Search: “inference”

arXiv:2601.16249v3 Announce Type: replace-cross Abstract: Learning DAG structures from purely observational data remains a long-standing challenge across scientific domains. An emerging line of resear

applicationsarxiv-cs-ai

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

PYPM-GGD: Pitman-Yor Process Mixture with Generalized Gaussian Density using ADAM

DGX agent

arXiv:2607.24583v1 Announce Type: new Abstract: Large scale Bayesian nonparametrics (BNP) learner such as Stochastic Variational Inference (SVI) can handle datasets with large class number and large t

researcharxiv-cs-lg

28 Jul 2026

Local Ai

Real-Time Human-Centric World Modeling for Upper-Body Human-Object Interaction

arXiv:2607.23517v1 Announce Type: new Abstract: We present a real-time human-centric world model for upper-body interactive generation, aiming to synthesize coherent local world dynamics centered on a

local-aiarxiv-cs-cv

Local Ai

Recurrent Autoregressive Diffusion: Global Memory Meets Local Attention

arXiv:2511.12940v2 Announce Type: replace Abstract: Recent advancements in video generation has shifted from bidirectional models for short videos to autoregressive ones for ultra long video generatio

local-aiarxiv-cs-cv

Hardware

Small-Pollinator Detection in Cluttered Field Video

arXiv:2607.22913v1 Announce Type: new Abstract: Detecting pollinators in field video is challenging: targets are small, visually similar, and observed against cluttered vegetation under blur and occlu

hardwarearxiv-cs-cv

Source-Free Controlled Adaptation of Teachers for Continual Test-Time Adaptation

model-releasesarxiv-cs-cv

arXiv:2607.23735v1 Announce Type: cross Abstract: In many real-world scenarios, encountering continual shifts in domain during inference is very common. Consequently, continual test-time adaptation (C

SyRuP: Enhancing System-Prompt Following via Reward-Guided Prediction in LLM Decoding

arXiv:2607.23991v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly controlled through system prompts that specify roles, styles, formats, and safety requirements. However,

TLRNet: Estimating Individual Treatment Effect based on Local Information and Single Learner Structure

model-releasesarxiv-cs-lg

arXiv:2607.22762v1 Announce Type: cross Abstract: Causal inference has become a central issue across various fields, including computer science, statistics, economics, education, healthcare, and medic

Verification-Notebook Learning for Source-Aware Multimodal Misinformation Detection

arXiv:2607.23581v1 Announce Type: new Abstract: Multimodal misinformation verification is challenging because misleading signals may come from different parts of a post and require different forms of

Adversarial Prompts for Acceptance Collapse in Speculative Decoding

arXiv:2607.21804v1 Announce Type: cross Abstract: Lossless acceleration schemes, such as speculative decoding, promise significant inference speedups by relying on dynamic token-level alignment betwee

safetyarxiv-cs-cl

Cloud-Native Evaluation-as-a-Service: A Microservices Architecture for Scalable AI Monitoring with Conformal Guarantees

arXiv:2607.21623v1 Announce Type: new Abstract: We present EaaS, a cloud-native reference architecture that operationalizes AI evaluation methods as six stateless Kubernetes microservices: conformal p

safetyarxiv-cs-lg

On the Identifiability of Controlled World Models

arXiv:2607.22430v1 Announce Type: new Abstract: Learning world models that infer environment dynamics from high-dimensional observations and predict outcomes under candidate actions is central to plan

safetyarxiv-cs-lg

Operator-Informed Gaussian Processes for Complex Helmholtz Wavefields: From Synthetic Benchmarks to In Vivo Brain Elastography

model-releasesarxiv-cs-lg

arXiv:2607.14193v2 Announce Type: replace-cross Abstract: The Helmholtz equation governs time-harmonic wave propagation, and in dissipative media a complex modulus renders its squared wavenumber kappa

Tutorials

Robot-Factored World Models via Robot Rendering

arXiv:2607.22535v1 Announce Type: cross Abstract: Action-conditioned video world models predict future observations from an initial observation and an action signal. In robotics, actions influence fut

tutorialsarxiv-cs-cv

SiPhy: Single-Image Physical Property Reasoning

arXiv:2607.22355v1 Announce Type: new Abstract: Inferring physical properties such as mass, stiffness, and elasticity from a single image is essential for simulation and embodied AI, yet most existing

