuning for Multimodal In-Context Alzheimer's Diagnosis

arXiv:2605.08585v1 Announce Type: cross Abstract: Deep learning models in medical imaging typically operate as parametric memory, diagnosing patients by recalling fixed knowledge learned during traini

SAGE: Agentic Framework for Interpretable and Clinically Translatable Computational Pathology Biomarker Discovery

arXiv:2602.00953v2 Announce Type: replace Abstract: Engineered image-based biomarkers offer a clinically interpretable alternative to black-box AI in computational pathology, yet their discovery remai

agentsarxiv-cs-lg

The Geometry of Forgetting: Temporal Knowledge Drift as an Independent Axis in LLM Representations

arXiv:2605.09195v1 Announce Type: new Abstract: Large language models confidently produce outdated answers, and no existing method can detect them. We show this is not an engineering failure but a str

VFM-SDM: A vision foundation model-based framework for training-free, marker-free, and calibration-free structural displacement measurement

model-releasesarxiv-cs-cv

arXiv:2605.09677v1 Announce Type: new Abstract: Reliable displacement measurement is fundamental for structural health monitoring and digital engineering workflows, as it provides direct structural re

Voice Biomarkers for Depression and Anxiety

arXiv:2605.09908v1 Announce Type: cross Abstract: Current approaches to detecting depression and anxiety from speech primarily rely on machine learning techniques that utilize hand-engineered paraling

tutorialsarxiv-cs-ai

A Systematic Investigation of The RL-Jailbreaker in LLMs

arXiv:2605.07032v1 Announce Type: cross Abstract: The evolution of generative models from next-token predictors to autonomous engines of complex systems necessitates rigorous safety hardening. Adversa