Search: “inference”

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning

arXiv:2607.29353v1 Announce Type: cross Abstract: With the ever-increasing pervasiveness of smart edge devices, the demand is growing for applications that can be tailored to users (e.g., custom keywo

local-aiarxiv-cs-ai

3 Aug 2026

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Applications

What Makes a Sale? Simulating End-to-End Seller--Buyer Retail Dynamics with LLM Agents

DGX agent

arXiv:2604.04468v2 Announce Type: replace Abstract: Evaluating retail strategies before deployment is difficult, as outcomes are determined across multiple stages, from seller-side persuasion through

applicationsarxiv-cs-ai

3 Aug 2026

Why It Hurts: Identifying the Drivers of Negative Thoughts in Emotional Support Conversations

model-releasesarxiv-cs-ai

arXiv:2607.28648v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for emotional support tasks, such as negative thought reframing. This task relies on modifying cogn

3 Aug 2026

ZeroR@CHiPSAL 2026: Two-Stage Vision-Language Adaptation with Contrastive Learning for Nepali Meme Classification

arXiv:2607.28637v1 Announce Type: new Abstract: This paper presents our system for the CHiPSAL 2026 shared task on multimodal hate speech and sentiment detection in Nepali memes. We address both subta

researcharxiv-cs-cl

3 Aug 2026

4DHumanDiff: Direct Text-to-4DGS Generation for Consistent 360-Degree Dynamic Humans

arXiv:2607.27634v1 Announce Type: new Abstract: Generating high-quality 360-degree dynamic human assets from text prompts is challenging. Existing methods usually synthesize monocular or multi-view vi

researcharxiv-cs-cv

A Lightweight Foundation Model for Collider Physics with Multi-Domain Adaptation

arXiv:2607.27501v1 Announce Type: new Abstract: We present a lightweight approach to foundation modeling (extbf{NEXUS}) that leverages pre-trained learning from collider physics data towards out-of-do

Hardware

A Query-Efficient Stochastic Volume Rendering Framework for Time-Varying Implicit Neural Volumes

arXiv:2607.28047v1 Announce Type: cross Abstract: Time-varying implicit neural representations (INRs) provide a compact representation of scientific volumes and, for modalities such as dynamic X-ray c

hardwarearxiv-cs-lg

APO: Unsupervised Atomic Policy Optimization for 3D Structure Prediction of Atomic Systems

arXiv:2607.28553v1 Announce Type: new Abstract: Predicting the 3D structures of atomic systems is fundamental to advancing material science and drug discovery. While flow-matching models (, FlowDPO) h

safetyarxiv-cs-lg

ARD-REFSM: Enhancing Reflection Symmetry Detection with Asymmetric Denoising and Rotation Equivariance

model-releasesarxiv-cs-cv

arXiv:2607.27927v1 Announce Type: new Abstract: Reflection symmetry detection remains challenging due to interference from asymmetric regions and arbitrary orientations of symmetric patterns. Asymmetr

Backbone-Agnostic Stochastic Perturbation Learning for End-to-End Real-World Image Dehazing

arXiv:2607.11623v3 Announce Type: replace Abstract: Real-world paired image dehazing remains challenging because haze degradation is spatially non-uniform, illumination-dependent, and physically ambig

tutorialsarxiv-cs-cv

Balancing Privacy and Efficiency: Music Information Retrieval via Additive Homomorphic Encryption

arXiv:2508.07044v2 Announce Type: replace-cross Abstract: Modern music retrieval runs on vector embeddings, and once these embeddings are shared for search or matching they can be copied, probed, or u

researcharxiv-cs-ai

BCNet: Bronchus Classification via Structure Guided Representation Learning

model-releasesarxiv-cs-cv

arXiv:2205.06947v3 Announce Type: replace-cross Abstract: CT-based bronchial tree analysis is essential for diagnosing lung and airway diseases, yet automatic bronchus classification remains challengi

Belief-Guided Decision Making with Uncertainty Gating in the Game of Go

arXiv:2607.26946v1 Announce Type: new Abstract: Recent advancements in Computer Go, driven by AlphaZero and MuZero, rely heavily on Monte Carlo Tree Search (MCTS) to correct the errors of the neural n

safetyarxiv-cs-ai

Beyond Similarity: Grounded Agentic Extraction and Expert-Adjudicated Evaluation of Intertextuality in Classical Chinese Histories

arXiv:2607.27595v1 Announce Type: new Abstract: Computational approaches to intertextuality have advanced from string matching to neural retrieval, yet their outputs, similarity scores and parallel-pa

