Search: “inference”

Bayesian-Optimized Superpixel-GrabCut for Traceable Optic Disc Segmentation

arXiv:2608.29196v1 Announce Type: new Abstract: Optic disc (OD) segmentation is essential for diagnosing ophthalmic pathologies from retinal fundus images. However, prevailing deep learning approaches

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Behavioral Latency as Weak Event-Time Supervision for EEG Reaction-Time Decoding

DGX agent

arXiv:2608.29428v1 Announce Type: new Abstract: Single-trial EEG analyses are often organized around events and latencies, yet EEG-based reaction-time (RT) prediction is posed as scalar regression on

safetyarxiv-cs-lg

1 Sept 2026

BiasMix-Finance: Post-Generation KYC Guardrails for LLM Portfolio Advice

arXiv:2608.28646v1 Announce Type: new Abstract: Large language models (LLMs) can generate plausible-sounding ETF portfolios while silently violating basic KYC-style constraints on risk, fees, and dive

Biomechanical 3D Body: Self-Supervised Distillation of Biomechanical Pose from a 3D Body Foundation Model

arXiv:2608.29928v1 Announce Type: new Abstract: State-of-the-art monocular body recovery methods predict mesh vertices and angles on the corresponding kinematic tree, but their outputs lack biomechani

Call Neighbours Yourself: Graph Walks with Destination-Conditioned On-Policy Self-Distillation

arXiv:2608.29588v1 Announce Type: new Abstract: Reasoning over text-attributed graphs (TAGs) requires large language models (LLMs) to combine a node's text with evidence distributed across its neighbo

safetyarxiv-cs-ai

CANVAS: Consistency-Aware Navigation via Visual Adaptive Sampling for Long-Context Text-to-SVG Generation

arXiv:2608.30689v1 Announce Type: new Abstract: Autoregressive large models have recently advanced Text-to-SVG generation from simple icons to complex, long-context graphics, yet standard autoregressi

researcharxiv-cs-cv

Ceiling-Clipped Acceptance Histograms Indicate Stranded Speed-up in Block-Diffusion Speculative Decoding

arXiv:2608.30427v1 Announce Type: new Abstract: Speculative decoding speeds up generation with an efficient draft model (drafter) that proposes tokens for a target model to verify in one pass, preserv

Chain-of-Thought Faithfulness of Reasoning Models Varies with Where and How Preference Cues Are Delivered

arXiv:2608.29464v1 Announce Type: cross Abstract: Chain-of-thought (CoT) monitoring assumes that reasoning traces faithfully record the information that shapes a model's answer. Existing faithfulness

Coarse composition suffices: tabular in-context learning for multi-activity antimicrobial peptide profiling

model-releasesarxiv-cs-lg

arXiv:2608.30337v1 Announce Type: new Abstract: Antimicrobial peptides (AMPs) often act against multiple pathogen classes, making multi-label activity prediction a more realistic screening target than

Compact and Infinite-Order Error Analysis for Null-Space SVD Estimation

arXiv:2608.30374v1 Announce Type: cross Abstract: We study null-space estimation from a noisy matrix. For a simple left null space, we first derive an exact compact expression for the error of the sma

researcharxiv-cs-lg

Local Ai

Computational Depth Measurement in Thermographic Video: Overcoming Spatial Overfitting via Spatio-Temporal Decoupling

arXiv:2608.29223v1 Announce Type: new Abstract: Accurate through-thickness measurement of subsurface delamination depth in Carbon Fiber Reinforced Polymer (CFRP) is important for structural assessment

local-aiarxiv-cs-ai

Conjoint Audio-to-Spikes Encoding and Processing for Efficient Neuromorphic Speech Recognition

arXiv:2608.30792v1 Announce Type: cross Abstract: Obtaining data from neuromorphic sensors and processing it with Spiking Neural Networks is a promising solution to lower the energy cost of artificial

Context-Aware Interpretable Representations for Retrieval and Graph Convolutional Network Classification

arXiv:2608.29004v1 Announce Type: cross Abstract: The advances in visual information modeling and representation during the last decades are remarkable, mainly supported by Convolutional Neural Networ

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Coronary Mask Guided Registration for Continuous Time 4D Cardiac CT Dataset Construction

arXiv:2608.28712v1 Announce Type: cross Abstract: Objective: Clinical cardiac CT multiphase reconstructions generally provide acceptable image quality in end-diastole (ED) or end-systole (ES) phases,

researcharxiv-cs-cv

CPR for LLMs: Critical-Point Routing against Catastrophic Forgetting in Domain Adaptation

arXiv:2608.30158v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) is the de facto standard for adapting large language models (LLMs) to target domains, but it often degrades the model's g

