Search: “models”

On the Brittleness of Maximum Likelihood Estimation for Gaussian Process Hyperparameter Optimization

arXiv:2608.13793v1 Announce Type: cross Abstract: Machine learning (ML) has become an indispensable part of modern engineering design workflows. A crucial step in training an ML model is the selection

researcharxiv-cs-lg

17 Aug 2026

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Safety

Responsiveness Verification: Will Predictions Change? How Much? How Often?

DGX agent

arXiv:2507.02169v2 Announce Type: replace Abstract: Machine learning models are often used in applications where their inputs change due to routine interactions, strategic manipulation, or noise. In s

safetyarxiv-cs-lg

17 Aug 2026

Wrong but Useful: Trajectory Value Beyond Answer Correctness in Multi-Agent Messages

arXiv:2608.14375v1 Announce Type: new Abstract: Multi-agent reasoning systems often use agreement, confidence, or automated scores to decide which messages should shape a final answer. Such filtering

17 Aug 2026

A Generative Approach for Improving Multi-Label Defect Classification in Photovoltaic Modules

model-releasesarxiv-cs-cv

arXiv:2608.12725v1 Announce Type: new Abstract: This paper addresses the challenge of multi-label defect classification in electroluminescence (EL) images of photovoltaic (PV) cells. Training models o

Are You Sure You're Sure? On the Impact of Instruction Tuning on Confidence and Lexical Diversity

arXiv:2608.13430v1 Announce Type: cross Abstract: Instruction-tuned language models achieve strong performance across a range of generation tasks, but have also recently been shown to exhibit verbaliz

AutoDesign: Meta-Harness Optimization for Long-Horizon Agentic Design

arXiv:2608.13560v1 Announce Type: cross Abstract: Transforming multimodal sources into condensed and structured media outputs can be fundamentally conceptualized as a long-horizon agentic process cent

CoMedBench: A Multi-Source Benchmark of Synthetic Medical Data Fidelity and Downstream Utility

arXiv:2608.12805v1 Announce Type: new Abstract: Access to clinical data is essential for developing reliable healthcare machine learning systems, but direct use of electronic health records is constra

Dead text or binding clause? Measuring and restoring constraint influence in black-box LLM dialogues

arXiv:2608.12599v1 Announce Type: new Abstract: Multi-turn dialogues let users revoke constraints as easily as impose them, but revocation does not reliably take effect: models keep enacting withdrawn

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding

arXiv:2608.13072v1 Announce Type: new Abstract: Electroencephalography (EEG) decoding models often generalize poorly across datasets and subjects due to domain shifts in acquisition protocols and indi

Local Ai

From Local Mismatch to Global Impact: Optimizing Cache Reuse Policy for Efficient Diffusion

arXiv:2608.13043v1 Announce Type: new Abstract: Diffusion models have achieved dominant performance in visual generation but suffer from substantial inference overhead. While cache-based acceleration

local-aiarxiv-cs-ai

Geometric and Behavioral Stratification in Transformer Residual Streams

arXiv:2608.12447v1 Announce Type: cross Abstract: Trained transformer models develop privileged bases: coordinate axes whose statistics differ from the rest of the residual stream. But what kind of di

researcharxiv-cs-cl

Applications

Incremental Evaluation and Training in Relational Deep Learning

arXiv:2608.13023v1 Announce Type: new Abstract: Relational Deep Learning (RDL) models multi-tabular databases as temporal heterogeneous graphs to enable end-to-end representation learning. However, pr

applicationsarxiv-cs-lg

Safety

Jagged Judges: Epistemic Stability Under Silence, Pressure, and Persistence

arXiv:2608.12645v1 Announce Type: new Abstract: LLM judges have become central infrastructure for model evaluations, online grading, and reward modeling. Judges are typically validated by accuracy on

safetyarxiv-cs-ai

Keep, Customize, or Exit: Default Design and Token Pricing in LLM Reasoning Services

arXiv:2608.13315v1 Announce Type: cross Abstract: We study a large language model (LLM) service in which a provider chooses a per-token price and a default reasoning-token allocation, while a user may

Masked diffusion LLMs can use EoS tokens for hidden reasoning

arXiv:2603.05197v2 Announce Type: replace Abstract: Diffusion LLMs have been proposed as an alternative to autoregressive LLMs. Curiously, they are especially capable if the generation length, i.e., t

researcharxiv-cs-cl

Tutorials

New Terms, New Toxicity: Consensus-based Chinese Neologism Toxicity Detection via Search-Augmented LLMs

arXiv:2608.12361v1 Announce Type: new Abstract: Neologisms, emerging terms in meaning or form, can serve as new vehicles for toxic expression, like 'country girl' as a stigmatizing label targeting fem

tutorialsarxiv-cs-cl

Predicting consumer-technology ownership without a diffusion history

arXiv:2608.12344v1 Announce Type: new Abstract: We test whether the perceived attributes of a consumer technology predict how widely it is owned. In a 2022 Prolific survey of US adults (n = 678), resp

