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Catching The Correct Answer Trap: Characterising AI Tutor Blind Spots When Analysing Student Reasoning

arXiv:2605.23925v1 Announce Type: cross Abstract: Intelligent tutoring systems increasingly provide automated feedback on student work, but robust feedback requires assessing reasoning, not only final

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Causality as the Statistical Conscience of Artificial Intelligence: From Pearl's Ladder to Trustworthy Machines

DGX agent

arXiv:2605.24076v1 Announce Type: cross Abstract: Modern Artificial Intelligence achieves remarkable predictive power by optimizing statistical risk functionals over vast corpora. Yet a gap separates

researcharxiv-cs-lg

26 May 2026

Channel-wise Vector Quantization

arXiv:2605.26089v1 Announce Type: cross Abstract: We present Channel-wise Vector Quantization (CVQ), a novel image tokenization paradigm that replaces patch-wise tokens with channel-wise tokens. Unlik

Characterizing the Representational Capacity of Neural Processes

arXiv:2605.24210v1 Announce Type: new Abstract: What functions can Neural Processes represent? We analyze the representational capacity of popular NP architectures: Conditional Neural Processes (CNPs)

CHESTNUT: A QoS Dataset for Mobile Edge Environments

arXiv:2410.19248v2 Announce Type: replace Abstract: Quality of Service (QoS) is an important metric to measure the performance of network services. Nowadays, it is widely used in mobile edge environme

Clarify, Abstain or Answer? Strategising in Conversation with Belief-Augmented Generation

arXiv:2605.25831v1 Announce Type: cross Abstract: Large language models (LLMs) define a distribution over text, which can be viewed as a probabilistic representation of uncertainty: sampling K respons

CLiViS: Unleashing Cognitive Map through Linguistic-Visual Synergy for Embodied Visual Reasoning

arXiv:2506.17629v2 Announce Type: replace-cross Abstract: Embodied Visual Reasoning (EVR) seeks to follow complex, free-form instructions based on egocentric video, enabling semantic understanding and

Closed-Form Node Classification with Exact Graph Unlearning

arXiv:2605.25662v1 Announce Type: new Abstract: Graph neural networks for node classification are typically trained by gradient descent over hundreds or thousands of epochs. Recent work has shown that

Cluster Frequency Conformal Prediction for Local Coverage

arXiv:2605.24872v1 Announce Type: new Abstract: Conformal prediction provides distribution-free coverage guarantees, but in many-class classification it may still under-cover specific classes or subpo

Clustering based on Stochastic Dominance with application for risk averters and risk seekers

arXiv:2605.24422v1 Announce Type: cross Abstract: Stochastic Dominance (SD) theory provides a rigorous framework for selecting superior assets tailored to the asset allocation needs of investors with

Coarse-to-Fine Domain Incremental Learning with Attentive Distillation for Mining Footprint Segmentation in Multispectral Imagery

arXiv:2605.24460v1 Announce Type: cross Abstract: Automatically mapping and segmenting global mining footprints using remote sensing and deep learning is critical for monitoring the socio-environmenta

Compliant Non-Prehensile Pushing Manipulation

arXiv:2605.25672v1 Announce Type: new Abstract: In this paper, we address the challenge of performing non-prehensile pushing operations with a compliant robotic manipulation system. To ensure safe ope

researcharxiv-cs-ro

Compositional Semantics for Open Vocabulary Spatio-semantic Representations

arXiv:2310.04981v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) transform environment percepts into vision-language semantics interpretable by LLMs. However, completing complex

Conditional KRR: Injecting Unpenalized Features into Kernel Methods with Applications to Kernel Thresholding

arXiv:2605.26067v1 Announce Type: cross Abstract: Conditionally positive definite (CPD) kernels are defined with respect to a function class F. It is well known that such a kernel K is associated with

Confidence and Calibration of Activation Oracles for Reliable Interpretation of Language Model Internals

arXiv:2605.26045v1 Announce Type: cross Abstract: Activation oracles aim to make the activations of other models legible to humans and yield promising results compared to white-box interpretability te

Confidence Calibration in Large Language Models

arXiv:2605.23909v1 Announce Type: new Abstract: We investigate the calibration of large language models' (LLMs') confidence across diverse tasks. The results of our preregistered study show that the c

