Search: “models”

RLM-Cascade: Response-Level Speculative Decoding for Cost-Efficient LLM API Serving

arXiv:2606.22840v1 Announce Type: new Abstract: We present RLM-Cascade, a proxy-layer system that applies speculative decoding at the response level to reduce LLM API costs without requiring model arc

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RouteJudge: An Open Platform for Reproducible and Preference-Aware LLM Routing

DGX agent

arXiv:2606.18774v2 Announce Type: replace Abstract: We present RouteJudge, an online pairwise preference evaluation framework for LLM routing systems, with a public platform available at https://route

model-releasesarxiv-cs-lg

23 Jun 2026

Sequential Minimal Optimization Algorithm for One-Class Support Vector Machines With Privileged Information

arXiv:2606.22210v1 Announce Type: new Abstract: One of the powerful techniques in data modeling is accounting for features that are available at the training stage, but are not available when the trai

Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study

arXiv:2602.22747v2 Announce Type: replace Abstract: Epistemic uncertainty in neural networks is commonly modeled using two second-order paradigms: distribution-based representations, which rely on pos

TeleStyle V2: Beyond Content-Preserving Style Transfer with Self-Distillation and Distribution-Matching-Distillation

arXiv:2606.20709v1 Announce Type: new Abstract: Given a content reference and a style reference, content-preserving style transfer requires the model to generate stylized outputs with content and styl

Towards Error-Free Long Video Generation

arXiv:2606.22370v1 Announce Type: new Abstract: Recent advances in video generation have made minute-level synthesis possible; however, generating long videos remains challenging due to error accumula

Understanding Parallel Samplers in Masked Diffusion via Random Walks on Graphs

arXiv:2606.22976v1 Announce Type: new Abstract: In this paper, we propose using random walks on graphs as a verifiable sandbox to study different parallel sampling strategies in masked diffusion model

UniRank: Unified Rank Allocation for Low-Rank LLM Compression

arXiv:2606.21847v1 Announce Type: new Abstract: Low-rank decomposition serves as a promising compression paradigm for large language models, however, rank allocation remains challenging: manual rules

A Lightweight Multi-Agent Framework for Automated Concrete Barrier Design

arXiv:2606.12040v1 Announce Type: new Abstract: The design of reinforced concrete highway barriers is a safety-critical process that requires strict compliance with regulatory provisions such as the A

Categorical Prior Lock-in: Why In-Context Learning Fails for Structured Data

arXiv:2606.11961v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as conditional generators for structured data, relying on in-context learning (ICL) to adapt to new

Safety

From Consumption to Reflection: Designing Human-AI Relations for Stable Reasoning

arXiv:2606.11195v1 Announce Type: cross Abstract: Large language models (LLMs) have transformed how humans access information, but not how we reason with it. Their fluency accelerates consumption whil

safetyarxiv-cs-ai

Hardware

INFRAMIND: Infrastructure-Aware Multi-Agent Orchestration

arXiv:2606.11440v1 Announce Type: new Abstract: Existing multi-agent LLM orchestration methods, ranging from brute-force ensembles to learned routers, select models and topologies based on task and mo

hardwarearxiv-cs-ai

Neural-Parameterized Cellular Automata for Wildfire Spread

arXiv:2606.11676v1 Announce Type: cross Abstract: Traditional wildfire models rely on rigid, low-dimensional parameters and static fuel maps, frequently underpredicting fire spread. To address this we

On Subquadratic Architectures: From Applications to Principles

arXiv:2606.12364v1 Announce Type: new Abstract: Transformers dominate modern sequence modeling, but their quadratic attention incurs substantial computational cost. Subquadratic architectures offer a

researcharxiv-cs-lg

RankVR: Low-Rank Structure Perception and Value Recalibration for Robust Composed Image Retrieval

arXiv:2606.11689v1 Announce Type: new Abstract: Composed Image Retrieval (CIR) constitutes a pivotal paradigm requiring models to perform joint reasoning on reference images and modification texts. Ho

SPEAR: A System for Post-Quantization Error-Adaptive Recovery Enabling Efficient Low-Bit LLM Serving

arXiv:2606.11244v1 Announce Type: cross Abstract: Efficient large language model (LLM) serving is increasingly constrained by deployment cost. Quantization is a key technique for reducing serving cost

