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Learning Process Rewards via Reasoning State Propagation

arXiv:2609.39220v1 Announce Type: new Abstract: Process reward models (PRMs) have demonstrated notable effectiveness in test-time scaling and reinforcement learning by providing fine-grained signals f

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Learning Skills from Historical Action Trajectories: Action Experience Dictionary for World Action Models

DGX agent

arXiv:2609.40219v1 Announce Type: cross Abstract: World Action Models (WAMs) couple visual dynamics prediction with action generation, yet they do not explicitly support the reuse of action experience

tutorialsarxiv-cs-ai

1 Oct 2026

Learning Steganography Is Easy, Learning Steganographic Reasoning Is Hard

arXiv:2609.39838v1 Announce Type: new Abstract: Chain-of-thought monitoring as an approach for AI oversight and control is threatened by the possibility of steganographic reasoning, where LLMs conceal

Learning Where to Steer: Noise-Space Geometry for Efficient Offline Multi-Objective Optimization with Generative Models

arXiv:2609.38920v1 Announce Type: new Abstract: Offline multi-objective optimization (MOO) seeks solutions with better objective trade-offs using only a fixed dataset, without querying the objectives.

Looking Back to Move Forward: Temporal Verification for Generative Robot Policies

arXiv:2609.39038v1 Announce Type: new Abstract: Generative policies have emerged as a promising paradigm for robot learning, combining expressive generative action modeling with scalable imitation lea

tutorialsarxiv-cs-ro

Matisse: Evidence-Space Reasoning for Active 3D Reconstruction

arXiv:2609.38746v1 Announce Type: new Abstract: How can a 3D reconstruction system acquire and retain useful information to understand the geometry of a scene from partial views under a limited comput

MIND: Marginal-Invariant Neural Dependency Diffusion for Mixed-Type Tabular Generation

arXiv:2609.39628v1 Announce Type: new Abstract: This paper proposes MIND, a marginal-invariant neural dependency diffusion model for mixed-type tabular data. MIND does not directly learn the joint dis

Minimax rates for learning spectral Barron functions by deep ReLU neural networks

arXiv:2609.39020v1 Announce Type: cross Abstract: We study how well deep neural networks approximate and learn spectral Barron functions. Recent studies have shown that these function classes can be e

ModSec-Learn: Boosting ModSecurity with Machine Learning

arXiv:2406.13547v2 Announce Type: replace Abstract: ModSecurity is widely recognized as the standard open-source Web Application Firewall (WAF), maintained by the OWASP Foundation. It detects maliciou

P-SRM: Selective Recovery of Rejected Predictions in Visual Tracking

arXiv:2609.39832v1 Announce Type: new Abstract: Many visual tracking methods use rejection mechanisms to suppress unreliable predictions. However, these mechanisms can also reject correctly localized

PAMI: Part Anchored Motion for Text to Human-Object Interaction Generation

arXiv:2609.38466v1 Announce Type: new Abstract: Text-conditioned full-body human-object interaction (HOI) generation requires synthesizing human motion and object trajectories that match the input tex

PrecipJEPA: JEPA-Regularized Future-State Prediction with Motion-Source Rendering for Precipitation Nowcasting

arXiv:2609.38926v1 Announce Type: cross Abstract: Long-term precipitation nowcasting requires modeling radar-echo evolution while preserving localized high-intensity structures. Recent radar-specific

Predicting Multi-View Rashomon Representation: Can We Learn Where Models Disagree?

arXiv:2609.39848v1 Announce Type: new Abstract: Foundation models are increasingly adopted across a wide range of applications, often serving as core blocks within AI systems. Yet different foundation

Proper Scoring Rule-based Diffusion for Probabilistic Weather Forecasting

arXiv:2609.38632v1 Announce Type: new Abstract: Recent probabilistic weather forecasters train stochastic predictors with the continuous ranked probability score (CRPS) to generate each ensemble membe

ProtScape: A molecular structure and energy-aware representation for protein conformation generation

arXiv:2410.20317v2 Announce Type: replace Abstract: Molecular dynamics (MD) simulations are a principled but computationally expensive approach for studying protein conformational variability, making

Pseudo-Label-Triggered Retraining from Forecast Errors for Online Time Series Forecasting

arXiv:2609.39789v1 Announce Type: cross Abstract: Real-world time series forecasting systems operate under non-stationary data streams, where forecasting performance may degrade over time. Although re