Constraint-Anchored Attribution: Feasibility-Certified Counterfactuals and Bonferroni-PAC Sufficient Subsets for Neural CO Policies

arXiv:2605.25235v1 Announce Type: cross Abstract: We give an attribution method for neural combinatorial-optimisation (CO) policies that (i) decomposes a decision by constraint families via LP-relaxat

Context-CoT: Enhancing Context Learning via High-Quality Reasoning Synthesis

arXiv:2605.25354v1 Announce Type: new Abstract: While LLMs excel at reasoning over prompts using static pretrained knowledge, they struggle significantly with context learning-the ability to dynamical

Context: Proactive Goal-Directed Intelligence via Composable Sandboxed Programs, Declarative Wiring, and Structured Interaction

arXiv:2605.23928v1 Announce Type: new Abstract: We present Context, the intelligence layer of the Magarshak Architecture, which replaces reactive query-response chatbots with proactive goal-directed a

Correcting Visual Blur Induced by Attention Distraction to Reduce Hallucinations: Algorithm and Theory

arXiv:2605.24602v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) frequently suffer from object hallucinations, yet the visual perceptual mechanism underlying this failure rem

Coupled Variational Reinforcement Learning for Language Model General Reasoning

arXiv:2512.12576v3 Announce Type: replace-cross Abstract: While reinforcement learning has achieved impressive progress in language model reasoning, it is constrained by the requirement for verifiable

Courant: a State-Adaptive Perceiver-Based Neural Surrogate with Local Support and Interpretable Field Decomposition

arXiv:2605.25115v1 Announce Type: cross Abstract: We introduce 'Courant', a Perceiver-based encoder-processor-decoder surrogate model that has latent features exhibiting adaptive specialization and lo

Creativity feeds on constraints

researchfrancois-chollet--x

Creativity often thrives within limitations rather than unlimited freedom, as constraints force innovative problem-solving and focused thinking. This insight from AI researcher François Chollet sugges

Cross-Stage Attention Multi-Expert Network for Radiologist-Inspired Breast Ultrasound Diagnosis

arXiv:2605.25518v1 Announce Type: cross Abstract: Breast ultrasound imaging is an important noninvasive method for early breast cancer diagnosis, but automatic benign/malignant classification remains

CUNY at CLPsych 2026: A Pipeline Approach to Classification and Summarization of Mental Health Changes

arXiv:2605.24164v1 Announce Type: new Abstract: We describe our submission to the CLPsych~2026 Shared Task on capturing and characterizing mental health changes through social media timeline dynamics.

Decoding Stimulus Reconstruction-Based Auditory Attention Robustly in Unbalanced EEG Datasets

arXiv:2605.25605v1 Announce Type: cross Abstract: In the past decade, numerous studies have applied deep neural networks (DNNs) to decode auditory attention (AAD) from Electroencephalogram (EEG) signa

Deep Learning-Enabled Prediction of Geoeffective CMEs Using SOHO and SDO Observations

arXiv:2605.24748v1 Announce Type: cross Abstract: Understanding and forecasting the geoeffectiveness of a coronal mass ejection (CME) is crucial for protecting infrastructure in the near-Earth space e

Deep ZakaiJ: Structured Filtering for Jump-Diffusion Time Series Forecasting

arXiv:2605.24548v1 Announce Type: new Abstract: Time series driven by unobserved latent states frequently exhibit abrupt jump discontinuities whose timing and magnitude cannot be predicted from observ

Delayed Assignments in Online Non-Centroid Clustering with Stochastic Arrivals

arXiv:2601.16091v2 Announce Type: replace-cross Abstract: Clustering is a fundamental problem, aiming to partition a set of elements, like agents or data points, into clusters such that elements in th

Design, Control, and Motion Strategy for DELTA: Transformable Multilink Multirotor for Air-Ground Hybrid Locomotion and Manipulation

arXiv:2403.06636v2 Announce Type: replace Abstract: In recent years, multimodal locomotion capabilities have enabled robots to maneuver in both terrestrial and aerial domains. However, most of these r

researcharxiv-cs-ro

Detecting Metastable Basins in High Dimensions via Marginal Trajectory Distribution Discrimination

arXiv:2605.24136v1 Announce Type: cross Abstract: We study the problem of identifying dynamically distinct basins of attraction in high dimensional time-homogeneous Markov processes using only traject

