Search: “models”

arXiv:2608.14692v1 Announce Type: cross Abstract: Personalized, generative AI systems increasingly adapt their behavior to individual users over time, fundamentally changing model behavior. While exis

tutorialsarxiv-cs-ai

Research

Implementation of a Metacognition Framework for Self-Awareness and Self-Regulation in Ensembles of LLMs

DGX agent

arXiv:2608.15400v1 Announce Type: new Abstract: Large Language Models (LLMs) are notorious for struggling with assessing their own uncertainty, detecting knowledge conflicts, or recognizing when probl

researcharxiv-cs-ai

18 Aug 2026

Iterative Self-Learning for Expressive Text-to-Speech Synthesis

arXiv:2608.15910v1 Announce Type: cross Abstract: Expressive text-to-speech (TTS) systems that use explicit conditioning labels provide direct and interpretable control over expressive attributes, in

researcharxiv-cs-cl

JoLT: Joint Latent Trajectories for Context-Guided High-Resolution Tiled Generation

arXiv:2608.15395v1 Announce Type: new Abstract: Although text-to-image generative models produce impressive results, they struggle to generate densely detailed, high-resolution (HR) images. Current li

KHiM-Mamba: Injecting Pathology Knowledge into Mamba via Hidden-State Modulation for Whole Slide Image Analysis

arXiv:2608.14757v1 Announce Type: cross Abstract: Whole slide image analysis is commonly formulated as multiple instance learning (MIL), where instance features are contextually updated and aggregated

Language-Guided Generation for Personalized Inspection Planning

arXiv:2506.02917v2 Announce Type: replace Abstract: We propose a training-free, Vision-Language Model (VLM)-guided approach for efficiently generating trajectories to facilitate target inspection plan

applicationsarxiv-cs-ro

Le Critique: Privileged Value Functions for LLM Reinforcement Learning

arXiv:2608.16739v1 Announce Type: new Abstract: Reinforcement learning algorithms for Large Language Models (LLMs) are largely distinguished by their variance reduction strategy. Group-relative method

Leveraging Machine Unlearning for Cost-Efficient Preference Alignment

arXiv:2504.06659v2 Announce Type: replace-cross Abstract: Despite advances in Preference Alignment (PA) for Large Language Models (LLMs), mainstream methods like reinforcement learning with human feed

LUNG-KGMM: Knowledge-Guided Multimodal Learning for Lung Cancer Incidence Prediction

arXiv:2608.14657v1 Announce Type: cross Abstract: Early identification of lung cancer risk is critical for timely intervention, yet existing prediction models are limited by their reliance on single d

applicationsarxiv-cs-cv

Mixture of experts architectures for machine learning interatomic potentials

arXiv:2603.07977v3 Announce Type: replace-cross Abstract: Machine Learning Interatomic Potentials (MLIPs) enable accurate large-scale atomistic simulations, yet improving their expressive capacity eff

researcharxiv-cs-lg

Multi-Bin Batching for Increasing LLM Inference Throughput

arXiv:2412.04504v2 Announce Type: replace Abstract: As large language models (LLMs) grow in popularity for their diverse capabilities, improving the efficiency of their inference systems has become in

safetyarxiv-cs-cl

MUSE: An Interactive Meta-Agent for Understanding and Steering LLM-powered Data Science Systems

arXiv:2608.16181v1 Announce Type: cross Abstract: Recent advances in large language models have enabled a new class of agentic data science systems that allow users to complete complex data science wo

Near-Optimal Sample Complexity Bounds for Constrained Average-Reward MDPs

arXiv:2509.16586v2 Announce Type: replace Abstract: Recent advances have significantly improved our understanding of the sample complexity of learning in average-reward Markov decision processes (AMDP

Neoclouds reshape traditional architectures to meet AI demands

The rise of neoclouds — AI-first cloud providers — is transforming enterprise infrastructure. The unique requirements of AI models and agents have paved the way for new collaborations between hardware

applicationssiliconangle

nobody takes Altman at his word anymore – nor should they.

