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Optimal Experiments for Partial Causal Effect Identification

arXiv:2605.06993v1 Announce Type: new Abstract: Causal queries are often only partially identifiable from observational data, and experiments that could tighten the resulting bounds are typically cost

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Human DGX agent

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Optimal Recourse Summaries via Bi-Objective Decision Tree Learning

DGX agent

arXiv:2605.07598v1 Announce Type: new Abstract: Actionable Recourse provides individuals with actions they can take to change an unfavorable classifier outcome. While useful at the instance level, it

safetyarxiv-cs-lg

11 May 2026

Optimising MFCC parameters for the automatic detection of respiratory diseases

arXiv:2408.07522v2 Announce Type: replace-cross Abstract: Voice signals originating from the respiratory tract are utilized as valuable acoustic biomarkers for the diagnosis and assessment of respirat

Optimizing Language Models for Crosslingual Knowledge Consistency

arXiv:2603.04678v2 Announce Type: replace-cross Abstract: Large language models are known to often exhibit inconsistent knowledge. This is particularly problematic in multilingual scenarios, where mod

OrchJail: Jailbreaking Tool-Calling Text-to-Image Agents by Orchestration-Guided Fuzzing

arXiv:2605.07414v1 Announce Type: cross Abstract: Tool-calling text-to-image (T2I) agents can plan and execute multi-step tool chains to accomplish complex generation and editing queries. However, thi

OrScale: Orthogonalised Optimization with Layer-Wise Trust-Ratio Scaling

model-releasesarxiv-cs-cl

arXiv:2605.07815v1 Announce Type: cross Abstract: Muon improves neural-network training by orthogonalizing matrix-valued updates, but it leaves each layer's update magnitude controlled mostly by a glo

Outlier Smoothing with Closed-Form Rotations for W4A4 Large Language Model Quantization

model-releasesarxiv-cs-lg

arXiv:2511.22316v2 Announce Type: replace Abstract: Large Language Models (LLMs) quantization facilitates deploying LLMs in resource-limited settings, but existing methods that combine incompatible gr

Local Ai

Overcoming data scarcity through multi-center federated learning for organs-at-risk segmentation in pediatric upper abdominal radiotherapy

arXiv:2605.06820v1 Announce Type: cross Abstract: Deep learning-based organs/structures-at-risk(OARs) auto-contouring models can improve radiotherapy workflows, but models trained on adult data often

local-aiarxiv-cs-ai

Overview of the TREC 2025 RAGTIME Track

arXiv:2602.10024v2 Announce Type: replace-cross Abstract: The principal goal of the RAG TREC Instrument for Multilingual Evaluation (RAGTIME) track at TREC is to study report generation from multiling

researcharxiv-cs-cl

PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents

arXiv:2605.07039v1 Announce Type: new Abstract: Large language models have become drivers of evolutionary search, but most systems rely on a fixed, prompt-elicited policy to sample next candidates. Th

safetyarxiv-cs-lg

PAIR-Former: Budgeted Relational Multi-Instance Learning for Functional miRNA Target Prediction

arXiv:2602.00465v3 Announce Type: replace-cross Abstract: Functional miRNA--mRNA targeting is a large-bag prediction problem where each transcript yields a heavy-tailed pool of candidate target sites

Agents

Palm-sized Omnidirectional Vision-Based UAV Exploration with Sparse Topological Map Guidance

arXiv:2605.07275v1 Announce Type: new Abstract: Classic exploration methods often rely on dense occupancy maps or high-resolution point clouds for frontier detection and path planning, resulting in su

agentsarxiv-cs-ro

Tutorials

PAMPOS: Causal Transformer-based Trajectory Prediction for Attack-Agnostic Misbehavior Detection in V2X Networks

arXiv:2605.06833v1 Announce Type: cross Abstract: Misbehavior detection in Vehicle-to-Everything (V2X) networks is a second line of defense against insider falsification attacks that cryptographic mec

tutorialsarxiv-cs-ai

Pan-FM: A Pan-Organ Foundation Model with Saliency-Guided Masking for Missing Robustness

arXiv:2605.07055v1 Announce Type: cross Abstract: Foundation models (FMs) have shown great promise in medical imaging, but most FMs are trained on unimodal data within isolated domains, such as brain

