Search: “safety”

Three Necessary Principles for Self-Supervised Visual Representation Learning

arXiv:2608.08309v1 Announce Type: cross Abstract: We argue that learning visual representations without labels requires a training signal jointly complete across three non-overlapping objectives: sema

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Topographic Constraints Shape Brain-Like Component Structure in Auditory Models

DGX agent

arXiv:2509.24039v2 Announce Type: replace-cross Abstract: If topography is a fundamental feature of the brain, it should influence both how neurons are arranged in space (i.e. explain brain maps) and

safetyarxiv-cs-ai

11 Aug 2026

Towards Expressive and Faithful Audio-to-Image Generation: A Unified Multimodal Dataset and Synthesis Framework

arXiv:2608.09529v1 Announce Type: new Abstract: As an important subfield of cross-modal generation, synthesizing static visual content in the form of images from audio, namely audio-to-image (A2I) gen

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation

arXiv:2608.09052v1 Announce Type: cross Abstract: Semi-supervised adaptation of vision foundation models (VFMs) commonly freezes the pretrained backbone and updates lightweight modules such as LoRA. H

Unaccountable Delegation, Fading Skills: Mapping the Risks of Workplace AI Agents

arXiv:2608.08601v1 Announce Type: new Abstract: To anticipate socio-technical risks from AI agents, organizations need taxonomies to classify them. However, existing AI risk taxonomies focus on broad

Uncertainty-Aware Variational Reward Factorization via Probabilistic Preference Bases for LLM Personalization

arXiv:2604.00997v2 Announce Type: replace Abstract: Reward factorization personalizes large language models (LLMs) by decomposing rewards into shared basis functions and user-specific weights. Yet, ex

safetyarxiv-cs-cl

Understanding Reasoning from Pretraining to Post-Training

arXiv:2607.16097v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become central to improving large language models (LLMs) on complex reasoning tasks, yet RL post-training is l

Unimodality-Promoting Regularized Learning for Ordinal Regression

arXiv:2608.08359v1 Announce Type: new Abstract: Ordinal regression, also called ordinal classification, is classification of ordinal data, in which the underlying target variable is categorical and co

VADER: Adaptive Debiasing for Hallucination Mitigation in Video Large Language Models

arXiv:2608.08622v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have demonstrated strong performance in open-ended video understanding, yet they remain prone to fluent responses u

VANE: Reliable Test-Time Training for Vision-Language-Action Models via Future Visual Representation Prediction

arXiv:2608.09448v1 Announce Type: cross Abstract: Test-time training (TTT) offers a lightweight way to adapt vision--language--action (VLA) policies from unlabeled deployment streams, but it remains d

Vid2WAM: Distilling Video Diffusion Priors into World Action Models

arXiv:2608.08558v1 Announce Type: new Abstract: World Action Models (WAMs) improve robot policy learning by jointly modeling future visual dynamics and actions. However, their scalability and generali

safetyarxiv-cs-ro

VoxZip: Semantic-Anchored Temporal KV Cache Compression for Long-Context Audio Inference

arXiv:2608.08569v1 Announce Type: new Abstract: Recent advancements in Speech Large Language Models have demonstrated remarkable capabilities in understanding complex audio tasks. Despite this progres

WDL-OPD: Weak-Driven On-Policy Distillation via Mixture-Constrained Co-Training

arXiv:2608.09447v1 Announce Type: cross Abstract: On-policy distillation (OPD) aligns a student with a teacher on trajectories sampled from the student itself, reducing the train-test state mismatch o

When Does Trace-Driven Evaluation Mislead MoE Expert Caching? Replay Semantics, Workload Contamination, and Operating Regimes

arXiv:2608.07911v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models have outgrown accelerator memory, and offloading expert weights to host memory is now standard. This makes expert cache

Who Built This Model? Tracing LLM Lineage via Spectral Fingerprints in Weight Space

arXiv:2608.07786v1 Announce Type: new Abstract: Open-weight large language models (LLMs) are increasingly developed through complex, multi-stage pipelines, leading to intricate lineage relationships t

World Tokens: Enhancing Embodied Policies with Training-Time World Modeling

arXiv:2608.09730v1 Announce Type: new Abstract: Vision-language-action (VLA) models are a widely adopted paradigm for embodied policies. They excel at efficient closed-loop control but do not explicit