nobody takes Altman at his word anymore – nor should they. Altman's claim that OpenAI is slowing their frontier model training in order to focus on safety doesn't pass the sniff test.👃 For several yea

safetygary-marcus--x

Not All Neighbors Matter: Understanding the Impact of Graph Sparsification on GNN Pipelines

arXiv:2603.06952v2 Announce Type: replace Abstract: As graphs scale to billions of nodes and edges, graph Machine Learning workloads are constrained by the cost of multi-hop traversals over exponentia

researcharxiv-cs-lg

OceanLight: Efficient Global Ocean Forecasting via Geometry-Adaptive Unstructured Mesh Representation

arXiv:2608.16070v1 Announce Type: cross Abstract: Reliable global ocean forecasting is critical for climate monitoring, marine navigation, and extreme event early warning. Physics-based ocean forecast

local-aiarxiv-cs-ai

p-Spin Glass Network Efficient Single-Batch Continual Learning

arXiv:2608.14774v1 Announce Type: new Abstract: Modern sequence models heavily rely on massive memory footprints and large-batch stochastic optimization, barriers that restrict sample efficiency and c

local-aiarxiv-cs-lg

PACE: Phase-Progress-Aware Credit for Long-Horizon Embodied Manipulation

arXiv:2608.15026v1 Announce Type: new Abstract: Post-training of vision-language-action (VLA) models typically relies on expert demonstrations and policy interaction trajectories. However, in long-hor

local-aiarxiv-cs-ro

Pallas: A Proactive KV Cache Migration Framework for LLM Inference in AI-RAN

arXiv:2608.16477v1 Announce Type: new Abstract: AI-RAN brings large language model (LLM) serving close to mobile users, but cellular handover can separate an active request from its inference state: t

local-aiarxiv-cs-lg

ParaTempo: Efficient Parallel Reasoning via Temporal Confidence

arXiv:2608.16425v1 Announce Type: new Abstract: Parallel reasoning improves the accuracy and robustness of large reasoning models by exploring multiple solution paths, but its computational cost grows

Physics of Agents: Statistical Mechanics Predicts Collective Behavior of AI Agents

arXiv:2608.16578v1 Announce Type: new Abstract: AI agents increasingly operate as part of interacting systems rather than in isolation. As agents exchange information and jointly make decisions, their

PickStyle: Video-to-Video Style Transfer with Context-Style Adapters

arXiv:2510.07546v2 Announce Type: replace Abstract: We address the task of video style transfer with diffusion models, where the goal is to preserve the context of an input video while rendering it in

safetyarxiv-cs-cv

PixelControl: Fine-Grained Condition Fidelity in Text-to-Image Diffusion

arXiv:2608.15705v1 Announce Type: new Abstract: Controllable text-to-image diffusion models can often follow the global layout of spatial conditions, yet still violate fine-grained structures such as

POI Recommendation with LLM-Augmented Multi-Graph Learning and Contrastive Alignment

arXiv:2608.16407v1 Announce Type: cross Abstract: Point-of-interest (POI) recommendation models based on graph neural networks achieve strong performance by propagating collaborative signals over user

PoseAdapter: Dual-Stream 2.5D Controllable Image Generation for Complex Multi-Object Scenes

arXiv:2608.15583v1 Announce Type: new Abstract: While Text-to-Image (T2I) diffusion models have achieved remarkable success, precise spatial and orientational control in multi-object scenes remains a

Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems

arXiv:2608.14667v1 Announce Type: new Abstract: Large language model-based agents are increasingly deployed as collaborators in scientific discovery yet most current work focuses on the autonomous cap

Probabilistic Circuits as Reasoning Machines in Artificial Intelligence (Part I)

arXiv:2608.16565v1 Announce Type: new Abstract: This cumulative habilitation thesis studies probabilistic circuits (PCs) as a powerful and tractable framework for reasoning and learning under uncertai

Probability-Preserving Transformer for the Time-Dependent Schrodinger Equation

arXiv:2608.15112v1 Announce Type: new Abstract: Solving the time-dependent Schrodinger equation (TDSE) via traditional numerical methods is computationally intensive. Transformer models offer a compel

researcharxiv-cs-lg

Prototype-Rectified Iterative Self-supervised Manifold Denoising under Severe Acoustic Shift

arXiv:2608.15037v1 Announce Type: cross Abstract: Audio-Text Foundation Models (ATMs) fail catastrophically under severe acoustic noise, yet existing adaptation strategies either rely on gradient-base

QUMem: Personalized Memory for Query-Conditioned User-State Inference in LLM Agents

arXiv:2608.16168v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly use external memory systems to support personalization by drawing on long and evolving interaction hist

