Search: “models”

ReportLogic: Evaluating Logical Quality in Deep Research Reports

arXiv:2602.18446v2 Announce Type: replace-cross Abstract: Users increasingly rely on Large Language Models (LLMs) for Deep Research, using them to synthesize diverse sources into structured reports th

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Model Releases

SamaVaani: Auditing and Debiasing Multilingual Clinical ASR for Indian Languages

DGX agent

arXiv:2606.26901v1 Announce Type: cross Abstract: Automatic Speech Recognition (ASR) is increasingly used to document clinical encounters, yet its reliability in multilingual and demographically diver

model-releasesarxiv-cs-ai

26 Jun 2026

See & Sniff: Learning Visuo-Olfactory Representations

arXiv:2606.27307v1 Announce Type: new Abstract: While modern multimodal models integrate vision with language, audio, or touch, olfaction remains largely unexplored due to the lack of paired visuo-olf

ShareLock: A Stealthy Multi-Tool Threshold Poisoning Attack Against MCP

arXiv:2606.27027v1 Announce Type: cross Abstract: With the rapid evolution of LLM-driven agents, Model Context Protocol (MCP), an open protocol bridging LLMs with external tools, has quickly become fo

Staying VIGILant: Mitigating Visual Laziness via Counterfactual Visual Alignment in MLLMs

arXiv:2606.26387v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) extend large language models (LLMs) with visual perception, enabling joint reasoning over images and text. De

safetyarxiv-cs-cl

Tutorials

Structure Before Collapse: Transient semantic geometry in next-token prediction

arXiv:2606.26749v1 Announce Type: cross Abstract: Neural Collapse predicts that balanced one-hot classification pushes model representations to be equally far from each other; a symmetric configuratio

tutorialsarxiv-cs-cl

Towards Video Anomaly Detection from Event Streams: A Baseline and Benchmark Datasets

arXiv:2603.24991v2 Announce Type: replace Abstract: Event-based vision, characterized by low redundancy, focus on dynamic motion, and inherent privacy-preserving properties, naturally fits the demands

When are likely answers right? On Sequence Probability and Correctness in LLMs

arXiv:2606.27359v1 Announce Type: cross Abstract: Many decoding methods for large language models can be understood as shifting probability mass toward outputs that are more likely under the model, ei

Zero-Shot Size Transfer for Neural ODEs on Sparse Random Graphs: Graphon Limits and Adjoint Convergence

arXiv:2606.26662v1 Announce Type: cross Abstract: Graph Neural Differential Equations (GNDEs) model continuous-time graph dynamics by parameterizing Neural ODE velocity fields with Graph Neural Networ

Auto-Configured Explainable Graph Neural Networks for Multi-Site Pollution Prediction

arXiv:2606.24978v1 Announce Type: new Abstract: Accurate particulate matter (PM) prediction is crucial for mitigating air pollution. Graph Neural Networks (GNNs) effectively model spatiotemporal depen

Beyond Function Calling: Benchmarking Tool-Using Agents under Tool-Environment Unreliability

arXiv:2606.25819v1 Announce Type: new Abstract: Large language models are increasingly deployed as agents that solve tasks by interacting with external tool environments. Although recent tool-use benc

Blasto-Net: An Explainable Multi-Task Learning for Blastocyst Segmentation, Grading, and Implantation Prediction

arXiv:2606.25463v1 Announce Type: cross Abstract: This study introduces Blasto-Net, a multi-task deep learning model for comprehensive blastocyst analysis. The proposed model performs three tasks simu

Constraint Tax in Open-Weight LLMs: An Empirical Study of Tool Calling Suppression Under Structured Output Constraints

arXiv:2606.25605v1 Announce Type: new Abstract: Tool Calling and Structured Output are two core capabilities of modern Agent systems, yet their interaction under joint deployment conditions remains in

DFMU: Data-Frugal Machine Unlearning

arXiv:2606.25410v1 Announce Type: new Abstract: Machine unlearning is an emerging domain that ensures the safe removal of elements (includes concepts, attributes, entity and class) from the trained mo

