Search: “inference”

SCENT: Aligning Mass Spectra with Molecular Structure for Olfactory Perception

arXiv:2605.27009v1 Announce Type: new Abstract: Predicting human olfactory perception from molecular structure has seen remarkable progress, yet these approaches require explicit chemical structure at

safetyarxiv-cs-lg

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Model Releases

Self-Verified Distillation: Your Language Model Is Secretly Its Own Synthetic Data Pipeline

DGX agent

arXiv:2605.26132v1 Announce Type: new Abstract: Can post-trained large language models (LLMs) further improve themselves using only unlabeled prompts, without external teachers or feedback from tools?

model-releasesarxiv-cs-cl

27 May 2026

Structured Relational Reasoning for Group Activity Assessment

model-releasesarxiv-cs-cv

arXiv:2508.07996v2 Announce Type: replace Abstract: Group Activity Detection (GAD) involves recognizing social groups and their collective behaviors in videos. Vision Foundation Models (VFMs), like DI

Targeted Remasking: Replacing Token Editing with Token-to-Mask Refinement in Discrete Diffusion Language Models

arXiv:2605.26436v1 Announce Type: cross Abstract: Discrete masked diffusion language models such as LLaDA generate text through iterative denoising, where mask tokens are progressively replaced with p

“The future of AI is going to be local models running on extraordinary desktop hardware.” - @Jason This line from the recent @theallinpod hi…

local-aiyohei-nakajima--x

“The future of AI is going to be local models running on extraordinary desktop hardware.” - @Jason This line from the recent @theallinpod hit hard. For years AI meant sending everything to the cloud,

The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence

arXiv:2605.26494v1 Announce Type: new Abstract: We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum

TWIST: Closed-Loop token Synchronization for Application-Aware Wireless Digital Twins

arXiv:2605.27205v1 Announce Type: cross Abstract: Wireless digital twins require repeated synchronization between a time-evolving physical scene and its digital counterpart under limited and time-vary

When Correct Demonstrations Hurt: Rethinking the Role of Exemplars in In-Context Learning

arXiv:2605.26350v1 Announce Type: cross Abstract: In-context learning (ICL) is often motivated by the intuition that demonstrations help because they provide correct input-output examples. However, we

A Controlled Synthetic Benchmark for Educational Aspect-Based Sentiment Analysis

arXiv:2605.25502v1 Announce Type: cross Abstract: Educational aspect-based sentiment analysis (ABSA) can support course improvement, but public aspect-labeled student feedback remains scarce because e

A general tensor-structured compression scheme for efficient large language models

arXiv:2605.25344v1 Announce Type: cross Abstract: Large language models (LLMs) are dominated by dense linear transformations, whose storage, memory and computational overheads hinder efficient adaptat

Advancing Graph Few-Shot Learning via In-Context Learning

arXiv:2605.24410v1 Announce Type: new Abstract: Graph few-shot learning, which aims to classify nodes from novel classes with only a few labeled examples, is a widely studied problem in graph learning

Agent-Centric Social Trajectory Prediction: A Free Energy Principle Perspective

arXiv:2605.25748v1 Announce Type: new Abstract: Trajectory prediction methods have demonstrated remarkable capabilities in capturing complex motion patterns. However, existing methods rely on global s

Agent Primitives: Reusable Latent Building Blocks for Multi-Agent Systems

arXiv:2602.03695v2 Announce Type: replace-cross Abstract: While existing multi-agent systems (MAS) can handle complex problems by enabling collaboration among multiple agents, they are often highly ta

Agent-ToM: Learning to Monitor Autonomous LLM Agents via Theory-of-Mind Reasoning

arXiv:2605.24216v1 Announce Type: cross Abstract: Monitoring autonomous large language model (LLM) agents for covert malicious behavior is challenging due to delayed, context-dependent, and long-horiz

Applications

Assessing the Operational Viability of Foundation Models for Time Series Forecasting

arXiv:2605.24381v1 Announce Type: cross Abstract: Time series forecasting drives operational decisions in areas like finance, transportation, and energy. While supervised learning approaches achieve s

applicationsarxiv-cs-ai

AstroMind: A High-Fidelity Benchmark for Spacecraft Behavior Reasoning Based on Large Language Models

arXiv:2605.24573v1 Announce Type: new Abstract: Understanding why a spacecraft maneuvers -- rather than simply that it did -- is an increasingly important problem for space domain awareness as Earth o

