Search: “papers”

Object Pose from 2-D to 3-D Point and Line Correspondences

arXiv:2610.19734v1 Announce Type: new Abstract: In this paper we present a method for optimally estimating the rotation and translation between a camera and a 3-D object from point and/or line corresp

tutorialsarxiv-cs-cv

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Old Ideas, Novel Problems: The Instability of LLM-Based Novelty Evaluation

DGX agent

arXiv:2610.02022v2 Announce Type: replace Abstract: AI-based ideation systems are often evaluated on the novelty of their ideas, and that judgment is increasingly delegated to large language models. S

researcharxiv-cs-cl

15 Oct 2026

S^3-Nav: Structured Spatial-Semantic Decision Making for Language-Guided Robot Navigation

arXiv:2610.19431v1 Announce Type: new Abstract: This paper presents S^3-Nav, a structured spatial-semantic decision-making framework for language-guided robot navigation under partial observability. R

safetyarxiv-cs-ro

SKG: A Law-Induced Engine from Inference to Induction

arXiv:2610.19505v1 Announce Type: new Abstract: This paper proposes the SKG (Symplectic-Kernel Gradient) reasoning engine for law-induced inference and induction. Laws are a priori; data provide insta

Student Copula Markov Networks

arXiv:2610.19135v1 Announce Type: cross Abstract: This paper extends the applicability of probabilistic graphical models to non Gaussian data. It establishes a characterization of the pairwise Markov

Toward a Universal Characterization of Sudoku Puzzle Difficulty

arXiv:2507.21137v2 Announce Type: replace Abstract: In this paper we try to answer the question 'What constitutes Sudoku difficulty rating across different Sudoku websites?' Using two distinct methods

researcharxiv-cs-ai

Training LLMs Beyond Next Token Prediction - Filling the Mutual Information Gap

model-releasesarxiv-cs-ai

arXiv:2511.00198v2 Announce Type: replace-cross Abstract: Autoregressive language models are usually trained with next-token prediction under a fixed left-to-right target order. This paper studies tar

Unlocking Token Rewards via Training-Free Reward Attribution

model-releasesarxiv-cs-ai

arXiv:2610.18704v1 Announce Type: new Abstract: In this paper, we propose an extremely efficient, training-free method to extract token-level reward signals directly from an existing deep reward model

Agents

Web3 x AI Agents: Landscape, Integrations, and Foundational Challenges

arXiv:2508.02773v4 Announce Type: replace-cross Abstract: The convergence of Web3 technologies and AI agents is a rapidly evolving frontier that is reshaping decentralized ecosystems. This paper prese

agentsarxiv-cs-ai

When Do Discrete Actions Make Good Continuous Controllers?

arXiv:2610.20429v1 Announce Type: new Abstract: Though modern AI models like LLMs predict discrete tokens, today's robotic foundation models represent actions as continuous vectors. In this paper, we

researcharxiv-cs-ro

Wong--Zakai Convergence and FNO Learning for Stochastic Volterra Equations

arXiv:2610.18939v1 Announce Type: cross Abstract: This paper studies Wong--Zakai convergence and Fourier neural operator learning for stochastic Volterra integral equations (SVIEs) describing random s

tutorialsarxiv-cs-lg

A Geometrical Approach to Constructing Neural Networks for Perfect Training-set Classification

arXiv:2610.17312v1 Announce Type: new Abstract: This paper proposes a method for constructing a neural network that achieves perfect classification of all samples in a given training set, without taki

All-Electric Heavy-Duty Robotic Manipulator: Surrogate-Enhanced Modeling and Adaptive Modular Control

arXiv:2508.06313v2 Announce Type: replace Abstract: This paper presents a unified system-level modeling and control framework for an all-electric heavy-duty robotic manipulator (HDRM) driven by electr

researcharxiv-cs-ro

AReA: Adaptive Receptive-Field Attention for Efficient High-Resolution Diffusion Transformers (for Flux1/2, Qwen-Image, Ideogram 4)

model-releasesr-stablediffusion

At the moment only the flux1-krea-dev code has been release. Qwen-Image, Ideogram 4, and Flux2 'coming soon'. Project: https://rajabi2001.github.io/area/ Paper: https://arxiv.org/abs/2610.17227 Demo:

Agents

Autonomous Cyber Operations Gym for Training and Evaluating AI Models for Defence

arXiv:2610.18594v1 Announce Type: cross Abstract: This paper proposes an Autonomous Cyber Operations (ACO) gym, a unified framework integrating high-fidelity network emulation, realistic user behaviou

agentsarxiv-cs-lg

CAST: Real-Time Motion Capture for Any Skeleton Topology

model-releasesarxiv-cs-cv

arXiv:2610.14856v1 Announce Type: new Abstract: This paper studies the problem of recovering motion from monocular video, even when the target skeleton topology differs from the subject observed in th

