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IndicContextEval: A Benchmark for Evaluating Context Utilisation in Audio Large Language Models Across 8 Indic Languages

model-releasesarxiv-cs-cl

arXiv:2606.19157v2 Announce Type: replace-cross Abstract: AudioLLMs enable speech recognition conditioned on textual prompts such as domain descriptions or entity lists. However, it remains unclear wh

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Internal Data Repetition Destroys Language Models

DGX agent

arXiv:2606.24998v1 Announce Type: new Abstract: Language models are running out of high-quality training data, and even aggressively deduplicated corpora retain some amount of repetition. Earlier cont

model-releasesarxiv-cs-lg

25 Jun 2026

Interpretable Concept-Guided Polynomial Tabular Kolmogorov-Arnold Network for EEG-Based Mild Cognitive Impairment Detection

arXiv:2606.25434v1 Announce Type: new Abstract: Early and scalable detection of mild cognitive impairment (MCI) remains an unresolved clinical challenge. Existing EEG-based screening approaches are co

Introducing corpora Hlava Cor and Hlava AD: Human Label Variation in Coreference and Discourse Relations

arXiv:2606.25383v1 Announce Type: new Abstract: As previous research on annotator disagreement in discourse phenomena has shown, understanding text coherence varies considerably from one individual to

Invariant Kalman filtering for extended pose estimation in multi-IMU articulated rigid-body systems

arXiv:2606.25083v1 Announce Type: new Abstract: Accurate extended pose estimation (orientation, velocity, and position) for IMU-instrumented articulated rigid-body systems is a key challenge in roboti

researcharxiv-cs-ro

Inverse Reinforcement Learning for Interpretable Keystroke Biomarkers in Parkinson's Disease

arXiv:2606.25270v1 Announce Type: new Abstract: Keystroke dynamics have been explored extensively as a passive digital biomarker for Parkinson's disease (PD), typically by extracting summary statistic

InvestPhilBench: A Multi-Layer Dynamic Benchmark for Evaluating Large Language Model Procedural Reasoning in Expert Investment Philosophy

arXiv:2606.25984v1 Announce Type: cross Abstract: Large language models are increasingly deployed as investment research assistants, yet no benchmark tests whether they can accurately reconstruct and

Invisible to humans, visible to machines: a preregistered audit of Unicode fidelity across four biomedical bibliographic APIs

arXiv:2606.24897v1 Announce Type: cross Abstract: Biomedical text mining, scientometrics, and the construction of training corpora for biomedical large language models (LLMs) all assume that the abstr

Invoice Haystack: Benchmarking Document Retrieval and Visual Question Answering Under Strong Visual Homogeneity

model-releasesarxiv-cs-cv

arXiv:2606.25343v1 Announce Type: new Abstract: Vision Language Models have achieved near-human performance on single-document Visual Question Answering, yet their effectiveness degrades significantly

Agents

Is GraphRAG Needed? From Basic RAG to Graph-/Agentic Solutions with Context Optimization

arXiv:2606.25656v1 Announce Type: new Abstract: As advanced RAG variants like GraphRAG and Agentic RAG emerge, one leading question is when and how to use them. Here, we introduce a framework for diff

agentsarxiv-cs-cl

Is Variational Monte Carlo Robust? Sharp Moment Thresholds and Heavy-tailed Stochastic Optimization

arXiv:2606.26009v1 Announce Type: new Abstract: Variational Monte Carlo (VMC) is a central algorithm in electronic structure theory and has gained renewed importance through modern neural-network ansa

Hardware

JetSpec: Breaking the Scaling Ceiling of Speculative Decoding with Parallel Tree Drafting

arXiv:2606.18394v2 Announce Type: replace Abstract: Speculative decoding (SD) accelerates autoregressive Large Language Models (LLMs) by drafting multiple tokens and verifying them in parallel, but it

hardwarearxiv-cs-cl

KidRisk: Benchmark Dataset for Children Dangerous Action Recognition

model-releasesarxiv-cs-cv

arXiv:2606.25298v1 Announce Type: new Abstract: Children are naturally energetic, and during their spontaneous activities, they often encounter potentially dangerous situations, especially when lackin

Knowledge Cascade: Reverse Knowledge Distillation on Nonparametric Multivariate Functional Estimation

arXiv:2606.25927v1 Announce Type: cross Abstract: As machine learning models and datasets continue to grow, developing complex models has become increasingly computationally demanding. Knowledge disti

Laplace--Fisher Gate Identities for Optimal Matrix-Gated Blended Score Estimation

arXiv:2606.25169v1 Announce Type: cross Abstract: Sampling from an unnormalized target by reversing an Ornstein--Uhlenbeck diffusion requires the score of each noise-perturbed marginal. Tweedie's iden

