Knowledge catalogue
arXiv:2506.12090v2 Announce Type: replace Abstract: This paper introduces ChatbotManip, a novel dataset for studying manipulation in Chatbots. It contains simulated generated conversations between a c
arXiv:2605.10005v1 Announce Type: cross Abstract: In this paper, we describe early work on a specification inference tool for the Move Prover that combines a weakest-precondition (WP) analysis over Mo
arXiv:2605.10516v1 Announce Type: new Abstract: This paper establishes a rigorous measurement science for AI agent reliability, providing a foundational framework for quantifying consistency under sem
arXiv:2605.09833v1 Announce Type: cross Abstract: This paper studies cross-domain lossy compression through the lens of minimum entropy coupling (MEC) with rate and classification constraints. In this
arXiv:2605.10295v1 Announce Type: new Abstract: This paper aims to construct a linguistic resource of Korean Multiword Expressions for Feature-Based Sentiment Analysis (FBSA): DECO-MWE. Dealing with m
arXiv:2605.08218v1 Announce Type: cross Abstract: This paper proposes latent visualization by optimization (LVO), a mechanistic interpretability technique that extends feature visualization by optimiz
arXiv:2505.05740v3 Announce Type: replace Abstract: This paper introduces the first globally optimal algorithm for the empirical risk minimization problem of two-layer maxout and ReLU networks, i.e.,
arXiv:2605.10015v1 Announce Type: cross Abstract: In this paper, we study the problem of sampling from a distribution under the constraint of differential privacy (DP). Prior works measure the utility
arXiv:2605.08104v1 Announce Type: new Abstract: This paper explores the application of the Soft Actor-Critic (SAC) algorithm within a Distributional Reinforcement Learning setting and introduces an im
arXiv:2605.09395v1 Announce Type: new Abstract: In this paper, we propose the first VLnderline{extbf{M}} nderline{extbf{a}}gentic nderline{extbf{r}}easoning framework for few-nderline{extbf{s}}hot mul
arXiv:2511.08644v3 Announce Type: replace-cross Abstract: This paper presents a detailed comparative analysis of the performance of three major Python data manipulation libraries - Pandas, Polars, and
arXiv:2605.10680v1 Announce Type: new Abstract: This paper proposes a paradigm shift linking machine unlearning directly to the structure of the data distributions rather than a mere update of the neu
arXiv:2605.09469v1 Announce Type: new Abstract: This paper explores the use of emojis in financial sentiment analysis, focusing on the social media platform StockTwits. Emojis, increasingly prevalent
arXiv:2509.15519v2 Announce Type: replace Abstract: This paper studies fully decentralized cooperative multi-agent reinforcement learning, where each agent solely observes the states, its local action
arXiv:2605.08702v1 Announce Type: cross Abstract: This paper tackles compositional personalization of vision-language models (VLMs). In this problem, multiple user-defined concepts must be recognized
arXiv:2605.10582v1 Announce Type: cross Abstract: This paper proposes a guaranteed defense method for large language models (LLMs) to safeguard against jailbreaking attacks. Drawing inspiration from t
arXiv:2605.10560v1 Announce Type: new Abstract: This paper describes our system to SemEval-2026 Task 3 Track A Subtask 1 on Dimensional Aspect Sentiment Regression (DimASR). We propose a lightweight a
arXiv:2605.10087v1 Announce Type: new Abstract: This paper describes an initiation of interaction(IoI) detection framework without keywords for human-robot interaction(HRI) based on audio and vision s
arXiv:2605.10151v1 Announce Type: new Abstract: This paper addresses the problem of learning to sparsify stochastic linear bandits, where a decision-maker sequentially selects actions from a high-dime
arXiv:2605.10148v1 Announce Type: new Abstract: The Vision Transformer (ViT) achieves remarkable accuracy across visual tasks but remains computationally expensive for edge deployment. This paper pres
arXiv:2511.01774v3 Announce Type: replace Abstract: This paper presents the MOBIUS platform, a bipedal robot capable of walking, crawling, climbing, and rolling. MOBIUS features four limbs, two 6-DoF
arXiv:2605.09364v1 Announce Type: new Abstract: This paper investigates robust representation learning in offline goal-conditioned reinforcement learning (GCRL). Particularly in sparse reward scenario
arXiv:2605.10813v1 Announce Type: new Abstract: LLM-powered multi-agent systems can now automate the full research pipeline from ideation to paper writing, but a fundamental question remains: automati
arXiv:2605.10299v1 Announce Type: new Abstract: This paper studies kernelized bandits (also known as Gaussian process bandits) in an adversarial environment, where the reward functions in a known repr
arXiv:2506.01250v2 Announce Type: replace Abstract: In this paper, we address the contextual dueling bandit problem by proposing variance-aware algorithms that leverage neural networks to approximate
arXiv:2605.08221v1 Announce Type: cross Abstract: This paper presents NoisyCoconut, a novel inference-time method that enhances large language model (LLM) reliability by manipulating internal represen
arXiv:2512.24552v2 Announce Type: replace Abstract: This paper proposes a novel second-order optimization algorithm based on the Optimal Control Principle (OCP), applicable to large-scale optimization
arXiv:2506.01404v2 Announce Type: replace Abstract: This paper introduces an innovative error feedback framework designed to mitigate quantization noise in distributed graph filtering, where communica
arXiv:2605.10611v1 Announce Type: cross Abstract: This paper proposes a jailbreaking prompt detection method for large language models (LLMs) to defend against jailbreak attacks. Although recent LLMs
arXiv:2605.10862v1 Announce Type: new Abstract: This paper demonstrates RUBEN, an interactive tool for discovering minimal rules to explain the outputs of retrieval-augmented large language models (LL