Reasoning for Social Audio-Visual Question Answering: Where Do We Stand?

model-releasesarxiv-cs-cv

arXiv:2608.13239v1 Announce Type: new Abstract: Training Multimodal Large Language Models for audio-visual social understanding is a crucial step toward embodied social intelligence. Chain-of-thought

Reliability-Aware Sexism Detection: Combining DPO with Annotator Agreement and Token-Level Confidence Scoring

arXiv:2608.12330v1 Announce Type: new Abstract: The detection of online sexism remains an open problem. Sexism detection is inherently subjective, yet most existing systems reduce multi-annotator labe

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills

model-releasesarxiv-cs-ro

arXiv:2608.12416v1 Announce Type: new Abstract: Achieving generalizable robotic manipulation remains a central challenge in embodied intelligence. Despite rapid advances in model architectures and lea

TEMPO: Makespan-Aware Expert-Parallel Load Balancing Across Memory- and Compute-Bound Regimes

arXiv:2608.13057v1 Announce Type: cross Abstract: In expert-parallel (EP) MoE serving, every layer synchronizes at the slowest GPU. Dispatchers balance token counts (EPLB, LPLB, UltraEP) or activated-

Toward a Gricean Retreat: Probing LLMs for Knowledge Boundaries and Referent Specificity

arXiv:2608.13484v1 Announce Type: cross Abstract: When asked about entities outside their knowledge boundary, LLMs routinely fabricate plausible-sounding details rather than backing off to safer, more

Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization

arXiv:2608.12953v1 Announce Type: new Abstract: Structured pruning is a promising approach for compressing large language models (LLMs), yet existing methods rely heavily on greedy heuristics that pro

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation

arXiv:2608.13337v1 Announce Type: new Abstract: Sparse autoencoders are meant to name the things a language model computes, and the usual way to check that a latent matters is to switch it off and see

A Neighborhood Attention Transformer Network for Enhanced 3D Segmentation of the Left Anterior Descending Artery

arXiv:2608.12274v1 Announce Type: cross Abstract: Background: Accurate segmentation of the Left Anterior Descending (LAD) artery in 3D free-breathing, non-contrast CT is critical for cardiac dose spar

Advancing MLLM-based UAV Image Understanding and Reasoning: A Benchmark and a Training-Free Multi-Agent System

arXiv:2608.11738v1 Announce Type: cross Abstract: Multimodal Large Language Model (MLLM)-based UAV aerial image understanding and reasoning is essential for aerial intelligence yet poses distinct chal

Agent Safety Should Be a Runtime Contract

arXiv:2608.11274v1 Announce Type: cross Abstract: The dominant paradigm treats AI safety as a property to be instilled during model training via RLHF, DPO, or Constitutional AI. We argue this is struc

APEX: Adaptive Expert Prefetching for Memory-Efficient Edge MoE Inference

arXiv:2608.11688v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models are attractive for edge deployment because they provide high model capacity while activating only a small subset of pa

Benchmarking Cyberattack Detection in Electric Vehicle Charging Infrastructure with Benign User Updates

arXiv:2608.11286v1 Announce Type: cross Abstract: Cyberattack detection in electric vehicle charging infrastructure is complicated by legitimate post-activation revisions to requested energy and depar

Local Ai

Diffusion-Based Data-Driven Assortment Optimization

arXiv:2608.11419v1 Announce Type: new Abstract: Assortment optimization is a fundamental problem in revenue management, typically addressed using parametric choice models such as the multinomial logit

local-aiarxiv-cs-lg

DREAMS: Density Functional Theory Based Research Engine for Agentic Materials Simulation

arXiv:2507.14267v2 Announce Type: replace Abstract: Large language model (LLM) agents can execute long-horizon scientific workflows, but their numerical outputs are difficult to trust: agents lose con

