Sandbar secures $23M Series A for its AI note-taking ring
Sandbar aims to ship the Stream, which can be used to take notes, chat with an AI assistant, and for media playback, this summer.
Yann LeCun’s AMI Labs raises $1.03B to build world models
“My prediction is that ‘world models’ will be the next buzzword,” AMI Labs CEO Alexandre LeBrun told TechCrunch. “In six months, every company will call itself a world model to raise funding.”
Language Shapes Mental Health Evaluations in Large Language Models
arXiv:2603.06910v1 Announce Type: new Abstract: This study investigates whether large language models (LLMs) exhibit cross-linguistic differences in mental health evaluations. Focusing on Chinese and English, we examine two widely used models, GPT-4o and Qwen3, to assess whether prompt language systematically...
Counting on Consensus: Selecting the Right Inter-annotator Agreement Metric for NLP Annotation and Evaluation
arXiv:2603.06865v1 Announce Type: new Abstract: Human annotation remains the foundation of reliable and interpretable data in Natural Language Processing (NLP). As annotation and evaluation tasks continue to expand, from categorical labelling to segmentation, subjective judgment, and continuous rating, measuring agreement...
Hierarchical Embedding Fusion for Retrieval-Augmented Code Generation
arXiv:2603.06593v1 Announce Type: new Abstract: Retrieval-augmented code generation often conditions the decoder on large retrieved code snippets. This ties online inference cost to repository size and introduces noise from long contexts. We present Hierarchical Embedding Fusion (HEF), a two-stage approach...
Hit-RAG: Learning to Reason with Long Contexts via Preference Alignment
arXiv:2603.07023v1 Announce Type: new Abstract: Despite the promise of Retrieval-Augmented Generation in grounding Multimodal Large Language Models with external knowledge, the transition to extensive contexts often leads to significant attention dilution and reasoning hallucinations. The surge in information density causes...
Emotion Transcription in Conversation: A Benchmark for Capturing Subtle and Complex Emotional States through Natural Language
arXiv:2603.07138v1 Announce Type: new Abstract: Emotion Recognition in Conversation (ERC) is critical for enabling natural human-machine interactions. However, existing methods predominantly employ categorical or dimensional emotion annotations, which often fail to adequately represent complex, subtle, or culturally specific emotional nuances....
Scaling Self-Supervised Speech Models Uncovers Deep Linguistic Relationships: Evidence from the Pacific Cluster
arXiv:2603.07238v1 Announce Type: new Abstract: Similarities between language representations derived from Self-Supervised Speech Models (S3Ms) have been observed to primarily reflect geographic proximity or surface typological similarities driven by recent expansion or contact, potentially missing deeper genealogical signals. We investigate...
To Predict or Not to Predict? Towards reliable uncertainty estimation in the presence of noise
arXiv:2603.07330v1 Announce Type: new Abstract: This study examines the role of uncertainty estimation (UE) methods in multilingual text classification under noisy and non-topical conditions. Using a complex-vs-simple sentence classification task across several languages, we evaluate a range of UE techniques...
How Much Noise Can BERT Handle? Insights from Multilingual Sentence Difficulty Detection
arXiv:2603.07346v1 Announce Type: new Abstract: Noisy training data can significantly degrade the performance of language-model-based classifiers, particularly in non-topical classification tasks. In this study we designed a methodological framework to assess the impact of denoising. More specifically, we explored a...
RILEC: Detection and Generation of L1 Russian Interference Errors in English Learner Texts
arXiv:2603.07366v1 Announce Type: new Abstract: Many errors in student essays can be explained by influence from the native language (L1). L1 interference refers to errors influenced by a speaker's first language, such as using stadion instead of stadium, reflecting lexical...
Cross-Modal Taxonomic Generalization in (Vision-) Language Models
arXiv:2603.07474v1 Announce Type: new Abstract: What is the interplay between semantic representations learned by language models (LM) from surface form alone to those learned from more grounded evidence? We study this question for a scenario where part of the input...
A Joint Neural Baseline for Concept, Assertion, and Relation Extraction from Clinical Text
arXiv:2603.07487v1 Announce Type: new Abstract: Clinical information extraction (e.g., 2010 i2b2/VA challenge) usually presents tasks of concept recognition, assertion classification, and relation extraction. Jointly modeling the multi-stage tasks in the clinical domain is an underexplored topic. The existing independent task...
Bolbosh: Script-Aware Flow Matching for Kashmiri Text-to-Speech
arXiv:2603.07513v1 Announce Type: new Abstract: Kashmiri is spoken by around 7 million people but remains critically underserved in speech technology, despite its official status and rich linguistic heritage. The lack of robust Text-to-Speech (TTS) systems limits digital accessibility and inclusive...
Accent Vector: Controllable Accent Manipulation for Multilingual TTS Without Accented Data
arXiv:2603.07534v1 Announce Type: new Abstract: Accent is an integral part of society, reflecting multiculturalism and shaping how individuals express identity. The majority of English speakers are non-native (L2) speakers, yet current Text-To-Speech (TTS) systems primarily model American-accented English due limited...
