Search: “inference”

LARP: Learner-Agnostic Robust Data Prefiltering

arXiv:2506.20573v4 Announce Type: replace-cross Abstract: Public datasets, crucial for modern machine learning and statistical inference, often contain low-quality or contaminated samples that can har

OmniMem: Perturbation-aware Memory Compression for Streaming Audio-Visual LLMs

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DGX agent

arXiv:2606.07577v1 Announce Type: new Abstract: Audio-visual large language models (LLMs) hold strong promise for long-form video understanding, yet their long-video inference is fundamentally limited

model-releasesarxiv-cs-ai

9 Jun 2026

Pretrained, Frozen, Still Leaking: Auditing Cross-Encoder Attribute Transfer in EEG Foundation Models

arXiv:2606.09189v1 Announce Type: cross Abstract: EEG foundation-model releases are usually audited one endpoint at a time: raw-reconstruction, membership inference, identity linkage, or DP-SGD on the

Q-Delta: Beyond Key-Value Associative State Evolution

arXiv:2606.08804v1 Announce Type: new Abstract: Linear attention reformulates sequence modeling as recurrent state evolution, enabling efficient linear-time inference. Under the key-value associative

Reformulate LLM Reinforcement Learning for Efficient Training under Black-box Discrepancy

model-releasesarxiv-cs-lg

arXiv:2606.08779v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a pivotal post-training paradigm, yet it frequently suffers from unpredictable sub-optimum performance or eve

RT-SDGOD: Real-Time Single-Domain Generalized Object Detection

arXiv:2606.09367v1 Announce Type: new Abstract: In real-world deployment under strict real-time constraints, weather and imaging variations induce significant distribution shifts, severely degrading d

applicationsarxiv-cs-cv

SceneConductor: 3D Scene Generation from Single Image with Multi-Agent Orchestration

arXiv:2606.08402v1 Announce Type: cross Abstract: Generating complete 3D scenes from a single image requires inferring globally consistent geometry, object relationships, and environmental context fro

Semantic Cache Distillation: Efficient State Transfer via Reuse and Selective Patching

arXiv:2606.07684v1 Announce Type: cross Abstract: Disaggregated serving alleviates memory bottlenecks in Large Language Model (LLM) inference but creates a severe communication bottleneck: transmittin

Local Ai

Sound and Complete Neurosymbolic Reasoning with LLM-Grounded Interpretations

arXiv:2507.09751v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but exhibit problems with l

local-aiarxiv-cs-ai

Steganography Without Modification: Hidden Communication via LLM Seeds

arXiv:2606.09135v1 Announce Type: cross Abstract: We demonstrate that widely deployed Large Language Model (LLM) inference stacks harbor a steganographic channel that requires no modification to model

Hardware

TAO: Tolerance-Aware Optimistic Verification for Floating-Point Neural Networks

arXiv:2510.16028v4 Announce Type: replace-cross Abstract: Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces). Yet ML-as-a-Service reveals litt

hardwarearxiv-cs-ai

TBD-VLA: Temporal Block Diffusion Vision Language Action Model

arXiv:2606.07895v1 Announce Type: new Abstract: Discrete Vision-Language-Action (VLA) models typically formulate action generation as next-token prediction over discretized action spaces, conditioning

applicationsarxiv-cs-cv

Think Before You Act: Intention-Guided Reasoning for LLM-Based Location Prediction

arXiv:2606.08122v1 Announce Type: new Abstract: Predicting a user's next Point-of-Interest (POI) based on their historical check-in records is a fundamental task in location-based services. While rece

TimpaTeks: Automatic In-place Text Sequence Modification via Diffusion Language Model Steering

arXiv:2606.08408v1 Announce Type: cross Abstract: We extend activation steering to diffusion language models (DLMs) and study a novel problem that arose due to the inference mechanism of DLMs: Modifyi

Training-Free Generalized Few-Shot Segmentation through Open-Vocabulary Semantic Arbitration

model-releasesarxiv-cs-cv

arXiv:2606.09474v1 Announce Type: new Abstract: Generalized Few-Shot Semantic Segmentation (GFSS) has traditionally been approached as a representation-learning problem, requiring task-specific adapta

UA-DCM: Uncertainty-aware Causal Decision Making via Effect Bound Decomposition

arXiv:2601.22736v2 Announce Type: replace-cross Abstract: Causal inference from observational data can provide strong evidence for finding the best action in a decision-making scenario without having