Cross-lingual Functional Vectors for Emotion Detection in Large Language Models

arXiv:2608.29613v1 Announce Type: new Abstract: Function vectors (FVs) have recently emerged as a promising mechanism for steering the behavior of large language models (LLMs) by injecting task-specif

Cross-Regional Grapevine Cold Hardiness Prediction via Learned Multimodal Latent Representations

arXiv:2608.31097v1 Announce Type: new Abstract: Accurate daily predictions of cold hardiness in woody plants are critical in regions where freezing temperatures can damage dormant buds and reduce seas

DASC: Decay-Aware State Compression for Hybrid Linear-Attention Serving

arXiv:2608.30386v1 Announce Type: cross Abstract: Hybrid linear-attention architectures have recently scaled to large open-weight models, offering quality competitive with full attention while substan

Data-to-Energy Stochastic Dynamics

arXiv:2509.26364v3 Announce Type: replace Abstract: The Schrodinger bridge problem is concerned with finding a stochastic dynamical system bridging two marginal distributions that minimises a certain

safetyarxiv-cs-lg

Dataset Protection via Watermarked Canaries in Retrieval-Augmented LLMs

arXiv:2502.10673v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has become an effective method for enhancing large language models (LLMs) with up-to-date knowledge. Howe

researcharxiv-cs-cl

Deciding When to Decide: Testing Operational Suboptimality Under Distributional Shift

model-releasesarxiv-cs-lg

arXiv:2608.29465v1 Announce Type: cross Abstract: Deployed decisions are often optimized once and retained because updates impose operational, regulatory, or switching costs. As operating conditions c

Designing for the Next Click: Bandits for Real-Time Page Layout

arXiv:2608.29850v1 Announce Type: new Abstract: E-commerce platforms increasingly personalize user experiences through machine learning, yet page layout decisions remain dominated by static rules and

safetyarxiv-cs-lg

Detecting Hidden Chain-of-Thought in Large Language Models with Linguistic, Behavioral, and Mechanistic Indicators

arXiv:2608.29956v1 Announce Type: new Abstract: Large language models often answer complex reasoning questions without revealing intermediate steps, raising whether they reason latently or complete pa

safetyarxiv-cs-cl

Diffusion-Based Inverse Design of Dielectric Resonator Metasurfaces for Shaping Smart Electromagnetic Environments

arXiv:2608.29907v1 Announce Type: new Abstract: Future wireless systems are expected to transform the surrounding space from a passive propagation medium into a smart electromagnetic environment, wher

researcharxiv-cs-lg

DIP: Dynamic In-Context Planner For Diffusion Language Models

arXiv:2601.03199v2 Announce Type: replace-cross Abstract: Diffusion language models (DLMs) have shown strong potential for general natural language tasks with in-context examples. Existing In-Context

Discrete Diffusion Bridges for Spatiotemporally Aligned Image Translation and Generation

arXiv:2608.29997v1 Announce Type: new Abstract: We propose Discrete Diffusion Bridges (DDB), a novel framework designed to resolve the fundamental spatiotemporal misalignment of standard discrete diff

safetyarxiv-cs-cv

Do Large Language Models Possess a Theory of Mind? A Comparative Evaluation Using the Strange Stories Paradigm

arXiv:2603.18007v2 Announce Type: replace-cross Abstract: The study explores whether current Large Language Models (LLMs) exhibit Theory of Mind (ToM) capabilities -- specifically, the ability to infe

Applications

Doc-REFRAG: Rethinking Multimodal Document Retrieval-Augmented Generation

arXiv:2608.30163v1 Announce Type: cross Abstract: Real-world knowledge resides in multimodal documents, necessitating retrieval-augmented generation (RAG) for accurate question answering. However, exi

applicationsarxiv-cs-cv

DriftingVLA: Native One-Step Vision-Language-Action Generation via Per-Dimension Temporal Drifting

arXiv:2608.29749v1 Announce Type: new Abstract: Conventional flow-based vision-language-action (VLA) models support expressive continuous action generation but rely on multi-step refinement to produce

safetyarxiv-cs-ro

Efficient and High-Quality Depth Estimation via Pixel-Space Diffusion with Linear Attention

arXiv:2608.30129v1 Announce Type: new Abstract: This work presents extbf{Lapis}, a extbf{l}inear-extbf{a}ttention-based extbf{pi}xel-extbf{s}pace generative framework that achieves efficient and high-

researcharxiv-cs-cv

Enhancing Low-Resource Language Reasoning via High-Resource Language Feature Transfer

arXiv:2608.30462v1 Announce Type: cross Abstract: Large language models exhibit substantial performance variation across languages, even when solving semantically equivalent tasks. Existing analyses o

