All entries

arXiv:2604.27846v1 Announce Type: new Abstract: How people narrate their experiences offers a window into how the mind organizes them. Computational approaches to therapeutic writing have evolved from

local-aiarxiv-cs-cl

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Multibit neural inference in a N-ary crossbar architecture

DGX agent

arXiv:2604.26979v1 Announce Type: cross Abstract: In-memory computing (IMC) enables energy-efficient neural network inference by computing analog matrix-vector multiplications (MVM) in memory crossbar

researcharxiv-cs-ai

1 May 2026

MultiBLiMP 1.0: A Massively Multilingual Benchmark of Linguistic Minimal Pairs

model-releasesarxiv-cs-cl

arXiv:2504.02768v4 Announce Type: replace Abstract: We introduce MultiBLiMP 1.0, a massively multilingual benchmark of linguistic minimal pairs, covering 101 languages and 2 types of subject-verb agre

NanoKnow: How to Know What Your Language Model Knows

arXiv:2602.20122v2 Announce Type: replace-cross Abstract: How do large language models (LLMs) know what they know? Answering this question has been difficult because pre-training data is often a 'blac

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria

model-releasesarxiv-cs-lg

arXiv:2510.18183v2 Announce Type: replace Abstract: Finding Nash equilibria in two-player zero-sum imperfect-information games remains a central challenge in multi-agent reinforcement learning. Recent

NeocorRAG: Less Irrelevant Information, More Explicit Evidence, and More Effective Recall via Evidence Chains

arXiv:2604.27852v1 Announce Type: cross Abstract: Although precise recall is a core objective in Retrieval-Augmented Generation (RAG), a critical oversight persists in the field: improvements in retri

Neural Aided Kalman Filtering for UAV State Estimation in Degraded Sensing Environments

arXiv:2604.28107v1 Announce Type: new Abstract: Accurate state estimation of nonlinear dynamical systems is fundamental to modern aerospace operations across air, sea, and space domains. Online tracki

tutorialsarxiv-cs-lg

Noise2Map: End-to-End Diffusion Model for Semantic Segmentation and Change Detection

arXiv:2604.27889v1 Announce Type: new Abstract: Semantic segmentation and change detection are two fundamental challenges in remote sensing, requiring models to capture either spatial semantics or tem

tutorialsarxiv-cs-cv

NORACL: Neurogenesis for Oracle-free Resource-Adaptive Continual Learning

arXiv:2604.27031v1 Announce Type: cross Abstract: In a continual learning setting, we require a model to be plastic enough to learn a new task and stable enough to not disturb previously learned capab

tutorialsarxiv-cs-ai

Normativity and Productivism: Ableist Intelligence? A Degrowth Analysis of AI Sign Language Translation Tools for Deaf People

arXiv:2604.28125v1 Announce Type: new Abstract: Sign languages, of any geographical or accentual variation, understandably face continuous scrutiny under the ever present popularity of verbal dictatio

Not All Memories Age the Same: Autodiscovery of Adaptive Decay in Knowledge Graphs

arXiv:2604.26970v1 Announce Type: cross Abstract: Knowledge graphs used for retrieval treat all facts as equally current. Existing temporal approaches apply uniform decay, using a single forgetting cu

ObjectGraph: From Document Injection to Knowledge Traversal -- A Native File Format for the Agentic Era

arXiv:2604.27820v1 Announce Type: new Abstract: Every document format in existence was designed for a human reader moving linearly through text. Autonomous LLM agents do not read - they retrieve. This

Omni-Attribute: Open-vocabulary Attribute Encoder for Visual Concept Personalization

arXiv:2512.10955v2 Announce Type: replace Abstract: Visual concept personalization aims to transfer only specific image attributes, such as identity, expression, lighting, and style, into unseen conte

tutorialsarxiv-cs-cv

Local Ai

OmniDrive-R1: Reinforcement-driven Interleaved Multi-modal Chain-of-Thought for Trustworthy Vision-Language Autonomous Driving

arXiv:2512.14044v3 Announce Type: replace-cross Abstract: The deployment of Vision-Language Models (VLMs) in safety-critical domains like autonomous driving (AD) is critically hindered by reliability

local-aiarxiv-cs-ai

OmniRobotHome: A Multi-Camera Platform for Real-Time Multiadic Human-Robot Interaction

arXiv:2604.28197v1 Announce Type: cross Abstract: Human-robot collaboration has been studied primarily in dyadic or sequential settings. However, real homes require multiadic collaboration, where mult

safetyarxiv-cs-cv

On the Expressive Power of GNNs to Solve Linear SDPs

arXiv:2604.27786v1 Announce Type: new Abstract: Semidefinite programs (SDPs) are a powerful framework for convex optimization and for constructing strong relaxations of hard combinatorial problems. Ho

