Search: “models”

RLBFF: Binary Flexible Feedback to bridge between Human Feedback & Verifiable Rewards

arXiv:2509.21319v3 Announce Type: replace-cross Abstract: Reinforcement Learning with Human Feedback (RLHF) and Reinforcement Learning with Verifiable Rewards (RLVR) are the main RL paradigms used in

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Safety

SADP: Subgoal-Aware Diffusion Policy for Explainable Robots Learned from Foundation Model Generated Demonstrations

DGX agent

arXiv:2605.16871v1 Announce Type: new Abstract: Explainable robots require not only successful task execution but also the ability to expose internal decision-making process in a user-friendly manner.

safetyarxiv-cs-ro

19 May 2026

Scales++: Compute Efficient Evaluation Subset Selection with Cognitive Scales Embeddings

arXiv:2510.26384v2 Announce Type: replace Abstract: The prohibitive cost of evaluating large language models (LLMs) on comprehensive benchmarks necessitates the creation of small yet representative da

StreamingTalker: Audio-driven 3D Facial Animation with Autoregressive Diffusion Model

arXiv:2511.14223v3 Announce Type: replace Abstract: This paper focuses on the task of speech-driven 3D facial animation, which aims to generate realistic and synchronized facial motions driven by spee

researcharxiv-cs-cv

StreamPro: From Reactive Perception to Proactive Decision-Making in Streaming Video

arXiv:2605.16381v1 Announce Type: cross Abstract: Proactive streaming video understanding requires models to continuously process video streams and decide when to respond, rather than merely what to r

Applications

STRIDE-AI: A Threat Modeling Framework for Generative AI Security Assessment

arXiv:2605.17163v1 Announce Type: cross Abstract: Traditional cybersecurity methodologies target deterministic systems and fail to address the probabilistic nature of AI, leaving systems vulnerable to

applicationsarxiv-cs-ai

Temporal Task Diversity: Inductive Biases Under Non-Stationarity in Synthetic Sequence Modelling

arXiv:2605.18281v1 Announce Type: new Abstract: Modern deep learning science often assumes that neural networks learn from a fixed data distribution. However, many practically important learning probl

safetyarxiv-cs-lg

Time-Efficient Hybrid Hyperparameter Tuning Approach for Cardiovascular Disease Classification

arXiv:2411.18234v2 Announce Type: replace-cross Abstract: Cardiovascular diseases (CVDs) are any serious illness of the heart, which require accurate diagnosis to prevent fatal consequences. Hyperpara

Why Modeling Human Haptic Material Perception with AI Is Difficult

arXiv:2605.16602v1 Announce Type: cross Abstract: Touch plays a central role in how humans perceive and recognize materials through physical contact. Despite decades of research, the mechanisms by whi

researcharxiv-cs-ai

Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making

arXiv:2605.16054v1 Announce Type: cross Abstract: Recent work has framed decision-making as a sequence modeling problem using generative models such as diffusion models. Although promising, these appr

Judge Circuits

arXiv:2605.16023v1 Announce Type: new Abstract: LLM-as-a-judge has become the dominant paradigm for grading model outputs at scale, yet the same model assigns systematically different scores when its

Linked Multi-Model Data on Russian Domestic and Foreign Policy Speeches

arXiv:2605.15886v1 Announce Type: new Abstract: This paper introduces a dataset of interlinked multimodal political communications from the Russian government, addressing persistent deficiencies in th

safetyarxiv-cs-cl

Minerva-Ego: Spatiotemporal Hints for Egocentric Video Understanding

model-releasesarxiv-cs-cv

arXiv:2605.15342v1 Announce Type: new Abstract: Video reasoning models are a core component of egocentric and embodied agents. However, standard benchmarks for assessing models provide only evaluation

Perforated Neural Networks for Keyword Spotting

arXiv:2605.15647v1 Announce Type: new Abstract: Edge machine learning presents a unique set of constraints not encountered in cloud-scale model deployment: strict memory budgets, limited compute, and

Agents

Training on Documents About Monitoring Leads to CoT Obfuscation

arXiv:2605.15257v1 Announce Type: new Abstract: Chain-of-thought (CoT) monitoring is one of the most promising tools we have for detecting model misbehavior, but its effectiveness depends on models fa

agentsarxiv-cs-lg

UAM: A Dual-Stream Perspective on Forgetting in VLA Training

arXiv:2605.15735v1 Announce Type: cross Abstract: Vision--language--action (VLA) models are typically built by fine-tuning a pretrained vision--language model (VLM) on action data. However, we show th