Learning-free L2-Accented Speech Generation using Phonological Rules
arXiv:2603.07550v1 Announce Type: new Abstract: Accent plays a crucial role in speaker identity and inclusivity in speech technologies. Existing accented text-to-speech (TTS) systems either require large-scale accented datasets or lack fine-grained phoneme-level controllability. We propose a accented TTS framework that...
Nw\=ach\=a Mun\=a: A Devanagari Speech Corpus and Proximal Transfer Benchmark for Nepal Bhasha ASR
arXiv:2603.07554v1 Announce Type: new Abstract: Nepal Bhasha (Newari), an endangered language of the Kathmandu Valley, remains digitally marginalized due to the severe scarcity of annotated speech resources. In this work, we introduce Nw\=ach\=a Mun\=a, a newly curated 5.39-hour manually transcribed...
Whitening Reveals Cluster Commitment as the Geometric Separator of Hallucination Types
arXiv:2603.07755v1 Announce Type: new Abstract: A geometric hallucination taxonomy distinguishes three failure types -- center-drift (Type~1), wrong-well convergence (Type~2), and coverage gaps (Type~3) -- by their signatures in embedding cluster space. Prior work found Types~1 and~2 indistinguishable in full-dimensional contextual...
An Efficient and Effective Evaluator for Text2SQL Models on Unseen and Unlabeled Data
arXiv:2603.07841v1 Announce Type: new Abstract: Recent advances in large language models has strengthened Text2SQL systems that translate natural language questions into database queries. A persistent deployment challenge is to assess a newly trained Text2SQL system on an unseen and unlabeled...
FuzzingRL: Reinforcement Fuzz-Testing for Revealing VLM Failures
arXiv:2603.06600v1 Announce Type: new Abstract: Vision Language Models (VLMs) are prone to errors, and identifying where these errors occur is critical for ensuring the reliability and safety of AI systems. In this paper, we propose an approach that automatically generates...
Scale Dependent Data Duplication
arXiv:2603.06603v1 Announce Type: new Abstract: Data duplication during pretraining can degrade generalization and lead to memorization, motivating aggressive deduplication pipelines. However, at web scale, it is unclear what constitutes a ``duplicate'': beyond surface-form matches, semantically equivalent documents (e.g. translations) may...
Structure-Aware Set Transformers: Temporal and Variable-Type Attention Biases for Asynchronous Clinical Time Series
arXiv:2603.06605v1 Announce Type: new Abstract: Electronic health records (EHR) are irregular, asynchronous multivariate time series. As time-series foundation models increasingly tokenize events rather than discretizing time, the input layout becomes a key design choice. Grids expose time$\times$variable structure but require...
OptiRoulette Optimizer: A New Stochastic Meta-Optimizer for up to 5.3x Faster Convergence
arXiv:2603.06613v1 Announce Type: new Abstract: This paper presents OptiRoulette, a stochastic meta-optimizer that selects update rules during training instead of fixing a single optimizer. The method combines warmup optimizer locking, random sampling from an active optimizer pool, compatibility-aware learning-rate scaling...
Correlation Analysis of Generative Models
arXiv:2603.06614v1 Announce Type: new Abstract: Based on literature review about existing diffusion models and flow matching with a neural network to predict a predefined target from noisy data, a unified representation is first proposed for these models using two simple...
Annealed Co-Generation: Disentangling Variables via Progressive Pairwise Modeling
arXiv:2603.06615v1 Announce Type: new Abstract: For multivariate co-generation in scientific applications, we advocate pairwise block rather than joint modeling of all variables. This design mitigates the computational burden and data imbalance. To this end, we propose an Annealed Co-Generation (ACG)...
Distilling and Adapting: A Topology-Aware Framework for Zero-Shot Interaction Prediction in Multiplex Biological Networks
arXiv:2603.06618v1 Announce Type: new Abstract: Multiplex Biological Networks (MBNs), which represent multiple interaction types between entities, are crucial for understanding complex biological systems. Yet, existing methods often inadequately model multiplexity, struggle to integrate structural and sequence information, and face difficulties...
Advances in GRPO for Generation Models: A Survey
arXiv:2603.06623v1 Announce Type: new Abstract: Large-scale flow matching models have achieved strong performance across generative tasks such as text-to-image, video, 3D, and speech synthesis. However, aligning their outputs with human preferences and task-specific objectives remains challenging. Flow-GRPO extends Group Relative...
Grouter: Decoupling Routing from Representation for Accelerated MoE Training
arXiv:2603.06626v1 Announce Type: new Abstract: Traditional Mixture-of-Experts (MoE) training typically proceeds without any structural priors, effectively requiring the model to simultaneously train expert weights while searching for an optimal routing policy within a vast combinatorial space. This entanglement often leads...
Graph Property Inference in Small Language Models: Effects of Representation and Inference Strategy
arXiv:2603.06635v1 Announce Type: new Abstract: Recent progress in language modeling has expanded the range of tasks that can be approached through natural language interfaces, including problems that require structured reasoning. However, it remains unclear how effectively limited-capacity language models can...