On the Predictive Skill of Artificial Intelligence-based Weather Models for Extreme Events using Uncertainty Quantification

arXiv:2511.17176v2 Announce Type: replace-cross Abstract: Accurate prediction of extreme weather events remains a major challenge for artificial intelligence-based weather prediction systems. While de

On the Proper Treatment of Units in Surprisal Theory

arXiv:2604.28147v1 Announce Type: new Abstract: Surprisal theory links human processing effort to the predictability of an upcoming linguistic unit, but empirical work often leaves the notion of a uni

researcharxiv-cs-cl

One Pass, Any Order: Position-Invariant Listwise Reranking for LLM-Based Recommendation

arXiv:2604.27599v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for recommendation reranking, but their listwise predictions can depend on the order in which candi

One Single Hub Text Breaks CLIP: Identifying Vulnerabilities in Cross-Modal Encoders via Hubness

arXiv:2604.27674v1 Announce Type: cross Abstract: The hubness problem, in which hub embeddings are close to many unrelated examples, occurs often in high-dimensional embedding spaces and may pose a pr

Online semi-supervised perception: Real-time learning without explicit feedback

arXiv:2604.27562v1 Announce Type: new Abstract: This paper proposes an algorithm for real-time learning without explicit feedback. The algorithm combines the ideas of semi-supervised learning on graph

safetyarxiv-cs-lg

OpAgent: Operator Agent for Web Navigation

arXiv:2602.13559v2 Announce Type: replace Abstract: To fulfill user instructions, autonomous web agents must contend with the inherent complexity and volatile nature of real-world websites. Convention

OpenAI o1 System Card

arXiv:2412.16720v2 Announce Type: replace Abstract: The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provi

OpenClassGen: A Large-Scale Corpus of Real-World Python Classes for LLM Research

arXiv:2504.15564v3 Announce Type: replace-cross Abstract: Existing class-level code generation datasets are either synthetic (ClassEval: 100 classes) or insufficient in scale for modern training needs

Optimal Diagonal Preconditioning Beyond Worst-Case Conditioning: Theory and Practice of Omega Scaling

arXiv:2509.23439v2 Announce Type: replace-cross Abstract: We study optimal diagonal preconditioning using the classical worst-case kappa-condition number and the averaging-based omega-condition number

Agents

Optimal Stop-Loss and Take-Profit Parameterization for Autonomous Trading Agent Swarm

arXiv:2604.27150v1 Announce Type: new Abstract: Autonomous crypto trading systems often spend most of their design effort on finding entries, while exits are left to fixed rules that are rarely tested

agentsarxiv-cs-ai

Optimization before Evaluation: Evaluation with Unoptimised Prompts Can be Misleading

arXiv:2604.27637v1 Announce Type: new Abstract: Current Large Language Model (LLM) evaluation frameworks utilize the same static prompt template across all models under evaluation. This differs from t

Applications

Optimized Deferral for Imbalanced Settings

arXiv:2604.27723v1 Announce Type: new Abstract: Learning algorithms can be significantly improved by routing complex or uncertain inputs to specialized experts, balancing accuracy with computational c

applicationsarxiv-cs-lg

Agents

OptimusKG: Unifying biomedical knowledge in a modern multimodal graph

arXiv:2604.27269v1 Announce Type: new Abstract: Biomedical knowledge graphs (KGs) are widely used in the life sciences, yet many are derived from unstructured documents and therefore lack schema-level

agentsarxiv-cs-ai

OR-VSKC: Resolving Visual-Semantic Knowledge Conflicts in Operating Rooms with Synthetic Data-Guided Alignment

arXiv:2506.22500v2 Announce Type: replace-cross Abstract: Automated identification of surgical safety risks is critical for improving patient outcomes; however, Multimodal Large Language Models (MLLMs

ORFS-agent: Tool-Using Agents for Chip Design Optimization

arXiv:2506.08332v3 Announce Type: replace Abstract: Machine learning has been widely used to optimize complex engineering workflows across numerous domains. In integrated circuit design, modern flows

PALCAS: A Priority-Aware Intelligent Lane Change Advisory System for Autonomous Vehicles using Federated Reinforcement Learning

arXiv:2604.27118v1 Announce Type: cross Abstract: We present a priority-aware intelligent lane change advisory system based on multi-agent federated reinforcement learning, namely PALCAS, for autonomo

