Search: “models”

Mixed-Modality Dual Face-Hair Retrieval

arXiv:2606.03470v1 Announce Type: new Abstract: We introduce Dual Face-Hair Retrieval (DFHR), a new mixed-modality dual-reference task in image retrieval where a query consists of a face image specify

MultiTurnPSB: Evaluating Multi-Turn Jailbreak Attacks an dClassifier-Based Defenses for Medical AI Safety

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DGX agent

arXiv:2606.02630v1 Announce Type: cross Abstract: Patient-facing medical chatbots are commonly evaluated on single-turn prompts, yet real users push back after refusals, add urgency, and invoke author

model-releasesarxiv-cs-ai

3 Jun 2026

OASIS: Outlier-Aware LUT-Based GEMM with Dual-Side Quantization for LLM Inference Acceleration

arXiv:2507.23035v4 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated impressive capabilities across a wide range of applications, but demand substantial memory and comput

Optimal Initialization in Depth: Lyapunov Initialization and Limit Theorems for Deep Leaky ReLU Networks

arXiv:2602.10949v2 Announce Type: replace-cross Abstract: Effective initialization in deep networks requires an understanding of random neural networks. In this work, a rigorous probabilistic analysis

Optimizing Explicit Unit-Distance Lower-Bound Certificates

arXiv:2606.03419v1 Announce Type: cross Abstract: The 2026 disproof of Erdos's unit-distance conjecture and Sawin's subsequent explicit quantitative refinement show that the maximum number u(n) of uni

Pathway-Structured Privileged Distillation for Deployable Computational Pathology

arXiv:2606.02877v1 Announce Type: new Abstract: Integrating transcriptomics and histopathology can improve cancer risk modelling, yet practical use is constrained by the limited availability of RNA pr

safetyarxiv-cs-cv

PerchRL: Vision-Based Agile Perching on Inclined Platforms under Rapid and Irregular Motion

arXiv:2606.03441v1 Announce Type: cross Abstract: Autonomous vision-based perching of quadrotors on moving inclined platforms is critical for air-ground collaboration but remains challenging due to th

PINNfluence: Interpreting PINNs through Influence Functions

arXiv:2409.08958v3 Announce Type: replace-cross Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful deep learning approach for solving partial differential equations (PDEs) i

Plan2Map: A Multimodal Benchmark for Document-Grounded Geospatial Boundary Reconstruction from Planning Records

arXiv:2606.02747v1 Announce Type: cross Abstract: Planning records define restrictions over geographic areas, but their source documents often provide only indirect spatial evidence rather than machin

Planning with Uncertainty: Symmetries, Policy Inference, and Solution Compression

arXiv:2403.19883v2 Announce Type: replace Abstract: Fully-observable non-deterministic (FOND) planning is at the core of artificial intelligence planning with uncertainty. It models uncertainty throug

safetyarxiv-cs-ai

Position: Adversarial ML for LLMs Is Not Making Any Progress

arXiv:2502.02260v2 Announce Type: replace Abstract: In the past decade, considerable research effort has been devoted to securing machine learning (ML) models that operate in adversarial settings. Yet

ProtocolBench: Which LLM MultiAgent Protocol to Choose?

arXiv:2510.17149v3 Announce Type: replace Abstract: As large-scale multi-agent systems evolve, the communication protocol layer has become a critical yet under-evaluated factor shaping performance and

PsychoPass: Geometric Profiling of Multi-Turn Adversarial LLM Conversations

arXiv:2606.03136v1 Announce Type: cross Abstract: Multi-turn jailbreak attacks on large language models (LLMs) reveal a mismatch in current guardrails: they operate on individual turns, while attacks

safetyarxiv-cs-cl

Agents

Reproducibility is the New Copyleft: Defining AGI-oriented Reproducible Builds

arXiv:2606.03019v1 Announce Type: cross Abstract: Copyleft, as implemented in licenses such as the GNU General Public License, was a legal hack that used copyright to guarantee user freedom by tying t

agentsarxiv-cs-ai

Rethinking the Idiomaticity Decomposability Hypothesis: Evidence from Distributional Learning

arXiv:2606.03817v1 Announce Type: new Abstract: Idioms can be analysed in terms of their decomposability, the extent to which constituent meanings contribute to the figurative whole. Decomposability i

researcharxiv-cs-cl

Right Makes Might: Aligning Verified Hidden States Empowers RL Reasoning

arXiv:2606.03234v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has become the dominant approach for improving mathematical reasoning in large language models, ye

safetyarxiv-cs-lg

ROBUST-WT: Robust Uncertainty-aware Segmentation Transform via Whitening and Training Enhancements

arXiv:2606.03069v1 Announce Type: cross Abstract: Generalized segmentation of medical images prevents performance degradation when different imaging devices and clinical protocols are used across mult