Agents

WorldVLN: Autoregressive World Action Model for Aerial Vision-Language Navigation

arXiv:2605.15964v1 Announce Type: cross Abstract: Aerial vision-language navigation (VLN) requires agents to follow natural-language instructions through closed-loop perception and action in 3D enviro

agentsarxiv-cs-cv

A cross-species neural foundation model for end-to-end speech decoding

arXiv:2511.21740v5 Announce Type: replace-cross Abstract: Speech brain-computer interfaces (BCIs) aim to restore communication for people with paralysis by translating neural activity into text. Most

CoCoEdit: Content-Consistent Image Editing via Region Regularized Reinforcement Learning

model-releasesarxiv-cs-cv

arXiv:2602.14068v2 Announce Type: replace Abstract: Image editing has achieved impressive results with the development of large-scale generative models. However, existing models mainly focus on the ed

Do Coding Agents Understand Least-Privilege Authorization?

arXiv:2605.14859v1 Announce Type: cross Abstract: As coding agents gain access to shells, repositories, and user files, least-privilege authorization becomes a prerequisite for safe deployment: an age

Local Ai

EVA: Editing for Versatile Alignment against Jailbreaks

arXiv:2605.14750v1 Announce Type: cross Abstract: Large Language Models (LLMs) and Vision Language Models (VLMs) have demonstrated impressive capabilities but remain vulnerable to jailbreaking attacks

local-aiarxiv-cs-ai

Tutorials

IG-Diff: Complex Night Scene Restoration with Illumination-Guided Diffusion Model

arXiv:2605.14337v1 Announce Type: new Abstract: In nighttime circumstances, it is challenging for individuals and machines to perceive their surroundings. While prevailing image restoration methods ad

tutorialsarxiv-cs-cv

Is Grep All You Need? How Agent Harnesses Reshape Agentic Search

arXiv:2605.15184v1 Announce Type: new Abstract: Recent advances in Large Language Model (LLM) agents have enabled complex agentic workflows where models autonomously retrieve information, call tools,

Applications

SoFFT: Spatial Fourier Transform for Modeling Continuum Soft Robots

arXiv:2502.17347v2 Announce Type: replace Abstract: Continuum soft robots, composed of flexible materials, exhibit theoretically infinite degrees of freedom, enabling notable adaptability in unstructu

applicationsarxiv-cs-ro

Synthesizing POMDP Policies: Sampling Meets Model-checking via Learning

arXiv:2605.14440v1 Announce Type: new Abstract: Partially Observable Markov Decision Processes (POMDPs) are the standard framework for decision-making under uncertainty. While sampling-based methods s

UMo: Unified Sparse Motion Modeling for Real-Time Co-Speech Avatars

arXiv:2605.14731v1 Announce Type: cross Abstract: Speech-driven gestures and facial animations are fundamental to expressive digital avatars in games, virtual production, and interactive media. Howeve

safetyarxiv-cs-cv

3D Primitives are a Spatial Language for VLMs

arXiv:2605.12586v1 Announce Type: cross Abstract: Vision-language models (VLMs) exhibit a striking paradox: they can generate executable code that reconstructs a 3D scene from geometric primitives wit

AcquisitionSynthesis: Targeted Data Generation using Acquisition Functions

arXiv:2605.13149v1 Announce Type: cross Abstract: Data quality remains a critical bottleneck in developing capable, competitive models. Researchers have explored many ways to generate top quality samp

researcharxiv-cs-ai

An Efficient Insect-inspired Approach for Visual Point-goal Navigation

arXiv:2601.16806v3 Announce Type: replace Abstract: In this work we develop a novel insect-inspired model for visual point-goal navigation. This combines abstracted models of two insect brain structur

Controllable Quantum Memory Capacity in Quantum Reservoir Networks with Tunable partial-SWAPs

arXiv:2605.12713v1 Announce Type: cross Abstract: In the field of quantum reservoir computing (QRC), many different computational models and architectures have been proposed. From these models, we ide

Agents

Multi-Dimensional Behavioral Evaluation of Agentic Stock Prediction Systems Using Large Language Model Judges with Closed-Loop Reinforcement Learning Feedback

arXiv:2605.05739v2 Announce Type: replace-cross Abstract: Forecast evaluation in finance has relied on aggregate accuracy metrics and predictive-accuracy tests built on point-forecast errors. These in

agentsarxiv-cs-ai

Tutorials

OceanCBM: A Concept Bottleneck Model for Mechanistic Interpretability in Ocean Forecasting

arXiv:2605.12639v1 Announce Type: new Abstract: Extreme ocean phenomena are challenging not only to predict but to diagnose, as accurate forecasts alone do not reveal the underlying physical drivers.

