Search: “research”

On Language Generation in the Limit with Bounded Memory

arXiv:2605.30324v1 Announce Type: cross Abstract: We study language generation in the limit under bounded memory. In this task, a learner observes examples from an unknown target language one at a tim

On the Optimizer Dependence of Neural Scaling Laws

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arXiv:2605.29387v1 Announce Type: cross Abstract: The scaling exponent alpha in neural scaling laws L(N) propto N^{-alpha} is commonly treated as a fixed constant set by architecture and data. We pres

researcharxiv-cs-ai

29 May 2026

One Click per Cell Type Suffices: Training-free Group Interaction for Cell Instance Segmentation

arXiv:2605.29429v1 Announce Type: new Abstract: Cell instance segmentation models trained on cell-specific datasets suffer severe performance drops on out-of-distribution cell types, while interactive

Open World Autoencoding Drift Detection with Novel Class Recognition in Tabular Non-stationary Data Streams

arXiv:2605.29834v1 Announce Type: new Abstract: Data stream processing has become a landmark in modern machine learning applications, with concept drifts and novel class appearances posing the primary

Optimal Gap-Dependent Regret for Private Stochastic Decision-Theoretic Online Learning

arXiv:2605.29148v1 Announce Type: new Abstract: We study stochastic decision-theoretic online learning with full information and event-level pure differential privacy. A COLT open problem of Hu and Me

Optimal Rates for Differentially Private Hypothesis Testing with E-values

arXiv:2605.28952v1 Announce Type: cross Abstract: E-values have attracted considerable interest in recent years as flexible tools for enabling anytime-valid and adaptive data analysis. Hypothesis test

Optimization and Generation in Aerodynamics Inverse Design

arXiv:2602.03582v3 Announce Type: replace Abstract: Aerodynamic inverse design can improve vehicle and aircraft efficiency, but practical design rarely seeks performance alone: vehicle refinement must

ParCo-SDF: Learning Prior-Free Partial-to-Complete Signed Distance Fields of Deformable Objects

arXiv:2605.29417v1 Announce Type: new Abstract: This study addresses the partial-to-complete geometry reconstruction of deformable objects (DOs) from point-cloud observations toward precise DO manipul

Phantom: Training Robots Without Robots Using Only Human Videos

arXiv:2503.00779v2 Announce Type: replace Abstract: Training general-purpose robots requires learning from large and diverse data sources. Current approaches rely heavily on teleoperated demonstration

researcharxiv-cs-ro

Privacy-Enhanced Zero-Order Federated Learning via xMK-CKKS over Wireless Channels

arXiv:2605.30123v1 Announce Type: cross Abstract: Homomorphic encryption (HE) enables privacy-preserving aggregation in federated learning (FL) by allowing the server to operate on encrypted data with

Procedural Pretraining: Warming Up Language Models with Abstract Data

arXiv:2601.21725v2 Announce Type: replace Abstract: Pretraining language models directly on web-scale corpora is the de facto paradigm. We study an alternative where the model is initially exposed to

Projectional Decoding: Towards Semantic-Aware LLM Generation

arXiv:2605.30054v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate software artifacts across many software engineering (SE) tasks, yet ensuring the semant

Provable Affine Identifiability of Nonlinear CCA under Latent Distributional Priors

arXiv:2510.04758v2 Announce Type: replace Abstract: In this work, we establish the sufficient conditions under which nonlinear Canonical Correlation Analysis (CCA) recovers ground-truth latent factors

Pushing the Limits of Block Rotations in Post-Training Quantization

arXiv:2601.22347v2 Announce Type: replace-cross Abstract: Recent post-training quantization (PTQ) methods have adopted block rotations to diffuse outliers prior to rounding. While this reduces the ove

Rare Event Analysis of Large Language Models

arXiv:2602.06791v2 Announce Type: replace Abstract: Being probabilistic models, during inference large language models (LLMs) display rare events: behaviour that is far from typical but highly signifi

Reasoning about Reasoning: BAPO Bounds on Chain-of-Thought Token Complexity in LLMs

arXiv:2602.02909v2 Announce Type: replace Abstract: Inference-time scaling via chain-of-thought (CoT) reasoning is a major driver of state-of-the-art LLM performance, but it comes with substantial lat

