research

Rethinking Speech-LLM Integration for ASR: Effective Joint Speech-Text Training by Interleaving

arXiv:2607.01733v1 Announce Type: new Abstract: Speech-LLM integration has shown promising results by leveraging extensive textual pretraining, yet its specific benefits for automatic speech recogniti

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Revisiting Chain-of-Thought Reasoning under Limited Supervision: Semi-supervised Chain-of-Thought Learning

DGX agent

arXiv:2607.01511v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning has emerged as an effective approach for activating latent reasoning capabilities in large language models. However, mo

researcharxiv-cs-ai

3 Jul 2026

Revisiting Decentralized Online Convex Optimization with Compressed Communication

arXiv:2607.01665v1 Announce Type: new Abstract: Decentralized online convex optimization (D-OCO) is a popular framework for distributed applications with streaming data. To tackle the communication bo

Robust and Explainable 3D Mode Shape Recognition Using Region-Aware Graph Neural Networks

arXiv:2607.01522v1 Announce Type: cross Abstract: Mode shape recognition is a fundamental task in automotive NVH development, yet it remains dependent on manual visual inspection by experienced engine

Role-Aware Neural Convex Divergence Heads for Asymmetric Representation Learning

arXiv:2607.01762v1 Announce Type: new Abstract: Many representation learning problems involve directed relations, such as lexical entailment, sentence entailment, ontology hierarchy, and citation link

RuleChef: Grounding LLM Task Knowledge in Human-Editable Rules

arXiv:2607.01293v1 Announce Type: new Abstract: We present RuleChef, a framework that uses large language models (LLMs) to generate executable rules for NLP tasks such as text classification, Named En

SA-HGNN: Sample-Adaptive Hyperbolic Graph Neural Network for EEG-Based Depression Recognition

arXiv:2607.02063v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) have been widely used to capture spatial functional connectivity patterns to improve electroencephalography (EEG)-based d

Scalable and Distributed Silhouette Approximation

arXiv:2607.01993v1 Announce Type: cross Abstract: The silhouette is one of the most widely used measures to assess the quality of a k-clustering of a dataset of n elements. Its evaluation requires no

Scaling Latent Reasoning via Looped Language Models

arXiv:2510.25741v5 Announce Type: replace Abstract: Modern LLMs are trained to 'think' primarily via explicit text generation, such as chain-of-thought (CoT), which defers reasoning to post-training a

Scaling to Multimodal and Multichannel Heart Sound Classification with Synthetic and Augmented Biosignals

arXiv:2509.11606v4 Announce Type: replace-cross Abstract: Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for approximately 17.9 million deaths each year. Early det

Scene-Conditioned PINN-GNN for Multipath RF Maps: Cross-Scene Generation and In-Scene Completion

arXiv:2607.01777v1 Announce Type: cross Abstract: Radio frequency (RF) maps provide a compact representation of multipath propagation characteristics and are fundamental to channel modeling, coverage

Self-Supervised Test-Time Tuning for Packet Loss Concealment

arXiv:2607.01823v1 Announce Type: cross Abstract: Packet loss concealment (PLC) reconstructs audio packets that are missing at the receiver, usually with a trained model whose parameters remain fixed

SemHash-LLM: A Multi-Granularity Semantic Hashing Framework for Document Deduplication

arXiv:2607.01601v1 Announce Type: new Abstract: Large scale document deduplication must preserve semantic equivalence while remaining efficient over massive corpora. We present SemHash LLM, a multi gr

Set Diffusion: Interpolating Token Orderings Between Autoregression and Diffusion for Fast and Flexible Decoding

arXiv:2607.01775v1 Announce Type: new Abstract: Discrete diffusion models have steadily improved in quality relative to autoregressive (AR) models. However, these models are normally constrained to fi

SINA: A Fully Automated Circuit Schematic Image to Netlist Generator Using Artificial Intelligence

arXiv:2607.01609v1 Announce Type: new Abstract: Recent advances in Artificial Intelligence (AI) have revolutionized Electronic Design Automation (EDA), particularly through Large Language Models (LLMs

Single-Channel EEG-Based Cognitive Load Assessment in Online Learning: A Hybrid Deep Learning Approach

arXiv:2607.01795v1 Announce Type: cross Abstract: Monitoring cognitive load during online learning could help instructors identify content that learners find difficult, but remote settings remove the

