Search: “models”

Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization

arXiv:2606.26002v1 Announce Type: new Abstract: We present HiReLC, a hierarchical ensemble-reinforcement learning framework for automated joint quantization and structured pruning of deep neural netwo

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Research

How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off

DGX agent

arXiv:2510.01163v2 Announce Type: replace Abstract: The factors driving the performance of in-context learning (ICL) in large language models (LLMs) remain poorly understood despite ICL's surprising e

researcharxiv-cs-lg

25 Jun 2026

How Modular Is a Frontier Mixture-of-Experts? A Pre-registered Causal Test in Which Apparent Expert Modularity Mostly Dissolves

arXiv:2606.25092v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) models route each token to a few of many experts, inviting the hypothesis that experts form functional modules tied to c

Improving Zero-Shot Offline RL via Behavioral Task Sampling

arXiv:2604.25496v2 Announce Type: replace Abstract: Offline zero-shot reinforcement learning (RL) aims to learn agents that optimize unseen reward functions without additional environment interaction.

Learning Subset-Shared Invariances for Domain Generalization with Mixture-of-Experts

arXiv:2606.25665v1 Announce Type: new Abstract: Domain generalization (DG) aims to learn a model from one or more source domains that generalizes to an unseen target domain without accessing target da

safetyarxiv-cs-lg

Learning with a Single Rollout via Monte Carlo Pass@k Critic

arXiv:2606.25451v1 Announce Type: new Abstract: Estimating token-level advantages in reinforcement learning (RL) for language models remains challenging because scaling up episodic experience collecti

safetyarxiv-cs-lg

LLM-ACES: Closed-Loop Discovery of Dynamical Systems with LLM-Guided Adaptive Search

arXiv:2606.25039v1 Announce Type: cross Abstract: Recovering governing Ordinary Differential Equations (ODEs) from data is a central challenge in modeling dynamical systems across scientific domains.

researcharxiv-cs-cl

Long-Term Simulation Exposes Cognitive-Developmental Risks in AI Companions

arXiv:2606.25396v1 Announce Type: new Abstract: AI companions powered by large language models increasingly interact with cognition-developing users, including children and adolescents, creating risks

MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction

arXiv:2606.25651v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed in healthcare settings, accurate error detection and correction in generated or existing text

safetyarxiv-cs-cl

Omni-Perception Policy Optimization for Multimodal Emotion Reasoning

arXiv:2606.25325v1 Announce Type: new Abstract: We find that current emotion-oriented Omni-MLLMs still lack reliable omni-modal perception: they (i) underutilize multimodal cues in their reasoning tra

On-Device Neural Architecture Search

arXiv:2606.24900v1 Announce Type: new Abstract: This paper proposes a new approach to near-sensor computing, in which a lightweight Neural Architecture Search (NAS) is performed directly on the deploy

Pre-Warm: Input-Conditioned Weight Initialization for Convolutional Neural Networks

model-releasesarxiv-cs-cv

arXiv:2606.25256v1 Announce Type: new Abstract: We introduce Pre-Warm, a simple yet effective zero-training-cost method for data-conditioned initialization of the first convolutional layer. Before the

Applications

Probing in the Wild: A Case Study of Self-Supervised Speech Representations on Mandarin Sub-dialects with Unsupervised Articulatory Analysis

arXiv:2606.25459v1 Announce Type: new Abstract: While self-supervised speech models have achieved strong performance across speech tasks, relatively little is known about how their internal phonetic r

applicationsarxiv-cs-cl

Rational Neural Networks have Expressivity Advantages

arXiv:2602.12390v2 Announce Type: replace Abstract: We study neural networks with trainable low-degree rational activation functions and show that they are more expressive and parameter-efficient than

Real-Time Voice AI Hears but Does Not Listen

arXiv:2606.26083v1 Announce Type: new Abstract: Speech conveys information through both words and vocal delivery. We evaluate four leading production realtime voice systems-OpenAI's GPT Realtime 2, Go

SARA: Unlocking Multilingual Knowledge in Mixture-of-Experts via Semantically Anchored Routing Alignment

arXiv:2606.25821v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) architectures have emerged as an increasingly influential paradigm as they offer a strategic balance between parameter s