Asymmetric Adaptation-based Real-time Fault Diagnosis Under Transitional Operating Conditions

arXiv:2605.24457v1 Announce Type: cross Abstract: Data streams in real-world industrial scenarios often contain transitional operating conditions that are uncovered during offline training, leading to

tutorialsarxiv-cs-lg

CARL-CXR: Continual Adapter-Based Routing for Task-Unknown Chest Radiograph Classification

arXiv:2602.15811v2 Announce Type: replace-cross Abstract: Clinical deployment of chest radiograph classifiers requires models that can be updated as new datasets become available without retraining on

Causal methods for LLM development and evaluation

arXiv:2605.25998v1 Announce Type: new Abstract: Large language model (LLM) development is currently driven by large-scale empirical iteration over data mixtures, reward models, routing strategies, and

safetyarxiv-cs-lg

Applications

ChainzRule: Sample-Efficient, Robust Deep Learning Across Tabular, NLP, and Vision Tasks

arXiv:2605.24340v1 Announce Type: new Abstract: Production deep learning systems across enterprise domains operate under constraints that academic benchmarks routinely obscure: labeled data is expensi

applicationsarxiv-cs-lg

Characterizing Linear Alignment Across Language Models

arXiv:2603.18908v4 Announce Type: replace Abstract: Language models increasingly appear to learn similar representations, despite differences in training objectives, architectures, and data modalities

DRInQ: Evaluating Conversational Implicature with Controlled Context Variation

arXiv:2605.24267v1 Announce Type: new Abstract: Human conversation relies heavily on conversational implicature, in which speakers convey meanings that are suggested rather than explicitly stated. Alt

Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models

arXiv:2605.24924v1 Announce Type: new Abstract: Diffusion models excel at generating diverse and multimodal trajectories for robotic planning, yet their iterative denoising process introduces latency

safetyarxiv-cs-ro

DynaPURLS: Dynamic Refinement of Part-Aware Representations for Skeleton-Based Zero-Shot Action Recognition

arXiv:2512.11941v2 Announce Type: replace-cross Abstract: Zero-shot skeleton-based action recognition (ZS-SAR) is fundamentally constrained by prevailing approaches that rely on aligning skeleton feat

EchoDistill:Alignment Noisy-to-Clean Self-Distillation for Robust Audio LLMs

arXiv:2605.23954v1 Announce Type: cross Abstract: Audio Large Language Models (ALLMs) are highly vulnerable to real-world noise, which often induces severe semantic drift and hallucinations. Existing

Generative modeling of granular flow on inclined planes using conditional flow matching

arXiv:2604.04453v2 Announce Type: replace-cross Abstract: Granular flows govern many natural and industrial processes, yet their interior kinematics and mechanics remain largely unobservable, as exper

researcharxiv-cs-lg

Guided Flow Matching for Forward and Inverse PDE Problems with Sparse Observations: Algorithm and Theory

model-releasesarxiv-cs-lg

arXiv:2605.25509v1 Announce Type: cross Abstract: Reconstructing PDE solutions from sparse observations is a core challenge in scientific computing. We present FM4PDE, a flow-matching generative frame

Hardware

H^{2}MT: Semantic Hierarchy-Aware Hierarchical Memory Transformer

arXiv:2605.24930v1 Announce Type: new Abstract: Transformer-based LLMs achieve strong results on many language tasks; however, long inputs remain challenging because context windows are finite, and pr

hardwarearxiv-cs-cl

JT-SAFE-V2: Safety-by-Design Foundation Model with World-Context Data

arXiv:2605.24414v1 Announce Type: new Abstract: We introduce JT-Safe-V2, a large language model designed to advance the safety and trustworthiness of foundation models, extending our previous JT-Safe

Learning to Trust: Bayesian Adaptation to Varying Suggester Reliability in Sequential Decision Making

arXiv:2511.12378v2 Announce Type: replace Abstract: Autonomous agents operating in sequential decision-making tasks under uncertainty can benefit from external action suggestions, which provide valuab

Looped Diffusion Language Models

model-releasesarxiv-cs-lg

arXiv:2605.26106v1 Announce Type: new Abstract: Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models for language modeling, yet the effective design of trans

Multi-Agent Coordination Adaptation via Structure-Guided Orchestration

arXiv:2605.25746v1 Announce Type: cross Abstract: As large language model (LLM)-based multi-agent systems scale to handle increasingly complex tasks, balancing structural stability and dynamic adaptab

