Search: “models”

Privacy-Preserving LLMs Routing

arXiv:2604.15728v1 Announce Type: cross Abstract: Large language model (LLM) routing has emerged as a critical strategy to balance model performance and cost-efficiency by dynamically selecting servic

researcharxiv-cs-ai

Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model

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arXiv:2604.16111v1 Announce Type: new Abstract: We study the sample complexity of learning an epsilon-optimal policy in the Stochastic Shortest Path (SSP) problem. We first derive sample complexity bo

safetyarxiv-cs-lg

20 Apr 2026

Scaling Behaviors of LLM Reinforcement Learning Post-Training: An Empirical Study in Mathematical Reasoning

arXiv:2509.25300v4 Announce Type: replace-cross Abstract: While scaling laws for large language models (LLMs) during pre-training have been extensively studied, their behavior under reinforcement lear

researcharxiv-cs-ai

Hardware

Taming Asynchronous CPU-GPU Coupling for Frequency-aware Latency Estimation on Mobile Edge

arXiv:2604.15357v1 Announce Type: cross Abstract: Precise estimation of model inference latency is crucial for time-critical mobile edge applications, enabling devices to calculate latency margins aga

hardwarearxiv-cs-ai

Applications

TriagerX: Dual Transformers for Bug Triaging Tasks with Content and Interaction Based Rankings

arXiv:2508.16860v2 Announce Type: replace-cross Abstract: Pretrained Language Models or PLMs are transformer-based architectures that can be used in bug triaging tasks. PLMs can better capture token s

applicationsarxiv-cs-ai

A Robust Approach for LiDAR-Inertial Odometry Without Sensor-Specific Modeling

arXiv:2509.06593v2 Announce Type: replace Abstract: Accurate odometry is a critical component in a robotic navigation stack, and subsequent modules such as planning and control often rely on an estima

researcharxiv-cs-ro

Local Ai

BitFlipScope: Scalable Fault Localization and Recovery for Bit-Flip Corruptions in LLMs

arXiv:2512.22174v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) deployed in practical and safety-critical settings are increasingly susceptible to bit-flip faults caused by hard

local-aiarxiv-cs-ai

Bridging the Gap between Learning and Inference for Diffusion-Based Molecule Generation

arXiv:2411.05472v2 Announce Type: replace Abstract: The paradigm shift toward structure-driven molecule generation has been propelled by advances in deep generative models, such as variational auto-en

Calibrate-Then-Delegate: Safety Monitoring with Risk and Budget Guarantees via Model Cascades

arXiv:2604.14251v1 Announce Type: new Abstract: Monitoring LLM safety at scale requires balancing cost and accuracy: a cheap latent-space probe can screen every input, but hard cases should be escalat

Can LLMs Score Medical Diagnoses and Clinical Reasoning as well as Expert Panels?

arXiv:2604.14892v1 Announce Type: new Abstract: Evaluating medical AI systems using expert clinician panels is costly and slow, motivating the use of large language models (LLMs) as alternative adjudi

DeepPrune: Parallel Scaling without Inter-trace Redundancy

arXiv:2510.08483v2 Announce Type: replace Abstract: Parallel scaling has emerged as a powerful paradigm to enhance reasoning capabilities in large language models (LLMs) by generating multiple Chain-o

researcharxiv-cs-cl

DPQuant: Efficient and Differentially-Private Model Training via Dynamic Quantization Scheduling

arXiv:2509.03472v2 Announce Type: replace Abstract: Differentially-Private SGD (DP-SGD) and its adaptive variant DP-Adam are powerful techniques to protect user privacy when using sensitive data to tr

Language Model as Planner and Formalizer under Constraints

arXiv:2510.05486v2 Announce Type: replace Abstract: LLMs have been widely used in planning, either as planners to generate action sequences end-to-end, or as formalizers to represent the planning doma

safetyarxiv-cs-cl

Applications

OmniLight: One Model to Rule All Lighting Conditions

arXiv:2604.15170v1 Announce Type: new Abstract: Adverse lighting conditions, such as cast shadows and irregular illumination, pose significant challenges to computer vision systems by degrading visibi

applicationsarxiv-cs-cv

SAGE: Sign-Adaptive Gradient for Memory-Efficient LLM Optimization

arXiv:2604.07663v2 Announce Type: replace Abstract: The AdamW optimizer, while standard for LLM pretraining, is a critical memory bottleneck, consuming optimizer states equivalent to twice the model's

Scalable Model-Based Clustering with Sequential Monte Carlo

arXiv:2604.14810v1 Announce Type: cross Abstract: In online clustering problems, there is often a large amount of uncertainty over possible cluster assignments that cannot be resolved until more data

