All entries

cantnlp@DravidianLangTech 2026: organic domain adaptation improves multi-class hope speech detection in Tulu

arXiv:2605.09795v1 Announce Type: new Abstract: This paper presents our systems and results for the Hope Speech Detection in Code-Mixed Tulu Language shared task at the Sixth Workshop on Speech, Visio

researcharxiv-cs-cl

Model Releases

Capacity-Aware Inference: Mitigating the Straggler Effect in Mixture of Experts

DGX agent

arXiv:2503.05066v5 Announce Type: replace-cross Abstract: The Mixture of Experts (MoE) is an effective architecture for scaling large language models by leveraging sparse expert activation to balance

model-releasesarxiv-cs-ai

12 May 2026

CapCLIP: A Vision-Language Representation Alignment Approach for Wireless Capsule Endoscopy Analysis

arXiv:2605.08493v1 Announce Type: new Abstract: Wireless capsule endoscopy (WCE) enables non-invasive visual assessment of the small bowel, but its clinical utility is constrained by the large volume

safetyarxiv-cs-cv

CapVector: Learning Transferable Capability Vectors in Parametric Space for Vision-Language-Action Models

model-releasesarxiv-cs-cv

arXiv:2605.10903v1 Announce Type: new Abstract: This paper proposes a novel approach to address the challenge that pretrained VLA models often fail to effectively improve performance and reduce adapta

CARL: Criticality-Aware Agentic Reinforcement Learning

arXiv:2512.04949v3 Announce Type: replace-cross Abstract: Agents capable of accomplishing complex tasks through multiple interactions with the environment have emerged as a popular research direction.

safetyarxiv-cs-ai

Case Study: Silencio’s Full-Stack Infrastructure Advantage with Vultr

Silencio leveraged Vultr's full-stack infrastructure platform to optimize their operations, likely achieving improvements in performance, scalability, or cost efficiency through Vultr's cloud computin

tutorialsvultr

Applications

CASISR: Circular Arbitrary-Scale Image Super-Resolution

arXiv:2605.08173v1 Announce Type: new Abstract: The generalization performance (GP) of deep learning-based arbitrary-scale image super-resolution (ASISR) methods is subject to limited training dataset

applicationsarxiv-cs-cv

CAST: Channel-Aware Spatial Transfer Learning with Pseudo-Image Radar for Sign Language Recognition

arXiv:2605.08663v1 Announce Type: new Abstract: We propose CAST, a dual-stream architecture that utilizes channel-aware spatial transfer learning for isolated sign language recognition addressing the

CATO: Charted Attention for Neural PDE Operators

arXiv:2605.09016v1 Announce Type: new Abstract: Neural operators have emerged as powerful data-driven solvers for PDEs, offering substantial acceleration over classical numerical methods. However, exi

CATS: Curvature Aware Temporal Selection for efficient long video understanding

arXiv:2605.09223v1 Announce Type: new Abstract: Understanding long videos with multimodal large language models (MLLMs) requires selecting a small subset of informative frames under strict computation

Causal Dimensionality of Transformer Representations: Measurement, Scaling, and Layer Structure

arXiv:2605.08740v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) decompose transformer residual streams into interpretable feature dictionaries, yet the relationship between SAE width and

Applications

Causal Discovery for Irregularly Time Series with Consistency Guarantees

arXiv:2507.03310v2 Announce Type: replace-cross Abstract: This paper studies causal discovery in irregularly sampled time series-a key challenge in risk-sensitive domains like finance, healthcare, and

applicationsarxiv-cs-ai

Applications

Causal Discovery Should Embrace the Wisdom of the Crowd

arXiv:2603.02678v3 Announce Type: replace Abstract: This paper argues for recognizing an emerging paradigm of causal learning by wisdom of the crowd. Recent developments in government, industry, and r

applicationsarxiv-cs-lg

Causal Explanations from the Geometric Properties of ReLU Neural Networks

arXiv:2605.10396v1 Announce Type: new Abstract: Neural networks have proved an effective means of learning control policies for autonomous systems, but these learned policies are difficult to understa

safetyarxiv-cs-lg

Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluation

arXiv:2605.09663v1 Announce Type: cross Abstract: Machine learning classifiers in dynamic environments face concept drift -- changes in the data-generating process that degrade performance. Convention

Causal Stories from Sensor Traces: Auditing Epistemic Overreach in LLM-Generated Personal Sensing Explanations

arXiv:2605.08590v1 Announce Type: cross Abstract: LLMs are increasingly used to explain personal sensing data, translating traces of activity and mood into natural-language accounts of why an anomalou