tutorialsarxiv-cs-lg

Separating Shortcut Transition from Cross-Family OOD Failure in a Minimal Model

arXiv:2605.12945v1 Announce Type: new Abstract: Shortcut features are often invoked to explain out-of-distribution (OOD) failure, but training correlation, learned shortcut use, and test-time failure

researcharxiv-cs-lg

What is Learnable in Valiant's Theory of the Learnable?

arXiv:2605.13840v1 Announce Type: cross Abstract: Valiant's 1984 paper is widely credited with introducing the PAC learning model, but it, in fact, introduced a different model: unlike PAC learning, t

researcharxiv-cs-lg

AcuityBench: Evaluating Clinical Acuity Identification and Uncertainty Alignment

arXiv:2605.11398v1 Announce Type: cross Abstract: We introduce AcuityBench, a benchmark for evaluating whether language models identify the appropriate urgency of care from user medical presentations.

Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation

arXiv:2510.04265v4 Announce Type: replace-cross Abstract: Pass@k is widely used to report the reasoning performance of LLMs, but it often produces unstable and potentially misleading rankings, especia

researcharxiv-cs-cl

Local Ai

EchoTracker2: Enhancing Myocardial Point Tracking by Modeling Local Motion

arXiv:2605.12140v1 Announce Type: new Abstract: Myocardial point tracking (MPT) has recently emerged as a promising direction for motion estimation in echocardiography, driven by advances in general-p

local-aiarxiv-cs-cv

Extending Kernel Trick to Influence Functions

arXiv:2605.11239v1 Announce Type: new Abstract: In this paper, we present a dual representation of the influence functions, whose computational complexity scales with dataset size rather than model si

Focusable Monocular Depth Estimation

model-releasesarxiv-cs-cv

arXiv:2605.11756v1 Announce Type: new Abstract: Monocular depth foundation models generalize well across scenes, yet they are typically optimized with uniform pixel-wise objectives that do not disting

Learning, Fast and Slow: Towards LLMs That Adapt Continually

arXiv:2605.12484v1 Announce Type: new Abstract: Large language models (LLMs) are trained for downstream tasks by updating their parameters (e.g., via RL). However, updating parameters forces them to a

Three Regimes of Context-Parametric Conflict: A Predictive Framework and Empirical Validation

arXiv:2605.11574v1 Announce Type: new Abstract: The literature on how large language models handle conflict between their training knowledge and a contradicting document presents a persistent empirica

Applications

Why Conclusions Diverge from the Same Observations: Formalizing World-Model Non-Identifiability via an Inference

arXiv:2605.12255v1 Announce Type: cross Abstract: When people share the same documents and observations yet reach different conclusions, the disagreement often shifts into a judgment that the other pa

applicationsarxiv-cs-lg

Behavioral Determinants of Deployed AI Agents in Social Networks: A Multi-Factor Study of Personality, Model, and Guardrail Specification

arXiv:2605.08463v1 Announce Type: new Abstract: Autonomous AI agents are increasingly deployed in open social environments, yet the relationship between their configuration specifications and their em

Beyond Accuracy: Evaluating Strategy Diversity in LLM Mathematical Reasoning

arXiv:2605.09292v1 Announce Type: new Abstract: Large language models now achieve high final-answer accuracy on mathematical reasoning benchmarks, but accuracy alone does not capture reasoning flexibi

CREATE: Testing LLMs for Associative Creativity

arXiv:2603.09970v2 Announce Type: replace Abstract: A key component of creativity is associative reasoning: the ability to draw novel yet meaningful connections between concepts. We introduce CREATE,

CUDAHercules: Benchmarking Hardware-Aware Expert-level CUDA Optimization for LLMs

arXiv:2605.08467v1 Announce Type: new Abstract: Large language models show promise for automated CUDA programming, however even the strongest coding models (e.g., Claude-Opus-4.6) may still fall short

Data Mixing Can Induce Phase Transitions in Knowledge Acquisition

arXiv:2505.18091v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are typically trained on data mixtures: most data come from web scrapes, while a small portion is curated from hi

researcharxiv-cs-ai

DeepTumorVQA: A Hierarchical 3D CT Benchmark for Stage-Wise Evaluation of Medical VLMs and Tool-Augmented Agents

arXiv:2605.09679v1 Announce Type: cross Abstract: Medical vision-language models (VLMs) and AI agents have made significant progress in learning to analyze and reason about clinical images. However, e