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Beyond ECE: Calibrated Size Ratio, Risk Assessment, and Confidence-Weighted Metrics

arXiv:2605.01796v1 Announce Type: new Abstract: Confidence calibration has been dominated by the Expected Calibration Error (ECE), a linear metric that counts calibration offset equally regardless of

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Human DGX agent

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Beyond Known Objects: A Novel Framework for Open-Set Object Detection using Negative-Aware Norm

DGX agent

arXiv:2605.02284v1 Announce Type: new Abstract: Open-Set Object Detection (OSOD) is crucial for autonomous driving, where perception systems must recognize and localize both known and previously unsee

agentsarxiv-cs-cv

5 May 2026

Beyond Perceptual Shortcuts: Causal-Inspired Debiasing Optimization for Generalizable Video Reasoning in Lightweight MLLMs

arXiv:2605.01324v1 Announce Type: new Abstract: Although reinforcement learning (RL) has significantly advanced reasoning capabilities in large multimodal language models (MLLMs), its efficacy remains

safetyarxiv-cs-cv

Beyond Perplexity: Character Distribution Signatures and the MDTA Benchmark for AI Text Detection

arXiv:2605.01647v1 Announce Type: new Abstract: Training-free AI text detection methods primarily rely on model log-probabilities, achieving strong performance through approaches like Binoculars and D

Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation

arXiv:2605.01302v1 Announce Type: new Abstract: Standard Retrieval-Augmented Generation (RAG) systems predominantly rely on semantic relevance as a proxy for utility. However, this assumption collapse

safetyarxiv-cs-cl

Agents

Beyond Sentiment: A Multi-Agent Pipeline for Actionable Business Advice from Reviews

arXiv:2601.12024v2 Announce Type: replace-cross Abstract: Customer reviews contain valuable signals about service quality, but converting large-scale review corpora into actionable business recommenda

agentsarxiv-cs-cl

Beyond Specialization: Robust Reinforcement Learning Navigation via Procedural Map Generators

arXiv:2605.02528v1 Announce Type: cross Abstract: Deep reinforcement learning (DRL) navigation policies often overfit to the structure of their training environments, as environmental diversity is typ

Beyond Structure: Revolutionising Materials Discovery via AI-Driven Synthesis Protocol-Property Relationships

arXiv:2605.00313v1 Announce Type: cross Abstract: The current structure-centric paradigm in artificial intelligence (AI)-driven materials discovery, despite delivering thousands of candidate structure

researcharxiv-cs-ai

Bi-Level Reinforcement Learning Control for an Underactuated Blimp via Center-of-Mass Reconfiguration

arXiv:2605.01289v1 Announce Type: new Abstract: This paper investigates goal-directed tracking control of underactuated blimps with center-of-mass (CoM) reconfiguration. Unlike conventional overactuat

safetyarxiv-cs-ro

BIM Information Extraction Through LLM-based Adaptive Exploration

arXiv:2605.01698v1 Announce Type: new Abstract: BIM models provide structured representations of building geometry, semantics, and topology, yet extracting specific information from them remains remar

Binary Rewards and Reinforcement Learning: Fundamental Challenges

arXiv:2605.02375v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a standard approach for improving reasoning in language models, yet models trained with

Local Ai

Biological Spatial Priors Regularize Foundation Model Representations for Cross-Site MSI Generalization in Colorectal Cancer

arXiv:2605.02660v1 Announce Type: cross Abstract: Predicting microsatellite instability (MSI) status from routine hematoxylin and eosin (H&E) whole slide images (WSIs) offers a practical alternative t

local-aiarxiv-cs-cv

Black-box optimization of noisy functions with unknown smoothness

arXiv:2605.02462v1 Announce Type: cross Abstract: We study the problem of black-box optimization of a function f of any dimension, given function evaluations perturbed by noise. The function is assume

Bolek: A Multimodal Language Model for Molecular Reasoning

arXiv:2605.02745v1 Announce Type: new Abstract: Molecular property models increasingly support high-stakes drug-discovery decisions, but their outputs are often difficult to audit: classical predictor

BoostDream: Efficient Refining for High-Quality Text-to-3D Generation from Multi-View Diffusion

arXiv:2401.16764v5 Announce Type: replace Abstract: Witnessing the evolution of text-to-image diffusion models, significant strides have been made in text-to-3D generation. Currently, two primary para

researcharxiv-cs-cv

Boosting Multimodal Remote Sensing Image Classification with Transformer-based Heterogeneously Salient Graph Representation

arXiv:2311.10320v3 Announce Type: replace Abstract: Data collected by different modalities can provide a wealth of complementary information, such as hyperspectral image (HSI) to offer rich spectral-s