arXiv:2605.09772v1 Announce Type: cross Abstract: This paper proposes a safe data-driven control framework for nonlinear systems with partially known dynamics. The method ensures stability and constra
arXiv:2605.10327v1 Announce Type: cross Abstract: In this paper, we present SCALAR (Symbolic Conjecture and LLM-Assisted Reasoning), a neurosymbolic framework for automated conjecture generation in qu
arXiv:2605.08453v1 Announce Type: cross Abstract: This paper studies the role of sinks and diagonal patterns as attention switch and anti-oversmoothing mechanisms. We analyze geometric conditions unde
arXiv:2507.17921v2 Announce Type: replace-cross Abstract: Canonical correlation analysis (CCA) is a technique for finding correlated sets of features between two datasets. In this paper, we propose a
arXiv:2605.05569v2 Announce Type: replace-cross Abstract: This paper shows that the semi-dual formulation of the optimal transport problem has a degenerate saddle-point structure, and that its numeric
arXiv:2605.08991v1 Announce Type: new Abstract: A series of papers has introduced the Heuristic Rating Estimation method, which evaluates a set of alternatives based on pairwise comparisons and the we
arXiv:2605.10811v1 Announce Type: cross Abstract: This paper develops a joint spectral radius (JSR) framework for analyzing rank-one deflated Q-value iteration (Q-VI) in discounted Markov decision pro
arXiv:2605.09020v1 Announce Type: new Abstract: This paper presents a novel Direct Integration Theorem (DIT), derived as a non-trivial corollary of the classical Central Slice Theorem (CST). The DIT p
arXiv:1706.00476v4 Announce Type: replace-cross Abstract: In this paper, we propose a low-rank coordinate descent approach to structured semidefinite programming with diagonal constraints. The approac
arXiv:2605.08379v1 Announce Type: cross Abstract: This paper proposes a time-warping transfer learning method, a technique for temporally rescaling the learned dynamics of a recurrent neural network (
arXiv:2605.08914v1 Announce Type: cross Abstract: This paper introduces a framework specifically designed for sparse and irregular time series {risk estimation}. It is based on a Transformer Autoencod
arXiv:2605.09396v1 Announce Type: cross Abstract: This paper relaxes the restrictive symmetry conditions adopted in [4], [5] and extends their universal feature selection framework to accommodate nois
arXiv:2605.09692v1 Announce Type: new Abstract: Unpredictable behavior is often taken as evidence of control, yet stochastic dispersion and structured action control need not coincide. This paper test
arXiv:2605.09769v1 Announce Type: new Abstract: This paper describes our system for classifying psychological defense mechanisms in emotional support dialogues using the Defense Mechanism Rating Scale
arXiv:2605.10567v1 Announce Type: new Abstract: In this paper, we aim to jointly model the geometry, appearance, and physical information of 3D scenes solely from dynamic multi-view videos, without re
arXiv:2605.05611v2 Announce Type: replace-cross Abstract: In this paper, we present X-Voice, a 0.4B multilingual zero-shot voice cloning model that clones arbitrary voices and enables everyone to spea
arXiv:2605.10704v1 Announce Type: cross Abstract: This paper introduces a proactive Unmanned Aerial Vehicle (UAV) mobility management xApp for Open Radio Access Network (O-RAN) Near Real-Time Radio In
arXiv:2605.06231v2 Announce Type: replace Abstract: This paper presents our system for SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization, which identifies p
arXiv:2605.08059v1 Announce Type: new Abstract: In this paper, we propose a modular framework for 6D pose estimation based on keypoint heatmap regression. Our approach combines YOLOv10m for object det
arXiv:2605.06741v1 Announce Type: new Abstract: Learning-rate steps are usually treated as hyperparameters. This paper isolates a local beliefspace calculation: when an update is modeled as a projecte
arXiv:2605.07094v1 Announce Type: new Abstract: This paper introduces the Active-Importance-Sampling Actor-Critic (AISAC) algorithm, an extension of the Actor-Critic framework for reducing variance in
arXiv:2605.06878v1 Announce Type: cross Abstract: This paper presents CARMEN, a runtime-adaptive, CORDIC-accelerated multi-precision vector engine for resource-efficient deep learning inference. The k
arXiv:2605.07830v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents in offensive cybersecurity. In this paper, we reveal an interesting phenom
arXiv:2605.07351v1 Announce Type: new Abstract: While Gaussian Splatting-based Feature Fields (GSFFs) have shown promise for visual localization, this paper highlights that photometrically optimized G
arXiv:2605.07041v1 Announce Type: cross Abstract: This paper introduces Dr-BA, a first-of-its-kind radar bundle adjustment (BA) framework that operates directly on 2D spinning radar intensity images.
arXiv:2602.06827v2 Announce Type: replace Abstract: In this paper, we introduce DynaRetarget, a complete pipeline for retargeting human motions to humanoid control policies. The core component of Dyna
Epistemological standards in SF venture capital are so low that you can write this, and everyone will simply nod along. The actual evidence, including a recent NBER paper on thousands of firms, finds:
arXiv:2512.12602v4 Announce Type: replace Abstract: In this paper, we introduce Exact Flow Linear Attention~(EFLA), an exact-flow formulation of delta-rule linear attention. We show that the delta-rul
arXiv:2605.07208v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used to brainstorm and evaluate research ideas, yet assessing such judgments is fundamentally difficult be
arXiv:2605.07703v1 Announce Type: new Abstract: This paper presents a finite-time analysis for Monte Carlo Tree Search (MCTS) in Partially Observable Markov Decision Processes (POMDPs), with probabili