CausalGS: Learning Physical Causality of 3D Dynamic Scenes with Gaussian Representations

arXiv:2605.10586v1 Announce Type: new Abstract: Learning a physical model from video data that can comprehend physical laws and predict the future trajectories of objects is a formidable challenge in

tutorialsarxiv-cs-cv

CauSim: Scaling Causal Reasoning with Increasingly Complex Causal Simulators

arXiv:2605.09079v1 Announce Type: new Abstract: Despite surpassing human performance across mathematics, coding, and other knowledge-intensive tasks, large language models (LLMs) continue to struggle

tutorialsarxiv-cs-ai

Cavity-Enhanced Collective Quantum Processing with Polarization-Encoded Qubits

arXiv:2605.10473v1 Announce Type: cross Abstract: We introduce a cavity-enhanced optical architecture for collective quantum processing in which logical qubits are encoded in the polarization subspace

CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG

arXiv:2605.08333v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is sensitive to the vast hyperparameters of the retriever and generator, yet optimizing them using given queries

Hardware

CellDX AI Autopilot: Agent-Guided Training and Deployment of Pathology Classifiers

arXiv:2605.10362v1 Announce Type: new Abstract: Training AI models for computational pathology currently requires access to expensive whole-slide-image datasets, GPU infrastructure, deep expertise in

hardwarearxiv-cs-cv

Central Limit Theorem for Two-Time-Scale Approximate Distributionally Robust RL

arXiv:2605.08417v1 Announce Type: new Abstract: Designing model-free algorithms for distributionally robust reinforcement learning (DRRL) poses fundamental challenges. The robust Bellman operator is n

tutorialsarxiv-cs-lg

CERSA: Cumulative Energy-Retaining Subspace Adaptation for Memory-Efficient Fine-Tuning

arXiv:2605.08174v1 Announce Type: cross Abstract: To mitigate the memory constraints associated with fine-tuning large pre-trained models, existing parameter-efficient fine-tuning (PEFT) methods, such

CFSPMNet: Cross-subject Fourier-guided Spatial-Patch Mamba Network for EEG Motor Imagery Decoding in Stroke Patients

arXiv:2605.10111v1 Announce Type: cross Abstract: Motor imagery electroencephalography (MI-EEG) decoding offers a non-invasive route for post-stroke rehabilitation, but cross-patient use remains diffi

safetyarxiv-cs-ai

CHAINTRIX: A multi-pipeline LLM-augmented framework for automated smart-contract security auditing

arXiv:2605.09350v1 Announce Type: new Abstract: Smart-contract exploits have caused billions of USD in cumulative losses, yet audits remain expensive and slow. Automated tools have emerged to close th

Challenges and opportunities for AI to help deliver fusion energy

arXiv:2603.25777v2 Announce Type: replace-cross Abstract: There is great potential for the application of AI tools in fusion research, and substantial worldwide benefit if fusion power is realised. Ho

Change My View? The Dynamics of Persuasion and Polarization in Online Discourse

arXiv:2605.08383v1 Announce Type: new Abstract: Philosophical accounts of persuasion often assume that shared evidence and rational argumentation should lead to a convergence of views between peers, y

safetyarxiv-cs-cl

ChaosNetBench: Benchmarking Spatio-Temporal Graph Neural Networks on Chaotic Lattice Dynamics

arXiv:2605.09676v1 Announce Type: cross Abstract: Spatio-temporal graph neural networks (STGNNs) are widely used for short-term forecasting in dynamic physical systems such as traffic and weather. How

Character-Level Transformer for Tajik-Persian Transliteration with a Parallel Lexical Corpus

model-releasesarxiv-cs-cl

arXiv:2605.09092v1 Announce Type: new Abstract: This study addresses automatic transliteration from Tajik (Cyrillic script) to Persian (Perso-Arabic script). We present a curated, lexicographically ve

Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentation

arXiv:2605.10290v1 Announce Type: cross Abstract: This paper aims at analyzing the regularization effect that data augmentation induces on supervised regression methods in the proportional regime, whe

researcharxiv-cs-lg

ChartDiff: A Large-Scale Benchmark for Comprehending Pairs of Charts

arXiv:2603.28902v2 Announce Type: replace Abstract: Charts are central to analytical reasoning, yet existing benchmarks for chart understanding focus almost exclusively on single-chart interpretation

Agents

Chat LangChain has been revamped, and re-open sourced We've been working on a few improvements for a while now, and are very excited to fina…

