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BubbleSpec: Turning Long-Tail Bubbles into Speculative Rollout Drafts for Synchronous Reinforcement Learning

arXiv:2605.08862v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has become a cornerstone for improving the performance of Large Language Models (LLMs). However, its rollout phase constit

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Building Korean linguistic resource for NLU data generation of banking app CS dialog system

DGX agent

arXiv:2605.10241v1 Announce Type: new Abstract: Natural language understanding (NLU) is integral to task-oriented dialog systems, but demands a considerable amount of annotated training data to increa

researcharxiv-cs-cl

12 May 2026

CachePrune: Teaching LLMs What Not to Follow via KV-Cache Editing

arXiv:2504.21228v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are susceptible to indirect prompt injection attacks, where the model inadvertently responds to instructions inje

CAGS: Color-Adaptive Volumetric Video Streaming with Dynamic 3D Gaussian Splatting

arXiv:2605.09279v1 Announce Type: cross Abstract: Volumetric video (VV) streaming enables real-time, immersive access to remote 3D environments, powering telepresence, ecological monitoring, and robot

CalibFree: Self-Supervised View Feature Separation for Calibration-Free Multi-Camera Multi-Object Tracking

arXiv:2605.09245v1 Announce Type: new Abstract: Multi-camera multi-object tracking (MCMOT) faces significant challenges in maintaining consistent object identities across varying camera perspectives,

Calibrate, Don't Curate: Label-Efficient Estimation from Noisy LLM Judges

arXiv:2605.09702v1 Announce Type: cross Abstract: Multi-judge evaluation is increasingly used to assess LLMs and reward models, and the prevailing heuristic is to curate: keep the most accurate judges

Can Language Models Analyze Data? Evaluating Large Language Models for Question Answering over Datasets

arXiv:2605.10419v1 Announce Type: cross Abstract: This paper investigates the effectiveness of large language models (LLMs) in answering questions over datasets. We examine their performance in two sc

Can Language Models Identify Side Effects of Breast Cancer Radiation Treatments?

arXiv:2605.08439v1 Announce Type: new Abstract: Accurately communicating the side effects of cancer treatments to cancer survivors is critical, particularly in settings such as informed consent, where

Can MLLMs Reason About Visual Persuasion? Evaluating the Efficacy and Faithfulness of Reasoning

arXiv:2605.08965v1 Announce Type: new Abstract: Despite strong performance of Multimodal Large Language Models (MLLMs) on multimodal tasks, predicting whether and why an image is persuasive remains ch

Can Muon Fine-tune Adam-Pretrained Models?

arXiv:2605.10468v1 Announce Type: new Abstract: Muon has emerged as an efficient alternative to Adam for pretraining, yet remains underused for fine-tuning. A key obstacle is that most open models are

Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key

arXiv:2605.06638v2 Announce Type: replace Abstract: Reinforcement learning (RL) has been applied to improve large language model (LLM) reasoning, yet the systematic study of how training scales with t

Can You Keep a Secret? Involuntary Information Leakage in Language Model Writing

arXiv:2605.10794v1 Announce Type: cross Abstract: Language models are deployed in settings that require compartmentalization: system prompts should not be disclosed, chain-of-thought reasoning is hidd

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

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 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

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

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

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

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

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

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

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

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-

Coarse-to-Fine: Progressive Image Compression for Semantically Hierarchical Classification

arXiv:2605.08266v1 Announce Type: cross Abstract: Recent advances in learned image compression (LIC) have enabled practical deployments, spurring active research into image compression for machines an

Coarsening Linear Non-Gaussian Causal Models with Cycles

arXiv:2605.10163v1 Announce Type: cross Abstract: Recent work on causal abstraction, in particular graphical approaches focusing on causal structure between clusters of variables, aims to summarize a

COGNOS: Universal Enhancement for Time Series Anomaly Detection via Constrained Gaussian-Noise Optimization and Smoothing

arXiv:2511.06894v3 Announce Type: replace-cross Abstract: Reconstruction-based methods are a dominant paradigm in time series anomaly detection (TSAD), however, their near-universal reliance on Mean S

CollabVR: Collaborative Video Reasoning with Vision-Language and Video Generation Models

arXiv:2605.08735v1 Announce Type: new Abstract: Recent 'Thinking with Video' approaches use Video Generation Models (VGMs) for visual reasoning by producing temporally coherent Chain-of-Frames as reas

Communicating Sound Through Natural Language

arXiv:2605.08750v1 Announce Type: cross Abstract: Natural language is widely used to describe, prompt, and control audio systems, but rarely serves as the representation carrying audio itself. We intr

Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning

arXiv:2605.08885v1 Announce Type: new Abstract: SO(3) equivariant graph neural networks have become the dominant paradigm for atomistic foundation models, achieving high accuracy and data efficiency b

Comparing Two Proxy Methods for Causal Identification

arXiv:2512.00175v3 Announce Type: replace-cross Abstract: Identifying causal effects in the presence of unmeasured variables is a fundamental challenge in causal inference, for which proxy variable me

Composing diffusion priors with explicit physical context via generative Gibbs sampling

arXiv:2605.10642v1 Announce Type: new Abstract: Pretrained diffusion models provide powerful learned priors, but in scientific sampling the target distribution often depends on physical context that i

Concise Geometric Description as a Bridge: Unleashing the Potential of LLM for Plane Geometry Problem Solving

arXiv:2601.21164v3 Announce Type: replace Abstract: Plane Geometry Problem Solving (PGPS) is a multimodal reasoning task that aims to solve a plane geometric problem based on a geometric diagram and p

ConfoundingSHAP: Quantifying confounding strength in causal inference

arXiv:2605.10533v1 Announce Type: new Abstract: In causal inference, confounders are variables that influence both treatment decisions and outcomes. However, unlike as in randomized clinical trials, t

Consistent Projection of Langevin Dynamics: Preserving Thermodynamics and Kinetics in Coarse-Grained Models

arXiv:2512.03706v2 Announce Type: replace-cross Abstract: Coarse graining (CG) is an important task for efficient modeling and simulation of complex multi-scale systems, such as the conformational dyn

Consolidation-Expansion Operator Mechanics:A Unified Framework for Adaptive Learning

arXiv:2605.09968v1 Announce Type: new Abstract: Every adaptive learning system must alternate between two operations: consolidating what it already knows and expanding into new evidence. We propose Co

Constant-Target Energy Matching: A Unified Framework for Continuous and Discrete Density Estimation

arXiv:2605.09085v1 Announce Type: new Abstract: Density estimation is a central primitive in probabilistic modeling, yet continuous, discrete, and mixed-variable domains are often treated by separate

Constructive conditional normalizing flows

arXiv:2602.08606v2 Announce Type: replace-cross Abstract: Motivated by applications in conditional sampling, given a probability measure mu and a diffeomorphism phi, we consider the problem of simulta

Contextual Plackett-Luce: An Efficient Neural Model for Probabilistic Sequence Selection under Ambiguity

arXiv:2605.09112v1 Announce Type: cross Abstract: Selecting a coherent sequence or subset of elements is a fundamental problem in structured prediction, arising in tasks such as detection, trajectory

Contour-Native Bridge Defect Detection and Compact Digital Archiving with Frequency-Supervised Fourier Contours

arXiv:2605.08781v1 Announce Type: new Abstract: AI-assisted bridge defect inspection often produces bounding boxes with crude geometry or raster masks that are costly to store, transmit, and reuse. Th

CONTRA: Conformal Prediction Region via Normalizing Flow Transformation

arXiv:2605.08561v1 Announce Type: cross Abstract: Density estimation and reliable prediction regions for outputs are crucial in supervised and unsupervised learning. While conformal prediction effecti

Control Your View: High-Resolution Global Semantic Manipulation in Learned Image Compression

arXiv:2605.08727v1 Announce Type: cross Abstract: Learned image compression (LIC) integrates deep neural networks (DNNs) to map high-dimensional images into compact latent representations, reducing re

Controlling Transient Amplification Improves Long-horizon Rollouts

arXiv:2605.08856v1 Announce Type: new Abstract: Autoregressive neural simulators now match classical solvers on short-horizon prediction of physical systems, yet their accuracy degrades rapidly when r

CoreQ: Learning-Free Mismatch Correction and Successive Rounding for Quantization

arXiv:2602.05902v2 Announce Type: replace-cross Abstract: Post-training quantization (PTQ) enables efficient deployment of large language models by mapping pretrained weights to low-bit formats withou

CORP: Closed-Form One-shot Representation-Preserving Structured Pruning for Transformers

arXiv:2602.05243v2 Announce Type: replace-cross Abstract: Transformers achieve strong accuracy but incur high compute and memory cost. Structured pruning reduces inference cost, but most methods rely

Cplus2ASP: Computing Action Language C+ in Answer Set Programming

arXiv:2605.09528v1 Announce Type: new Abstract: We present Version 2 of system Cplus2ASP, which implements the definite fragment of action language C+. Its input language is fully compatible with the

Cross-Cultural Transfer of Emoji Semantics and Sentiment in Financial Social Media

arXiv:2605.09414v1 Announce Type: new Abstract: Emojis are widely used in online financial communication, but it is unclear whether they provide transferable sentiment signals across languages, platfo