Tutorials

CLOAK: Contrastive Guidance for Latent Diffusion-Based Data Obfuscation

arXiv:2512.12086v2 Announce Type: replace Abstract: Data obfuscation is a promising technique for mitigating attribute inference attacks by semi-trusted parties with access to time-series data emitted

tutorialsarxiv-cs-lg

Codec-Gauge: Learning Compression-Friendly Gauges for Transformer KV Caches

arXiv:2607.20538v1 Announce Type: cross Abstract: Long-context Transformer inference increasingly relies on KV-cache compression or quantization. Prior rotation and transform-coding results suggest th

DecodeShare: Tracing the Shared Subspace of LLM Decode-Time Decisions

arXiv:2607.20469v1 Announce Type: new Abstract: Large language models (LLMs) handle many tasks with one set of parameters, but under KV-cached inference it is unclear what task-general structure, if a

Dropping the Anchor: Statistical Context Summarization for Distributed Systems via Pulsar Attention

arXiv:2607.20457v1 Announce Type: cross Abstract: Inference with large language models (LLMs) on long sequences is computationally expensive due to the quadratic complexity of self-attention. Distribu

From Noise to Diversity: Random Embedding Injection in LLM Reasoning

arXiv:2605.11936v2 Announce Type: replace Abstract: Recent soft prompt research has tried to improve reasoning by inserting trained vectors into LLM inputs, yet whether the gain comes from the learned

Out of Sight, Still in Mind: Token Compression for Omni-LLMs

arXiv:2607.21179v1 Announce Type: new Abstract: The goal of this paper is to reduce the input token cost of Omni-modal large language models (Omni-LLMs) at inference time. Omni-LLMs reason jointly ove

PersonaTrail: Benchmarking Personalized Web Agents through Browsing Trails

arXiv:2607.20482v1 Announce Type: new Abstract: Recent advances in large language models have enabled web agents to autonomously execute complex tasks. In practice, users frequently provide underspeci

SonicSampler: Unified Tile-Aware Kernels for LLM Sampling and Speculative Verification

arXiv:2607.20475v1 Announce Type: new Abstract: Sampling in LLM inference comprises a combinatorial set of logit processing, token selection, and verification operations for speculative decoding. Howe

Tutorials

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training

arXiv:2603.06642v2 Announce Type: replace-cross Abstract: Test-Time Training (TTT) language models replace the KV-cache with fast weights updated during inference, achieving O(1) memory but suffering

tutorialsarxiv-cs-ai

The Hidden Footprint: Making Storage a First-Class Metric for LLM Agent Evaluation

arXiv:2607.11149v3 Announce Type: replace Abstract: LLM agent benchmarks measure task completion, reliability, and inference cost, but not the persistent data an agent run leaves on disk, including lo

Adaptive Bayesian Online Learning via Expert Aggregation

arXiv:2607.20239v1 Announce Type: cross Abstract: Bayesian online learning promises uncertainty-aware prediction on data streams, but its performance hinges on inferential choices, including learning

researcharxiv-cs-lg

Cognitive Dual-Process Planning for Autonomous Driving with Structured Scene Knowledge and Verifiable Reasoning-Action Consistency

arXiv:2607.19194v2 Announce Type: cross Abstract: High-level planning for autonomous driving is a knowledge-intensive engineering decision task that requires accurate scene understanding, timely infer

safetyarxiv-cs-cv

Context-structured Video Anomaly Detection with Large Vision-Language Models

arXiv:2607.19077v1 Announce Type: new Abstract: Training video anomaly detectors is challenging due to the difficulty and cost of annotating diverse and rare abnormal events. Although recent large vis

Applications

Efficient Tracking and Understanding Object Transformations

arXiv:2607.19743v1 Announce Type: new Abstract: Tracking objects through state transformations is essential for understanding real-world dynamics. However, existing methods are computationally expensi

applicationsarxiv-cs-cv

Hardware

MoA-Structured Decode Attention DNF Derivation, KV-Cache Accumulation, GQA/MQA, and OpenACC Kernel

arXiv:2607.19456v1 Announce Type: cross Abstract: We derive four memory-optimal inference artifacts for transformer attention using the Mathematics of Arrays (MoA), each following directly from the fo

hardwarearxiv-cs-ai

Now We Know? A Systematic Comparison of TerraMind and THOR

arXiv:2607.18504v1 Announce Type: cross Abstract: Benchmarks for Geospatial Foundation Models (GFMs) increasingly rank models by aggregate score, but such rankings obscure why models differ: how much

OPIUM: Mitigating Steering Externalities and Over-Refusal via Dual Objective Latent Optimization

arXiv:2607.19806v1 Announce Type: cross Abstract: Activation steering provides a lightweight mechanism for controlling large language models at inference time, but steering vectors can have unintended