Chat LangChain has been revamped, and re-open sourced We've been working on a few improvements for a while now, and are very excited to finally open source them again! Want to see how a production Q&A

agentsharrison-chase--x

ChatbotManip: A Dataset to Facilitate Evaluation and Oversight of Manipulative Chatbot Behaviour

model-releasesarxiv-cs-cl

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

Cheap Thrills: Effective Amortized Optimization Using Inexpensive Labels

arXiv:2603.05495v2 Announce Type: replace Abstract: To scale optimization and simulation, prior work has explored training machine-learning surrogates that map problem parameters to solutions inexpens

researcharxiv-cs-lg

Chebyshev Center-Based Direction Selection for Multi-Objective Optimization and Training PINNs

arXiv:2605.09975v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) are a promising approach for solving partial differential equations (PDEs). Their training, however, is often d

researcharxiv-cs-lg

Chinese Cyberbullying Detection: Dataset, Method, and Validation

arXiv:2505.20654v2 Announce Type: replace-cross Abstract: Existing cyberbullying detection benchmarks were organized by the polarity of speech, such as 'offensive' and 'non-offensive', which were esse

ChladniSonify: A Visual-Acoustic Mapping Method for Chladni Patterns in New Media Art Creation

arXiv:2605.09846v1 Announce Type: cross Abstract: In new media art creation, the mapping between vision and hearing is often subjective. As a classic carrier of sound visualization, Chladni patterns h

Local Ai

Chroma1-HD Character Transfer with Flux.2 Dev

local-air-stablediffusion

Chroma1-HD is an 8.9B parameter text-to-image foundational model based on FLUX.1-schnell , ideal for finetuning on specific styles, concepts, or characters . The Reddit discussion likely covers techni

CIVeX: Causal Intervention Verification for Language Agents

arXiv:2605.09168v1 Announce Type: new Abstract: A valid tool call is not necessarily a valid intervention. Tool-using language agents are guarded by schema validators, policy filters, provenance check

Classification-Head Bias in Class-Level Machine Unlearning: Diagnosis, Mitigation, and Evaluation

model-releasesarxiv-cs-lg

arXiv:2605.08730v1 Announce Type: new Abstract: Class-level machine unlearning aims to remove the influence of specified classes while preserving model utility on retained classes. Existing methods ar

🤩🤯🤩 Claude Code (still not AGI but biggest advance since GPT-4) is the most neurosymbolic thing I have ever seen in my life. 53 symbolic …

model-releasesgary-marcus--x

🤩🤯🤩 Claude Code (still not AGI but biggest advance since GPT-4) is the most neurosymbolic thing I have ever seen in my life. 53 symbolic tools, 500,000 lines of symbolic code, combined with a state-of

Claude Opus 4.7 (fast mode) is now available in Windsurf! Full Claude Opus 4.7 intelligence ~2.5x higher output speeds We can't wait to see …

model-releaseswindsurf--x

Windsurf has released Claude Opus 4.7 in fast mode, offering the full intelligence of Claude Opus 4.7 with approximately 2.5x faster output speeds. This integration allows users of Windsurf to access

CLEAR: Context-Aware Learning with End-to-End Mask-Free Inference for Adaptive Video Subtitle Removal

arXiv:2603.21901v2 Announce Type: replace Abstract: Video subtitle removal aims to distinguish text overlays from background content while preserving temporal coherence. Existing diffusion-based metho

CLEF: EEG Foundation Model for Learning Clinical Semantics

arXiv:2605.10817v1 Announce Type: new Abstract: Clinical EEG interpretation requires reasoning over full EEG sessions and integrating signal patterns with clinical context. Existing EEG foundation mod

Industry

@ClementDelangue @huggingface worth it ! when i saw my kid walk into my office and start talking to it .. worth every penny

Clem Delangue of Hugging Face expresses satisfaction with their AI product, highlighting a personal moment where his child interacted with it as a meaningful validation of the investment. The post sug

industryclem-delangue--x

Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories

arXiv:2605.10840v1 Announce Type: cross Abstract: We present Clin-JEPA, a multi-phase co-training framework for joint-embedding predictive (JEPA) pretraining on EHR patient trajectories. JEPA architec

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking

arXiv:2605.09858v1 Announce Type: new Abstract: Multi-Object Tracking (MOT) in dynamic environments relies on robust temporal reasoning to maintain consistent object identities over time. Transformer-

CLR-voyance: Reinforcing Open-Ended Reasoning for Inpatient Clinical Decision Support with Outcome-Aware Rubrics

arXiv:2605.09584v1 Announce Type: cross Abstract: Inpatient clinical reasoning is a sequential decision under partial observability: the clinician sees the admission so far and must choose the next ac