SPARCLE: SPeaker-aware Aligned Representations via Contrastive Language Embeddings

arXiv:2607.01238v1 Announce Type: cross Abstract: Recent advances in speech synthesis have shifted from phoneme representations to direct grapheme modeling. While phonemes address the one-to-many mapp

Spatial Support Matters: Geometry-Aware Graph Fusion for Rainfall Field Reconstruction

arXiv:2607.01621v1 Announce Type: new Abstract: Fine-scale rainfall reconstruction is critical for urban flood modeling, but real rainfall sensing systems observe the field through incompatible spatia

Spin-Weighted Spherical Harmonics Enable Complete and Scalable E(3)-Equivariant Networks

arXiv:2607.01408v1 Announce Type: cross Abstract: E(3)-equivariant networks are promising for 3D atomistic system modeling, yet their scalability is limited by the O(L^6) complexity of the Clebsch-Gor

Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability

arXiv:2503.07570v3 Announce Type: replace-cross Abstract: Deep learning, when integrated with a large amount of training data, has the potential to outperform machine learning in terms of high accurac

Statistical Properties of k-means Clustering for Data Missing Completely at Random

arXiv:2607.01945v1 Announce Type: cross Abstract: The classical k-means clustering cannot be directly used to incomplete data, and existing k-means-based clustering for missing data primarily focus on

Subliminal Clocks: Latent Time Modelling in Diffusion Language Models

arXiv:2607.01774v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) have recently emerged as a promising alternative to autoregressive models. Unlike standard diffusion-based approaches,

SUNTA: Hierarchical Video Prediction with Surprise-based Chunking

arXiv:2607.02087v1 Announce Type: new Abstract: Hierarchical state-space models (HSSMs) offer a promising approach to long-horizon prediction by segmenting sequences into temporal chunks. However, the

The Download: a smoking “endgame” and a new Elizabeth Bear story

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. The UK’s generational tobacco ban might not work. I’m supporti

researchmit-tech-review

The Dual Nature of LLM Persona: Aggregated Tendencies and Frame-Dependent Geometry

arXiv:2607.02368v1 Announce Type: cross Abstract: Evaluations of LLM personas via psychometric questionnaires typically rely on aggregate scores, discarding within-instance correlation structure. We t

The Future of NLP may not be at NLP Conferences: Scholarly Migration Patterns in Natural Language Processing

arXiv:2607.02416v1 Announce Type: new Abstract: Natural Language Processing (NLP) has traditionally been published in its core disciplinary venues like ACL. However, advances in Large Language Models

The Grammar Does the Work: Functional vs. Lexical Dependency Length Minimization Across Universal Dependencies

arXiv:2607.01899v1 Announce Type: new Abstract: Dependency length minimization (DLM) is a well-documented processing universal, but previous studies report a single mean dependency distance (MDD) per

The jobs of the future will require high adaptability and creativity, focusing on complex problem framing rather than repetitive execution o…

researchfrancois-chollet--x

Future jobs will prioritize adaptability and creativity over routine task execution, with emphasis on complex problem-framing skills. This reflects how automation and AI are shifting labor demands tow

The Moving Eye: Enhancing VLA Spatial Generalization via Hybrid Dynamic Data Collection

arXiv:2607.02322v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown remarkable promise in generalized robotic manipulation. However, their spatial generalization remains fra

researcharxiv-cs-ro

The risk of KV cache compression

arXiv:2607.01520v1 Announce Type: new Abstract: Transformer inference on long sequences is expensive because softmax attention repeatedly reads from a large KV cache. The prevalent approach to this bo

The Three Dimensions of ROS 2 Middleware

arXiv:2607.01304v1 Announce Type: new Abstract: ROS 2 (Robot Operating System 2) has emerged as the de facto standard for modern robot software development, with middleware implementations such as the

researcharxiv-cs-ro

The UK’s generational tobacco ban might not work. I’m supporting it anyway.

