research

Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum Bayes Risk Decoding

arXiv:2605.29336v1 Announce Type: new Abstract: Improving the quality of model-generated summaries, especially factuality, the accuracy of a summary with respect to its source content, remains a chall

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Enhancing LLM Training via Spectral Clipping

DGX agent

arXiv:2603.14315v2 Announce Type: replace Abstract: While spectral-based optimizers like Muon operate directly on the spectrum of updates, standard adaptive methods such as AdamW do not account for th

researcharxiv-cs-lg

29 May 2026

Enhancing Membership Inference Attacks on Diffusion Models from a Frequency-Domain Perspective

arXiv:2505.20955v4 Announce Type: replace-cross Abstract: Diffusion models have achieved tremendous success in image generation, but they also raise significant concerns regarding privacy and copyrigh

Ensemble Score Filtering for Real-Data Energy Consumption Forecast Correction

arXiv:2605.29072v1 Announce Type: new Abstract: Accurate estimation and forecasting of energy consumption are important for power-system operation, planning, and demand-side management. In practice, h

Eulerian Gaussian Splatting using Hashed Probability Pyramids

arXiv:2605.29136v1 Announce Type: new Abstract: We introduce a probabilistic splat-based radiance field framework that retains the fast rasterization and test-time efficiency of 3D Gaussian Splatting

EvA: An Evidence-First Audio Understanding Paradigm for LALMs

arXiv:2603.27667v2 Announce Type: replace-cross Abstract: Large Audio Language Models (LALMs) still struggle in complex acoustic scenes because they often fail to preserve task-relevant acoustic evide

EVADE: LLM-Based Explanation Generation and Validation for Error Detection in NLI

arXiv:2511.08949v2 Announce Type: replace Abstract: High-quality datasets are critical for training and evaluating reliable NLP models. In tasks like natural language inference (NLI), human label vari

Evaluating Skill and Stability of ArchesWeather and ArchesWeatherGen under Multi-Decadal Climate Simulations

arXiv:2605.29976v1 Announce Type: cross Abstract: We evaluate the climate simulation capabilities of ArchesWeather and ArchesWeatherGen, two machine learning models originally trained for weather fore

EviLink: Multi-Path Schema Linking with Uncertainty-Guided Evidence Acquisition for Large-Scale Text-to-SQL

arXiv:2605.29670v1 Announce Type: cross Abstract: Schema linking is a difficult and important step in large-scale Text-to-SQL, where systems must identify a compact yet sufficient schema context from

ExDBSCAN: Explaining DBSCAN with Counterfactual Reasoning -- Additional Material

arXiv:2605.30225v1 Announce Type: new Abstract: Clustering is an unsupervised technique for grouping data points by similarity. While explainability methods exist for supervised machine learning, they

Extreme dynamic symmetry enables omnidirectional and multifunctional robots

arXiv:2605.29254v1 Announce Type: cross Abstract: Symmetry is a central organizing principle in natural systems, yet its use as a unifying design strategy in robotics has largely remained limited to g

Fingerprinting Inference Systems of Large Language Models

arXiv:2605.29979v1 Announce Type: cross Abstract: The behavior of LLMs does not depend solely on the model itself. Components of the inference system, such as the inference engine, attention backend,

FormalEvolve: Neuro-Symbolic Evolutionary Search for Diverse Autoformalization

arXiv:2603.19828v3 Announce Type: replace Abstract: Autoformalization aims to produce formal statements that compile and faithfully preserve the intended meaning of informal mathematics. Yet standard

FreeForm: Reduced-Order Deformable Simulation from Particle-Based Skinning Eigenmodes

arXiv:2605.29318v1 Announce Type: cross Abstract: We present a novel formulation for mesh-free, reduced-order simulation of deformable hyperelastic objects. Existing work in reduced-order elastodynami

From Data to Insights: Exploring Program-of-Thoughts Prompting for Chart Summarization

arXiv:2605.28874v1 Announce Type: new Abstract: Charts play a critical role in conveying numerical data insights through structured visual representations. However, semantic visual understanding and n

From Meta-Thought to Execution: Cognitively Aligned Post-Training for Generalizable and Reliable LLM Reasoning

arXiv:2601.21909v2 Announce Type: replace Abstract: Current LLM post-training methods optimize complete reasoning trajectories through Supervised Fine-Tuning (SFT) followed by outcome-based Reinforcem

