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arXiv:2605.05121v1 Announce Type: new Abstract: Automated mental health prediction using textual data has shown promising results with deep learning and large language models. However, deploying these

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Research

Denoising Particle Filters: Learning State Estimation with Single-Step Objectives

DGX agent

arXiv:2602.19651v2 Announce Type: replace-cross Abstract: Learning-based methods commonly treat state estimation in robotics as a sequence modeling problem. While this paradigm can be effective at max

researcharxiv-cs-lg

7 May 2026

DiffCap-Bench: A Comprehensive, Challenging, Robust Benchmark for Image Difference Captioning

arXiv:2605.04503v1 Announce Type: new Abstract: Image Difference Captioning (IDC) generates natural language descriptions that precisely identify differences between two images, serving as a key bench

DSVM-UNet : Enhancing VM-UNet with Dual Self-distillation for Medical Image Segmentation

arXiv:2601.19690v2 Announce Type: replace Abstract: Vision Mamba models have been extensively researched in various fields, which address the limitations of previous models by effectively managing lon

researcharxiv-cs-cv

Tutorials

From Diffusion to Rectified Flow: Rethinking Text-Based Segmentation

arXiv:2605.04590v1 Announce Type: new Abstract: Text-based image segmentation aims to delineate object boundaries within an image from text prompts, offering higher flexibility and broader application

tutorialsarxiv-cs-cv

Hermes🪽

Hermes is an AI model developed by Nous Research that focuses on instruction-following and reasoning capabilities. Based on Nous Research's focus, this likely covers the model's architecture, performa

researchnous-research--x

Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency

arXiv:2510.08431v3 Announce Type: replace Abstract: Although continuous-time consistency models (e.g., sCM, MeanFlow) are theoretically principled and empirically powerful for fast academic-scale diff

researcharxiv-cs-cv

Local Ai

Learning to Feel the Future: DreamTacVLA for Contact-Rich Manipulation

arXiv:2512.23864v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have shown remarkable generalization by mapping web-scale knowledge to robotic control, yet they remain bl

local-aiarxiv-cs-cv

Making Knowledge Accessible: Divergent Readability-Accuracy Strategies of Mistral and QWen in Biomedical Text Simplification

arXiv:2511.05080v4 Announce Type: replace Abstract: The growing public demand for accessible biomedical information calls for scalable text simplification. While large language models (LLMs) offer sol

MULTIBENCH++: A Unified and Comprehensive Multimodal Fusion Benchmarking Across Specialized Domains

arXiv:2511.06452v3 Announce Type: replace Abstract: Although multimodal fusion has made significant progress, its advancement is severely hindered by the lack of adequate evaluation benchmarks. Curren

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization

arXiv:2605.04738v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities. However, their massive parameter scale leads to significant resource consumption

Physics-Guided Regime Unmixing

arXiv:2605.04247v1 Announce Type: new Abstract: The Linear Mixing Model (LMM) dominates spectral unmixing for its simplicity, but fails under multiple scattering; existing nonlinear models compensate

researcharxiv-cs-cv

Privacy-Preserving Empathy Detection in Video Interactions

arXiv:2504.10808v3 Announce Type: replace Abstract: Detecting empathy from video interactions has emerging applications, yet raw videos that could be used for training AI models are rarely available d

Safety

Scalable Multi Agent Diffusion Policies for Coverage Control

arXiv:2509.17244v2 Announce Type: replace Abstract: We propose MADP, a novel diffusion-model-based approach for collaboration in decentralized robot swarms. MADP leverages diffusion models to generate

safetyarxiv-cs-ro

Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics

arXiv:2605.04893v1 Announce Type: cross Abstract: Large language models hallucinate in predictable ways: attention routing fails by over-concentrating on a narrow set of positions, or by spreading so

Tutorials

Self-Improvement for Fast, High-Quality Plan Generation

arXiv:2605.03625v1 Announce Type: new Abstract: Generative models trained on synthetic plan data are a promising approach to generalized planning. Recent work has focused on finding any valid plan, ra

tutorialsarxiv-cs-ai

Single-Position Intervention Fails: Distributed Output Templates Drive In-Context Learning

arXiv:2605.04061v1 Announce Type: cross Abstract: Understanding how large language models encode task identity from few-shot demonstrations is a central open problem in mechanistic interpretability. P

