Search: “inference”

WPG-MoE: Weak-Prior-Guided Dense Mixture-of-Experts for User-Level Social Media Depression Detection

arXiv:2607.04350v1 Announce Type: new Abstract: Online social media posts provide scalable signals for early depression screening, and recent studies mainly improve pre-classification evidence through

local-aiarxiv-cs-cl

7 Jul 2026

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Research

A Mathematical Introduction to Diffusion Models

DGX agent

arXiv:2607.01693v1 Announce Type: new Abstract: These notes give a proof-oriented introduction to diffusion models from the viewpoint of sampling, tracing a single arc from classical sampling dynamics

researcharxiv-cs-lg

3 Jul 2026

ACID: Action Consistency via Inverse Dynamics for Planning with World Models

arXiv:2607.02403v1 Announce Type: cross Abstract: Decision-time planning with action-conditioned world models has become a popular paradigm for embodied control. However, the standard planning cost ju

Adaptive Companionship for Group-Following Robots: Handling Dynamically Changing Group Formations

arXiv:2607.01287v1 Announce Type: cross Abstract: Accompanying a group of humans is an essential aspect of developing human-like social cognition in robots. However, human groups typically do not foll

Hardware

AI-enabled gravitational-waves searches for binary neutron stars at optimal sensitivity

arXiv:2607.01372v1 Announce Type: cross Abstract: Gravitational Waves (GWs) represent the newest window of astronomy, furthering our understanding of compact objects like black holes and neutron stars

hardwarearxiv-cs-ai

Ask the Right Comparison:Bias-Aware Bayesian Active Top-k Ranking with LLM Judges

arXiv:2607.02104v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as cheap, scalable judges that compare candidate outputs pairwise -- to rank responses, select models

Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation

model-releasesarxiv-cs-cl

arXiv:2607.02182v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit remarkable reasoning capabilities, but their task-specific fine-tuning is notoriously plagued by overconfidence,

Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification

arXiv:2607.01272v1 Announce Type: cross Abstract: Deploying 3D point cloud analysis in privacy-sensitive, resource-constrained settings faces two barriers: data cannot be centralized, and models must

BRIDGE: Predicting Human Task Completion Time From Model Performance

arXiv:2602.07267v2 Announce Type: replace Abstract: Evaluating the real-world capabilities of AI systems requires grounding benchmark performance in human-interpretable measures of task difficulty. Ex

Bridge-WA: Predicting Where and How the World Changes for Robotic Action

arXiv:2607.02195v1 Announce Type: new Abstract: General-purpose vision-language-action models benefit from large vision-language priors, but effective manipulation also requires anticipating action-re

local-aiarxiv-cs-ro

CheckRLM: Effective Knowledge-Thought Coherence Checking in Retrieval-Augmented Reasoning

arXiv:2607.02262v1 Announce Type: new Abstract: Reasoning Language Models (RLMs) have significantly improved performance on complex tasks by extending the reasoning chain. However, these chains are pr

local-aiarxiv-cs-cl

Composite Reward Design in PPO-Driven Adaptive Filtering

arXiv:2506.06323v2 Announce Type: replace-cross Abstract: Model-free and reinforcement learning-based adaptive filtering methods are gaining traction for denoising in dynamic, non-stationary environme

Controllable Sim Agents with Behavior Latents

arXiv:2607.02496v1 Announce Type: cross Abstract: Realistic traffic simulation requires agents that imitate logged behavior and can also be steered along interpretable axes. Such controllability enabl

Criticality-Based Guard Rail Validation for AI Agent Decisions in Autonomous Telecom Networks

arXiv:2607.02210v1 Announce Type: new Abstract: The evolution toward fully autonomous telecommunications networks (Autonomous Network Levels 4-5) requires AI/ML agents to make real-time network decisi

Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning

arXiv:2607.02037v1 Announce Type: cross Abstract: Autonomous surface vehicles vary widely in hydrodynamic and actuation characteristics, yet most controllers are designed for single-platform deploymen

safetyarxiv-cs-lg

DecompRL: Solving Harder Problems by Learning Modular Code Generation

arXiv:2607.02390v1 Announce Type: new Abstract: How can Large Language Models (LLMs) solve problems they currently cannot? Repeated sampling scales test-time compute but GPU cost grows linearly with a