Emergent Capabilities Arise Randomly from Learning Sparse Attention Patterns

arXiv:2606.25010v1 Announce Type: cross Abstract: Neural scaling laws for transformer language models predict smooth improvements in pretraining loss with increasing parameters, but downstream capabil

researcharxiv-cs-cl

Enhancing Pathological VLMs with Cross-scale Reasoning

arXiv:2606.17412v3 Announce Type: replace Abstract: Pathological images are inherently multi-scale, requiring pathologists to integrate evidence from global tissue architecture at low magnification to

Evaluating LLMs on Real-World Software Performance Optimization

arXiv:2606.25530v1 Announce Type: cross Abstract: Software performance optimization is a notoriously complex and manual task. Despite the growing use of Large Language Models (LLMs) for code refinemen

HG-Bench: A Benchmark for Multi-Page Handwritten Answer-Region Grounding in Automated Homework Assessment

arXiv:2606.25491v1 Announce Type: new Abstract: Automated homework assessment depends not only on recognizing student answers, but also on accurately locating where each answer and each intermediate r

Homogeneity Bias in Open-Weight LLMs Is Robust to Decoding Hyperparameters

arXiv:2501.02211v2 Announce Type: replace-cross Abstract: Large language models (LLMs) reproduce homogeneity bias -- the tendency to portray marginalized groups as more internally similar than dominan

safetyarxiv-cs-cl

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

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

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

Applications

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

MacroLens: A Multi-Task Benchmark for Contextual Financial Reasoning under Macroeconomic Scenarios

model-releasesarxiv-cs-lg

arXiv:2606.24950v1 Announce Type: new Abstract: Financial decision-making is contextual: forecasting prices, valuing companies, and assessing event exposure weigh price history, accounting fundamental

Multilingual Hematology Visual Question Answering Dataset

arXiv:2606.25246v1 Announce Type: cross Abstract: Vision Language Models (VLMs) have shown promising capabilities in medical image analysis by jointly understanding visual and textual information for

Pulmonary Embolism Risk Stratification from CTPA and Medical Records: Vascular Graphs Are Not All You Need

arXiv:2606.25956v1 Announce Type: new Abstract: Risk stratification for pulmonary embolism (PE) is critical for clinical decision-making. Stratification guidelines are based on patient medical records

Applications

Quantifying Explainable AI-introduced signal noise on ECG data with Spectral Entropy

arXiv:2606.24974v1 Announce Type: new Abstract: Explainability techniques are used to assess the output of various deep learning models. This is especially true in healthcare, where models need to be

applicationsarxiv-cs-lg

RoboAtlas: Contextual Active SLAM

arXiv:2606.26046v1 Announce Type: cross Abstract: We present RoboAtlas, a contextual Active SLAM framework that adaptively balances geometric exploration and semantic reasoning using a scalable 3D sem

RoboRouter: Training-Free Policy Routing for Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2603.07892v4 Announce Type: replace Abstract: Research on robotic manipulation has developed a diverse set of policy paradigms, including vision-language-action (VLA) models, vision-action (VA)

Sarashina2.2-TTS: Tackling Kanji Polyphony in Japanese Speech Generation via Data Scaling and Targeted Data Synthesis

arXiv:2606.25369v1 Announce Type: cross Abstract: While large language model (LLM)-based text-to-speech (TTS) systems have achieved high-quality speech synthesis, most existing systems focus on Englis

Scale or Reason? A Compute-Equivalent Analysis of Reasoning Distillation

arXiv:2509.22193v2 Announce Type: replace Abstract: Distilling reasoning traces from strong teacher models has become the standard recipe for building capable small language models. Yet reasoning trac

researcharxiv-cs-cl

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding

arXiv:2606.25552v1 Announce Type: new Abstract: Prompt-based spoken language understanding (SLU) with large language models (LLMs) often suffers from inconsistent intent--slot structures due to decodi

ShutterMuse: Capture-Time Photography Guidance with MLLMs

arXiv:2606.25763v1 Announce Type: new Abstract: Real-world photography requires capture-time guidance for both camera framing and subject pose. Yet existing aesthetic cropping benchmarks mainly evalua

Speculative Decoding at Temperature Zero: A Scoped Safety-Invariance Screen with a 48,072-Sample Expansion

arXiv:2606.25097v1 Announce Type: new Abstract: Speculative decoding accelerates inference by letting a draft model propose tokens for a target model to verify, raising a concrete safety question: at

safetyarxiv-cs-lg

Speech Codec Probing from Semantic and Phonetic Perspectives

arXiv:2603.10371v2 Announce Type: replace-cross Abstract: Speech tokenizers are essential for connecting speech to large language models (LLMs) in multimodal systems. Speech tokenizers are expected to