Unsupervised Learning of Local Updates for Maximum Independent Set in Dynamic Graphs

arXiv:2505.13754v3 Announce Type: replace Abstract: We present the first unsupervised learning model for Maximum-Independent-Set (MaxIS) in dynamic graphs where edges change over time. Our method comb

Applications

Blind Bitstream-corrupted Video Recovery via Metadata-guided Diffusion Model

arXiv:2604.13906v1 Announce Type: new Abstract: Bitstream-corrupted video recovery aims to restore realistic content degraded during video storage or transmission. Existing methods typically assume th

applicationsarxiv-cs-cv

Caption First, VQA Second: Knowledge Density, Not Task Format, Drives Multimodal Scaling

arXiv:2604.13054v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved rapid progress, yet their scaling behavior remains less clearly characterized and often less pred

researcharxiv-cs-cl

Getting the Numbers Rightnicode{x2014}Modelling Multi-Class Object Counting in Dense and Varied Scenes

arXiv:2510.02213v2 Announce Type: replace Abstract: Density map estimation enables accurate object counting in heavily occluded, and densely packed scenes where detection-based counting fails. In mult

researcharxiv-cs-cv

Hessian-Enhanced Token Attribution (HETA): Interpreting Autoregressive LLMs

arXiv:2604.13258v1 Announce Type: new Abstract: Attribution methods seek to explain language model predictions by quantifying the contribution of input tokens to generated outputs. However, most exist

Monthly Diffusion v0.9: A Latent Diffusion Model for the First AI-MIP

arXiv:2604.13481v1 Announce Type: new Abstract: Here, we describe Monthly Diffusion at 1.5-degree grid spacing (MD-1.5 version 0.9), a climate emulator that leverages a spherical Fourier neural operat

Neural Mean-Field Games: Extending Mean-Field Game Theory with Neural Stochastic Differential Equations

arXiv:2504.13228v4 Announce Type: replace Abstract: Mean-field game theory relies on approximating games that are intractable to model due to a very large to infinite population of players. While thes

SpatialEvo: Self-Evolving Spatial Intelligence via Deterministic Geometric Environments

arXiv:2604.14144v1 Announce Type: cross Abstract: Spatial reasoning over three-dimensional scenes is a core capability for embodied intelligence, yet continuous model improvement remains bottlenecked

The cognitive companion: a lightweight parallel monitoring architecture for detecting and recovering from reasoning degradation in LLM agents

arXiv:2604.13759v1 Announce Type: cross Abstract: Large language model (LLM) agents on multi-step tasks suffer reasoning degradation, looping, drift, stuck states, at rates up to 30% on hard tasks. Cu

Analyzing the Effect of Noise in LLM Fine-tuning

arXiv:2604.12469v1 Announce Type: new Abstract: Fine-tuning is the dominant paradigm for adapting pretrained large language models (LLMs) to downstream NLP tasks. In practice, fine-tuning datasets may

ASGuard: Activation-Scaling Guard to Mitigate Targeted Jailbreaking Attack

arXiv:2509.25843v2 Announce Type: replace Abstract: Large language models (LLMs), despite being safety-aligned, exhibit brittle refusal behaviors that can be circumvented by simple linguistic changes.

safetyarxiv-cs-ai

CoLA: A Choice Leakage Attack Framework to Expose Privacy Risks in Subset Training

arXiv:2604.12342v1 Announce Type: cross Abstract: Training models on a carefully chosen portion of data rather than the full dataset is now a standard preprocess for modern ML. From vision coreset sel

researcharxiv-cs-cv

ContextLens: Modeling Imperfect Privacy and Safety Context for Legal Compliance

arXiv:2604.12308v1 Announce Type: new Abstract: Individuals' concerns about data privacy and AI safety are highly contextualized and extend beyond sensitive patterns. Addressing these issues requires

safetyarxiv-cs-cl

Generative Refinement Networks for Visual Synthesis

model-releasesarxiv-cs-cv

arXiv:2604.13030v1 Announce Type: new Abstract: While diffusion models dominate the field of visual generation, they are computationally inefficient, applying a uniform computational effort regardless

Nucleus-Image: Sparse MoE for Image Generation

model-releasesarxiv-cs-cv

arXiv:2604.12163v1 Announce Type: new Abstract: We present Nucleus-Image, a text-to-image generation model that establishes a new Pareto frontier in quality-versus-efficiency by matching or exceeding

SceneCritic: A Symbolic Evaluator for 3D Indoor Scene Synthesis

arXiv:2604.13035v1 Announce Type: cross Abstract: Large Language Models (LLMs) and Vision-Language Models (VLMs) increasingly generate indoor scenes through intermediate structures such as layouts and

researcharxiv-cs-cl

SOLARIS: Speculative Offloading of Latent-bAsed Representation for Inference Scaling

arXiv:2604.12110v1 Announce Type: new Abstract: Recent advances in recommendation scaling laws have led to foundation models of unprecedented complexity. While these models offer superior performance,