PACT: Proactive Asking for Continual Task Assistance in Human-Robot Collaboration

arXiv:2605.24350v1 Announce Type: new Abstract: Robotic assistants in long-term human-robot collaboration need to assist users under partial observations while leveraging cross-day interaction history

researcharxiv-cs-ro

Retrieved In-Context Principles from Previous Mistakes

arXiv:2407.05682v2 Announce Type: replace Abstract: In-context learning (ICL) has been instrumental in adapting Large Language Models (LLMs) to downstream tasks using correct input-output examples. Re

researcharxiv-cs-cl

Selective Test-Time Compute Scaling for Click-Through Rate Prediction via Uncertainty-Triggered Feature Path Exploration

arXiv:2605.24989v1 Announce Type: cross Abstract: Scaling test-time compute has proven highly effective for language models, yet this opportunity remains largely unexplored for industrial Click-Throug

tutorialsarxiv-cs-ai

Test-Time Deep Thinking to Explore Implicit Rules

arXiv:2605.24828v1 Announce Type: new Abstract: With the continuous advancement of Large Language Models (LLMs), intelligent agents are becoming increasingly vital. However, these agents often fail in

Towards the Connection between Activation Sparsity and Flat Minima

arXiv:2605.25612v1 Announce Type: cross Abstract: The observation that activation sparsity emerges in MLP blocks of standardly trained Transformers offers an opportunity to drastically reduce computat

Universal Boosts, Specific Suppressors: Sparse Autoencoder Steering of Medical Vision-Language Models

arXiv:2605.24977v1 Announce Type: cross Abstract: Medical vision-language models (VLMs) often hallucinate findings when generating chest X-ray reports: they fabricate findings that are not present in

safetyarxiv-cs-cl

Your Embedding Model is SMARTer Than You Think

arXiv:2605.24938v1 Announce Type: cross Abstract: Multimodal retrieval relies heavily on single-vector retrievers, which compress rich, sequential token sequences into one single global representation

local-aiarxiv-cs-ai

A mathematical theory of balancing relational generalization and memorization

arXiv:2605.22972v1 Announce Type: cross Abstract: Humans, animals, and modern machine learning models exhibit impressive abilities to learn complex behaviors and generalize these behaviors to unseen s

tutorialsarxiv-cs-ai

Beyond Normal References: Discriminative Few-Shot Anomaly Detection

arXiv:2605.23231v1 Announce Type: new Abstract: This paper considers a practical few-shot anomaly detection (FSAD) setting, termed discriminative FSAD, where a limited number of both normal and anomal

tutorialsarxiv-cs-cv

Can AI Guess What You Know? Performance Comparison of Large Language Models for Human Domain Knowledge Estimation From Communication Logs

arXiv:2605.22971v1 Announce Type: new Abstract: Employees often struggle to identify ``who knows what,'' leading to organizational productivity losses. We investigate whether Large Language Models (LL

CHASD: Language Increment-Calibrated Contrastive Decoding against Hallucination in LVLMs

arXiv:2605.23344v1 Announce Type: cross Abstract: Large Vision-Language Models have shown strong multimodal reasoning capabilities, yet they remain susceptible to object hallucinations when language p

local-aiarxiv-cs-ai

Contrastive Distribution Matching for Amortized Sequential Monte Carlo in Discrete Diffusion

arXiv:2605.23346v1 Announce Type: new Abstract: Discrete diffusion models have emerged as powerful frameworks for generating structured categorical data. However, efficiently sampling from reward-tilt

safetyarxiv-cs-lg

DFKI-MLT at SemEval-2026 TASK 7: Steering Multilingual Models Towards Cultural Knowledge

arXiv:2605.23069v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used across diverse linguistic and cultural contexts, yet their cultural knowledge remains uneven across r

Evaluating PhaseNet on Teleseismic Data with MsPASS

arXiv:2605.22837v1 Announce Type: cross Abstract: Numerous studies have shown that the machine-learning picker PhaseNet produces accurate P and S picks on local earthquake signals, but its performance

local-aiarxiv-cs-lg

FastKernels: Benchmarking GPU Kernel Generation in Production

arXiv:2605.23215v1 Announce Type: cross Abstract: LLM-based agents for GPU kernel generation are advancing rapidly, yet their progress is fundamentally constrained by the benchmarks they optimize agai

InfiGFusion: Graph-on-Logits Distillation via Efficient Gromov-Wasserstein for Model Fusion

arXiv:2505.13893v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have intensified efforts to fuse heterogeneous open-source models into a unified system that inherit

researcharxiv-cs-cl