Streaming-dLLM: Accelerating Diffusion LLMs via Suffix Pruning and Dynamic Decoding

arXiv:2601.17917v3 Announce Type: replace-cross Abstract: Diffusion Large Language Models (dLLMs) offer a compelling paradigm for natural language generation, leveraging parallel decoding and bidirect

researcharxiv-cs-cl

V-Zero: Answer-Label-Free On-Policy Distillation with Contrastive Evidence Gating for Fine-Grained Visual Reasoning

arXiv:2606.25319v1 Announce Type: new Abstract: Fine-grained visual reasoning requires multimodal large language models (MLLMs) to identify task-relevant visual evidence and ground their reasoning in

VPA-Guard: Defending and Benchmarking Image-to-Video Generation Against Visual Prompt Attacks

arXiv:2606.25592v1 Announce Type: new Abstract: Recent advancements in Image-to-Video (I2V) generation have transformed input images from simple appearance references into interactive control interfac

What Does It Mean to Break a Distillation Defense?

arXiv:2606.25059v1 Announce Type: cross Abstract: Black-box LLMs (accessible only via API) are vulnerable to distillation attacks, in which an attacker queries the model and trains a student on its ou

safetyarxiv-cs-ai

What Does the Brain See? Multiview Neural Representations to Demystify the Brain-Visual Alignment

arXiv:2606.25718v1 Announce Type: new Abstract: Zero-shot visual decoding from electroencephalography (EEG) aims to infer visual semantics from non-invasive neural recordings, but remains challenging

Are We Ready For An Agent-Native Memory System?

arXiv:2606.24775v1 Announce Type: new Abstract: Memory for large language model (LLM) agents has rapidly evolved from simple retrieval-augmented mechanisms into a data management system that supports

Applications

ArtiTwinSplat: Interactable Digital Twin Reconstruction via Gaussian Splatting from RGB-D videos

arXiv:2606.24628v1 Announce Type: cross Abstract: Deploying robots in unstructured real-world environments needs accurate, interactive models of the objects. Constructing these models at scale remains

applicationsarxiv-cs-cv

Assessing Distribution Shift in Human Activity Recognition for Domain Generalization

arXiv:2606.24781v1 Announce Type: new Abstract: While the field of Human Activity Recognition (HAR) continues to draw interest from researchers and advance in important ways, some key challenges remai

AutoSpecNER: A Fine-Grained Named Entity Recognition Dataset for Vehicle Specification Extraction

arXiv:2606.24387v1 Announce Type: new Abstract: Vehicle advertisements contain rich specification information, but automotive NER resources remain limited. We introduce AutoSpecNER, an expert-annotate

Average Rankings Mask Per-Subject Optimality: A Friedman-Nemenyi Benchmark of EEG Motor-Imagery BCI Decoders

arXiv:2606.24394v1 Announce Type: cross Abstract: Electroencephalography (EEG) is the dominant non-invasive modality for brain-computer interfaces (BCIs), yet reliable decoding of motor imagery is ham

BioMedArena: An Open-source Toolkit for Building and Evaluating Biomedical Deep Research Agents

arXiv:2605.06177v2 Announce Type: replace Abstract: Reproducing and comparing deep research agents today is hard: the same backbone evaluated on the same benchmark can report different accuracies acro

CanadaFireSat: Toward high-resolution wildfire forecasting with multiple modalities

arXiv:2506.08690v3 Announce Type: replace Abstract: Canada experienced in 2023 one of the most severe wildfire seasons in recent history, causing damage across ecosystems, destroying communities, and

CANDLE: Character-level Arabic Noise Deduplication using Lightweight Encoder

arXiv:2606.24758v1 Announce Type: new Abstract: Handling repeated characters in text can be tricky, since they can represent either the correct spelling of a word or informal character elongation ofte

CineCap: Structured Reasoning with Spatio-Temporal Anchors for Cinematographic Video Captioning

arXiv:2606.24636v1 Announce Type: new Abstract: Cinematographic captioning aims to describe how a video is filmed using professional film-language concepts such as camera movement, shot size, depth of