Tutorials

EquiReg: Equivariance Regularized Diffusion for Inverse Problems

arXiv:2505.22973v3 Announce Type: replace-cross Abstract: Diffusion models represent the state-of-the-art for solving inverse problems such as image restoration tasks. Diffusion-based inverse solvers

tutorialsarxiv-cs-ai

Equivariant Sheaf Neural Networks: Learning Geometric Transport on Graphs

arXiv:2608.28853v1 Announce Type: cross Abstract: Equivariant graph neural networks provide a principled way to model geometric systems, but efficient first-order architectures remain limited in how v

Evaluating the Hidden Costs of Personalization in Large Language Models

arXiv:2608.28833v1 Announce Type: new Abstract: While Large language models (LLMs) incorporate user personalization signals to improve usability and helpfulness, they increasingly shift from providing

safetyarxiv-cs-ai

EviAnchor: Mitigating Hallucinations in Large Vision-Language Models via Regional Visual Evidence Compensation

arXiv:2608.29092v1 Announce Type: new Abstract: Large vision-language models (LVLMs) frequently generate content unsupported by visual inputs. Preliminary experiments show that visual evidence is prim

Evolutionary Soups: Evolving Mixture-of-Experts for Multi-Objective LLM Alignment

arXiv:2608.29978v1 Announce Type: new Abstract: Large language models are increasingly required to generate responses that satisfy multiple competing objectives. Since optimal trade-offs depend on bot

safetyarxiv-cs-cl

Extending TotalSegmentator: Predicting Patient and Acquisition Characteristics from CT and MR Images

arXiv:2608.29348v1 Announce Type: new Abstract: Background: Patient details and acquisition metadata are important for clinical decisions, image quality control, and automated research pipelines, but

Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction

arXiv:2506.15626v3 Announce Type: replace-cross Abstract: extbf{Objective:} Brain-predicted age difference (BrainAGE) is a neuroimaging biomarker reflecting brain health. However, training robust Brai

Fine-Tuning Low-Bit Models with Gradient in Quantized Code Space

arXiv:2608.30908v1 Announce Type: new Abstract: Fine-tuning Low-bit models aims to adapt a quantized model while keeping the final deployed checkpoint in the same low-bit form. This setting is practic

researcharxiv-cs-lg

Foundational feature fusion for conditional flow matching in 6D pose estimation

arXiv:2608.29183v1 Announce Type: new Abstract: Conditional flow matching has enabled a step forward in object 6D pose estimation, achieving state-of-the-art performance by progressively denoising and

From Intent to Evidence: Policy-Steered Multi-Strategy Retrieval for Long-Video Agents

arXiv:2608.31005v1 Announce Type: new Abstract: Existing long-video agents acquire evidence through one uniform behavior, ignoring whether the required evidence is concentrated, requires broad occurre

safetyarxiv-cs-cv

FuncRoom-Agent: Sequential Feed-Forward 3D Functional Indoor Scene Generation

arXiv:2608.29519v1 Announce Type: new Abstract: We introduce Function-Room Generation, a new indoor 3D scene generation setting that creates rooms supporting explicit functional goals rather than mere

Generative vs. Encoder Models for Multilingual NER: A Comprehensive Empirical Study on Naamapadam

arXiv:2608.29959v1 Announce Type: new Abstract: Language is humanity's most consequential technology, yet for over a billion speakers across India's twenty-two constitutionally recognised languages, i

GenRubric: Self-Evolving Rubric Generation for Scalable LLM Evaluation

arXiv:2608.29856v1 Announce Type: new Abstract: Large language models are increasingly used as scalable evaluators for open-ended tasks. However, many LLM judges derive query-specific criteria during

researcharxiv-cs-cl

GeoRay: Gauge-Aware Feed-Forward Satellite 3D Reconstruction in the Geodetic Frame

arXiv:2608.29680v1 Announce Type: new Abstract: Feed-forward 3D foundation models reconstruct perspective scenes in one pass. Satellite photogrammetry needs a different product, one that domain adapta

HiVe: Beyond Static Prompts for Multitask Learning via Hierarchy-based Vertical Mixture-of-Experts

arXiv:2608.29790v1 Announce Type: new Abstract: As large language models (LLMs) continue to scale, parameter-efficient fine-tuning (PEFT) has become a practical alternative to full-parameter adaptatio

How Identity and Opinion Shape Political Sycophancy in LLMs

arXiv:2608.29198v1 Announce Type: new Abstract: As Large Language Models (LLMs) increasingly encourage users to disclose personal profiles for tailored assistance, measuring their political alignment

safetyarxiv-cs-ai

HSRM: Hidden-State Reward Models for Test-Time Verification

arXiv:2608.30841v1 Announce Type: new Abstract: Large language models can often generate plausible mathematical reasoning traces, but reliably identifying the correct solution among multiple candidate