PTNO: Training Neural Operators with Noisy Monte Carlo Estimates for Particle Transport Problems

arXiv:2609.40090v1 Announce Type: new Abstract: Particle transport under multiple scattering is central to radiative transfer and plasma physics, yet high-fidelity Monte Carlo (MC) simulations must tr

Residuals Are Not Enough: Limits of Physics-Informed Pre-Training for Scientific Foundation Models

arXiv:2503.19081v2 Announce Type: replace Abstract: Scientific foundation models (SciFMs) aim to learn generalizable representations of physical systems governed by partial differential equations (PDE

Robust Budget Pacing with a Single Sample

arXiv:2302.02006v2 Announce Type: replace Abstract: Major Internet advertising platforms offer budget pacing tools as a standard service for advertisers to manage their ad campaigns. Given the inheren

RouteRec: Behavior-Guided Sparse Routing for Sequential Recommendation

arXiv:2609.39007v1 Announce Type: cross Abstract: Sessionized interaction histories contain behavioral patterns that can improve sequential recommendation. However, existing models process all session

SAGE: Salient Factor Discovery and Generation with Visual Foundation Representations

arXiv:2609.39635v1 Announce Type: new Abstract: Given a target dataset, such as faces with eyeglasses, and a background dataset, such as faces without, contrastive analysis separates extit{salient} fa

Seeing as Humans Do: Learning from Motion to Segment Anything Without Supervision

arXiv:2609.39785v1 Announce Type: new Abstract: The Segment Anything Model (SAM) relies heavily on massive manual annotations, creating a fundamental bottleneck for model scaling. While unsupervised m

SPOON: Towards Coherent Compositional 3D Scene Generation from Uncalibrated Multi-view Images

arXiv:2609.39590v1 Announce Type: new Abstract: Compositional 3D scene generation aims to recover complete 3D object shapes and their spatial arrangement from visual observations. Recent image-conditi

Synchronous Multi-view Neural Diffusion

arXiv:2609.39019v1 Announce Type: new Abstract: Multi-view learning seeks to learn more comprehensive representations by exploiting the complementarity and consistency across diverse modalities or vie

TACIT: Optimization Models that Learn from Their Mistakes

arXiv:2609.38434v1 Announce Type: cross Abstract: Real-world optimization problems are difficult to model accurately because many objectives and constraints reside in domain experts' tacit knowledge,

Tissue Detection Determines False Positives in Diffusion-Based Histopathology Artifact Detection

arXiv:2609.40083v1 Announce Type: cross Abstract: One-class artifact detectors for whole-slide images learn normal tissue from a clean training pool and flag departures from it. The pool is built by a

Towards Open-Ended Visual Scientific Discovery with Sparse Autoencoders

arXiv:2511.17735v2 Announce Type: replace Abstract: Foundation models in several scientific domains, including visual domains, learn representations that capture complex semantics from their respectiv

TripleFlow: Training-Free Video Object Removal by Bridging Residual Editing and Native Generation

arXiv:2609.39157v1 Announce Type: new Abstract: Video object removal presents a uniquely difficult editing challenge. Because a removal prompt specifies only what to erase rather than what to generate

TSGL: Teacher-Student Graph Learning for 3DGS Compression

arXiv:2609.38635v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) is a popular representation for novel view synthesis. However, 3DGS contains millions of Gaussian primitives, each with r

What Pretraining and Midtraining Make Learnable from Rewards?

arXiv:2609.38446v1 Announce Type: cross Abstract: A reward can identify a correct answer while leaving the computation needed for new inputs undetermined. We study how pretraining and midtraining supp

When Correct Memory Goes Wrong: Fuzzing Persistent Memory Use in LLM Agents

arXiv:2609.38275v1 Announce Type: cross Abstract: Persistent memory helps LLM agents carry information across long interactions, but correct memory can still be used incorrectly when queries change or

Why Do Conventional World Models Fail to Learn Cellular Automata?

arXiv:2609.39604v1 Announce Type: new Abstract: Although conventional world models - auto-regressive or diffusion models based on transformers or convolutional networks - may learn surface statistics