Constrained Diffusion Models with Primal-Dual Inference

arXiv:2606.17192v2 Announce Type: replace Abstract: This paper develops constrained diffusion models with primal-dual inference (PDI) to sample from optimal distributions of entropy-regularized optimi

Convex Chance-Constrained Stochastic Control under Uncertain Specifications with Application to Learning-Based Hybrid Powertrain Control

arXiv:2601.18313v2 Announce Type: replace-cross Abstract: This paper proposes a strictly convex chance-constrained stochastic control framework that accounts for uncertainty in control specifications

Data-Topology Compatibility Enables Personalization in Decentralized Learning

arXiv:2610.15126v1 Announce Type: cross Abstract: Decentralized learning must balance collaboration with personalization across heterogeneous agents. This paper studies when cooperation can support st

Diagnosing Generalization Boundaries in AI-Based Vulnerability Detection via Calibrated Embedding Distances

arXiv:2610.14296v1 Announce Type: cross Abstract: High benchmark scores in learning-based vulnerability detection mask severe degradation under distribution shifts. This paper demonstrates that calibr

Applications

Dissipative Lagrangian Neural Networks for the Interpretable Modelling of Passive Mechanical Systems

arXiv:2610.15996v1 Announce Type: new Abstract: In this paper, we propose the Dissipative Lagrangian Neural Network (dLNN), a continuous-time system identification technique that separately learns the

applicationsarxiv-cs-lg

Distributional Regression for Arbitrary Response Variables Using ReLU Neural Networks

arXiv:2610.16142v1 Announce Type: cross Abstract: This paper develops a unified framework for distributional regression with arbitrary response variables. Rather than relying on scalar thresholds or E

Ellipsoids from Tangents

model-releasesarxiv-cs-cv

arXiv:2610.15580v1 Announce Type: new Abstract: This paper presents an algorithm for detecting and fitting multiple ellipsoids in 2D, 3D and higher-dimensional data. The representation is based on tan

Agents

Enhanced Trading Systems with Intelligent Market Analysis

arXiv:2610.13998v1 Announce Type: new Abstract: This paper proposes a hybrid reinforcement learning trading framework integrating intraday market data with transformer-based financial sentiment signal

agentsarxiv-cs-lg

Fast Rates for Offline Q-Learning with KL Regularization

arXiv:2610.18839v1 Announce Type: new Abstract: This paper investigates offline Q-learning for finite-horizon Markov decision processes with Kullback-Leibler (KL) regularization. Recent studies have s

Local Ai

From Prototype to Massing: Learning Graph Correspondence for Scalable Facade Modeling

arXiv:2610.14098v1 Announce Type: new Abstract: Facade articulation at building scale is usually resolved manually or through parametric systems that encode design intent as explicit rules. This paper

local-aiarxiv-cs-lg

Gemini 4 TikZ unicorn

model-releasesethan-mollick--x

Gemini 4 TikZ unicorn From GPT-4 to GPT-6. Sparks was a remarkably prescient paper that got a lot of pushback at the time, but absolutely sensed where the vibes were heading with LLMs based on a lot o

Geometry-Aware Predictive Control for Walk-to-Run Bipedal Obstacle Crossing

model-releasesarxiv-cs-ro

arXiv:2610.15735v1 Announce Type: new Abstract: This paper presents a geometry-aware predictive control framework for bipedal obstacle crossing across walking and running regimes. The proposed approac

Global Optimality of Pose Graph Optimization

arXiv:2610.14655v1 Announce Type: new Abstract: Pose Graph Optimization (PGO) is a classical and widely applied problem in state estimation for robotics, playing a fundamental role in localization, ma

researcharxiv-cs-ro

GpFlash: Maximizing Speed and Minimizing Memory for Gaussian Process Training and Prediction

arXiv:2610.16861v1 Announce Type: new Abstract: This paper presents GpFlash, a Gaussian process library that is focused on fast training and posterior evaluations, minimal memory usage, and scalabilit

tutorialsarxiv-cs-lg

High-Order Error Bounds for Markovian LSA with Richardson-Romberg Extrapolation

arXiv:2508.05570v2 Announce Type: replace-cross Abstract: In this paper, we study the bias and high-order error bounds of the Linear Stochastic Approximation (LSA) algorithm with Polyak-Ruppert (PR) a

Inference on the Value of Policy Improvement in Adaptive Experiments

arXiv:2610.15789v1 Announce Type: cross Abstract: In this paper, we develop an asymptotically normal estimator for the value of policy improvement (VPI) under adaptively collected data. This value, wh

Applications

Is Complex Query Answering Really Complex?