Parameter-Efficient Architectural Modifications for Translation-Invariant CNNs

model-releasesarxiv-cs-cv

arXiv:2604.27870v1 Announce Type: new Abstract: Convolutional Neural Networks (CNNs) are widely assumed to be translation-invariant, yet standard architectures exhibit a startling fragility: even a si

Path-Lock Expert: Separating Reasoning Mode in Hybrid Thinking via Architecture-Level Separation

arXiv:2604.27201v1 Announce Type: cross Abstract: Hybrid-thinking language models expose explicit think and no-think modes, but current designs do not separate them cleanly. Even in no-think mode, mod

People-Centred Medical Image Analysis

arXiv:2604.26991v1 Announce Type: cross Abstract: Recent advances in data-centric medical AI have produced highly accurate diagnostic systems, but the emphasis on data curation and performance metrics

Performance-Driven QUBO for Recommender Systems on Quantum Annealers

arXiv:2410.15272v3 Announce Type: replace-cross Abstract: Quantum annealers offer a promising hardware platform for solving combinatorial optimization problems, especially those formulated as Quadrati

Perturbation Probing: A Two-Pass-per-Prompt Diagnostic for FFN Behavioral Circuits in Aligned LLMs

model-releasesarxiv-cs-cl

arXiv:2604.27401v1 Announce Type: new Abstract: Perturbation probing generates task-specific causal hypotheses for FFN neurons in large language models using two forward passes per prompt and no backp

PGOT: A Physics-Geometry Operator Transformer for Complex PDEs

arXiv:2512.23192v3 Announce Type: replace Abstract: While Transformers have demonstrated remarkable potential in modeling Partial Differential Equations (PDEs), modeling large-scale unstructured meshe

PhotIQA: A photoacoustic image data set with image quality ratings

arXiv:2507.03478v2 Announce Type: replace-cross Abstract: Image quality assessment (IQA) is crucial in the evaluation stage of novel algorithms operating on images, including traditional and machine l

researcharxiv-cs-cv

PhyCo: Learning Controllable Physical Priors for Generative Motion

arXiv:2604.28169v1 Announce Type: cross Abstract: Modern video diffusion models excel at appearance synthesis but still struggle with physical consistency: objects drift, collisions lack realistic reb

Physical Foundation Models: Fixed hardware implementations of large-scale neural networks

model-releasesarxiv-cs-lg

arXiv:2604.27911v1 Announce Type: new Abstract: Foundation models are deep neural networks (such as GPT-5, Gemini~3, and Opus~4) trained on large datasets that can perform diverse downstream tasks --

Physically-Informed Fuzzy Clustering of Vertical Sounding Ionograms

arXiv:2604.27721v1 Announce Type: cross Abstract: This paper presents a physically-informed fuzzy clustering of vertical sounding ionograms for automatically separating the ionogram into tracks suitab

researcharxiv-cs-cv

PiCSAR: Probabilistic Confidence Selection And Ranking for Reasoning Chains

arXiv:2508.21787v2 Announce Type: replace-cross Abstract: Best-of-n sampling improves the accuracy of large language models (LLMs) and large reasoning models (LRMs) by generating multiple candidate so

PINN-Cast: Exploring the Role of Continuous-Depth NODE in Transformers and Physics Informed Loss as Soft Physical Constraints in Short-term Weather Forecasting

arXiv:2604.27313v1 Announce Type: cross Abstract: Operational weather prediction has long relied on physics-based numerical weather prediction (NWP), whose accuracy comes at the cost of substantial co

researcharxiv-cs-cv

Local Ai

Policy-Governed LLM Routing with Intent Matching for Instrument Laboratories

arXiv:2604.26955v1 Announce Type: cross Abstract: AI tutoring systems in engineering labs face a tension between providing sufficient assistance and preserving learning opportunities. Existing systems

local-aiarxiv-cs-ai

Policy-Grounded Safety Evaluation of 20 Large Language Models

arXiv:2507.14719v2 Announce Type: replace Abstract: As large language models (LLMs) become increasingly integrated into real-world applications, scalable and rigorous safety evaluation is essential. T

Political Bias Audits of LLMs Capture Sycophancy to the Inferred Auditor

arXiv:2604.27633v1 Announce Type: new Abstract: Large language models (LLMs) are commonly evaluated for political bias based on their responses to fixed questionnaires, which typically place frontier

Applications

Position-Aware Drafting for Inference Acceleration in LLM-Based Generative List-Wise Recommendation

arXiv:2604.27747v1 Announce Type: cross Abstract: Large language model (LLM)-based generative list-wise recommendation has advanced rapidly, but decoding remains sequential and thus latency-prone. To

applicationsarxiv-cs-ai