Beyond the Best Teacher: Expanding and Compressing the Reasoning Solution Manifold

arXiv:2607.27770v1 Announce Type: new Abstract: A single reinforcement-learning run can produce a strong reasoner yet an incomplete teacher: it often amplifies only a subset of the valid solution mode

local-aiarxiv-cs-lg

Bridging the Gap in Ophthalmic AI: MM-Retinal-Reason Dataset and OphthaReason Model toward Dynamic Multimodal Reasoning

arXiv:2508.16129v3 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have recently demonstrated remarkable reasoning abilities with reinforcement learning paradigm. Although se

tutorialsarxiv-cs-ai

Capturing Token Tendencies for Training-Free Token Pruning in Multimodal Large Language Models

arXiv:2607.28341v1 Announce Type: new Abstract: While visual token pruning is essential for efficient Multimodal Large Language Models (MLLMs), existing training-free methods suffer from a critical li

local-aiarxiv-cs-cv

Applications

CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations

arXiv:2505.04999v2 Announce Type: replace-cross Abstract: Learning robot control policies from demonstrations typically requires action-labeled expert data, which is expensive to collect through teleo

applicationsarxiv-cs-lg

Closed-Loop Bayesian Bandit Encoder with GRAND Receiver for a Bursty Interference Channel

arXiv:2607.15404v2 Announce Type: replace-cross Abstract: Interleaving mitigates burst errors but introduces decoding delay and removes temporal error structure that a channel-aware decoder could expl

Compact Task-Aligned Imitation Learning for Laboratory Automation

arXiv:2603.01110v2 Announce Type: replace Abstract: Robotic laboratory automation has traditionally relied on carefully engineered motion pipelines and task-specific hardware interfaces, resulting in

local-aiarxiv-cs-ro

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation

arXiv:2603.12055v3 Announce Type: replace Abstract: Continual learning of pretrained vision-language models (VLMs) is prone to catastrophic forgetting, yet current approaches adapt to new tasks withou

researcharxiv-cs-cv

Agents

CRMWeaver: Building Powerful Business Agent via Agentic RL and Shared Memories

arXiv:2510.25333v2 Announce Type: replace Abstract: Recent years have witnessed the rapid development of LLM-based agents, which shed light on using language agents to solve complex real-world problem

agentsarxiv-cs-cl

DinoLizer: Separating VAE and Diffusion Artifacts in Generative Inpainting Localization

arXiv:2511.20722v2 Announce Type: replace Abstract: We introduce DinoLizer, a DINOv2-based localizer of manipulated areas in generative inpainting. The model is trained to focus on semantically altere

local-aiarxiv-cs-cv

Distributions In, Distributions Out: The Case for Soft-Label Training

arXiv:2511.14117v2 Announce Type: replace Abstract: Supervised classifiers output a distribution over classes but are typically trained against a single label obtained by collapsing multiple annotator

Eco3S: Complex Socio-Economic System Simulation via Agent-Based Models

arXiv:2607.26588v1 Announce Type: new Abstract: The rapid development of large language models (LLMs) has renewed interest in agent-based modeling (ABM). However, current LLM-based ABM research faces

safetyarxiv-cs-ai

EgoGVAE: Ego-body Mesh Reconstruction via Guided Variational Autoencoder

model-releasesarxiv-cs-cv

arXiv:2607.27755v1 Announce Type: new Abstract: We address the problem of recovering the full-body mesh from only the head pose. This task has become essential for various applications based on head-m

Applications

Enhancing Irregular Time Series Forecasting with Continuous-Time Modeling Framework

arXiv:2607.28035v1 Announce Type: new Abstract: Irregular multivariate time series are widely encountered in applications such as healthcare monitoring, human activity recognition, and environmental s

applicationsarxiv-cs-lg

Enhancing Scene Transition Awareness in Video Generation via Post-Training

arXiv:2507.18046v2 Announce Type: replace Abstract: Recent advances in AI-generated video have shown strong performance on text-to-video tasks, particularly for short clips depicting a single scene. H

tutorialsarxiv-cs-cv

Exact Action Values Are Not Enough: Rollout-Verified Reinforcement Fine-Tuning of a Reasoning Model for Multi-Zone VAV Control

model-releasesarxiv-cs-lg

arXiv:2607.27914v1 Announce Type: new Abstract: Multi-zone variable-air-volume control must balance thermal comfort, indoor air quality, and electricity use across several continuous actuators. Model

Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation

arXiv:2607.27372v1 Announce Type: cross Abstract: The deep learning revolution, kicked off by AlexNet, taught us that end-to-end training beats decomposing a problem into hand-designed stages. Generat

FA-RDP: A Frequency-Adaptive Reactive Diffusion Policy for Contact-Rich Manipulation

arXiv:2607.28596v1 Announce Type: new Abstract: In contact-rich manipulation, action multimodality and reactivity dominate different stages of a single episode. Before contact, multiple trajectories m

safetyarxiv-cs-ro

Fairness Pruning: Locating Demographic Bias in GLU-MLP Layers via Differential Activations

arXiv:2607.28319v1 Announce Type: new Abstract: This work presents Fairness Pruning, a lightweight structural intervention method designed for the management and future mitigation of demographic bias