Telegraph English: Semantic Prompt Compression via Structured Symbolic Rewriting

arXiv:2605.04426v1 Announce Type: new Abstract: We introduce Telegraph English (TE), a prompt-compression protocol that rewrites natural language into a symbol-rich, formally-structured dialect. Where

Wasserstein-Aligned Localisation for VLM-Based Distributional OOD Detection in Medical Imaging

arXiv:2605.05161v1 Announce Type: new Abstract: Zero-shot anomaly localisation via vision-language models (VLMs) offers a compelling approach for rare pathology detection, yet its performance is funda

6G Needs Agents: Toward Agentic AI-Native Networks for Autonomous Intelligence

arXiv:2605.01546v1 Announce Type: cross Abstract: Sixth-generation (6G) networks are increasingly envisioned as AI-native infrastructures integrating communication, sensing, and computing into a unifi

A-CODE: Fully Atomic Protein Co-Design with Unified Multimodal Diffusion

arXiv:2605.03360v1 Announce Type: cross Abstract: We present A-CODE, a fully atomic unified one-stage protein co-design model that simultaneously refines discrete atom types and continuous atom coordi

researcharxiv-cs-lg

Accelerating Inference of Discrete Autoregressive Normalizing Flows by Selective Jacobi Decoding

arXiv:2505.24791v2 Announce Type: replace Abstract: Discrete normalizing flows are promising generative models with advantages such as analytical log-likelihood computation and end-to-end training. Ho

researcharxiv-cs-lg

Compress Then Adapt? No, Do It Together via Task-aware Union of Subspaces

arXiv:2605.02829v1 Announce Type: new Abstract: Adapting large pretrained models to diverse tasks is now routine, yet the two dominant strategies of parameter-efficient fine-tuning (PEFT) and low-rank

CuraView: A Multi-Agent Framework for Medical Hallucination Detection with GraphRAG-Enhanced Knowledge Verification

arXiv:2605.03476v1 Announce Type: new Abstract: Discharge summaries require extracting critical information from lengthy electronic health records (EHRs), a process that is labor-intensive when perfor

Distributed Deep Variational Approach for Privacy-preserving Data Release

arXiv:2605.03069v1 Announce Type: cross Abstract: Federated learning (FL) lets distributed nodes train a shared model without exchanging their raw data, but in privacy-sensitive deployments medical se

From Code to Prediction: Fine-Tuning LLMs for Neural Network Performance Classification in NNGPT

arXiv:2605.03686v1 Announce Type: cross Abstract: Automated Machine Learning (AutoML) frameworks increasingly leverage Large Language Models (LLMs) for tasks such as hyperparameter optimization and ne

From Laboratory to Real-World Applications: Benchmarking Agentic Code Reasoning at the Repository Level

arXiv:2601.03731v3 Announce Type: replace-cross Abstract: As large language models (LLMs) evolve into autonomous agents, evaluating repository-level reasoning, the ability to maintain logical consiste

GOAT: A Training Framework for Goal-Oriented Agent with Tools

arXiv:2510.12218v2 Announce Type: replace Abstract: Current approaches rely on zero-shot evaluation due to the absence of training data; while proprietary models such as GPT-4 exhibit strong reasoning

Graph Neural Networks in the Wilson Loop Representation of Abelian Lattice Gauge Theories

arXiv:2605.03901v1 Announce Type: cross Abstract: Local gauge structures play a central role in a wide range of condensed matter systems and synthetic quantum platforms, where they emerge as effective

Joint Energy Management and Coordinated AIGC Workload Scheduling for Distributed Data Centers: A Diffusion-Aided Reward Shaping Approach

arXiv:2605.02965v1 Announce Type: new Abstract: Artificial intelligence-generated content (AIGC) has emerged as a transformative paradigm for automating the creation of diverse and customized content,

Meta-Inverse Physics-Informed Neural Networks for High-Dimensional Ordinary Differential Equations

arXiv:2605.03511v1 Announce Type: new Abstract: Solving inverse problems in dynamical systems governed by high-dimensional coupled ordinary differential equations (ODEs) is a ubiquitous challenge in s

MILE: Mixture of Incremental LoRA Experts for Continual Semantic Segmentation across Domains and Modalities

arXiv:2605.03555v1 Announce Type: new Abstract: Continual semantic segmentation requires models to adapt to new domains or modalities without sacrificing performance on previously learned tasks. Exper

Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling

arXiv:2605.01566v1 Announce Type: new Abstract: Advances in inference methods have enabled language models to improve their predictions without additional training. These methods often prioritize raw

Neuro-Symbolic Agents for Hallucination-Free Requirements Reuse

arXiv:2605.01562v1 Announce Type: cross Abstract: The Object-Oriented Method for Requirements Authoring and Management (OOMRAM) is a requirements reuse framework that relies on exact identifier matchi

PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMs

arXiv:2605.01123v1 Announce Type: new Abstract: Large language models (LLMs) can provide automated feedback in educational settings, but aligning an LLMs style with a specific instructors tone while m

PHBench: A Benchmark for Predicting Startup Series A Funding from Product Hunt Launch Signals

arXiv:2605.02974v1 Announce Type: cross Abstract: Structured launch signals on Product Hunt contain statistically significant predictive information for Series A funding outcomes. We construct PHBench

PIIGuard: Mitigating PII Harvesting under Adversarial Sanitization

arXiv:2605.03129v1 Announce Type: cross Abstract: Browsing-enabled LLM assistants can fetch webpages and answer contact-seeking queries, creating a practical channel for scraping contact-style persona

Retrieval and Multi-Hop Reasoning in 1M-Token Context Windows: Evaluating LLMs on Classical Chinese Text

arXiv:2605.02173v1 Announce Type: new Abstract: We evaluate the long-context retrieval and reasoning capabilities of five frontier large language models with advertised 1M-token context windows on a c

Agents

small workflow note that adds up. /staged-pr is a skill (via slash command) i run when wrapping up a PR. it takes my staged code changes and…

small workflow note that adds up. /staged-pr is a skill (via slash command) i run when wrapping up a PR. it takes my staged code changes and drafts a concise PR title & description, based on my prefer

agentsharrison-chase--x

TimeTok: Granularity-Controllable Time-Series Generation via Hierarchical Tokenization

arXiv:2605.01418v1 Announce Type: new Abstract: Time-series generative models often lack control over temporal granularity, forcing users to accept whatever granularity the model produces. To enable t

researcharxiv-cs-ai

Agents

WMF-AM: Probing LLM Working Memory via Depth-Parameterized Cumulative State Tracking

arXiv:2603.27343v2 Announce Type: replace Abstract: Existing large language models (LLMs) evaluations use fixed-difficulty benchmarks that cannot adapt as models improve, and rarely isolate specific c

agentsarxiv-cs-ai

Accurate Legal Reasoning at Scale: Neuro-Symbolic Offloading and Structural Auditability for Robust Legal Adjudication

arXiv:2605.02472v1 Announce Type: new Abstract: Legal texts often contain computational legal clauses--provisions whose understanding requires complex logic. While frontier Large Reasoning Models (LRM

Activation Compression in LLMs: Theoretical Analysis and Efficient Algorithm

arXiv:2605.01255v1 Announce Type: new Abstract: Training large language models (LLMs) is highly memory-intensive, as training must store not only weights and optimizer states but also intermediate act

Adoption and Use of LLMs at an Academic Medical Center

arXiv:2602.00074v2 Announce Type: replace-cross Abstract: While large language models (LLMs) can support clinical documentation needs, standalone tools struggle with 'workflow friction' from manual da

Agents

Adversarial Flow Matching for Imperceptible Attacks on End-to-End Autonomous Driving

arXiv:2605.00880v1 Announce Type: new Abstract: Autonomous driving (AD) is evolving towards end-to-end (E2E) frameworks through two primary paradigms: monolithic models exemplified by Vision-Language-

agentsarxiv-cs-cv

ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoning

arXiv:2505.16850v2 Announce Type: replace-cross Abstract: Federated Learning (FL) has emerged as a promising paradigm for collaborative model training while preserving data privacy across decentralize

Benchmarking Retrieval Strategies for Biomedical Retrieval-Augmented Generation: A Controlled Empirical Study

arXiv:2605.02520v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) offers a well-established path to grounding large language model (LLM) outputs in external knowledge, yet the quest

Breaking the Silence: A Dataset and Benchmark for Bangla Text-to-Gloss Translation

arXiv:2504.02293v3 Announce Type: replace Abstract: Gloss is a written approximation that bridges Sign Language (SL) and its corresponding spoken language. Despite a deaf and hard-of-hearing populatio