Agents

Born-Qualified: An Autonomous Framework for Deploying Advanced Energy and Electronic Materials

arXiv:2605.00639v1 Announce Type: cross Abstract: Autonomous science is transforming how we discover materials and chemical systems for advanced energy technologies. However, many initially promising

agentsarxiv-cs-ai

Boundary Mass and the Soft-to-Hard Limit in Mixture-of-Experts

arXiv:2605.02124v1 Announce Type: new Abstract: Softmax-routed mixture-of-experts models approach hard routing as the temperature tends to zero, but this limit is singular near routing ties. This pape

Break the Block: Dynamic-size Reasoning Blocks for Diffusion Large Language Models via Monotonic Entropy Descent with Reinforcement Learning

model-releasesarxiv-cs-lg

arXiv:2605.02263v1 Announce Type: new Abstract: Recent diffusion large language models (dLLMs) have demonstrated both effectiveness and efficiency in reasoning via a block-based semi-autoregressive ge

Breaking the Computational Barrier: Provably Efficient Actor-Critic for Low-Rank MDPs

arXiv:2605.01242v1 Announce Type: new Abstract: Reinforcement learning (RL) is a fundamental framework for sequential decision-making, in which an agent learns an optimal policy through interactions w

Breaking the Resolution Barrier: Arbitrary-resolution Deep Image Steganography Framework

arXiv:2601.15739v2 Announce Type: replace Abstract: Deep image steganography (DIS) has achieved significant results in capacity and invisibility. However, current paradigms enforce the secret image to

researcharxiv-cs-cv

Breaking the Silence: A Dataset and Benchmark for Bangla Text-to-Gloss Translation

arXiv:2504.02293v3 Announce Type: replace Abstract: Gloss is a written approximation that bridges Sign Language (SL) and its corresponding spoken language. Despite a deaf and hard-of-hearing populatio

Local Ai

Bridging the Experimental Last Mile: Digitizing Laboratory Know-How for Safe AI-Assisted Support

arXiv:2604.16345v2 Announce Type: replace-cross Abstract: While advances in materials informatics have accelerated the development of Self-Driving Laboratories (SDLs), human-led experiments remain sta

local-aiarxiv-cs-ai

Bridging the Gap Between Average and Discounted TD Learning

arXiv:2605.02103v1 Announce Type: new Abstract: The analysis of Temporal Difference (TD) learning in the average-reward setting faces notable theoretical difficulties because the Bellman operator is n

Bringing Order to Asynchronous SGD: Towards Optimality under Data-Dependent Delays with Momentum

arXiv:2605.02043v1 Announce Type: new Abstract: Asynchronous stochastic gradient descent (SGD) enables scalable distributed training but suffers from gradient staleness. Existing mitigation strategies

BRITE: A Benchmark for Reliable and Interpretable T2V Evaluation on Implausible Scenarios

arXiv:2605.00873v1 Announce Type: cross Abstract: The rapid advancement of photorealistic Text-to-Video (T2V) generation brings in an urgent need for up-to-date evaluation methods. Existing benchmarks

Bucketing the Good Apples: A Method for Diagnosing and Improving Causal Abstraction

arXiv:2605.02234v1 Announce Type: cross Abstract: We present a method for diagnosing interpretation in neural networks by identifying an input subspace where a proposed interpretation is highly faithf

researcharxiv-cs-cl

Applications

CADFit: Precise Mesh-to-CAD Program Generation with Hybrid Optimization

arXiv:2605.01171v1 Announce Type: new Abstract: Despite recent progress, recovering parametric CAD construction sequences from geometric input, such as meshes or point clouds, is a key challenge for d

applicationsarxiv-cs-cv

Applications

CADFS: A Big CAD Program Dataset and Framework for Computer-Aided Design with Large Language Models

arXiv:2605.01925v1 Announce Type: new Abstract: We introduce CADFS, a data-centric framework that enables large vision-language models to generate complex CAD design histories. Existing generative CAD

applicationsarxiv-cs-cv

Hardware

Can AI Debias the News? LLM Interventions Improve Cross-Partisan Receptivity but LLMs Overestimate Their Own Effectiveness

arXiv:2605.01006v1 Announce Type: new Abstract: Partisan news media erode cross-partisan trust, but large language models (LLMs) offer a potential means of debiasing such content at scale. Across two

hardwarearxiv-cs-cl

Applications

Can Causal Discovery Algorithms Help in Generating Legal Arguments?

arXiv:2605.02318v1 Announce Type: cross Abstract: In 2011, Judea Pearl received the Turing Award, considered the Nobel Prize in Computing, for fundamental contributions to artificial intelligence thro

applicationsarxiv-cs-lg

Can LLMs Compress (and Decompress)? Evaluating Code Understanding and Execution via Invertibility

model-releasesarxiv-cs-lg

arXiv:2601.13398v2 Announce Type: replace Abstract: LLMs demonstrate strong performance on code benchmarks, yet consistent reasoning across forward and backward execution remains elusive. We present R