Scalable Uncertainty Quantification for Extreme Weather Forecasting via Empirical Neural Tangent Kernels

arXiv:2606.02886v1 Announce Type: cross Abstract: Deep learning weather models now match numerical weather prediction accuracy while running orders of magnitude faster, but produce deterministic forec

researcharxiv-cs-ai

ScoreStop: Gradient-based early stopping using functional score tests

arXiv:2606.02740v1 Announce Type: cross Abstract: Gradient boosted decision trees require a stopping rule to avoid overfitting. The standard rule monitors a validation loss and stops if the loss fails

Local Ai

Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference

arXiv:2606.03332v1 Announce Type: new Abstract: Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation.

local-aiarxiv-cs-lg

Temporal Action Selection for Action Chunking

arXiv:2511.04421v2 Announce Type: replace Abstract: Action chunking is a widely adopted approach in Learning from Demonstration (LfD). By modeling multi-step action chunks rather than single-step acti

safetyarxiv-cs-ro

TeX-1500: A Paired Real-World LWIR Hyperspectral Dataset and Benchmark for Temperature-Emissivity-Texture Decomposition

arXiv:2606.03806v1 Announce Type: new Abstract: Temperature-emissivity-texture (TeX) decomposition seeks to recover object heat state, material spectral response, and visible-like geometric texture fr

Text-attributed Graph Condensation via Text Selection and Attribute Matching

arXiv:2606.03839v1 Announce Type: new Abstract: Text-Attributed Graph (TAG) is an important type of graph structured data, where each node has a text description. TAG models usually train a Graph Neur

The Geometry of LLM-as-Judge: Why Inter-LLM Consensus Is Not Human Alignment

model-releasesarxiv-cs-cl

arXiv:2606.03043v1 Announce Type: new Abstract: LMs-as-judges are now standard, yet judges agree strongly with one another while agreeing only weakly with humans. We test whether this reflects shared

The Impact of Configuring Agentic AI Coding Tools on Build-vs-Buy Decisions: A Study Protocol

arXiv:2606.03907v1 Announce Type: cross Abstract: Agentic AI coding tools write code with increasing autonomy and in doing so decide when to import a library and when to implement functionality from s

The Ringelmann Effect in Multi-Agent LLM Systems: A Scaling Law for Effective Team Size

arXiv:2606.02646v1 Announce Type: cross Abstract: Inference-time multi-agent LLM scaling lacks a shared unit: counting nominal agents conflates cost with independent evidence. We derive a two-paramete

The Shadow Price of Reasoning: Economic Perspective on Optimal Budget Allocation for LLMs

arXiv:2606.03092v1 Announce Type: new Abstract: Inference-time scaling has emerged as a critical avenue for enhancing Large Language Models' performance, yet real-world deployment is constrained by st

safetyarxiv-cs-ai

Tool-Aware Optimization with Entropy Guidance for Efficient Agentic Reinforcement Learning

arXiv:2606.03762v1 Announce Type: cross Abstract: Agentic reinforcement learning (RL) equips large language models (LLMs) with tool-use capabilities that substantially improve reasoning on complex tas

safetyarxiv-cs-ai

Topology-Aware Gaussian Graph Repair for Robust Graph Neural Networks

arXiv:2606.03462v1 Announce Type: new Abstract: Graph neural networks have achieved strong performance on graph-structured data, but their effectiveness depends heavily on the quality of the observed

Local Ai

Toward a Modular Architecture for Embedded AI Agent Systems at the Edge

arXiv:2606.02862v1 Announce Type: new Abstract: The rise of Large Language Models (LLMs) has enabled agentic AI capable of complex reasoning and tool use; however, deploying such autonomy in pervasive

local-aiarxiv-cs-ai

TrAction: Action Recognition with Sparse Trajectories

arXiv:2606.03490v1 Announce Type: new Abstract: Modern action recognition models operate on memory- and compute-intensive dense RGB video volumes and frequently exploit appearance and background short

researcharxiv-cs-cv

Training a Predictive Coding Network on ImageNet using Equilibrium Propagation

arXiv:2606.03584v1 Announce Type: new Abstract: Equilibrium Propagation (EP) is a physics-based training framework that has primarily been employed in energy-based models, including continuous Hopfiel