arXiv:2410.12537v4 Announce Type: replace Abstract: Complex query answering (CQA) on knowledge graphs (KGs) is gaining momentum as a challenging reasoning task. In this paper, we show that the current

applicationsarxiv-cs-lg

Last-Iterate Convergence of Clipped SGD with Linear Decay

arXiv:2610.18555v1 Announce Type: new Abstract: In this paper, we study the last-iterate convergence of clipped stochastic gradient descent under heavy-tailed noise. We assume that the stochastic grad

Learning Families of Fixed-Wing Aircraft Control Policies with Hypernetwork-Conditioned Reinforcement Learning

arXiv:2604.03392v2 Announce Type: replace-cross Abstract: This paper investigates whether it is more effective to condition reinforcement learning policies across changing dynamics using standard inpu

Learning Shortest Paths with Generative Flow Networks

arXiv:2603.01786v2 Announce Type: replace Abstract: In this paper, we present a novel learning framework for finding shortest paths in graphs utilizing Generative Flow Networks (GFlowNets). First, we

MoSAIC: Learning DAG Scheduling Heuristics from Optimal Schedules through Motif-Aware Recovery and LLM-Guided Reasoning

arXiv:2610.15205v1 Announce Type: cross Abstract: Efficient task scheduling in heterogeneous computing systems remains a fundamental challenge in embedded and high-level synthesis flows. This paper pr

tutorialsarxiv-cs-lg

Naga: Vedic Encoding for Deep State Space Models

arXiv:2511.13510v2 Announce Type: replace Abstract: This paper presents Naga, a deep State Space Model (SSM) encoding approach inspired by structural concepts from Vedic mathematics. The proposed meth

New impressive work from Microsoft and collaborators. And it's one of my favorite recent topics: RL environments. They show that Qwen3.6-35B…

New impressive work from Microsoft and collaborators. And it's one of my favorite recent topics: RL environments. They show that Qwen3.6-35B-A3B goes from 41.1 to 53.3 on Terminal-Bench 2.1 by trainin

model-releasesdair-ai--x

Local Ai

Optimal Transport Aggregation for Distributed Mixture-of-Experts

arXiv:2312.09877v3 Announce Type: replace Abstract: This paper considers the setting in which local Mixture-of-experts (MoE) models are fitted independently on distributed datasets and must be combine

local-aiarxiv-cs-lg

Orientation Estimation of Cryo-EM Images for Molecules with Arbitrary Symmetry by Semidefinite Programming

arXiv:2610.18691v1 Announce Type: new Abstract: This paper extends the non-unique games framework introduced in Bandeira et al. (2020) to address symmetric structures. Precisely, we consider the probl

researcharxiv-cs-cv

PC7 Model: Dynamics of Expanding and Contracting Soft Robots using Piece-wise Constant 7D Deformation Based on Similarity Transformation Group

model-releasesarxiv-cs-ro

arXiv:2610.14586v1 Announce Type: new Abstract: This paper proposes a novel soft robot model named piece-wise constant 7D deformation (PC7) model that can represent expanding and contracting deformati

[Re] Boosting the Visual Interpretability of CLIP via Adversarial Fine-Tuning

arXiv:2610.14832v1 Announce Type: new Abstract: This paper presents a reproducibility study of ``Boosting the Visual Interpretability of CLIP via Adversarial Fine-Tuning'', published at ICLR 2025, whi

safetyarxiv-cs-cv

Strong Gaussian Approximation of Constant-Learning-Rate SGD: Bias and Trajectory Inference

arXiv:2610.14567v1 Announce Type: cross Abstract: This paper develops strong Gaussian approximations (KMT type) for constant learning rate stochastic gradient descent (SGD). We work with heavy tailed

Towards Physically Grounded Articulated 3D Assets: A Multi-Agent Pipeline and Benchmark

model-releasesarxiv-cs-cv

arXiv:2610.13947v2 Announce Type: replace Abstract: In this paper, we study physically grounded articulated 3D asset generation---moving the goal from reproducing how objects look to capturing how the

TRIG: Toward Measuring Texture Realism in Image Generation

arXiv:2610.15164v1 Announce Type: new Abstract: This paper studies the backbone used by metrics for evaluating the quality of generated images. Although generative models have advanced rapidly, some o

safetyarxiv-cs-cv

Universal Distributed Feature Extraction Complementary to Side Information via H-Score

arXiv:2610.16707v1 Announce Type: cross Abstract: This paper considers a distributed estimation system where edge nodes transmit averaged features to a center node for parameter estimation. A key chal

Applications

Watermarking Continuous Tabular Data Generation

arXiv:2405.14018v2 Announce Type: replace-cross Abstract: In this paper, we introduce a simple yet effective tabular data watermarking mechanism with statistical guarantees. We show theoretically that

applicationsarxiv-cs-lg