STEAM: Squeeze and Transform Enhanced Attention Module

arXiv:2412.09023v3 Announce Type: replace Abstract: Channel and spatial attention mechanisms introduced in earlier work enhance the representational capabilities of deep convolutional neural networks

TAHOE: Text-to-SQL with Automated Hint Optimization from Experience

arXiv:2606.12387v1 Announce Type: cross Abstract: Large Language Models (LLMs) have democratized database access through Text-to-SQL, but moving from prototypes to production remains difficult. Real d

Applications

Unifying Learning Dynamics and Generalization in Transformers Scaling Law

arXiv:2512.22088v3 Announce Type: replace-cross Abstract: The scaling law, a cornerstone of Large Language Model (LLM) development, predicts improvements in model performance with increasing computati

applicationsarxiv-cs-ai

Visualizing LLM Latent Space Geometry Through Dimensionality Reduction

arXiv:2511.21594v3 Announce Type: replace Abstract: Large language models (LLMs) achieve state-of-the-art results across many natural language tasks, but their internal mechanisms remain difficult to

When Generic Prompt Improvements Hurt: Evaluation-Driven Iteration for LLM Applications

arXiv:2601.22025v2 Announce Type: replace-cross Abstract: Evaluating Large Language Model (LLM) applications differs from conventional software testing because outputs are probabilistic, semantically

Achieving Cloud-Grade SLOs for Local Mixture-of-Experts Inference through CPU-GPU Hybrid Design

arXiv:2606.10493v1 Announce Type: cross Abstract: Local deployment of large Mixture-of-Experts (MoE) models falls short of the service quality achieved in cloud-scale environments, even under low-conc

ASyMOB: Algebraic Symbolic Mathematical Operations Benchmark

arXiv:2505.23851v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly applied to symbolic mathematics, yet existing evaluations often conflate pattern memorization wi

AuRA: Internalizing Audio Understanding into LLMs as LoRA

arXiv:2606.11033v1 Announce Type: cross Abstract: Recent efforts to extend large language models (LLMs) to speech inputs typically rely on cascaded ASR-LLM pipelines, end-to-end speech-language models

Beyond APIs: Probing the Limits of MLLMs in Physical Tool Use

arXiv:2606.10803v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) excel at utilizing digital APIs and increasingly serve as the 'brain' of embodied AI, instructing robots to i

CoCoSI: Collaborative Cognitive Map Construction for Spatial Intelligence

arXiv:2606.10401v1 Announce Type: new Abstract: Spatial intelligence is a key frontier for multimodal large language models (MLLMs), enabling them to reason about the physical world from visual experi

ComBench: A Benchmark for Rigorous Proof Reasoning and Constructive Realization in Olympiad-Level Combinatorics

arXiv:2606.10479v1 Announce Type: new Abstract: Combinatorics is central to Olympiad-level mathematical problem solving, requiring deep discrete reasoning, creative constructions, and rigorous structu

CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data

model-releasesarxiv-cs-cl

arXiv:2601.18026v2 Announce Type: replace Abstract: Language identification (LID) is a fundamental step in curating multilingual corpora. However, LID models still perform poorly for many languages, e

Compile Once, Differentiate Everywhere: A Differentiable Meta-Circular Interpreter

arXiv:2606.09930v1 Announce Type: cross Abstract: The boundary between program execution and gradient-based optimization has long limited the use of code itself as a learnable scientific model. We pre

Cost-Aware Routing for Efficient Text-To-Image Generation

arXiv:2506.14753v3 Announce Type: replace Abstract: Diffusion models are well known for their ability to generate a high-fidelity image for an input prompt through an iterative denoising process. Unfo

researcharxiv-cs-cv

Expert-Level Crisis Detection in Mental Health Conversations

arXiv:2606.10380v1 Announce Type: cross Abstract: Real-world crisis intervention is inherently conversational, yet existing research largely focuses on static texts.Real-world crisis intervention is i

Fact-Augmented Lookahead Planning for LLM Agents

arXiv:2506.09171v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly capable, but LLM agents still struggle to plan effectively in interactive, partially observable,