Applications

TRAP: Hijacking VLA CoT-Reasoning via Adversarial Patches

arXiv:2603.23117v2 Announce Type: cross Abstract: By integrating Chain-of-Thought (CoT) reasoning, Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation, pa

applicationsarxiv-cs-ai

VLESA: Vision-Language Embodied Safety Agent for Human Activity Monitoring

arXiv:2606.03954v1 Announce Type: new Abstract: As AI systems increasingly assist humans in physical tasks, ensuring safety becomes paramount -- physical actions carry immediate and irreversible conse

WaterSIC: Information-Theoretically (Near) Optimal Linear Layer Quantization

arXiv:2603.04956v2 Announce Type: replace Abstract: This paper considers the problem of converting a given dense linear layer to low precision. The tradeoff between compressed length and output discre

What Makes Interaction Trajectories Effective for Training Terminal Agents?

arXiv:2606.03461v1 Announce Type: new Abstract: Stronger code agents are commonly assumed to be superior teachers for post-training, yet this assumption remains poorly disentangled from task difficult

Agents

When Helping Hurts and How to Fix It: Multi-Agent Debate for Data Cleaning

arXiv:2606.02866v1 Announce Type: new Abstract: When does multi-agent debate help data cleaning, and when does it hurt? Across three benchmarks, four model families, and over 6,000 task-condition pair

agentsarxiv-cs-ai

When Should the Teacher Move? Temporal Coupling and Stability in Self On-Policy Distillation

arXiv:2606.03532v1 Announce Type: cross Abstract: Self on-policy distillation trains a student policy against a teacher derived from its own parameter history, yet the teacher's update schedule -- whi

Why Are Linear RNNs More Parallelizable?

arXiv:2603.03612v3 Announce Type: replace-cross Abstract: The community is increasingly exploring linear RNNs (LRNNs) as language models, motivated by their expressive power and parallelizability. Whi

researcharxiv-cs-cl

Will Accurate Fields Mislead Photonic Design? FromGlobal Accuracy to Port Readout

arXiv:2606.03038v1 Announce Type: new Abstract: Neural field surrogates can accelerate photonic design loops, but a surrogate that looks accurate in global field error can still mis-rank candidate dev

3rd Place at CVPR 2026 CASTLE Challenge: Agentic Multi-View Long-Context Video Understanding via Hierarchical Knowledge Graph Retrieval

arXiv:2606.01933v1 Announce Type: new Abstract: This paper presents our winning methodology for the CASTLE 2026 Challenge at the CVPR 2026 EgoVis Workshop, where our team secured third place globally.

A Finite-Calibration Regime Map for LLM Judge Panels

model-releasesarxiv-cs-cl

arXiv:2606.01034v1 Announce Type: new Abstract: We study when LLM judge panels should be calibrated with low-dimensional stackers versus joint output tables under finite human-label budgets. Low-dimen

A Methodological Framework for Explicit Control of the Speed-Accuracy Trade-off in Brain-Computer Interfaces

arXiv:2606.00106v1 Announce Type: cross Abstract: Brain-computer interfaces (BCIs) are limited by low signal-to-noise ratio in modalities such as electroencephalography, which requires multiple trials

A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis

arXiv:2507.12645v1 Announce Type: cross Abstract: The increasing need for accurate and unified analysis of diverse biological signals, such as ECG and EEG, is paramount for comprehensive patient asses

A Per-Component Diagnostic Protocol for Neural HJB-PIDE Solvers under Control-Dependent Levy Jumps

arXiv:2606.01122v1 Announce Type: new Abstract: We propose a five-step diagnostic protocol for residual-trained neural HJB-PIDE solvers with control-dependent Levy jumps, targeting a general failure m

A Primer in Post-Training Reasoning Data: What We Know About How It Works

arXiv:2606.02113v1 Announce Type: cross Abstract: Post-training has become a primary driver of recent progress in large reasoning models, and reasoning data are often the key variable determining whet

researcharxiv-cs-ai

A Theoretical Framework for Self-Play Theorem Proving Algorithms

arXiv:2606.01861v1 Announce Type: new Abstract: Self-play, a type of training algorithm that enables a model to self-improve, has recently shown promising empirical results in the context of formal th

Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture

arXiv:2606.01110v1 Announce Type: cross Abstract: Full waveform inversion (FWI) reconstructs heterogeneous material properties from receiver data but remains computationally demanding. Physics-informe