Reducing Experimental Testing in Space Propulsion Film Cooling Analyses by Pixelwise Generative Image Interpolation

arXiv:2605.29911v1 Announce Type: cross Abstract: We propose a machine learning approach for image regression from sparse experimental measurements. We show the application of the proposed method on f

Reinforcement Learning with Robust Rubric Rewards

arXiv:2605.30244v1 Announce Type: cross Abstract: While Reinforcement Learning with Verifiable Rewards (RLVR) is effective for deterministically checkable tasks, many vision-language tasks are partial

Rel-MOSS: Towards Imbalanced Relational Deep Learning on Relational Databases

arXiv:2603.07916v2 Announce Type: replace Abstract: In recent advances, to enable a fully data-driven learning paradigm on relational databases (RDB), relational deep learning (RDL) is proposed to str

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability

arXiv:2605.29687v1 Announce Type: new Abstract: Large Language Models (LLMs) excel at understanding natural language but struggle with optimisation tasks involving multiple constraints and user-define

Resolution Diagnostics for Paired LLM Evaluation

arXiv:2605.30315v1 Announce Type: new Abstract: Across two public LLM leaderboards, many displayed pairwise rankings do not meet a conventional paired-test resolution target under the actual paired ev

Rethinking FID Through the Geometry of the Reference Dataset

arXiv:2605.29335v1 Announce Type: cross Abstract: Frechet Inception Distance (FID) is widely used to evaluate image generators, yet lower FID does not always correspond to better sample quality. We sh

Rethinking Stepwise Model Routing: A Cost-Efficient Table Reasoning Perspective

arXiv:2605.29319v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) achieve strong performance on table reasoning tasks but incur substantial inference cost due to long reasoning traces. Ste

Ridge Regression from Poisson Resetting: A Renewal Perspective on Spectral Regularization

arXiv:2605.30059v1 Announce Type: new Abstract: We connect stochastic resetting from non-equilibrium statistical physics with ridge regularization in statistical learning. For linear gradient flow, re

Riemannian AmbientFlow: Towards Simultaneous Manifold Learning and Generative Modeling from Corrupted Data

arXiv:2601.18728v2 Announce Type: replace Abstract: Modern generative modeling methods have demonstrated strong performance in learning complex data distributions from clean samples. In many scientifi

Robust Frequency-Calibrated Virtual EEG Channel Generation from Four Frontal Electrodes for Wearable EEG Augmentation

arXiv:2605.29263v1 Announce Type: new Abstract: Low-channel wearable electroencephalography (EEG) is attractive for long-term monitoring, but four frontal electrodes provide only a sparse and spatiall

S2MDF: A Plug-And-Play Layer for Intersection-Free Multi-Object Signed Distance Fields

arXiv:2605.29761v1 Announce Type: new Abstract: Compositional implicit surface representations model scenes as collections of objects, each encoded by a Signed Distance Field (SDF). A fundamental limi

SADA: Safe and Adaptive Aggregation of Multiple Black-Box Predictions in Semi-Supervised Learning

arXiv:2509.21707v3 Announce Type: replace-cross Abstract: Semi-supervised learning (SSL) arises in practice when labeled data are scarce or expensive to obtain, while large quantities of unlabeled dat

SalsaAgent: A multimodal embodied language model for interactive dance generation

arXiv:2605.29219v1 Announce Type: new Abstract: Interaction between humanoids involves bidirectional and nonverbal reactivity, coordination and synchrony. Toward socially aware robots and interactive

SAVAA: Mitigating Hallucinations in LVLMs via Step-wise Adaptive Visual Attention Amplification

arXiv:2602.13600v2 Announce Type: replace Abstract: A line of recent training-free methods for mitigating hallucinations in large vision-language models (LVLMs) operates by amplifying attention to vis

SCoOP: Semantic Consistent Opinion Pooling for Uncertainty Quantification in Multiple Vision-Language Model Systems

arXiv:2603.23853v3 Announce Type: replace Abstract: Combining multiple Vision-Language Models (VLMs) can enhance multimodal reasoning and robustness, but aggregating heterogeneous models' outputs ampl

SCOPE: A Lightweight-training LLM Framework for Air Traffic Control Readback Monitoring

arXiv:2605.29543v1 Announce Type: cross Abstract: Pilot readback of Air Traffic Control (ATC) voice instructions is a primary safeguard against miscommunication in air transportation. However, readbac

