Search: “models”

Escaping the Context Bottleneck: Active Context Curation for LLM Agents via Reinforcement Learning

arXiv:2604.11462v1 Announce Type: new Abstract: Large Language Models (LLMs) struggle with long-horizon tasks due to the 'context bottleneck' and the 'lost-in-the-middle' phenomenon, where accumulated

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Model Releases

FedKLPR: KL-Guided Pruning-Aware Federated Learning for Person Re-Identification

DGX agent

arXiv:2508.17431v3 Announce Type: replace-cross Abstract: Person re-identification (re-ID) is a fundamental task in intelligent surveillance and public safety. Federated learning (FL) provides a priva

model-releasesarxiv-cs-ai

14 Apr 2026

Local Ai

FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher

arXiv:2408.07587v4 Announce Type: replace Abstract: Federated Learning (FL) enables the collaborative training of machine learning models without requiring centralized collection of user data. To comp

local-aiarxiv-cs-lg

FinTrace: Holistic Trajectory-Level Evaluation of LLM Tool Calling for Long-Horizon Financial Tasks

arXiv:2604.10015v1 Announce Type: new Abstract: Recent studies demonstrate that tool-calling capability enables large language models (LLMs) to interact with external environments for long-horizon fin

FlexMS is a flexible framework for benchmarking deep learning-based mass spectrum prediction tools in metabolomics

arXiv:2602.22822v2 Announce Type: replace Abstract: The identification and property prediction of chemical molecules is of central importance in the advancement of drug discovery and material science,

From Scalars to Tensors: Declared Losses Recover Epistemic Distinctions That Neutrosophic Scalars Cannot Express

arXiv:2604.09602v1 Announce Type: new Abstract: Leyva-Vazquez and Smarandache (2025) demonstrated that neutrosophic T/I/F evaluation, where Truth, Indeterminacy, and Falsity are independent dimensions

GeoFormer: A Lightweight Swin Transformer for Joint Building Height and Footprint Estimation from Sentinel Imagery

arXiv:2602.09932v2 Announce Type: replace Abstract: Building height (BH) and footprint (BF) are fundamental urban morphological parameters required by climate modelling, disaster-risk assessment, and

GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning

arXiv:2505.17022v2 Announce Type: replace-cross Abstract: Visual generation models have made remarkable progress in creating realistic images from text prompts, yet struggle with complex prompts that

Hardware

Hardware Utilization and Inference Performance of Edge Object Detection Under Fault Injection

arXiv:2604.09631v1 Announce Type: cross Abstract: As deep learning models are deployed on resource constrained edge platforms in autonomous driving systems, reli able knowledge of hardware behavior un

hardwarearxiv-cs-ai

Safety

HDR Video Generation via Latent Alignment with Logarithmic Encoding

arXiv:2604.11788v1 Announce Type: new Abstract: High dynamic range (HDR) imagery offers a rich and faithful representation of scene radiance, but remains challenging for generative models due to its m

safetyarxiv-cs-cv

Improving LLM Unlearning Robustness via Random Perturbations

arXiv:2501.19202v5 Announce Type: replace Abstract: Here, we show that current LLM unlearning methods inherently reduce models' robustness, causing them to misbehave even when a single non-adversarial

researcharxiv-cs-cl

LARY: A Latent Action Representation Yielding Benchmark for Generalizable Vision-to-Action Alignment

arXiv:2604.11689v1 Announce Type: new Abstract: While the shortage of explicit action data limits Vision-Language-Action (VLA) models, human action videos offer a scalable yet unlabeled data source. A

Tutorials

Learning Long-term Motion Embeddings for Efficient Kinematics Generation

arXiv:2604.11737v1 Announce Type: new Abstract: Understanding and predicting motion is a fundamental component of visual intelligence. Although modern video models exhibit strong comprehension of scen

tutorialsarxiv-cs-cv

LIDARLearn: A Unified Deep Learning Library for 3D Point Cloud Classification, Segmentation, and Self-Supervised Representation Learning

arXiv:2604.10780v1 Announce Type: new Abstract: Three-dimensional (3D) point cloud analysis has become central to applications ranging from autonomous driving and robotics to forestry and ecological m

LiveCLKTBench: Towards Reliable Evaluation of Cross-Lingual Knowledge Transfer in Multilingual LLMs

arXiv:2511.14774v3 Announce Type: replace-cross Abstract: Evaluating cross-lingual knowledge transfer in large language models is challenging, as correct answers in a target language may arise either