GeoFlow: Efficient Driving Video Generation via Geometry-Aligned Priors

arXiv:2608.12203v1 Announce Type: new Abstract: Generative models like Diffusion Models and Flow Matching have demonstrated remarkable capabilities in synthesizing high-fidelity driving videos, but ar

researcharxiv-cs-cv

LEMUR: Latent Entropy-aware Multimodal Unlearning via Visual-anchored Reasoning Redirection

arXiv:2608.11691v1 Announce Type: cross Abstract: Reinforcement-learning (RL) post-training equips multimodal large reasoning models (MLRMs) with exploratory chains of thought (CoT), substantially imp

researcharxiv-cs-cl

Lifecycle-Optimal Tokenization: Vocabulary Size as a Deployment-Regime-Dependent Infrastructure Parameter

arXiv:2608.11361v1 Announce Type: cross Abstract: Tokenizer vocabulary size is a foundational design choice in large language model (LLM) infrastructure, yet it is typically fixed at training time bas

Located but Not Releasable: Silent Gate Inversion and Bounded Linear Release

arXiv:2608.11822v1 Announce Type: new Abstract: A growing body of work reports that language models represent task-relevant latent structure that they fail to use. Whether such structure, once located

Applications

Look What the Probes Dragged In! Real-World Chest X-ray Shortcuts in MedCLIP

arXiv:2608.12086v1 Announce Type: new Abstract: Vision-language models, such as contrastive language-image pre-training (CLIP)-based approaches, have reached state-of-the-art (SOTA) results in medical

applicationsarxiv-cs-cv

LoRAQuant: Mixed-Precision Quantization of LoRA to Ultra-Low Bits

arXiv:2510.26690v3 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has become a popular technique for parameter-efficient fine-tuning of large language models (LLMs). In many real-world sc

Safety

Quantifying the Relationship Between Clinical Safety and Environmental Impact in Therapeutic LLMs

arXiv:2608.11830v1 Announce Type: cross Abstract: The deployment of large language models (LLMs) in mental health contexts raises questions about the relationship between clinical safety and environme

safetyarxiv-cs-cl

SoftWater: Class-Aware Rate Allocation for Softmax Quantization

arXiv:2608.12026v1 Announce Type: new Abstract: Post-training quantization pipelines routinely leave the softmax output layer in high precision. Yet in small LLMs with modern vocabularies, the head ho

Benchmarking LLM-Guided Control-Plane Policies for Backend Fault Isolation in HAProxy

arXiv:2608.10532v1 Announce Type: cross Abstract: Static load balancers cannot mitigate a backend that is degraded rather than down: round-robin and least-connections keep routing traffic to a server

ComBodied Agents: a New Paradigm of Human-Centric Agentic AI

arXiv:2608.10915v1 Announce Type: new Abstract: After an older adult misses a medication dose, a software agent can send another reminder and an embodied agent can bring the medication. Yet neither ex

E^3mo-Bench: A Scalable Benchmark for Multimodal Evoked and Expressed Emotion Understanding via Bayesian Pairwise Alignment

model-releasesarxiv-cs-cv

arXiv:2608.10796v1 Announce Type: new Abstract: Understanding both expressed and evoked emotions is critical for multimodal large language models (MLLMs) to achieve comprehensive affect-aware interact

Edge Phoneme Recognition for Children's Speech through Age-Aware Training

arXiv:2608.10206v1 Announce Type: new Abstract: Detecting phonemes from children's speech has historically been difficult due to the scarcity of training data, and unique characteristics of children's

Efficient Reinforcement Learning for Long-Horizon Tool-Use Agentic Tasks

arXiv:2608.10357v1 Announce Type: cross Abstract: Long-horizon tool-using agents must reason over user goals, domain policies, tool calls, simulator state, and delayed verifiable rewards. Reinforcemen

Agents

MT-PingEval: Evaluating Multi-Turn Collaboration with Private Information Games

arXiv:2602.24188v2 Announce Type: replace Abstract: We present a scalable and verifiable methodology for evaluating language models in multi-turn interactions, using a suite of collaborative games tha

agentsarxiv-cs-cl

Local Ai

MultiModal Code-Switching: Interleaving Visual Objects into Language for Explicit Object-Level Alignment

arXiv:2608.11167v1 Announce Type: cross Abstract: Existing Multimodal Large Language Models (MLLMs) predominantly rely on image-text pairs for modality alignment pretraining, mapping global image repr

local-aiarxiv-cs-cl

Order Matters in Retrosynthesis: Structure-aware Generation via Reaction-Center-Guided Discrete Flow Matching

arXiv:2602.13136v2 Announce Type: replace Abstract: Template-free retrosynthesis methods treat the task as black-box sequence generation, limiting learning efficiency, while semi-template approaches r

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference

arXiv:2608.10288v1 Announce Type: cross Abstract: The Large Language Model from Power Law Decoder Representations (PLDR-LLM) and its attention, Power Law Graph Attention (PLGA), replace the fixed bili