Detecting Unfaithful Chain-of-Thought via Circuit-Guided Internal-External Discrepancy

arXiv:2605.25603v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning improves the problem-solving ability of large language models (LLMs), but generated reasoning traces may not faithfully

Developer productivity is hard to manage.

researchfrancois-chollet--x

Developer productivity is hard to manage. Uber’s COO has said that it’s getting “harder to justify” its AI costs because there was no way to show a link between AI spend and any meaningful increase in

Different Statistical Perspectives for Understanding Generalisation in Graph Neural Networks

arXiv:2605.25452v1 Announce Type: cross Abstract: Graph Neural Networks (GNN) are currently the most popular approach for learning and prediction on graph-structured data and are deployed in various f

Distinguishing Right from Wrong in Debates: Attribution Analysis of Chinese Harmful Memes

arXiv:2605.24344v1 Announce Type: new Abstract: Research on harmful meme detection has garnered significant attention, resulting in the development of numerous datasets and methods. However, progress

Does Continued Pretraining on a Learner Corpus Improve Automated Essay Scoring on English Proficiency Tests? Evidence from EFCAMDAT

arXiv:2605.25924v1 Announce Type: new Abstract: Recent automated essay scoring (AES) studies increasingly use pretrained transformer models, but these models are usually pretrained on general-domain E

Don't Retrain, Just Reuse: Recovering Dual-Target Molecules from Single-Target Diffusion Models

arXiv:2605.25681v1 Announce Type: cross Abstract: Designing a single molecule that modulates two targets is a promising strategy for polypharmacology, but it remains substantially harder than standard

DriftingMol: Decoder-Coupled Drift for One-Pass Property-Conditional Molecular Generation

arXiv:2605.24841v1 Announce Type: new Abstract: Property-conditional molecular generation should produce valid, diverse molecules while responding to continuous target values at low sampling cost. We

DRIVE: Modeling Skills at the Reasoning and Interaction Levels for Web Agents under Continual Learning

arXiv:2605.23939v1 Announce Type: new Abstract: Web agents require both high-level reasoning (for task decomposition) and low-level interactions (for page elements manipulation) to conduct different t

DUEL: Adversarial Self-Play for Multimodal Reasoning

arXiv:2605.24794v1 Announce Type: cross Abstract: Reinforcement learning (RL) has emerged as an effective paradigm for improving the reasoning capability of vision-language models (VLMs). However, RL-

Efficient and Scalable Neural Symbolic Search for Knowledge Graph Complex Query Answering

arXiv:2505.08155v4 Announce Type: replace Abstract: Complex Query Answering (CQA) is a crucial reasoning task over Knowledge Graphs (KGs), which aims to answer first-order logical queries from incompl

Efficient Imputation for Patch-based Missing Single-cell Data via Cluster-regularized Optimal Transport

arXiv:2601.14653v2 Announce Type: replace Abstract: Missing data in single-cell sequencing datasets poses significant challenges for extracting meaningful biological insights. However, existing imputa

End-to-End Intracortical Speech Decoding from Neural Activity

arXiv:2605.24313v1 Announce Type: new Abstract: Current high-performing intracortical speech neuroprostheses achieve low word error rates but typically rely on external language models during inferenc

Estimating Mixture Distributions via Stochastic Mirror Descent

arXiv:2605.24929v1 Announce Type: cross Abstract: We revisit the classical problem of estimating an unknown distribution from its samples by fitting a mixture model that minimizes cross-entropy loss.

EVA: Accelerating LLM Decoding via an Efficient Vector Quantization Architecture

arXiv:2605.24144v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved impressive performance across diverse domains but remain inefficient during the autoregressive decoding pha

Evaluating passing decision-making in professional football: An enhanced MPNN approach to Receiver Selection

arXiv:2605.25696v1 Announce Type: new Abstract: The process of decision-making in football is characterized by a complex interplay between spatial positioning, opponent pressure, and player intent. Th

Everything at Every Scale: Scale-Invariant Diffusion with Continuous Super-Resolution

arXiv:2605.26032v1 Announce Type: cross Abstract: Creating images from noise is image generation; reconstructing fine details from coarse inputs is super-resolution. Despite their practical difference

EXOTIC: An Exact, Optimistic, Tree-Based Algorithm for Min-Max Optimization

arXiv:2508.12479v2 Announce Type: replace-cross Abstract: Min-max optimization arises in many domains such as game theory, adversarial machine learning, etc. For these problems, gradient-based methods