A Practical Evaluation Method for Long-Form Simultaneous Speech-to-Speech Translation

arXiv:2606.15059v2 Announce Type: replace Abstract: Simultaneous speech-to-speech translation (SimulS2ST) enables real-time cross-lingual communication, but existing evaluation has focused largely on

safetyarxiv-cs-cl

An AI4AI Framework for Visual Token Pruning

arXiv:2608.07193v1 Announce Type: cross Abstract: Visual-token pruning can substantially reduce the inference cost of multimodal large language models (MLLMs), yet existing methods largely rely on fix

AutoIntervene: Calibrated Intervention for Action-Chunking Imitation Learning Policies

arXiv:2608.07065v1 Announce Type: cross Abstract: Action-chunking visuomotor policies learn from demonstrations and improve temporal consistency by predicting short action sequences rather than single

Automated item evaluation: Predicting item acceptance and rejection using LLM-generated critiques

arXiv:2608.06609v1 Announce Type: new Abstract: Automated item evaluation (AIE) refers to the use of computational methods to assess item quality without requiring manual expert review or field testin

Automated Terminal-to-Housing Assembly System for Flat Ribbon Cable Harness

arXiv:2608.06996v1 Announce Type: new Abstract: This paper presents a sensor-minimal automated assembly system for bidirectional single-row flat ribbon cable harnesses (FRCHs). Unlike conventional peg

safetyarxiv-cs-ro

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration

arXiv:2608.07419v1 Announce Type: new Abstract: Preference alignment often makes large language models (LLMs) overconfident and poorly calibrated. Traditional post-hoc temperature scaling is inherentl

Bootstrap-Conditioned Action Selection with Tabular Foundation Models

arXiv:2608.06559v1 Announce Type: new Abstract: Contextual bandits offer a natural framework for sample-efficient personalization, but practical deployment remains difficult under sparse, biased inter

Bypassing Krum: Selection-Aware Backdoor Attacks in Federated Learning

arXiv:2608.06637v1 Announce Type: cross Abstract: Robust aggregation methods are widely used in federated learning to mitigate the impact of adversarial client behavior. Distance-based aggregation rul

Calibrating WEAT Against Anisotropy: ZCA Whitening as a Geometric Pre-Processing Step for Embedding Association Tests

arXiv:2608.06908v1 Announce Type: cross Abstract: We propose Zero-phase Component Analysis (ZCA) whitening as a geometric pre-processing step for the Word Embedding Association Test (WEAT). WEAT is a

Cascade: Exploiting SLO-Aware latency budget for fair and high goodput LLM inference serving

arXiv:2608.06557v1 Announce Type: cross Abstract: The reasoning and agentic capabilities of large language models have expanded the range of applications they support, from short interactive exchanges

CEDAR: Agent-Orchestrated Tree Search for Goal-Directed Optimization of Complex Systems

arXiv:2608.06871v1 Announce Type: new Abstract: Complex systems, core objects of study in artificial life, model diverse phenomena through nonlinear, feedback-driven interactions that produce emergent

CHIME: A Case for Efficient Long-Context Attention-FC Disaggregated Inference with DIMM-PIM

arXiv:2504.17584v2 Announce Type: replace-cross Abstract: Attention-FC Disaggregated (AFD) LLM inference systems offload memory-bound Attention operations to memory-rich accelerators (e.g., CPUs, HBM-

Confidence Estimation for Financial Vision-Language Models in Chart and Document Understanding

arXiv:2608.06532v1 Announce Type: new Abstract: LVLMs are increasingly used to read financial charts, tables, and documents, where a single misread figure can move a decision and the most authoritativ

safetyarxiv-cs-cl

Counterfactual Shapley Credit Assignment

arXiv:2607.16999v2 Announce Type: replace-cross Abstract: The Credit Assignment Problem (CAP) is fundamental to developing efficient and explainable Reinforcement Learning (RL) agents. Existing framew

CreativeInstruct: Scalably Teaching LLMs to Balance Quality, Creativity, and Diversity

arXiv:2608.07460v1 Announce Type: cross Abstract: While post-training improves the capabilities of large language models (LLMs), it generally lowers their output diversity and creativity, negatively i

CrystalGRPO: Target-Aligned and Coverage-Preserving Reinforcement Learning for Flow-Based Crystal Structure Prediction

arXiv:2608.06582v1 Announce Type: new Abstract: Flow-based generative models can efficiently produce candidate structures for crystal structure prediction (CSP), but their pretrained objectives do not