Do Newer Lightweight CNNs Perform Better Under Resource Constraints? A Controlled Multigenerational Study of Architecture, Initialization, Training Budget, and Efficiency

arXiv:2607.01984v1 Announce Type: cross Abstract: Newer lightweight convolutional neural networks are often presented as improving predictive performance and deployment efficiency, but such claims req

Fourier Neural Operators for Rayleigh-Benard Convection

arXiv:2607.02088v1 Announce Type: new Abstract: We propose an improved Fourier Neural Operator (FNO) for modeling two-dimensional Rayleigh-Benard convection by predicting time increments instead of fu

researcharxiv-cs-lg

Fourier Preconditioning for Neural Feature Learning

arXiv:2607.02199v1 Announce Type: cross Abstract: Mutual information (MI)-inspired feature learning techniques are capable of generating low-dimensional embeddings that retain nonlinear dependence str

researcharxiv-cs-lg

Full Bayesian Reinforcement Learning via LF-IBIS

arXiv:2607.01741v1 Announce Type: cross Abstract: Reinforcement Learning (RL) is a sequential decision-making framework in which an agent learns optimal policies through interaction with an environmen

Hidden Forgetting in Continual Multimodal Learning: When Accuracy Survives but Grounding Fails

arXiv:2607.02020v1 Announce Type: new Abstract: Multimodal large language models must continually adapt to evolving tasks and domains, yet standard continual learning metrics mainly measure whether ol

Kara: Efficient Reasoning LLM Serving via Sliding-Window KV Cache Compression

arXiv:2607.01237v1 Announce Type: cross Abstract: Reasoning language models often generate long chain-of-thought (CoT), which accumulates a massive KV cache during the decoding phase and incurs high d

kNNGuard: Turning LLM Hidden Activations into a Training-Free Configurable Guardrail

arXiv:2607.02072v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in domains requiring guardrails to detect unsafe, off-topic, or adversarial prompts. Existing g

Latent Policy Barrier: Learning Robust Visuomotor Policies by Staying In-Distribution

arXiv:2508.05941v2 Announce Type: replace Abstract: Visuomotor policies trained via behavior cloning are vulnerable to covariate shift, where small deviations from expert trajectories can compound int

Learning Semantic Atomic Skills for Multi-Task Robotic Manipulation

arXiv:2512.18368v2 Announce Type: replace Abstract: Scaling imitation learning to diverse multi-task robot manipulation remains challenging due to suboptimal demonstrations, behavioral multi-modality,

Morphology-Aware Sample Assignment: Overcoming IoU Insensitivity for Surface Defect Detection

arXiv:2606.13723v2 Announce Type: replace-cross Abstract: Intersection-over-Union (IoU), as a pivotal metric for evaluating the spatial alignment between candidate proposals and ground-truth annotatio

MultAttnAttrib: Training-Free Multimodal Attribution in Long Document Question Answering

arXiv:2607.01420v1 Announce Type: cross Abstract: As grounded QA systems are increasingly deployed in AI assistants, accurately attributing generated answers to evidence is critical for user trust and

Object Aligner: A Configurable JSON Schema Similarity Score for Graphs, Applied to LLM Prompt Optimization

arXiv:2607.01972v1 Announce Type: cross Abstract: Large language models (LLMs) are often asked to produce JSON conforming to a fixed schema, powering information extraction, tool calling, agentic plan

Optimizing Visual Generative Models via Distribution-wise Rewards

arXiv:2607.02291v1 Announce Type: new Abstract: Conventional reinforcement learning strategies for visual generation typically employ sample-wise reward functions, yet this practice frequently results

safetyarxiv-cs-lg

OrbitQuant: Data-Agnostic Quantization for Image and Video Diffusion Transformers

arXiv:2607.02461v1 Announce Type: cross Abstract: Diffusion transformers (DiTs) achieve state-of-the-art image and video generation, but their multi-step sampling and growing parameter count make infe

PairCoder++: Pair Programming as a Universal Paradigm for Verified Code-Driven Multimodal and Structured-Artifact Generation

model-releasesarxiv-cs-cl

arXiv:2607.01883v1 Announce Type: new Abstract: Code is the medium through which large language models generate structured artifacts: charts, scientific figures, vector graphics, CAD models, 3D scenes