LLMs for Text-Based Exploration and Navigation Under Partial Observability

arXiv:2604.09604v1 Announce Type: new Abstract: Exploration and goal-directed navigation in unknown layouts are central to inspection, logistics, and search-and-rescue. We ask whether large language m

Lung Cancer Detection Using Deep Learning

arXiv:2604.10765v1 Announce Type: cross Abstract: Lung cancer, the second leading cause of cancer-related deaths, is primarily linked to long-term tobacco smoking (85% of cases). Surprisingly, 10-15%

Masked Contrastive Pre-Training Improves Music Audio Key Detection

arXiv:2604.10021v1 Announce Type: cross Abstract: Self-supervised music foundation models underperform on key detection, which requires pitch-sensitive representations. In this work, we present the fi

researcharxiv-cs-lg

MCAT: Scaling Many-to-Many Speech-to-Text Translation with MLLMs to 70 Languages

arXiv:2512.01512v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have achieved great success in Speech-to-Text Translation (S2TT) tasks. However, current research is constr

Measuring What Matters!! Assessing Therapeutic Principles in Mental-Health Conversation

arXiv:2604.05795v2 Announce Type: replace Abstract: The increasing use of large language models in mental health applications calls for principled evaluation frameworks that assess alignment with psyc

MPAC: A Multi-Principal Agent Coordination Protocol for Interoperable Multi-Agent Collaboration

arXiv:2604.09744v1 Announce Type: cross Abstract: The AI agent ecosystem has converged on two protocols: the Model Context Protocol (MCP) for tool invocation and Agent-to-Agent (A2A) for single-princi

Network Effects and Agreement Drift in LLM Debates

arXiv:2604.11312v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated an unprecedented ability to simulate human-like social behaviors, making them useful tools for simulati

One of my passions is that education should be dispersed freely and as widely as possible, especially for technologies as dynamic and crucia…

model-releasesjeremy-howard--x

One of my passions is that education should be dispersed freely and as widely as possible, especially for technologies as dynamic and crucial as LLMs/AI. I'm proud to have friends who would disown me

Panoptic Pairwise Distortion Graph

arXiv:2604.11004v1 Announce Type: cross Abstract: In this work, we introduce a new perspective on comparative image assessment by representing an image pair as a structured composition of its regions.

Point2Pose: Occlusion-Recovering 6D Pose Tracking and 3D Reconstruction for Multiple Unknown Objects Via 2D Point Trackers

arXiv:2604.10415v1 Announce Type: new Abstract: We present Point2Pose, a model-free method for causal 6D pose tracking of multiple rigid objects from monocular RGB-D video. Initialized only from spars

Agents

PoTable: Towards Systematic Thinking via Plan-then-Execute Stage Reasoning on Tables

arXiv:2412.04272v5 Announce Type: replace-cross Abstract: In recent years, table reasoning has garnered substantial research interest, particularly regarding its integration with Large Language Models

agentsarxiv-cs-ai

Preference Learning Unlocks LLMs' Psycho-Counseling Skills

arXiv:2502.19731v2 Announce Type: replace Abstract: Applying large language models (LLMs) to assist in psycho-counseling is an emerging and meaningful approach, driven by the significant gap between p

researcharxiv-cs-cl

SignReasoner: Compositional Reasoning for Complex Traffic Sign Understanding via Functional Structure Units

arXiv:2604.10436v1 Announce Type: new Abstract: Accurate semantic understanding of complex traffic signs-including those with intricate layouts, multi-lingual text, and composite symbols-is critical f

Tutorials

Specificity-aware reinforcement learning for fine-grained open-world classification

arXiv:2603.03197v3 Announce Type: replace Abstract: Classifying fine-grained visual concepts under open-world settings, i.e., without a predefined label set, demands models to be both accurate and spe

tutorialsarxiv-cs-cv

StarVLA-alpha: Reducing Complexity in Vision-Language-Action Systems

arXiv:2604.11757v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for building general-purpose robotic agents. However, the VLA landsc

Tutorials

Structured State-Space Regularization for Compact and Generation-Friendly Image Tokenization

arXiv:2604.11089v1 Announce Type: new Abstract: Image tokenizers are central to modern vision models as they often operate in latent spaces. An ideal latent space must be simultaneously compact and ge

tutorialsarxiv-cs-cv

Thinking Fast, Thinking Wrong: Intuitiveness Modulates LLM Counterfactual Reasoning in Policy Evaluation

arXiv:2604.10511v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for causal and counterfactual reasoning, yet their reliability in real-world policy evaluation remain

