Search: “inference”

Instance Hardness-Based Relevance for Imbalanced Regression

arXiv:2607.20173v1 Announce Type: new Abstract: Imbalanced regression problems arise when the target variable has an asymmetric distribution, resulting in underrepresented value ranges in the dataset.

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Logic-Guided Data Extraction with Answer Set Programming and Large Language Models

DGX agent

arXiv:2607.19365v1 Announce Type: new Abstract: When Large Language Models (LLMs) are used for semantic data extraction from unstructured text, producing candidate relational facts from natural langua

tutorialsarxiv-cs-ai

23 Jul 2026

PoTRE: Test-Time Reasoning inspired by Cognitive Heterogeneity

arXiv:2607.20268v1 Announce Type: new Abstract: While Large Language Models (LLMs) excel at many tasks, they frequently struggle with complex reasoning that requires long-horizon planning and iterativ

Stale but Stable: Staleness-Adaptive Trust Regions for Stabilizing Asynchronous Reinforcement Learning

arXiv:2607.18722v2 Announce Type: replace-cross Abstract: Asynchronous reinforcement learning improves throughput by decoupling rollout generation from optimization, but staleness is an inevitable byp

safetyarxiv-cs-cl

StrokeSeg2: Stroke Lesion Segmentation in Clinical Research Workflows

model-releasesarxiv-cs-cv

arXiv:2607.19901v1 Announce Type: new Abstract: Deep learning frameworks like nnU-Net achieve state-of-theart brain lesion segmentation performance but remain difficult to deploy in clinical research

Weakly Supervised Pathology-Informed Representation Learning for PET-Based Content Retrieval of Intra-Tumour Heterogeneity

arXiv:2607.18762v1 Announce Type: new Abstract: We propose a weakly supervised 18FFDG PET representation-learning framework for content based medical image retrieval, using H&E derived information dur

tutorialsarxiv-cs-cv

A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping

arXiv:2603.22006v2 Announce Type: replace-cross Abstract: Upcoming stage-IV surveys such as Euclid and Rubin will deliver vast amounts of high-precision data, opening new opportunities to constrain co

Analyzing Curricular Pattern Complexity Using AI to Improve On-Time Graduation Rates

arXiv:2607.13094v1 Announce Type: cross Abstract: The rise of Artificial Intelligence (AI) enables automatic analysis of large amounts of data. Previously time-consuming and labor-intensive tasks can

researcharxiv-cs-ai

Boogu-Image-0.1: Boosting Open-Source Unified Multimodal Understanding and Generation

arXiv:2607.13125v1 Announce Type: cross Abstract: We introduce Boogu-Image-0.1, an open-source unified multimodal understanding and generation model family, comprising Base, Turbo, Edit, and Edit-Turb

Local Ai

HRO: Hierarchical Room-to-Object Framework for Zero-Shot Object Goal Navigation with Large Language Models

arXiv:2607.13072v1 Announce Type: cross Abstract: Zero-shot object-goal navigation aims to enable an intelligent agent to explore and navigate to objects of unknown categories in an unfamiliar environ

local-aiarxiv-cs-ai

Applications

LPM: Industrial-Scale Generative Video Restoration

arXiv:2607.13460v1 Announce Type: new Abstract: We present the Large Processing Model (LPM), a diffusion-based generative framework for photorealistic video restoration under complex, in-the-wild degr

applicationsarxiv-cs-cv

Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling

arXiv:2607.13558v1 Announce Type: new Abstract: Urban region profiling constitutes a core problem in urban computing, supporting applications such as population estimation, economic assessment, and en

PiVoT: A Variational Solution for Real-time Large-scale Multi-object Detection and Tracking under Heavy Clutter

model-releasesarxiv-cs-cv

arXiv:2607.13891v1 Announce Type: cross Abstract: Multi-object detection and tracking from noisy point clouds remain challenging in many data-scarce radar applications. Current Bayesian trackers based

Representation-Based Exploration for Language Models: From Test-Time to Post-Training

arXiv:2510.11686v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) promises to expand the capabilities of language models, but it is unclear if current RL techniques promote the dis

Local Ai

Visual Place Recognition Using Rate-Encoded Spiking Neural Networks with Discrete STDP Learning

arXiv:2607.13584v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) trained through unsupervised Spike-Timing-Dependent Plasticity (STDP) have been explored as solutions to visual loop cl

local-aiarxiv-cs-cv

A Neurosymbolic Approach to Natural Language Formalization and Verification

arXiv:2511.09008v2 Announce Type: replace-cross Abstract: Large Language Models perform well at natural language interpretation and reasoning, but their lack of formal correctness guarantees limits th