Synthetic POMDPs to Challenge Memory-Augmented RL: Memory Demand Structure Modeling

arXiv:2508.04282v3 Announce Type: replace Abstract: Recent benchmarks for memory-augmented reinforcement learning (RL) have introduced partially observable Markov decision process (POMDP) environments

researcharxiv-cs-ai

TIPSv2: Advancing Vision-Language Pretraining with Enhanced Patch-Text Alignment

model-releasesarxiv-cs-cv

arXiv:2604.12012v1 Announce Type: new Abstract: Recent progress in vision-language pretraining has enabled significant improvements to many downstream computer vision applications, such as classificat

Local Ai

ViLL-E: Video LLM Embeddings for Retrieval

arXiv:2604.12148v1 Announce Type: new Abstract: Video Large Language Models (VideoLLMs) excel at video understanding tasks where outputs are textual, such as Video Question Answering and Video Caption

local-aiarxiv-cs-cv

Local Ai

A Two-Stage Dual-Modality Model for Facial Emotional Expression Recognition

arXiv:2603.12221v2 Announce Type: replace Abstract: This paper addresses the expression (EXPR) recognition challenge in the 10th Affective Behavior Analysis in-the-Wild (ABAW) workshop and competition

local-aiarxiv-cs-cv

bioLeak: Leakage-Aware Modeling and Diagnostics for Machine Learning in R

arXiv:2604.10965v1 Announce Type: cross Abstract: Data leakage remains a recurrent source of optimistic bias in biomedical machine learning studies. Standard row-wise cross-validation and globally est

Closed-Form Concept Erasure via Double Projections

arXiv:2604.10032v1 Announce Type: cross Abstract: While modern generative models such as diffusion-based architectures have enabled impressive creative capabilities, they also raise important safety a

safetyarxiv-cs-ai

Cross-Validated Cross-Channel Self-Attention and Denoising for Automatic Modulation Classification

arXiv:2604.10054v1 Announce Type: new Abstract: This study addresses a key limitation in deep learning Automatic Modulation Classification (AMC) models, which perform well at high signal-to-noise rati

Agents

Dual-Control Frequency-Aware Diffusion Model for Depth-Dependent Optical Microrobot Microscopy Image Generation

arXiv:2604.11680v1 Announce Type: new Abstract: Optical microrobots actuated by optical tweezers (OT) are important for cell manipulation and microscale assembly, but their autonomous operation depend

agentsarxiv-cs-ro

End-to-end Contrastive Language-Speech Pretraining Model For Long-form Spoken Question Answering

arXiv:2511.09282v3 Announce Type: replace-cross Abstract: Significant progress has been made in spoken question answering (SQA) in recent years. However, many existing methods, including large audio l

safetyarxiv-cs-cl

H-SPAM: Hierarchical Superpixel Anything Model

arXiv:2604.11218v1 Announce Type: new Abstract: Superpixels offer a compact image representation by grouping pixels into coherent regions. Recent methods have reached a plateau in terms of segmentatio

researcharxiv-cs-cv

HumanVBench: Probing Human-Centric Video Understanding in MLLMs with Automatically Synthesized Benchmarks

model-releasesarxiv-cs-ai

arXiv:2412.17574v3 Announce Type: replace-cross Abstract: Evaluating the nuanced human-centric video understanding capabilities of Multimodal Large Language Models (MLLMs) remains a great challenge, a

Infusing Theory of Mind into Socially Intelligent LLM Agents

arXiv:2509.22887v2 Announce Type: replace Abstract: Theory of Mind (ToM)-an understanding of the mental states of others-is a key aspect of human social intelligence, yet, chatbots and LLM-based socia

Agents

MASH: Modeling Abstention via Selective Help-Seeking

arXiv:2510.01152v2 Announce Type: replace Abstract: LLMs cannot reliably recognize their parametric knowledge boundaries and often hallucinate answers to outside-of-boundary questions. In this paper,

agentsarxiv-cs-cl

MatRes: Zero-Shot Test-Time Model Adaptation for Simultaneous Matching and Restoration

arXiv:2604.10081v1 Announce Type: cross Abstract: Real-world image pairs often exhibit both severe degradations and large viewpoint changes, making image restoration and geometric matching mutually in

safetyarxiv-cs-ai

MemDLM: Memory-Enhanced DLM Training

arXiv:2603.22241v2 Announce Type: replace Abstract: Diffusion Language Models (DLMs) offer attractive advantages over Auto-Regressive (AR) models, such as full-attention parallel decoding and flexible