As the parent of two little girls, I often think about how their childhood is different from mine. The seven-year-old is learning about AI at school. The five-year-old is given internet-based homework

researchmit-tech-review

This is the sort of early prediction you can make when you pay close attention to ARC-AGI scores

researchfrancois-chollet--x

Francois Chollet discusses how careful observation of ARC-AGI benchmark performance can enable early predictions about AI system capabilities and progress. The post likely highlights patterns or trend

ThreadWeaver: Adaptive Threading for Efficient Parallel Reasoning in Language Models

arXiv:2512.07843v2 Announce Type: replace-cross Abstract: Scaling inference-time computation has enabled Large Language Models (LLMs) to achieve strong reasoning performance, but their inherently sequ

TokenScope: Token-Level Explainability and Interpretability for Code-Oriented Tasks in Large Language Models

arXiv:2607.01235v1 Announce Type: cross Abstract: Understanding how Large Language Models (LLMs) make token-level decisions during code generation remains a major challenge for both researchers and pr

Towards Cellular-Scale Interpretability in Pathology Foundation Models for Biomarker Assessment

arXiv:2511.05150v2 Announce Type: replace-cross Abstract: Molecular biomarker testing in pathology is often costly and tissue-consuming, limiting scalable clinical deployment. Artificial intelligence

Trump’s huge windfall has few known global precedents. His earnings in office are at a level once unimaginable for any leader of a liberal d…

Trump’s huge windfall has few known global precedents. His earnings in office are at a level once unimaginable for any leader of a liberal democracy, particularly a sitting American president. https:/

researchyann-lecun--x

Uncertain but Useful: Leveraging CNN Training Variability into Data Augmentation

arXiv:2509.05238v2 Announce Type: replace-cross Abstract: Deep learning (DL) has transformed neuroimaging by delivering state-of-the-art performance with reduced computation times. Yet, the numerical

UniSE: A Unified Framework for Decoder-Only Autoregressive LM-Based Speech Enhancement

arXiv:2510.20441v2 Announce Type: replace-cross Abstract: Neural audio codecs have largely promoted the application of language models (LMs) for speech applications. However, the effectiveness of auto

Unlocking Speech-Text Compositional Powers: Instruction-Following Speech Language Models without Instruction Tuning

arXiv:2607.02214v1 Announce Type: new Abstract: Instruction tuning for speech language models (SLMs) is substantially more challenging than for text-based large language models (LLMs), as it requires

Unveiling the Non-Monotonic Effect of Privacy on Generalization under Byzantine Robustness

arXiv:2607.01492v1 Announce Type: new Abstract: Recent work has established a fundamental trilemma between Byzantine robustness, local differential privacy (LDP), and optimization error in distributed

Using AI to improve cancer immunotherapy outcomes, via training from transcriptomes of 10,000 tumor samples, 33 cancer types @NatureMedicine…

Researchers used machine learning trained on transcriptomic data from 10,000 tumor samples across 33 cancer types to develop AI models that predict and improve outcomes in cancer immunotherapy. The ap

researchyann-lecun--x

Using embeddings to predict spoken word duration and pitch in Mandarin monosyllabic words

arXiv:2607.02002v1 Announce Type: new Abstract: Time-normalized f0 contours of Mandarin words in conversational speech have been shown to be predictable in part from their contextualized embeddings (C

VaSST: Variational Inference for Symbolic Regression using Soft Symbolic Trees

arXiv:2602.23561v2 Announce Type: replace-cross Abstract: Symbolic regression (SR) has gained recent traction in AI-driven scientific discovery for learning closed-form physical laws. Yet existing met

Verifiable Knowledge Expansion through Retrieval-Grounded Formal Concept Analysis

arXiv:2607.01773v1 Announce Type: new Abstract: Ontology construction requires deciding which objects, attributes, and structural relations should be accepted as valid knowledge. Language models can p

We are pleased to present our latest research at #ICML2026, “Bridging Spherical Black-Box Optimizers” https://arxiv.org/abs/2606.25761 When …

We are pleased to present our latest research at #ICML2026, “Bridging Spherical Black-Box Optimizers” https://arxiv.org/abs/2606.25761 When optimizing through simulators, external APIs, or in reinforc

researchdavid-ha--x

When Does Generating More Help? Disentangling Fixed-Source Synthesis from Source Expansion in Synthetic Data Scaling

arXiv:2607.01727v1 Announce Type: new Abstract: Synthetic data can be scaled along two routes: Source Expansion (SE), which enlarges the source by adding seed materials or generators, and Fixed-Source

Will Scaling Improve Social Simulation with LLMs?

arXiv:2607.02464v1 Announce Type: new Abstract: Large Language Model (LLM) social simulations are a promising research method, but they are not yet faithful enough to be adopted widely. In this work,