Accuracy and Normalized Accuracy under Length Bias: Analysis, Guidelines, and a Bayesian Alternative

arXiv:2607.12767v1 Announce Type: new Abstract: Multiple-choice benchmarks that rank candidate completions by conditional log-probability suffer from a length bias: because log-probabilities sum over

CANDI: Contextual Alignment for Niche Domains Question Answering

arXiv:2607.11891v1 Announce Type: cross Abstract: The deployment of large language models (LLMs) in specialized domains like medical diagnostics and financial advisory necessitates evaluating capabili

CASHEW: Stabilizing Multimodal Reasoning via Iterative Trajectory Aggregation

arXiv:2601.08010v3 Announce Type: replace Abstract: Vision-language models achieve strong performance across a wide range of multimodal understanding and reasoning tasks, yet their multi-step reasonin

safetyarxiv-cs-cv

ChunkFlow: Towards Continuity-Consistent Chunked Policy Learning

arXiv:2607.12992v1 Announce Type: new Abstract: Vision-language action (VLA) models increasingly adopt chunked action heads to satisfy real-time constraints; however, this introduces boundary jitter:

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Code-MUE: Measuring Code LLMs' Uncertainty through Execution-based Semantic Interaction Graphs

arXiv:2607.12273v1 Announce Type: cross Abstract: As Code Large Language Models (LLMs) become central to modern software engineering, their inherent stochasticity poses significant real-world risks, w

Applications

Cost-Governed RAG: Unified Per-Tenant Cost Attribution Across Retrieval and Generation in Multi-Tenant LLM Systems

arXiv:2607.12188v1 Announce Type: new Abstract: Enterprise Retrieval-Augmented Generation (RAG) deployments face a critical governance gap: while LLM generation cost is metered per token, the retrieva

applicationsarxiv-cs-ai

Do We Really Need Multimodal Emotion Language Models Larger Than 1B Parameters?

arXiv:2607.12787v1 Announce Type: new Abstract: Recent advances in multimodal large language models (MLLMs) have significantly improved the performance of multimodal emotion recognition (MER) and enab

Hardware

FlashDiff: Efficient Regional Execution and Scheduling for Diffusion Model Serving

arXiv:2607.12121v1 Announce Type: cross Abstract: Diffusion models have become the central backbone for modern image, video, and audio generation, but their efficient service remains a challenge. Unli

hardwarearxiv-cs-lg

Generating Developable 3D Molecules via Pocket-Conditioned Diffusion and Property-Aware Optimization

arXiv:2607.12349v1 Announce Type: new Abstract: Drug discovery and development is time-consuming and resource-intensive, motivating computational approaches such as diffusion models for de novo drug d

In-Context Reinforcement Learning under Non-Stationarity: A Survey

arXiv:2607.11906v1 Announce Type: new Abstract: The development of decision-pretrained transformers, algorithm distillation, long-context meta-RL, and retrieval-augmented agents has renewed interest i

LatentFlow: A General Framework for Conditioning Stochastic Processes

arXiv:2607.12922v1 Announce Type: cross Abstract: Stochastic-process models are, as a rule, far easier to simulate than to condition. Non-linear observations, non-Gaussian likelihoods, black-box infor

researcharxiv-cs-lg

Learning-based Probabilistic Load Forecasting with Post-hoc and In-model Uncertainty

arXiv:2607.12730v1 Announce Type: cross Abstract: Smart-building load forecasters are often trained offline on dense, multivariate, high-frequency data, but deployment may provide only hourly, feature

researcharxiv-cs-ai

Lightweight Multi-Scale Anomaly Detection for Resource-Constrained Edge Devices

arXiv:2607.12599v1 Announce Type: new Abstract: Time-series anomaly detection is increasingly important in IoT systems, sensor networks, and edge monitoring applications, where models must operate und

Practical Judgment, Virtue, and Intuition in the Use of Opaque AI-Enabled Systems

arXiv:2607.12755v1 Announce Type: cross Abstract: AI-enabled systems are seeing increasing deployment across numerous domains, with many being 'black boxes' with respect to core functions and capabili

Quantification of Credal Uncertainty: A Distance-Based Approach

arXiv:2603.27270v2 Announce Type: replace Abstract: Credal sets, i.e., closed convex sets of probability measures, provide a natural framework to represent aleatoric and epistemic uncertainty in machi

tutorialsarxiv-cs-ai

Quantum Port-Hamiltonian Neural Networks: Learning Conservative and Dissipative Dynamics via Measurement-Induced Nonlinearity

arXiv:2607.12269v1 Announce Type: new Abstract: We introduce Quantum Port-Hamiltonian Neural Networks (Q-pHNNs), a family of parameterised quantum circuits that learn classical dynamics in a structure