Random Quadratic Form with random forcing: Metastable synchronization by noise

arXiv:2608.16664v1 Announce Type: cross Abstract: We study the Random Quadratic Form (RQF) on a sphere in the presence of random Brownian forcing. We show that the forcing does not effectively change

Rethinking Reverse KL as Adaptive Entropy Distillation

arXiv:2608.14685v1 Announce Type: new Abstract: Knowledge distillation (KD) is widely used to transfer the capabilities of large language models (LLMs) to smaller students, but existing objectives oft

Step-Level On-Policy Distillation: Interpolating Between On-Policy Distillation and Supervised Fine-Tuning

arXiv:2608.16333v1 Announce Type: cross Abstract: On-policy distillation (OPD) aligns a student model with a teacher's logit distribution on student-generated trajectories. This approach has achieved

Synchronized Logit Steering: Real-world Steganography

arXiv:2608.14697v1 Announce Type: new Abstract: Steganography in large language models offers a way to embed hidden messages within natural-sounding text. Existing token and logit-level methods typica

applicationsarxiv-cs-ai

SysEvolve: An AI-native, safe, autonomous adversarial attack-defense co-evolutionary system

arXiv:2608.15012v1 Announce Type: cross Abstract: The rapid advancement of large language models (LLMs) has created a growing asymmetry in cybersecurity, where attack accelerates toward autonomous exe

T-LLM Compiler: Trusted LLM-based Code Optimization and Verification Framework

arXiv:2608.14953v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have opened opportunities to apply high-level code transformations to the field of code optimization, an

Tail-Aware Top-k On-Policy Distillation

arXiv:2608.14728v1 Announce Type: cross Abstract: On-policy distillation (OPD) has emerged as an effective paradigm for transferring knowledge between language models, where a student is trained to al

Test-Time Instance Selection for Improved Whole Slide Image Analysis

arXiv:2608.14759v1 Announce Type: cross Abstract: Whole Slide Image (WSI) analysis has been widely studied for cancer diagnosis. Conventionally, a gigapixel WSI is divided into small patches and proce

The Many Senses of Visual Similarity: A Text-Prompted Image Perceptual Metric

arXiv:2607.18237v2 Announce Type: replace Abstract: Human visual similarity judgments are context-dependent. For example, two images may be similar in shape but distinct in color. Existing perceptual

The Note-Chord-Voice Framework: Structured Source Separation and Causal Inference for EV Charging Data

arXiv:2608.14756v1 Announce Type: cross Abstract: Real-world EV charging data exhibit three interlocking pathologies: hardware fragmentation (network timeouts and billing resets split sessions), physi

The Null Token Knows: Reducing Message-Free Hallucination in ASR and NMT

arXiv:2608.15940v1 Announce Type: new Abstract: Modern encoder-decoder systems can produce fluent text even when their input contains no recoverable message. We study this failure in ASR and NMT throu

researcharxiv-cs-cl

Think Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLMs: A Case Study of UN Regulation No. 152

arXiv:2608.16394v1 Announce Type: new Abstract: Generating regulation-compliant test scenarios is essential for validating safety-critical automotive systems, yet Large Language Models (LLMs) struggle

Tutorials

Train Yourself as an LLM: Exploring Effects of AI Literacy on Persuasion via Role-playing LLM Training

arXiv:2604.02637v2 Announce Type: replace Abstract: As large language models (LLMs) become increasingly persuasive, there is concern that people's opinions and decisions may be influenced across vario

tutorialsarxiv-cs-cl

Training-Free Multi-Concept Image Editing

arXiv:2602.20839v3 Announce Type: replace Abstract: Training-free image editing with diffusion models is highly desirable yet is complex and remains a significant challenge. While recent optimisation-

TRCA: Transition-wise Rubric Credit Assignment for Long-horizon LLM Agents

arXiv:2608.16156v1 Announce Type: new Abstract: Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, making fine-grained credit assignment across multi

Turning spectra into images improves plant trait retrieval with 2D-CNNs

arXiv:2608.16661v1 Announce Type: new Abstract: Hyperspectral reflectance spectroscopy enables non-destructive estimation of plant functional traits, yet current deep learning approaches process spect

Understanding Cognition-Induced Risks in Agentic AI Systems

arXiv:2608.15304v1 Announce Type: new Abstract: Frontier agentic systems powered by large language models (LLMs) exhibit human-like patterns of cognition. As these systems become deeply integrated acr