Agents

FaithEyes: Towards Faithful Tool Use via Multi-Agent Process-Image Verification

arXiv:2607.28225v1 Announce Type: new Abstract: Agentic vision-language models (VLMs), which interleave textual reasoning with explicit tool calls such as cropping and code-based image manipulation, h

agentsarxiv-cs-cv

Fewer Clarifications, Better Code: Benchmarking Cross-Session Personalized Ambiguity Adaptation in Coding Assistants

model-releasesarxiv-cs-ai

arXiv:2607.26611v1 Announce Type: new Abstract: AI-assisted coding increasingly translates informal user intent into executable software, yet coding requests often contain ambiguities that recur in us

Hardware

FinCacheServe: Dependency-Consistent Answer Reuse for Cost-Efficient RAG Serving over Mutable Enterprise Documents

arXiv:2607.26076v1 Announce Type: cross Abstract: Retrieval-augmented generation services over mutable enterprise documents repeatedly execute semantically equivalent analysis requests. Answer reuse c

hardwarearxiv-cs-ai

Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering

arXiv:2607.28568v1 Announce Type: new Abstract: Recursive self-improvement (RSI) requires AI systems that improve the process of building AI (i.e., AI4AI); machine learning engineering (MLE) offers a

GGC: Selective Query Correction for Reliable Text-to-SPARQL Generation

arXiv:2607.28082v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong capabilities in structured query generation, making them a natural choice for Text-to-SPARQL, whic

researcharxiv-cs-cl

Good Rankers, Bad Objectives: Bilinear Contrastive Critics under Expressive Policy Search

model-releasesarxiv-cs-lg

arXiv:2607.27422v1 Announce Type: new Abstract: Good action rankings do not make a contrastive critic safe to maximize. These critics increasingly act as value-like objectives for best-of-K selection,

Graph Is the Verifier: Agentic Reinforcement Learning for Interprocedural Vulnerability Detection

arXiv:2607.26656v1 Announce Type: cross Abstract: Real-world vulnerabilities often span multiple functions, yet most learning-based detectors classify each function in isolation: on a sample of real C

safetyarxiv-cs-ai

Harnessing the Potential of Optimizing Data Mixtures via Bayesian Domain Reweighting

arXiv:2607.27928v1 Announce Type: new Abstract: The performance of Large Language Models (LLMs) is fundamentally influenced by the distributional composition of multi-domain pre-training data. While m

safetyarxiv-cs-lg

HOMER: Huber-of-Means for Efficient and Robust Estimation in Hilbert Spaces

arXiv:2607.27532v1 Announce Type: cross Abstract: Heavy tails weaken high-confidence control for the empirical mean. Geometric median-of-means (MOM) also lacks a threshold that moves toward mean effic

Improving Item Discoverability in e-Commerce Search via Related Intent Generation

arXiv:2607.27172v1 Announce Type: cross Abstract: Traditional search systems are optimized to retrieve items that strictly match a query, often prioritizing precision over recall. In e-commerce market

researcharxiv-cs-ai

Applications

Information Bottleneck Learning for Faithful Time Series Forecasting Explanations

arXiv:2607.28124v1 Announce Type: new Abstract: As forecasts increasingly drive decisions in fields such as energy, transportation, and healthcare, understanding the historical data behind these predi

applicationsarxiv-cs-lg

Kohn-Sham Spectral Embedding on Sparse Graphs at the Nishimori Temperature for Image Classification

arXiv:2607.28428v1 Announce Type: cross Abstract: We introduce Kohn--Sham Spectral Embedding (KSSE), a physics-inspired energy-based model replacing dense CNN classifiers with a sparse-graph spectral

local-aiarxiv-cs-cv

Large scale cross-regional remote sensing flood monitoring framework for operative mapping and impact analysis

arXiv:2607.28401v1 Announce Type: new Abstract: Effective flood monitoring is critical for minimizing the impacts of flood disasters on populations and infrastructure. Yet reliable remote sensing acro

researcharxiv-cs-cv

Latent Matters: Learning Deep State-Space Models

arXiv:2602.23050v2 Announce Type: replace Abstract: Deep state-space models (DSSMs) enable temporal predictions by learning the underlying dynamics of observed sequence data. They are often trained by

tutorialsarxiv-cs-lg

Learning to Detect Cyber Attacks: Neural Anomaly Detection for Cybersecurity with Theoretical Insights

arXiv:2409.08521v2 Announce Type: replace-cross Abstract: In cybersecurity practice, new forms of cyberattacks continuously emerge, deliberately designed to evade defense systems that rely on previous

tutorialsarxiv-cs-lg

LM-GRASP: Instance-Specific Language Models for Combinatorial Construction via Online Imitation Learning

model-releasesarxiv-cs-lg

arXiv:2607.28135v1 Announce Type: new Abstract: Machine learning for combinatorial optimization typically relies on neural constructors trained via reinforcement learning on large offline datasets for