Large-Scale Tunnel Air--Ground Collaboration With FLISP: Fast LiDAR-IMU Synchronized Path Planne

arXiv:2606.25393v1 Announce Type: new Abstract: Hydropower tunnel inspection is critical for infrastructure integrity yet remains inefficient and hazardous using manual methods. We propose FLISP (Fast

researcharxiv-cs-ro

Latent Block-Diffusion Temporal Point Processes: A Semi-Autoregressive Framework for Asynchronous Event Sequence Generation

arXiv:2606.24982v1 Announce Type: new Abstract: Modeling and sampling from the underlying distribution of asynchronous event sequences are crucial in various real-world applications, including social

Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification

arXiv:2506.18414v3 Announce Type: replace Abstract: Melanoma is a highly aggressive skin cancer, making early and accurate diagnosis critical. While deep learning excels in skin lesion classification,

researcharxiv-cs-cv

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks

arXiv:2606.25589v1 Announce Type: new Abstract: As graph neural networks (GNNs) become standard tools for critical tasks in circuit design and analysis, their security and privacy risks require carefu

Learning Action Priors for Cross-embodiment Robot Manipulation

arXiv:2606.26095v1 Announce Type: cross Abstract: Most Vision-Language-Action (VLA) models build on a Vision-Language Model (VLM) backbone by attaching an action module and optimizing the full policy

safetyarxiv-cs-cv

Learning Asynchronous Upper-body Task-space Trajectory Tracking Policy for Humanoid Robots

arXiv:2606.25706v1 Announce Type: new Abstract: High-level humanoid planners often output sparse task-space, low-rate trajectories, whereas whole-body controllers run at high frequency. This creates t

safetyarxiv-cs-ro

Learning Diachronic Representations of Ancient Greek Letterforms

arXiv:2606.24984v1 Announce Type: cross Abstract: Learning representations that remain robust across centuries of variation in handwriting is a key challenge in diachronic representation learning. Tak

applicationsarxiv-cs-cl

Learning Dynamical Systems from Multiple Sparse Datasets: A Hierarchical Bayesian Modeling Approach

arXiv:2606.24966v1 Announce Type: new Abstract: Estimating parameters of dynamical systems from sparse, noisy, and irregularly sampled data is often severely ill-conditioned. When multiple related dat

Learning Non-Vacuous Generalization Bounds from Optimization

arXiv:2206.04359v3 Announce Type: replace Abstract: One of the fundamental challenges in the deep learning community is to theoretically understand how well a deep neural network generalizes to unseen

Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Application

arXiv:2606.25362v1 Announce Type: cross Abstract: Sequential contextual stochastic programs model real-time decision systems in which each time epoch commits to an action under uncertainty whose conse

Learning Perceptive Platform Adaptive Locomotion Controllers for Quadrupedal Robots

arXiv:2606.25179v1 Announce Type: new Abstract: Universal quadrupedal locomotion remains limited by the difficulty of integrating perception across diverse robot morphologies. State-of-the-art control

researcharxiv-cs-ro

Learning Robot Visual Navigation in Crowds via Intention-Aware Scene Representations

arXiv:2606.26047v1 Announce Type: new Abstract: Robot crowd navigation requires the ability to infer human intentions while accounting for the structural constraints of the environment. Currently, dee

safetyarxiv-cs-ro

Learning Structural Hardness for Combinatorial Auctions: Instance-Dependent Algorithm Selection via Graph Neural Networks

arXiv:2602.14772v2 Announce Type: replace Abstract: The Winner Determination Problem (WDP) in combinatorial auctions is NP-hard, and no existing method reliably predicts which instances will defeat fa

applicationsarxiv-cs-lg

Learning Subset-Shared Invariances for Domain Generalization with Mixture-of-Experts

arXiv:2606.25665v1 Announce Type: new Abstract: Domain generalization (DG) aims to learn a model from one or more source domains that generalizes to an unseen target domain without accessing target da

Learning task-specific subspaces via interventional post-training of speech foundation models

arXiv:2606.17967v2 Announce Type: replace Abstract: Speech foundation models, pre-trained on large corpora of unlabelled speech data, produce general-purpose representations which are useful across ta

tutorialsarxiv-cs-cl

Learning to Adapt: Reptile-D-Learning for Robust and Efficient Control Under Parametric Uncertainty

model-releasesarxiv-cs-ro

arXiv:2606.25659v1 Announce Type: new Abstract: Learning-based Lyapunov Control (LLC) provides formal stability guarantees for nonlinear systems, but its validity relies heavily on accurate system mod