From Rubrics to Reliable Scores: Evidence-Grounded Text Evaluation with LLM Judges

arXiv:2601.08654v2 Announce Type: replace-cross Abstract: Rubric-based text evaluation increasingly uses large language models (LLMs) as scalable judges, but aligning frozen black-box models with huma

Gesture-Aware Indoor THz ISAC Systems for Adaptive Resource Allocation

arXiv:2605.29913v1 Announce Type: cross Abstract: This paper investigates a multi-user indoor integrated sensing and communication (ISAC) system operating in the terahertz (THz) band, designed for ada

Getting to the Point: Pointing Improves LVLMs at Counting

arXiv:2603.21746v2 Announce Type: replace Abstract: Pointing-based methods decompose complex tasks as sequential grounding and reasoning steps. Given a query, the model first grounds the relevant obje

GiPL: Generative augmented iterative Pseudo-Labeling for Cross-Domain Few-Shot Object Detection

arXiv:2605.29539v1 Announce Type: cross Abstract: Vision-language foundation models have shown promising zero-shot generalization for Cross-Domain Few-Shot Object Detection (CD-FSOD). However, they fa

GPS-Enhanced Tourist Mobility Modeling with Seasonal Spatial Priors and LLM-Based Activity Chain Generation

arXiv:2605.29578v1 Announce Type: new Abstract: Tourist mobility poses a distinct challenge for urban transportation planning. Unlike resident commuting, tourist travel is largely non-routine, attract

Gradient Preconditioning for Efficient and Reliable Reward-Guided Generation

arXiv:2602.08646v2 Announce Type: replace Abstract: We propose a gradient preconditioning method that makes reward-guided generation with one-step generative models both efficient and reliable. Test-t

GRASP: Plan-Guided Graph Retrieval with Adaptive Fusion and Reranking on Semi-Structured Knowledge Bases

arXiv:2605.30237v1 Announce Type: cross Abstract: Semi-structured knowledge bases (SKBs) embed textual documents in a typed graph of entities and relations, and underpin applications such as product s

HaluNet: Learning Hallucination Risk from Internal Signals in LLM Question Answering

arXiv:2512.24562v2 Announce Type: replace Abstract: Large language models (LLMs) achieve strong question answering (QA) performance but can produce fluent answers unsupported by available evidence. Ex

HARP: Hadamard-Preconditioned Adaptive Rotation Processor for Extreme LLM Quantization

arXiv:2605.29843v1 Announce Type: cross Abstract: Post-training quantization (PTQ) is essential for deploying LLMs under memory and bandwidth constraints. However, extreme low-bit quantization remains

HiKEY: Hierarchical Multimodal Retrieval for Open-Domain Document Question Answering

arXiv:2605.29606v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) for document-based Open-domain Question Answering (ODQA) on large-scale industrial corpora faces two critical bottl

HM-Talker: Hybrid Motion Modeling for High-Fidelity Talking Head Synthesis

arXiv:2508.10566v3 Announce Type: replace Abstract: Audio-driven talking head generation faces a fundamental trade-off between personalization and generalization, limiting its practical application. I

HoliTok:A Coutinuous Holistic Tokenization with Robust Dual Capabilities of Speech Generation and Understanding

arXiv:2605.29948v1 Announce Type: cross Abstract: Unified speech foundation models require a holistic tokenization space that is both learnable by language models and decodable into high-quality wavef

Honest Lying: Understanding Memory Confabulation in Reflexive Agents

arXiv:2605.29463v1 Announce Type: cross Abstract: Reflexion-style agents rely on self-generated reflections as memory, implicitly assuming that agents can accurately diagnose their own failures.We sho

Horizon Activation Mapping for Neural Networks in Time Series Forecasting

arXiv:2601.02094v4 Announce Type: replace Abstract: Neural networks for time series forecasting have relied on error metrics and architecture-specific interpretability approaches for model selection t

How Much Is a Dataset Worth? Scaling Laws, the Vendi Score, and Matrix Spectral Functions

arXiv:2605.29448v1 Announce Type: cross Abstract: Neural scaling laws appraise data through dataset size, while the Vendi Score uses quantum entropy to measure dataset value. We show both that common

How the Pope’s Magnifica Humanitas offers a template for individuals to meet the AI moment