Density-Functional Excited-State Gradients and Nonadiabatic Couplings on a Consumer GPU from a Contraction-DAG

arXiv:2608.06536v1 Announce Type: cross Abstract: Nonadiabatic dynamics needs an excited-state gradient and an interstate nonadiabatic coupling matrix element (NACME) at every nuclear geometry, and a

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training

arXiv:2608.07147v1 Announce Type: new Abstract: Reinforcement learning with Verifiable Reward (RLVR) has emerged as a powerful paradigm for training coding agents, where the execution feedback from co

Do 3D Medical Foundation Models See Through MRI Artifacts? A Controlled Study of Representation Robustness

arXiv:2608.06613v1 Announce Type: cross Abstract: Self-supervised 3D medical foundation models are increasingly used as general-purpose feature extractors, yet their sensitivity to MRI artifacts remai

Does Splitting a Triage Decision Across Agents Hide Bias or Help Catch It? A Multi-Agent Simulation Study of LLM-Based Resource Allocation Under Audit Capacity Constraints

arXiv:2608.06949v1 Announce Type: new Abstract: Prior benchmarking work has shown that a single large language model (LLM), forced to make life-or-death resource-allocation decisions, exhibits measura

Dual-Space Modality Consistency Learning for Universal Cross-Modal Re-Identification

arXiv:2608.06943v1 Announce Type: new Abstract: Cross-modal Re-Identification (ReID) aims to retrieve the same identity across heterogeneous imaging modalities and has been widely studied in visible-i

Explore or Converge? Stage-Guided Per-Step Optimization for Diffusion Models

arXiv:2608.06768v1 Announce Type: new Abstract: Diffusion models have strong generative capabilities. However, their maximum likelihood training objective only focuses on reconstructing the data distr

From Optimal Actions to World Models: Identifiability of Transition Kernels in Discounted MDPs

arXiv:2608.07301v1 Announce Type: new Abstract: We study what can be recovered about the transition probabilities of a Markov decision process from optimal actions alone. This is closely related to th

Gated-BEPO: Confidence-Gated Bellman Credit Assignment for Large Language Model Agents

arXiv:2608.06861v1 Announce Type: new Abstract: Training large language model agents in long-horizon environments requires assigning credit from sparse terminal outcomes to individual actions. Existin

GOPI: Generation-Oriented 3D Pose Inference for Furniture Insertion from Single-View RGB-D Indoor Scenes

arXiv:2608.06836v1 Announce Type: new Abstract: We study the problem of inserting new furniture into indoor scene images. Under masked single-view 2D image-plane conditioning, however, the physical sc

Grad-ECLIP: Gradient-based Visual and Textual Explanations for CLIP

arXiv:2502.18816v3 Announce Type: replace Abstract: Significant progress has been achieved on the improvement and downstream usages of the Contrastive Language-Image Pre-training (CLIP) vision-languag

Graph Machine: Exploring Edge Mechanisms as an Inductive Bias

arXiv:2608.06834v1 Announce Type: new Abstract: Transformers provide a powerful architecture for global content-based matching, but reasoning problems may benefit from a stronger inductive bias toward

High-dimensional ridgeless least squares interpolation under spiked covariance structures

arXiv:2608.07281v1 Announce Type: cross Abstract: This paper investigates the asymptotic behavior of the out-of-sample prediction risk of the high-dimensional ridgeless least-squares estimator when th

How Much, Then Where: Credit-Conserving Action-to-Token Allocation for Multi-Turn Agent Reinforcement Learning

arXiv:2608.07118v1 Announce Type: new Abstract: Credit assignment in multi-turn agent reinforcement learning operates at two levels: assigning trajectory-level credit to actions and distributing each

Identity as Presence: Towards Appearance and Voice Personalized Joint Audio-Video Generation

arXiv:2603.17889v4 Announce Type: replace Abstract: Recent advances in video synthesis have enabled realistic integration of real individuals, driving demand for identity-aware generation. While emerg

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons

arXiv:2506.03392v2 Announce Type: replace Abstract: We propose a new ternary spiking neuron model to improve the representation capacity of binary spiking neurons in deep Q-learning. Although a ternar

Interpretable reinforcement learning with decision-tree pruning

arXiv:2608.07151v1 Announce Type: cross Abstract: Reinforcement learning policies are difficult to inspect, but interpreting them is a prerequisite for trustworthiness. Converting a trained policy int