Learning to Erase Private Knowledge from Multi-Documents for Retrieval-Augmented Large Language Models

arXiv:2504.09910v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) is a promising technique for applying LLMs to proprietary domains. However, retrieved documents may contain sen

Learning with a Single Rollout via Monte Carlo Pass@k Critic

arXiv:2606.25451v1 Announce Type: new Abstract: Estimating token-level advantages in reinforcement learning (RL) for language models remains challenging because scaling up episodic experience collecti

Learning with Monotone Adversarial Corruptions

arXiv:2601.02193v2 Announce Type: replace Abstract: We study the extent to which standard machine learning algorithms rely on exchangeability and independence of data by introducing a monotone adversa

LEVIRDet: A Million-Scale 159-Category Dataset and Foundation Model for Universal Remote Sensing Object Detection

model-releasesarxiv-cs-cv

arXiv:2606.25312v1 Announce Type: new Abstract: Remote sensing object detection has advanced rapidly with the development of large-scale benchmarks and modern detection architectures. However, existin

LibEvoBench: Probing Temporal Knowledge Stratification in Code Generation Models

model-releasesarxiv-cs-ai

arXiv:2606.25402v1 Announce Type: cross Abstract: Large software projects often depend on older versions of libraries, even as APIs continue to evolve across releases. This creates a challenge for LLM

Lifelong In-Context Learning with Transformers Requires Parametric Forms of Attention

arXiv:2606.25342v1 Announce Type: new Abstract: Lifelong continual learning remains an obstacle on the path to human-like intelligence. Modern transformers show sparks of intelligence with in-context

Lightweight PCGAE-Net: Parallel CrossGate Attention and Bottleneck AutoEncoder for Efficient 5G Channel Prediction

arXiv:2606.25401v1 Announce Type: cross Abstract: Accurate channel state information (CSI) prediction is essential for proactive beamforming and resource management in 5G massive MIMO systems, yet the

safetyarxiv-cs-ai

Limitations of SGD for Multi-Index Models Beyond Statistical Queries

arXiv:2602.05704v2 Announce Type: replace Abstract: Understanding the limitations of gradient methods, and stochastic gradient descent (SGD) in particular, is a central challenge in learning theory. T

LinStereo: Linear-Complexity Global Attention for Multi-Scale Iterative Stereo Matching

arXiv:2606.25437v1 Announce Type: new Abstract: Existing Vision Foundation Model (VFM)-based iterative stereo pipelines under-exploit three information pathways: multi-scale backbone features are coll

applicationsarxiv-cs-cv

LLM-ACES: Closed-Loop Discovery of Dynamical Systems with LLM-Guided Adaptive Search

arXiv:2606.25039v1 Announce Type: cross Abstract: Recovering governing Ordinary Differential Equations (ODEs) from data is a central challenge in modeling dynamical systems across scientific domains.

LLM-Based Scientific Peer Review: Methods, Benchmarks, and Reliability Challenges

arXiv:2606.25057v1 Announce Type: new Abstract: The rapid growth of scientific submissions has pushed traditional peer review toward its scalability limits, motivating the exploration of large languag

safetyarxiv-cs-cl

LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning

arXiv:2606.24901v1 Announce Type: new Abstract: Continual learning capability is critical for Industrial LLMs, as deployed models must be continuously updated to meet evolving requirements and environ

applicationsarxiv-cs-lg

LLM Performance on a Real, Double-Marked GCSE Benchmark

model-releasesarxiv-cs-cl

arXiv:2606.24973v1 Announce Type: new Abstract: We introduce a dataset of 32,534 double-marked real student responses to GCSE mock exams (GCSEs are the UK's national exams, taken at age ~16), spanning

LLM Program Optimization via Retrieval Augmented Search

arXiv:2501.18916v2 Announce Type: replace Abstract: Recent work has demonstrated the potential of large language models (LLMs) for program optimization, a key challenge in programming languages. We pr

Logit Distance Bounds Representational Similarity

arXiv:2602.15438v3 Announce Type: replace Abstract: For a broad family of discriminative models that includes autoregressive language models, identifiability results imply that if two models induce th

Long-Term Simulation Exposes Cognitive-Developmental Risks in AI Companions

arXiv:2606.25396v1 Announce Type: new Abstract: AI companions powered by large language models increasingly interact with cognition-developing users, including children and adolescents, creating risks

safetyarxiv-cs-ai

Low-Complexity Policy Tessellations in Structured Markov Decision Processes

arXiv:2606.25593v1 Announce Type: new Abstract: We study optimal-policy geometry in structured Markov decision processes. While approximate dynamic programming and reinforcement learning typically app