Real-time fall detection based on vision for low-power edge platforms

arXiv:2607.12909v1 Announce Type: cross Abstract: Falling detection is vital for elderly care and intelligent surveillance; however, prevailing vision-based approaches predominantly frame it as static

Rethinking the Evaluation of Harness Evolution for Agents

arXiv:2607.12227v1 Announce Type: new Abstract: We revisit the evaluation of automatic harness evolution for LLM agents. Existing harness evolution methods use unit test cases to search for harness co

Same Compression Principle, Different Geometry: Rate-Distortion Signatures Dissociate Biological and Artificial Visual Systems

arXiv:2603.01568v2 Announce Type: replace-cross Abstract: Efficient coding theory predicts that biological perceptual systems compress sensory input optimally under resource constraints, with the syst

researcharxiv-cs-cv

TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale

arXiv:2607.13028v1 Announce Type: cross Abstract: Training robust autonomous driving agents requires a simulator that is fast enough for reinforcement learning at scale, realistic enough to ground beh

The Seriality Gap in Video Diffusion Models

arXiv:2607.13031v1 Announce Type: cross Abstract: When one ball strikes another, then another, video models should predict the consequences of each bounce. In controlled experiments on multi-ball hard

researcharxiv-cs-cv

Tracing Agentic Failure from the Flow of Success

arXiv:2607.12747v1 Announce Type: new Abstract: Failure attribution for LLM-based agentic systems, i.e., identifying which steps in a failure trajectory caused the task to fail, is critical for debugg

Translation as a Computationally Efficient Bridge: Feasibility of English BERT for Low-Resource Languages

arXiv:2607.12612v1 Announce Type: new Abstract: BERT models have revolutionised Natural Language Processing (NLP) through their ability to process unstructured text across diverse domains. However, de

researcharxiv-cs-cl

Visual Access Boundaries in Vision-Language Model Reasoning

arXiv:2607.12815v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting is widely used as a test-time scaling strategy for Vision-Language Models (VLMs), but it remains unclear what is extend

researcharxiv-cs-ai

When Close Enough Is Not Enough: Autoregressive Drift in Quantum Circuit Synthesis

arXiv:2607.12780v1 Announce Type: cross Abstract: Quantum circuit optimization for fault-tolerant computing requires exact functional equivalence while minimizing expensive non-Clifford resources such

A First-Principles Theory of Slow Thinking and Active Perception

arXiv:2607.08196v1 Announce Type: new Abstract: As part of a series on first-principles modeling of cognitive functions, this paper attempts to provide a mathematical formulation of thinking and perce

Bayesian Deep Learning for Discrete Choice

arXiv:2505.18077v3 Announce Type: replace-cross Abstract: Discrete choice models (DCMs) are used to analyze individual decision-making in contexts such as transportation choices, political elections,

Cognitive-structured Multimodal Agent for Multimodal Understanding, Generation, and Editing

arXiv:2607.08497v1 Announce Type: cross Abstract: Recent unified multimodal models show a single architecture can jointly perform vision/language understanding and image generation/editing. However, t

Local Ai

Collate: Collaborative Neural Network Learning for Latency-Critical Edge Systems

arXiv:2607.08013v1 Announce Type: new Abstract: Federated Learning (FL) empowers multiple clients to collaboratively learn a model, enlarging the training data of each client for high accuracy while p

local-aiarxiv-cs-lg

Native Video-Action Pretraining for Generalizable Robot Control

arXiv:2607.08639v1 Announce Type: cross Abstract: The advent of video-action models offers a promising path for robot control. Nevertheless, we argue that repurposing video generative models designed

safetyarxiv-cs-cv

Safe Flow Q-Learning: Offline Safe Reinforcement Learning with Reachability-Based Flow Policies

arXiv:2603.15136v2 Announce Type: replace-cross Abstract: Offline safe reinforcement learning (RL) seeks reward-maximizing policies from static datasets under strict safety constraints. Existing metho

SeFA-Policy: Fast and Accurate Visuomotor Policy Learning with Selective Flow Alignment

arXiv:2511.08583v2 Announce Type: replace-cross Abstract: Developing efficient and accurate visuomotor policies poses a central challenge in robotic imitation learning. While recent rectified flow app

safetyarxiv-cs-lg