A Digital Simulation Toolkit for Physics-Based Generation of Realistic Experimental Scanning Tunneling Microscopy Images

arXiv:2609.36639v1 Announce Type: new Abstract: Scanning Tunneling Microscopy (STM) is a widely used tool for characterizing surfaces of materials at the atomic scale, playing a crucial role in discov

Abductive World Modeling via Causal Representation Learning

arXiv:2609.36985v1 Announce Type: cross Abstract: The central challenge of world modeling is to learn representations that capture how the world evolves. However, existing world models predominantly r

AESOP: Asymmetric Human-Camera Generation with Translation-Intensity Control

arXiv:2609.37229v1 Announce Type: new Abstract: Human motion defines an action, while a camera trajectory determines how it is presented. Camera generation for a given human motion and joint human-cam

Amadeus: When Models of People Meet

arXiv:2609.35835v1 Announce Type: cross Abstract: With the sheer constant advancements raining down in the field of Artificial Intelligence, one particular possibility that may cross our mind is wheth

Anisotropic Representations Improve Planning in JEPA World Models

arXiv:2609.37441v1 Announce Type: new Abstract: Latent world models learn action-conditioned dynamics in representation space and often score candidate actions by Euclidean distance to a goal represen

tutorialsarxiv-cs-ro

Benchmarking graph-based models for in-silico toxicity prediction in drug discovery

arXiv:2609.37555v1 Announce Type: new Abstract: Drug discovery is a costly and high-risk process, where toxicity-related failures remain a major cause of attrition in both preclinical and clinical sta

Best Practices in EEG Analysis: Preprocessing, Modeling, and Machine Learning

arXiv:2609.36609v1 Announce Type: cross Abstract: Electroencephalography (EEG) analysis requires careful choices in preprocessing, statistical modeling, and machine learning because EEG signals are hi

Bilinear World Models: Learning Representations with Structured Dynamics for Efficient Control

arXiv:2609.36305v1 Announce Type: new Abstract: World models jointly learn latent representations and dynamics that predict how high-dimensional observations evolve under actions. In this work, we pro

tutorialsarxiv-cs-ro

Co-PiLOT: Constrained Physics-Informed Latent Optimization for Target-Driven Inverse Design

arXiv:2609.37875v1 Announce Type: new Abstract: Inverse design of physical systems (molecules, devices, microstructures) often reduces to optimizing a high-dimensional structure against an expensive b

Corpus-Guided Dual-Path Propagation for Graph Retrieval-Augmented Generation

arXiv:2609.37661v1 Announce Type: new Abstract: Graph-based retrieval-augmented generation supports multi-hop retrieval by organizing corpus information into graphs. However, existing relation-free gr

tutorialsarxiv-cs-cl

Cross-Linguistic Effects in Bilingual Phoneme BabyLMs

arXiv:2609.37121v1 Announce Type: new Abstract: Cross-linguistic effects are a central topic in bilingual first-language acquisition. Artificial learners can help investigate L1-L2 interactions by ena

tutorialsarxiv-cs-cl

DiffReID: Discriminative Diffusion Model for Object Re-Identification

arXiv:2609.36894v1 Announce Type: new Abstract: As a fundamental image processing task, object Re-Identification (ReID) aims to retrieve objects across non-overlapping cameras. Recently, with the deve

Do LLMs Really Forget? Hidden-State Leakage in Model Unlearning and How to Fix it

arXiv:2609.36612v1 Announce Type: new Abstract: Unlearning in large language models (LLMs) is typically evaluated at the output level, where a model appears to suppress sensitive or undesirable conten

DraftTrace: A Multi-View Analytics Environment for AI-Integrated Writing

arXiv:2609.36544v1 Announce Type: cross Abstract: Generative AI has changed how students produce writing assignments. The final artifact is no longer sufficient to understand the process through which

EnJoi: Ensemble Joint Score Filter for Generative Data Assimilation

arXiv:2609.35944v1 Announce Type: new Abstract: Data Assimilation (DA) aims to recover the full state of a dynamical system that is only partially observed. A solution is to use Score-based models to

GenLimitLib: A Formal Library for Language Generation in the Limit and AI-Assisted Mathematical Research

arXiv:2609.36663v1 Announce Type: new Abstract: We present GenLimitLib, a source-aligned Lean 4 library for language generation in the limit. Introduced by Kleinberg and Mullainathan at NeurIPS 2024,