Opto-ViT-v2: Noise-Resilient On-Chip Fine-Tuning for Photonic Near-Sensor Vision Transformer Accelerators

arXiv:2607.19421v1 Announce Type: cross Abstract: Silicon-photonic (SiPh) accelerators have emerged as a promising platform for Vision Transformer (ViT) inference by performing matrix multiplications

SoftReason: A Fully Differentiable Neuro-Soft-Symbolic Deductive Reasoning Architecture over High-Dimensional Perceptual Data

arXiv:2607.20402v1 Announce Type: new Abstract: In many reasoning problems, the premises are not observed as discrete symbols, but must be inferred from high-dimensional inputs. Further, the predicate

Spatiotemporal Facial Action Unit Detection using Twin Cycle Autoencoders for Driver Monitoring

arXiv:2607.16760v2 Announce Type: replace-cross Abstract: Driver monitoring systems (DMS) increasingly rely on facial cues to infer drowsiness, distraction, and cognitive load in real time. Facial Act

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing

arXiv:2607.19368v1 Announce Type: new Abstract: Long-prompt inference remains expensive because prefill attention scales quadratically with sequence length. We propose Spectral-LSH, a training-free pr

Total Variation Distance Estimation in Autoregressive Models

arXiv:2607.19510v1 Announce Type: new Abstract: Modern LLM deployments use a number of implementation choices and inference optimizations (e.g., batching, custom kernels, and quantization) on top of f

researcharxiv-cs-lg

A Causal Model of Theory of Mind in Conflict for Artificial Intelligence

arXiv:2606.16944v2 Announce Type: replace Abstract: Theory of mind (ToM), the capacity to ascribe mental states to others and use those ascriptions for prediction and inference, is widely assumed to b

16 Jul 2026

NSNQuant: A Double Normalization Approach for Calibration-Free Low-Bit Vector Quantization of KV Cache

arXiv:2505.18231v3 Announce Type: replace-cross Abstract: Large Language Model (LLM) inference is typically memory-intensive, especially when processing large batch sizes and long sequences, due to th

16 Jul 2026

WaterMoE: Expert-Routing-based Watermarking for High Fidelity and Efficiency

arXiv:2607.13099v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable success but raise growing concerns about content provenance and misuse, motivating the need for

16 Jul 2026

Attention Misses Visual Risk: Risk-Adaptive Steering for Multimodal Safety Alignment

arXiv:2510.13698v4 Announce Type: replace Abstract: Even modern AI models often remain vulnerable to multimodal queries in which harmful intent is embedded in images. A widely used approach for safety

safetyarxiv-cs-cv

Applications

Causal Supervision of Attention for Affective Behaviour Analysis

arXiv:2607.12091v1 Announce Type: new Abstract: Affective Behaviour Analysis aims to enable machines to infer human affective states from behavioural signals, particularly facial expressions, in real-

applicationsarxiv-cs-cv

Egocentric Bias in Vision-Language Models

arXiv:2602.15892v2 Announce Type: replace-cross Abstract: Visual perspective taking--inferring how the world appears from another's viewpoint--is foundational to social cognition. We introduce FlipSet

Agents

Fine-Tuned Multi-Agent Framework for Detecting OCEAN in Life Narratives

arXiv:2607.12215v1 Announce Type: new Abstract: Accurately assessing personality from text is challenging because traits are latent, context-dependent, and often subtly expressed across long narrative

agentsarxiv-cs-cl

Local Ai

Follow the Latent Roadmap: Navigating Revocable Decoding for Diffusion LLMs with Anchor Tokens

arXiv:2606.16847v3 Announce Type: replace-cross Abstract: Diffusion Large Language Models (dLLMs) offer a promising avenue for parallel generation but face a trade-off between decoding speed and quali

local-aiarxiv-cs-ai

GeoSEAN: Explainable Country-Level Image Geolocation for ASEAN Regions

arXiv:2607.12284v1 Announce Type: new Abstract: Image geolocation aims to infer the geographic origin of an image from visual content alone. However, this task remains challenging in regions where cou

TerraLogic: A Benchmark for Hierarchical Geospatial Reasoning in Earth Observation

model-releasesarxiv-cs-cv

arXiv:2607.12497v1 Announce Type: new Abstract: Beyond perception, reasoning is essential in remote sensing for advanced interpretation, inference, and decision-making. Recent advances in large langua

VisCo: Leveraging Large Language Models as Intrinsic Encoders for Visual Token Compression

model-releasesarxiv-cs-cv

arXiv:2607.12756v1 Announce Type: new Abstract: Vision-language models (VLMs) process large numbers of visual tokens, resulting in substantial inference latency and memory overhead. This has motivated