Tracking High-order Evolutions via Cascading Low-rank Fitting

model-releasesarxiv-cs-lg

arXiv:2604.10980v1 Announce Type: new Abstract: Diffusion models have become the de facto standard for modern visual generation, including well-established frameworks such as latent diffusion and flow

Safety

Triviality Corrected Endogenous Reward

arXiv:2604.11522v1 Announce Type: new Abstract: Reinforcement learning for open-ended text generation is constrained by the lack of verifiable rewards, necessitating reliance on judge models that requ

safetyarxiv-cs-cl

UK gov's Mythos AI tests help separate cybersecurity threat from hype

model-releasesars-technica

The UK's AI Security Institute (AISI) conducted evaluations of Anthropic's Claude Mythos Preview, finding it represents a meaningful step up over previous frontier AI models in cybersecurity capabilit

Ultra-Low-Dimensional Prompt Tuning via Random Projection

arXiv:2502.04501v3 Announce Type: replace Abstract: Large language models achieve state-of-the-art performance but are increasingly costly to fine-tune. Prompt tuning is a parameter-efficient fine-tun

VeriSim: A Configurable Framework for Evaluating Medical AI Under Realistic Patient Noise

arXiv:2604.10441v1 Announce Type: new Abstract: Medical large language models (LLMs) achieve impressive performance on standardized benchmarks, yet these evaluations fail to capture the complexity of

Local Ai

WebLLM: A High-Performance In-Browser LLM Inference Engine

arXiv:2412.15803v2 Announce Type: replace-cross Abstract: Advancements in large language models (LLMs) have unlocked remarkable capabilities. While deploying these models typically requires server-gra

local-aiarxiv-cs-ai

AlphaLab: Autonomous Multi-Agent Research Across Optimization Domains with Frontier LLMs

arXiv:2604.08590v1 Announce Type: cross Abstract: We present AlphaLab, an autonomous research harness that leverages frontier LLM agentic capabilities to automate the full experimental cycle in quanti

CORA: Conformal Risk-Controlled Agents for Safeguarded Mobile GUI Automation

arXiv:2604.09155v1 Announce Type: cross Abstract: Graphical user interface (GUI) agents powered by vision language models (VLMs) are rapidly moving from passive assistance to autonomous operation. How

Cross-Lingual Attention Distillation with Personality-Informed Generative Augmentation for Multilingual Personality Recognition

arXiv:2604.08851v1 Announce Type: new Abstract: While significant work has been done on personality recognition, the lack of multilingual datasets remains an unresolved challenge. To address this, we

Detecting Diffusion-generated Images via Dynamic Assembly ForestsDetecting Diffusion-generated Images via Dynamic Assembly Forests

arXiv:2604.09106v1 Announce Type: new Abstract: Diffusion models are known for generating high-quality images, causing serious security concerns. To combat this, most efforts rely on deep neural netwo

researcharxiv-cs-cv

Safety

Do LLMs Follow Their Own Rules? A Reflexive Audit of Self-Stated Safety Policies

arXiv:2604.09189v1 Announce Type: cross Abstract: LLMs internalize safety policies through RLHF, yet these policies are never formally specified and remain difficult to inspect. Existing benchmarks ev

safetyarxiv-cs-ai

EGMOF: Efficient Generation of Metal-Organic Frameworks Using a Hybrid Diffusion-Transformer Architecture

arXiv:2511.03122v2 Announce Type: replace-cross Abstract: Designing materials with targeted properties remains challenging due to the vastness of chemical space and the scarcity of property-labeled da

EMA Is Not All You Need: Mapping the Boundary Between Structure and Content in Recurrent Context

arXiv:2604.08556v1 Announce Type: cross Abstract: What exactly do efficient sequence models gain over simple temporal averaging? We use exponential moving average (EMA) traces, the simplest recurrent

Envisioning the Future, One Step at a Time

arXiv:2604.09527v1 Announce Type: cross Abstract: Accurately anticipating how complex, diverse scenes will evolve requires models that represent uncertainty, simulate along extended interaction chains

Exploiting Web Search Tools of AI Agents for Data Exfiltration

arXiv:2510.09093v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are now routinely used to autonomously execute complex tasks, from natural language processing to dynamic workflo

researcharxiv-cs-cl

From Paper to Program: Accelerating Quantum Many-Body Algorithm Development via a Multi-Stage LLM-Assisted Workflow

arXiv:2604.04089v2 Announce Type: replace-cross Abstract: Large language models (LLMs) can generate code rapidly but remain unreliable for scientific algorithms whose correctness depends on structural