Hardware

PixGS: Pixel-Space Diffusion for Direct 3D Gaussian Splat Generation

arXiv:2607.01803v1 Announce Type: cross Abstract: Recent advances in 3D content generation from text or images have achieved impressive results, yet view inconsistency from 2D generators and the scarc

hardwarearxiv-cs-ro

Procedural Memory Distillation: Online Reflection for Self-Improving Language Models

arXiv:2607.01480v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR), along with recent selfdistillation variants such as SDPO, evaluates each rollout against a verifi

local-aiarxiv-cs-ai

Program-as-Weights: A Programming Paradigm for Fuzzy Functions

arXiv:2607.02512v1 Announce Type: cross Abstract: Many everyday programming tasks resist clean rule-based implementation, such as alerting on important log lines, repairing malformed JSON, or ranking

Quantifying the Uncertainty of Blindly Estimated Room Embeddings Using a Dispersion-Calibrated Score

arXiv:2607.01527v1 Announce Type: cross Abstract: Room embeddings derived from reverberant speech are often unreliable: speech content and recording degradation can alter the representation even when

safetyarxiv-cs-lg

Agents

Real-Time Visual Intelligence on Low-Cost UAVs: A Modular Approach for Tracking, Scanning, and Navigation

arXiv:2607.02298v1 Announce Type: new Abstract: Autonomous drones are rapidly transforming modern warfare and civil applications alike. This paper presents the development of an integrated intelligent

agentsarxiv-cs-ro

ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning

arXiv:2607.02509v1 Announce Type: new Abstract: Understanding and reasoning over long contexts has become a key requirement for deploying large language models (LLMs) in realistic applications. Althou

Restoring Linguistic Grounding in VLA Models via Train-Free Attention Recalibration

arXiv:2603.06001v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models enable robots to perform manipulation tasks directly from natural language instructions and are increasing

Self-Supervised Test-Time Tuning for Packet Loss Concealment

arXiv:2607.01823v1 Announce Type: cross Abstract: Packet loss concealment (PLC) reconstructs audio packets that are missing at the receiver, usually with a trained model whose parameters remain fixed

researcharxiv-cs-cl

Single-Channel EEG-Based Cognitive Load Assessment in Online Learning: A Hybrid Deep Learning Approach

arXiv:2607.01795v1 Announce Type: cross Abstract: Monitoring cognitive load during online learning could help instructors identify content that learners find difficult, but remote settings remove the

Subliminal Clocks: Latent Time Modelling in Diffusion Language Models

arXiv:2607.01774v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) have recently emerged as a promising alternative to autoregressive models. Unlike standard diffusion-based approaches,

The Future of NLP may not be at NLP Conferences: Scholarly Migration Patterns in Natural Language Processing

arXiv:2607.02416v1 Announce Type: new Abstract: Natural Language Processing (NLP) has traditionally been published in its core disciplinary venues like ACL. However, advances in Large Language Models

researcharxiv-cs-cl

Transport Discrepancy as a Reliability Signal for Vision-Language-Action Models

arXiv:2512.01715v2 Announce Type: replace Abstract: Vision-language-action (VLA) models that generate continuous action chunks via flow matching lack an internal signal for judging whether a given pre

What LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent Debates

arXiv:2607.02507v1 Announce Type: new Abstract: LLM agents will increasingly act in socially structured settings where role, audience, and relational context can shape what is advantageous or costly t

Hardware

Accelerating Discrete Diffusion Models with Parallel-In-Time Sampling

arXiv:2607.00773v1 Announce Type: new Abstract: Discrete diffusion models are widely used for learning and generating discrete distributions. As the generation process is inherently sequential, the ac

hardwarearxiv-cs-lg

Active Spatial Guidance: Eliminating Injected Positional Mechanisms in Vision Transformers

arXiv:2607.00580v1 Announce Type: new Abstract: Vision Transformers (ViTs) commonly rely on injected positional mechanisms to address self-attention's permutation invariance. Motivated by the spatial

safetyarxiv-cs-cv

AI Native Games: A Survey and Roadmap

arXiv:2607.00527v1 Announce Type: new Abstract: Generative AI now enables games to produce dialogue, quests, characters, images, and worlds at runtime. Yet generation alone does not make a game AI-nat

AutoSpeed: Annotation-Free Stage-Adaptive Motion Speed Learning for Robot Manipulation

arXiv:2607.01051v1 Announce Type: new Abstract: Different stages of manipulation tasks exhibit varying levels of difficulty, suggesting stage-dependent motion speeds and temporal prediction horizons.