Selecting Hyperparameters for Tree-Boosting

arXiv:2602.05786v2 Announce Type: replace Abstract: Tree-boosting is a widely used machine learning technique for tabular data. However, its out-of-sample accuracy is critically dependent on multiple

Self-Trained Verification for Training- and Test-Time Self-Improvement

arXiv:2605.30290v1 Announce Type: cross Abstract: Self-improvement at scale has been a longstanding goal for reasoning models, and there are two natural places to do it: at test time, through verifica

ShapleyLaw: A Game-Theoretic Approach to Multilingual Scaling Laws

arXiv:2603.17945v2 Announce Type: replace Abstract: In multilingual pretraining, the test loss of a pretrained model is heavily influenced by the proportion of each language in the pretraining data, n

Simulation-based planning of Motion Sequences for Automated Procedure Optimization in Multi-Robot Assembly Cells

arXiv:2507.23270v2 Announce Type: replace Abstract: Reconfigurable multi-robot cells offer a promising approach to meet fluctuating assembly demands. However, the recurrent planning of their configura

researcharxiv-cs-ro

Singularity-aware Optimization via Randomized Geometric Probing: Towards Stable Non-smooth Optimization

arXiv:2605.29547v1 Announce Type: cross Abstract: Deep learning optimization relies heavily on the assumption of smooth loss landscapes, a condition systematically violated by modern architectures due

Singularity-free dynamical invariants-based quantum control

arXiv:2510.15340v2 Announce Type: replace-cross Abstract: State preparation is a cornerstone of quantum technologies, underpinning applications in computation, communication, and sensing. Its importan

SM2ITH: Safe Mobile Manipulation with Interactive Human Prediction via Task-Hierarchical Bilevel Model Predictive Control

arXiv:2511.17798v2 Announce Type: replace Abstract: Mobile manipulators are designed to perform complex sequences of navigation and manipulation tasks in human-centered environments. While recent opti

researcharxiv-cs-ro

Soften the Mask: Adaptive Temporal Soft Mask for Efficient Dynamic Facial Expression Recognition

arXiv:2502.21004v2 Announce Type: replace Abstract: Dynamic Facial Expression Recognition (DFER) facilitates the understanding of psychological intentions through non-verbal communication. Existing me

Solved in Unit Domain: JacobiNet for Differentiable Coordinate-Transformed PINNs

arXiv:2508.02537v3 Announce Type: replace Abstract: Physics-Informed Neural Networks (PINNs) offer a powerful framework for solving PDEs by embedding physical laws into the learning process. However,

Solving Integer Linear Programming with Parallel Tempering

arXiv:2605.29366v1 Announce Type: new Abstract: Integer Linear Programming (ILP) serves as a versatile framework for modeling a wide range of combinatorial optimization problems, typically addressed b

Solving the Offline and Online Min-Max Problem of Non-smooth Submodular-Concave Functions: A Zeroth-Order Approach

arXiv:2601.21243v2 Announce Type: replace-cross Abstract: We consider max-min and min-max problems with objective functions that are possibly non-smooth, submodular with respect to the minimiser and c

Source-Grounded Semantic Reinforcement Learning for Low-Resource Target-Language Generation

arXiv:2605.29502v1 Announce Type: cross Abstract: Low-resource target-language generation is often limited by scarce parallel data, while high-resource source-language monolingual data is abundant but

Spectral Guidance for Flexible and Efficient Control of Diffusion Models

arXiv:2605.28900v1 Announce Type: new Abstract: We introduce Spectral Guidance, a framework for controlling diffusion models by leveraging the intrinsic geometry of the generative process. As data is

SpeedCP: Fast Kernel-based Conditional Conformal Prediction

arXiv:2509.24100v2 Announce Type: replace-cross Abstract: Conformal prediction provides distribution-free prediction sets with finite-sample conditional guarantees. We build upon the RKHS-based framew

Spurious Prompts: Can Irrelevant Prompts Steer Large Language Models?

arXiv:2605.29678v1 Announce Type: new Abstract: Large language models are highly sensitive to prompts, but this sensitivity is usually studied through task-relevant instructions, demonstrations, or re

STAP: A Shuffle-Tokenized App Predictor with Ultra Long Context for Vocabulary-Free Mobile App Prediction

arXiv:2605.29863v1 Announce Type: new Abstract: Predicting the next mobile application a user will launch is essential for intelligent device resource management and proactive assistance. Existing mod