Parallel Lifted Planning via Semi-Naive Datalog Evaluation

arXiv:2605.07584v1 Announce Type: new Abstract: Lifted classical planners operate directly on first-order planning tasks to avoid the computationally demanding grounding step. However, lifted planning

researcharxiv-cs-ai

PaT: Planning-after-Trial for Efficient Test-Time Code Generation

arXiv:2605.07248v1 Announce Type: new Abstract: Beyond training-time optimization, scaling test-time computation has emerged as a key paradigm to extend the reasoning capabilities of Large Language Mo

safetyarxiv-cs-cl

Tutorials

Path Integration and Object-Location Binding Emerge in an Action-Conditioned Predictive Sequence Network

arXiv:2602.03490v2 Announce Type: replace Abstract: Adaptive cognition requires structured internal models of objects and their relations. Predictive neural networks are often proposed to learn such w

tutorialsarxiv-cs-lg

PathPainter: Transferring the Generalization Ability of Image Generation Models to Embodied Navigation

model-releasesarxiv-cs-ro

arXiv:2605.07496v1 Announce Type: new Abstract: Bird's-eye-view (BEV) images have been widely demonstrated to provide valuable prior information for navigation. Given the global information provided b

Penalty-Based First-Order Methods for Bilevel Optimization with Minimax and Constrained Lower-Level Problems

arXiv:2605.08006v1 Announce Type: cross Abstract: We study a class of bilevel optimization problems in which both the upper- and lower-level problems have minimax structures. This setting captures a b

PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning

model-releasesarxiv-cs-lg

arXiv:2605.07267v1 Announce Type: new Abstract: Personalized healthcare decisions require reasoning about how physiological and behavioral variables influence an individual patient over time. Existing

PerfCoder: Large Language Models for Interpretable Code Performance Optimization

arXiv:2512.14018v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved remarkable progress in automatic code generation, yet their ability to produce high-performance cod

Persistent-Transient Policy Evaluation for Markov Chains via Minimal Peripheral Quotients

arXiv:2602.00474v2 Announce Type: replace-cross Abstract: We study fixed-policy evaluation for finite Markov chains that may be reducible and periodic. Classical evaluation methods with gain and bias

safetyarxiv-cs-lg

PersonaGest: Personalized Co-Speech Gesture Generation with Semantic-Guided Hierarchical Motion Representation

arXiv:2605.07252v1 Announce Type: cross Abstract: Co-speech gesture generation aims to synthesize realistic body movements that are semantically coherent with speech and faithful to a user-specified g

PET-Adapter: Test-Time Domain Adaptation for Full and Limited-Angle PET Image Reconstruction

arXiv:2605.08030v1 Announce Type: new Abstract: Positron Emission Tomography (PET) image reconstruction is inherently challenged by Poisson noise and physical degradation factors, which are further ex

Physical Simulators as Do-Operators: Causal Discovery under Latent Confounders for AI-for-Science

arXiv:2605.07467v1 Announce Type: cross Abstract: Existing interventional causal discovery methods -- IGSP, DCDI, ENCO -- assume causal sufficiency (no latent confounders) and rely on virtual interven

Physics-Based Benchmarking Metrics for Multimodal Synthetic Images

arXiv:2511.15204v3 Announce Type: replace-cross Abstract: Current state of the art measures like BLEU, CIDEr, VQA score, SigLIP-2 and CLIPScore are often unable to capture semantic or structural accur

Physics-based Digital Twins for Integrated Thermal Energy Systems Using Active Learning

arXiv:2605.06756v1 Announce Type: new Abstract: Real-time supervisory control of thermal energy distribution systems requires digital twins that are accurate, interpretable, and uncertainty-aware, yet

Hardware

Physics-Based Flow Matching for Full-Field Prediction of Silicon Photonic Devices

arXiv:2605.06929v1 Announce Type: cross Abstract: Designing photonic integrated circuits requires accurate electromagnetic field simulations, which remain computationally expensive even for simple dev

hardwarearxiv-cs-lg

Physics-Informed Reduced-Order Operator Learning for Hyperelasticity in Continuum Micromechanics

arXiv:2605.07738v1 Announce Type: cross Abstract: Physics-informed operator learning is an attractive candidate for surrogate modeling of microstructures, especially in multiscale finite-element simul

PhySPRING: Structure-Preserving Reduction of Physics-Informed Twins via GNN

model-releasesarxiv-cs-ro

arXiv:2605.07687v1 Announce Type: new Abstract: Physics-based digital twins aim to predict the dynamics of real-world objects under interaction, enabling real-to-sim-to-real applications in robotics.