Vector Space of Cycles

arXiv:2606.08202v1 Announce Type: cross Abstract: Most statistical and machine learning methods for directed interactions focus on pairwise effects among variables. Even existing cyclic models represe

A Comprehensive Anatomy of Human and DeepSeek-R1 LLM Mathematical Reasoning

arXiv:2606.07410v1 Announce Type: cross Abstract: The emergence of 'Aha moments' in large language models, particularly DeepSeek-R1-0120, has raised the question of whether these systems genuinely rea

Auditing Training Data in Domain-adapted LLMs: LoRA-MINT

arXiv:2606.06946v1 Announce Type: cross Abstract: We present LoRA-MINT, a new methodology for Membership Inference Test (MINT) applied to recent Large Language Models (LLMs) fine-tuned for specific Na

Data-Efficient Autoregressive-to-Diffusion Language Models via On-Policy Distillation

arXiv:2606.06712v1 Announce Type: cross Abstract: We study the transformation of autoregressive models (ARLMs) into diffusion language models (DLMs). Rather than pretraining from scratch, prior work r

MalTree: Tracing Malware Evolution from Embeddings at Scale

arXiv:2606.06570v1 Announce Type: cross Abstract: Malware detection remains largely reactive: machine learning models trained on known samples degrade as threats evolve. Understanding evolutionary rel

OffQ: Taming Structured Outliers in LLM Quantization by Offsetting

arXiv:2606.07116v1 Announce Type: cross Abstract: Low-bit quantization has been widely adopted to accelerate the inference of large language models (LLMs) by significantly reducing computational cost

pTNAS: Progressive Neural Architecture Search for Tabular Data

arXiv:2403.10318v3 Announce Type: replace Abstract: Recent advances have shifted the paradigm of tabular learning toward tabular foundation models, yet their accuracy relies on a heavy inference cost

What Is My Robot Thinking? Design Considerations for Transparent and Trustworthy Shared Autonomy

arXiv:2606.06870v1 Announce Type: new Abstract: Assistive robots operating under shared autonomy must balance user control with autonomous assistance. Because robot actions depend on internal intent i

safetyarxiv-cs-ro

When to Think Deeply: Inhibitory Deliberation for LLM Reasoning

model-releasesarxiv-cs-cl

arXiv:2606.06745v1 Announce Type: new Abstract: Reasoning Large Language Models can improve problem-solving performance through deliberative inference, but invoking slow reasoning for every input is c

Agents

2-Step Agent: A Framework for the Interaction of a Decision Maker with AI Decision Support

arXiv:2602.21889v2 Announce Type: replace Abstract: Predictions from ML models support human decision making in several fields, including high-stakes ones such as healthcare and the judiciary. Yet, we

agentsarxiv-cs-ai

6 Jun 2026

Beyond Output Matching: Preserving Internal Geometry in NVFP4 LLM Distillatio

arXiv:2606.05682v1 Announce Type: new Abstract: Demand for low-precision inference, including NVFP4-based approaches, has grown as large language models are increasingly deployed in latency and cost c

6 Jun 2026

Analysis of the Neglect-Zero Effect in Large Language Models

arXiv:2606.05864v1 Announce Type: new Abstract: We investigate the extent to which the language processing of LLMs resembles human cognitive processes, focusing on a human cognitive bias called the ex

safetyarxiv-cs-cl

CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2606.06219v1 Announce Type: new Abstract: End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints. While diffusion mod

Local Ai

GenTract: Generative Global Tractography

arXiv:2511.13183v2 Announce Type: replace Abstract: Tractography is the process of inferring the trajectories of white-matter pathways in the brain from diffusion magnetic resonance imaging (dMRI). Lo

local-aiarxiv-cs-cv

Imagine Before You Predict: Interleaved Latent Visual Reasoning for Video Event Prediction

arXiv:2606.05769v1 Announce Type: new Abstract: Video event prediction (VEP) requires models to infer unobserved future states from partial video evidence. Existing video MLLMs usually verbalize inter

researcharxiv-cs-cv

Learning Visual Spatial Planning from Symbolic State via Modality-Gap-Aware Self-Distillation

arXiv:2606.06076v1 Announce Type: cross Abstract: While vision-language models excel at general multimodal understanding, they still struggle with visual spatial planning. We attribute this to a perce