Pope Leo XIV’s new encyclical on artificial intelligence includes a statement that warrants serious attention from technologists and policymakers: “Technology is never neutral.” Magnifica Humanitas (“

researchmit-tech-review

Implicit Identity Technologies for LLMs: Fingerprinting and Watermarking across Datasets, Models, and Generated Content

arXiv:2605.29245v1 Announce Type: cross Abstract: This paper presents a survey and taxonomy of LLM fingerprinting and watermarking for identity, ownership verification, provenance, and generated-conte

Improved Guarantees for Heterogeneous Treatment-Effect Estimation via Matrix Completion

arXiv:2605.30319v1 Announce Type: cross Abstract: A central goal of modern causal inference is estimating heterogeneous treatment effects to answer questions like 'how does an intervention affect each

In-Place Feedback: Reliable Refinement for Multi-Turn Expert-LLM Collaboration

arXiv:2510.00777v2 Announce Type: replace Abstract: LLM-generated drafts often contain subtle factual or logical errors, yet prior work shows that models struggle to reliably integrate multi-turn feed

Inspectorch: Efficient rare event exploration in solar observations

arXiv:2602.20316v2 Announce Type: replace-cross Abstract: The Sun is observed in unprecedented detail, enabling studies of its activity on very small spatiotemporal scales. However, the large volume o

Instance-dependent Stochastic Lipschitz bandit

arXiv:2605.29748v1 Announce Type: cross Abstract: We study the Lipschitz bandit problem, where a learner sequentially maximizes an unknown Lipschitz function f over a domain X subset [0,1]^d using noi

Interactive In-Meeting Speaker Correction with Human Feedback

arXiv:2509.18377v2 Announce Type: replace Abstract: Most automatic speech processing systems operate in ``open loop'' mode without user feedback about who said what, yet human-in-the-loop workflows ca

IP-Adapter Is All You Need: Towards Fine-Tuning-Free Diffusion-Based Talking Face Generation

arXiv:2605.30230v1 Announce Type: new Abstract: With the rapid advancement of diffusion models, talking face generation has made remarkable progress. However, existing diffusion-based methods still re

Is Your Diffusion Sampler Actually Correct? A Sampler-Centric Evaluation of Discrete Diffusion Language Models

arXiv:2602.19619v2 Announce Type: replace Abstract: Discrete diffusion language models (dLLMs) provide a fast and flexible alternative to autoregressive models (ARMs) via iterative denoising with para

Joint Angle Estimation with Customized Wristband Based on Online Incremental Learning

arXiv:2605.29771v1 Announce Type: new Abstract: Intelligent wearable technology plays an increasingly important role in human-computer interaction, motion, and health monitoring. To ensure comfort and

researcharxiv-cs-ro

Joint Model and Data Sparsification via the Marginal Likelihood

arXiv:2605.29908v1 Announce Type: cross Abstract: Sparse recovery in linear systems underpins applications from signal processing to high-dimensional regression. Sparse Bayesian Learning, grounded in

KLAS: Using Similarity to Stitch Neural Networks for Improved Accuracy-Efficiency Tradeoffs

arXiv:2605.29259v1 Announce Type: cross Abstract: Given the wide range of deployment targets, flexible model selection is essential for optimizing performance within a given compute budget. Recent wor

LaRA: Layer-wise Representation Analysis for Detecting Data Contamination in RL Post-Training

arXiv:2605.29888v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training has shown to improve reasoning in large language models (LLMs). However, there has been little exploration o

Large-Scale AI and Foundation Models for Neuroscience: A Comprehensive Review

arXiv:2510.16658v3 Announce Type: replace Abstract: The development of large-scale artificial intelligence (AI) models is influencing neuroscience research by enabling end-to-end learning from raw bra

Learning Context-Conditioned Predicate Semantics via Prototype Feedback

arXiv:2605.29610v1 Announce Type: cross Abstract: In scene graph generation, a central challenge is modeling polysemous predicates whose meanings shift across contexts. Prior approaches address this i

Learning to Perturb Hidden Representations for Generalizable Deep Learning

arXiv:2605.29525v1 Announce Type: new Abstract: Deep neural networks process data through a cascade of representations: input features, hidden activations, logits, and loss. While perturbations at the

Leave a Window Out: Modifying the Jackknife for Predictive Inference in Time Series

arXiv:2605.30292v1 Announce Type: cross Abstract: Conformal prediction methods enjoy strong theoretical and empirical predictive inference performance, provided the data is exchangeable, and predictor