Human-AI Teaming Through the Lens of Calibration

arXiv:2606.10906v1 Announce Type: cross Abstract: We study models for human-AI teaming through the lens of statistical calibration. We assume the team consists of an AI model and human -- both of whic

Kwai Keye-VL-2.0 Technical Report

arXiv:2606.10651v1 Announce Type: new Abstract: We introduce Kwai Keye-VL-2.0-30B-A3B, an open-source Mixture-of-Experts (MoE) multimodal foundation model designed to advance long-video understanding

Linguistically Augmented Audio Speech Data (LinguAS)

arXiv:2606.10246v1 Announce Type: cross Abstract: Maliciously-created fake speech, including deepfaked and spoofed audio, is proliferating at an alarming rate, and detection models are racing to stay

LLM-as-a-Discriminator: When Synthetic Tables Still Look Real

arXiv:2606.09865v1 Announce Type: new Abstract: Privacy and data sharing are often in tension. Many organizations use synthetic data to reduce privacy risk and still share useful data. For tabular dat

Local Is Not a Sufficient Privacy Boundary: Governing OS-Integrated On-Device AI

arXiv:2606.10173v1 Announce Type: cross Abstract: As AI systems move into operating systems, privacy no longer turns only on whether a model runs locally. A local assistant may assemble email, calenda

MIRAGE: A Polarity-Flipping Encoding Subspace in LLM Agents

model-releasesarxiv-cs-cl

arXiv:2606.10304v1 Announce Type: new Abstract: When LLM agents are coerced into covertly encoding sensitive data (Base64, ROT13, acrostic, synonym chains, and beyond), the resulting outputs evade out

Optimal Post-Training Quantization Scales and Where to Find Them

arXiv:2606.10890v1 Announce Type: cross Abstract: Post-training quantization (PTQ) compresses large language models by mapping weights to low-bit representations. The scaling factor that defines the q

Piper: A Programmable Distributed Training System

arXiv:2606.11169v1 Announce Type: cross Abstract: Large-scale model training increasingly relies on composing multiple parallelism strategies, such as data, pipeline, and expert parallelism, together

Pre-AF 13: An Interpretable Atrial Fibrillation Risk Score Mined from Discharge Reports

arXiv:2606.10725v1 Announce Type: cross Abstract: Background. Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and a major determinant of prognosis. Established AF risk scores rely on

researcharxiv-cs-cl

RankLLM: Weighted Ranking of LLMs by Quantifying Question Difficulty

arXiv:2602.12424v2 Announce Type: replace-cross Abstract: Benchmarks establish a standardized evaluation framework to systematically assess the performance of large language models (LLMs), facilitatin

SPACE: Source-free Proxy Anchor Concept Erasure for MLLMs

arXiv:2606.09868v1 Announce Type: cross Abstract: As Multimodal Large Language Models (MLLMs) face growing privacy risks and regulatory constraints, machine unlearning (MU) has emerged as a crucial so

The Interlocutor Effect: Why LLMs Leak More Personal Data to Agents Than Humans

arXiv:2606.09844v1 Announce Type: cross Abstract: Large Language Models (LLMs) alter their privacy behavior based on the perceived identity of their interlocutor. While safety mechanisms typically pre

VFUSE: Virulent Feature Understanding with Sparse autoEncoders

arXiv:2606.10080v1 Announce Type: cross Abstract: Generative models have shown remarkable progress in a variety of domains such as protein design, but such power enables the opaque generation of hazar

A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection

arXiv:2510.26307v3 Announce Type: replace-cross Abstract: Anomaly detection is a critical task in cybersecurity, where identifying insider threats, access violations, and coordinated attacks is essent

9 Jun 2026

Local Ai

Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules

arXiv:2606.08802v1 Announce Type: new Abstract: Standard flow and diffusion pre-training matches the distribution of available data (e.g., molecules), which often covers only a small fraction of the v

local-aiarxiv-cs-lg

9 Jun 2026

AutoMegaKernel: A Statically-Checked Agent Harness for Self-Retargeting Megakernel Synthesis

arXiv:2606.09682v1 Announce Type: new Abstract: AutoMegaKernel (AMK) compiles a HuggingFace Llama-family model into a single persistent cooperative CUDA kernel that runs the whole forward pass in one