safetyarxiv-cs-cv

Agents

SkillComposer: Learning to Evolve Agent Skills for Specification and Generalization

arXiv:2606.06079v1 Announce Type: new Abstract: Agent skills, which consist of reusable strategies that guide agent reasoning and action, have shown strong potential for improving model capability at

agentsarxiv-cs-cl

Agents

Unsupervised Skill Discovery for Agentic Data Analysis

arXiv:2606.06416v1 Announce Type: cross Abstract: Inference-time skill augmentation provides a lightweight way to improve data-analytic agents by injecting reusable procedural knowledge without updati

agentsarxiv-cs-cl

Beyond Static Priors: Dynamic Neural Guidance for Large-Scale Ant Colony Optimization

arXiv:2606.04039v1 Announce Type: cross Abstract: Neural-guided Ant Colony Optimization (ACO) suffers from a fundamental training-inference misalignment: policies are typically trained to generate sta

CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts

arXiv:2606.04661v1 Announce Type: new Abstract: Prompts tuned for accuracy often grow long, raising inference cost on every model call. The best accuracy-cost trade-off depends on the task and the bud

researcharxiv-cs-cl

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models

arXiv:2606.04446v1 Announce Type: cross Abstract: Speculative decoding accelerates autoregressive large language model inference by drafting multiple tokens and verifying them in a single target-model

Depth-Attention: Cross-Layer Value Mixing for Language Models

arXiv:2606.05014v1 Announce Type: new Abstract: Self-attention selects information freely across the sequence, but across depth, Transformers merely add each layer's output to the residual stream, so

researcharxiv-cs-cl

GroupToM-Bench: Benchmarking Group Theory of Mind and Nonlinear Social Emergence in MLLMs

model-releasesarxiv-cs-cv

arXiv:2606.04184v1 Announce Type: new Abstract: True general intelligence requires not only a model of the physical world but also a social world model: the capacity to infer how individual mental sta

Long-Term and Short-Term Transistor Aging in Deep Neural Networks: Impact and Mitigation

arXiv:2606.04266v1 Announce Type: cross Abstract: Deep neural networks (DNNs) are used in a variety of real-world applications including, for example, image classification and speech recognition. The

applicationsarxiv-cs-lg

Local Ai

Recover-LoRA for Aggressive Quantization: Reclaiming Accuracy in 2-Bit Language Models via Low-Rank Adaptation with Knowledge Distillation on Synthetic Data

arXiv:2606.04238v1 Announce Type: cross Abstract: Aggressive weight quantization to 2-bit precision offers substantial throughput and memory gains for large language model (LLM) inference, but typical

local-aiarxiv-cs-ai

SSSD: Simply-Scalable Speculative Decoding

arXiv:2411.05894v3 Announce Type: replace-cross Abstract: Speculative Decoding has emerged as a popular technique for accelerating inference in Large Language Models. However, most existing approaches

Toward Multi-Domain and Long-Tailed Quantization via Feature Alignment and Scaling

arXiv:2606.04920v1 Announce Type: cross Abstract: Quantizing deep neural networks is essential for efficient inference on resource-constrained devices. However, most existing methods are designed for

safetyarxiv-cs-cv

Transmuting prompts into weights

arXiv:2510.08734v3 Announce Type: replace Abstract: A growing body of research has demonstrated that the behavior of large language models can be effectively controlled at inference time by directly m

Uncertainty-Aware End-to-End Co-Design of Neural Network Processors: From Training and Mapping to Fabrication

arXiv:2606.04850v1 Announce Type: cross Abstract: Designing a neural network processor is an end-to-end co-design problem: network architecture and training budget determine the inference workload; ha

WAM-Nav: Asymmetric Latent World-Action Modeling for Unified Visual Navigation

arXiv:2606.04907v1 Announce Type: new Abstract: Visual navigation requires generating smooth and collision-free trajectories under complex geometric and physical constraints. Existing reactive policie

safetyarxiv-cs-ro

Are we really tilting? The mechanics of reward guidance in flow and diffusion models

arXiv:2606.02884v1 Announce Type: cross Abstract: Reward guidance algorithms steer a learned generative process toward the reward-tilted measure at inference time. While empirically powerful, these me

3 Jun 2026

Denoising Tells When to Replan: Denoising-Variance Adaptive Chunking for Flow-Based Robot Policies