PicoEyes: Unified Gaze Estimation Framework for Mixed Reality with a Large-Scale Multi-View Dataset

arXiv:2605.07188v1 Announce Type: new Abstract: We present PicoEyes, a unified gaze estimation framework that directly predicts all key attributes of gaze, including 3D eye parameters, eye-region segm

PISTO: Proximal Inference for Stochastic Trajectory Optimization

arXiv:2605.07215v1 Announce Type: new Abstract: Stochastic trajectory optimization methods like STOMP enable planning with non-differentiable costs, offering substantial flexibility over gradient-base

researcharxiv-cs-ro

PLOT: Progressive Localization via Optimal Transport in Neural Causal Abstraction

arXiv:2605.06979v1 Announce Type: cross Abstract: Causal abstraction offers a principled framework for mechanistic interpretability, aligning a high-level causal model with the low-level computation r

POETS: Uncertainty-Aware LLM Optimization via Compute-Efficient Policy Ensembles

arXiv:2605.07775v1 Announce Type: cross Abstract: Balancing exploration and exploitation is a core challenge in sequential decision-making and black-box optimization. We introduce POETS (extbf{Po}licy

PolarAdamW: Disentangling Spectral Control and Schur Gauge-Equivariance in Matrix Optimisation

arXiv:2605.07067v1 Announce Type: new Abstract: Muon's matrix-level update couples two distinct effects: spectral control via a polar map, and equivariance under orthogonal changes of multiplicity-spa

PolarVLM: Bridging the Semantic-Physical Gap in Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2605.07574v1 Announce Type: new Abstract: Mainstream vision-language models (VLMs) fundamentally struggle with severe optical ambiguities, such as reflections and transparent objects, due to the

PolySQL: Scaling Text-to-SQL Evaluation Across SQL Dialects via Automated Backend Isomorphism

arXiv:2605.07796v1 Announce Type: new Abstract: SQL dialects vary in syntax, types, and functions across database engines. Text-to-SQL benchmarks, however, predominantly support only SQLite. This crea

researcharxiv-cs-cl

Position: Mechanistic Interpretability Must Disclose Identification Assumptions for Causal Claims

arXiv:2605.08012v1 Announce Type: cross Abstract: Mechanistic interpretability papers increasingly use causal vocabulary: circuits, mediators, causal abstraction, monosemanticity. Such claims require

Post-training makes large language models less human-like

arXiv:2605.07632v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as surrogates for human participants, but it remains unclear which models best capture human behavi

PPI-Net connects molecular protein interactions to functional processes in disease

arXiv:2605.07838v1 Announce Type: cross Abstract: Understanding how molecular alterations propagate across biological systems to drive disease remains a central challenge. Although high-throughput pro

researcharxiv-cs-ai

Pre-trained Tabular Foundation Models as Versatile Summary Networks for Neural Posterior Estimation

arXiv:2605.07765v1 Announce Type: new Abstract: In this work, we study TabPFN as a training-free, modular summary network for simulation-based Bayesian inference (SBI). Tabular foundation models such

Pre-training Enables Extraordinary All-optical Image Denoising

arXiv:2605.07810v1 Announce Type: cross Abstract: Optical neural networks are emerging as powerful machine learning and information processing tools because of their potential advantages in speed and

Local Ai

Predictive but Not Plannable: RC-aux for Latent World Models

arXiv:2605.07278v1 Announce Type: cross Abstract: A latent world model may achieve accurate short-horizon prediction while still inducing a latent space that is poorly aligned with planning. A key iss

local-aiarxiv-cs-ai

Pretraining a Foundation Model for Small-Molecule Natural Products

arXiv:2503.17656v4 Announce Type: replace-cross Abstract: Natural products, as metabolites from microorganisms, animals, or plants, exhibit diverse biological activities, making them crucial for drug

researcharxiv-cs-ai

Pretraining Induces a Reusable Spectral Basis for Downstream Task Adaptation

model-releasesarxiv-cs-lg

arXiv:2605.07302v1 Announce Type: new Abstract: Finetuning pretrained models occurs in a low-dimensional subspace of the full parameter space. Prior work has focused on characterizing this optimizatio

Pretty Good Measurement for Radiomics: A Quantum-Inspired Multi-Class Classifier for Lung Cancer Subtyping and Prostate Cancer Risk Stratification

arXiv:2603.00223v2 Announce Type: replace Abstract: We investigate a quantum-inspired approach to supervised multi-class classification based on the Pretty Good Measurement (PGM), viewed as an operato

PRIMED: Adaptive Modality Suppression for Referring Audio-Visual Segmentation via Biased Competition

model-releasesarxiv-cs-cv

arXiv:2605.07154v1 Announce Type: new Abstract: Referring Audio-Visual Segmentation (Ref-AVS) seeks to localize and segment target objects in video frames based on visual, auditory, and textual referr

Privately Estimating Black-Box Statistics

arXiv:2510.00322v3 Announce Type: replace-cross Abstract: Standard techniques for differentially private estimation, such as Laplace or Gaussian noise addition, require guaranteed bounds on the sensit