Can professional translators identify machine-generated text?

arXiv:2601.15828v3 Announce Type: replace Abstract: This study investigates whether professional translators without prior specialized training can reliably identify short stories generated in Italian

researcharxiv-cs-cl

CARD: Coarse-to-fine Autoregressive Modeling with Radix-based Decomposition for Transferable Free Energy Estimation

arXiv:2605.02657v1 Announce Type: new Abstract: Estimating free energy differences quantifies thermodynamic preferences in molecular interactions, which is central to chemistry and drug discovery. Des

CASE: An Agentic AI Framework for Enhancing Scam Intelligence in Digital Payments

model-releasesarxiv-cs-ai

arXiv:2508.19932v2 Announce Type: replace Abstract: The proliferation of digital payment platforms has transformed commerce, offering unmatched convenience and accessibility globally. However, this gr

Causal Foundations of Collective Agency

arXiv:2605.00248v1 Announce Type: new Abstract: A key challenge for the safety of advanced AI systems is the possibility that multiple simpler agents might inadvertently form a collective agent with c

safetyarxiv-cs-ai

Causal Representation Learning with Optimal Compression under Complex Treatments

arXiv:2603.11907v2 Announce Type: replace Abstract: Estimating Individual Treatment Effects (ITE) in multi-treatment scenarios faces two critical challenges: the Hyperparameter Selection Dilemma for b

Causal2Vec: Improving Decoder-only LLMs as Embedding Models through a Contextual Token

arXiv:2507.23386v3 Announce Type: replace Abstract: Decoder-only large language models (LLMs) have been increasingly adopted to build embedding models for diverse tasks. To overcome the inherent limit

Tutorials

CCNETS: A Modular Causal Learning Framework for Pattern Recognition in Imbalanced Datasets

arXiv:2401.04139v4 Announce Type: replace Abstract: Handling class imbalance remains a central challenge in machine learning, particularly in pattern recognition tasks where identifying rare but criti

tutorialsarxiv-cs-lg

Cell-induced densification and tether formation in fibrous extracellular matrices with biomimetic physics-informed neural networks

arXiv:2603.29184v3 Announce Type: replace Abstract: Nonconvex multi-well energies in cell-induced phase transitions give rise to fine-scale microstructures, low-regularity transition layers and sharp

Certified vs. Empirical Adversarial Robust-ness via Hybrid Convolutions with Attention Stochasticity

arXiv:2605.01519v1 Announce Type: new Abstract: We introduce Hybrid Convolutions with Attention Stochasticity (HyCAS), an adversarial defense that narrows the long-standing gap between provable robust

researcharxiv-cs-cv

CEZSAR: A Contrastive Embedding Method for Zero-Shot Action Recognition

arXiv:2605.01165v1 Announce Type: new Abstract: This paper proposes a novel Zero-Shot Action Recognition~(ZSAR) method based on contrastive learning. In ZSAR, we aim to classify examples from classes

CGFformer: Cluster-Guidance Frequency Transformer for Pansharpening

arXiv:2605.01490v1 Announce Type: new Abstract: Pansharpening aims to generate high-resolution multispectral (HRMS) images by fusing low-resolution multispectral (LRMS) images with high-resolution pan

CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining

arXiv:2605.00933v1 Announce Type: new Abstract: Continuous Glucose Monitoring (CGM) can detect early metabolic subphenotypes (insulin resistance, IR; eta-cell dysfunction), but population-scale deploy

Chain of Evidence: Pixel-Level Visual Attribution for Iterative Retrieval-Augmented Generation

arXiv:2605.01284v1 Announce Type: cross Abstract: Iterative Retrieval-Augmented Generation (iRAG) has emerged as a powerful paradigm for answering complex multi-hop questions by progressively retrievi

researcharxiv-cs-cl

Agents

Change-Robust Online Spatial-Semantic Topological Mapping

arXiv:2605.02227v1 Announce Type: new Abstract: Autonomous robots require change-robust spatial-semantic reasoning: using spatial and semantic knowledge to decide where to go, how to get there, and wh

agentsarxiv-cs-ro

Channel-Level Relation to Attentive Aggregation with Neighborhood-Homogeneity Constraint for Point Cloud Analysis

arXiv:2605.02357v1 Announce Type: new Abstract: In 3D point cloud understanding, the core challenge lies in accurately capturing discriminative features within complex neighborhoods, which directly af

Chart-FR1: Visual Focus-Driven Fine-Grained Reasoning on Dense Charts

arXiv:2605.01882v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown considerable potential in chart understanding and reasoning tasks. However, they still struggle with