Self-Modulating Quantum Fast-Weight Programmers for Efficient Adaptive Sequential Learning

arXiv:2606.24933v1 Announce Type: cross Abstract: Recent advances in quantum machine learning have motivated efficient models for sequential data processing. In this paper, we propose Self-Modulating

Shapley-Inspired Feature Weighting in k-means with No Additional Hyperparameters

arXiv:2508.07952v2 Announce Type: replace Abstract: Clustering algorithms often assume all features contribute equally to the data structure, an assumption that usually fails in high-dimensional or no

Solving Markov Decision Processes with Future Information via MPC

arXiv:2606.24991v1 Announce Type: cross Abstract: Model Predictive Control (MPC) is widely used in industrial and robotic systems for enforcing constraints and embedding domain knowledge through finit

safetyarxiv-cs-lg

Spatio-Temporal Mixture-of-Modality-Experts Diffusion for Quantitative DCE-MRI Synthesis from Incomplete MR Sequences

model-releasesarxiv-cs-cv

arXiv:2606.25535v1 Announce Type: new Abstract: Quantitative maps from dynamic contrast-enhanced MRI (DCE-MRI) are essential for tumor assessment but are often unavailable due to contrast-agent risks

Spotlighting Task-Relevant Features: Object-Centric Representations for Better Generalization in Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2601.21416v2 Announce Type: replace Abstract: The generalization capabilities of robotic manipulation policies are heavily influenced by the choice of visual representations. Existing approaches

Tutorials

Stabilizing black-box algorithms through task-oriented randomization

arXiv:2606.25269v1 Announce Type: cross Abstract: As black-box models become foundational to modern research, ensuring their stability is paramount for the realization of trustworthy artificial intell

tutorialsarxiv-cs-lg

Stable-Shift: Biologically Structured Prediction of Transcriptional Responses to Unseen Gene Perturbations

arXiv:2606.24940v1 Announce Type: cross Abstract: Predicting transcriptional responses to genetic perturbations could reduce the experimental burden of functional genomics, but extrapolation to genes

Staying In Character: Perspective-Bounded Memory For Book-Based Role-Playing Agents

arXiv:2606.25632v1 Announce Type: new Abstract: Recent LLM role-playing systems build character agents from novels by extracting characters, scenes, and relations. Yet long-narrative role-playing suff

Story Operators: Decomposing the Original o Sequel Transformation in Embedding Space

arXiv:2606.25379v1 Announce Type: new Abstract: I treat a book as a point in a sentence-embedding space and a literary transformation as an operation on points. Given an original novel and its sequel,

Swazure: Swarm Measurement of Pose for Flying Light Specks

model-releasesarxiv-cs-ro

arXiv:2606.25222v1 Announce Type: new Abstract: One may construct a 3D multimedia display using miniature drones configured with light sources, Flying Light Specks (FLSs). Swarms of FLSs localize to i

TacVerse: A Multi-Sensor Dataset and Benchmark for Cross-Sensor Vision-Based Tactile Perception

model-releasesarxiv-cs-ro

arXiv:2606.25877v1 Announce Type: new Abstract: Vision-based tactile sensors (VBTSs) enable robots to infer contact geometry and force-related cues by imaging deformation through an internal camera, y

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems

arXiv:2606.24937v1 Announce Type: cross Abstract: The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems. The book covers the full stack fr

safetyarxiv-cs-cl

Three Buddhist Vocabularies: Computational Stylometry of the English Pali Canon across Sutta, Vinaya, and Abhidhamma

arXiv:2606.25372v1 Announce Type: new Abstract: We present a computational stylometric analysis of the Tipitaka across all three Pitakas in English translation, extending earlier work on the Sutta Pit

Towards a Dynamic and Fixed-budget Memory Bank for Efficient Streaming Video Understanding

arXiv:2606.25658v1 Announce Type: new Abstract: Currently, streaming video understanding is still a daunting task for existing multimodal large language models (MLLMs). Its difficulties not only lie i

researcharxiv-cs-cv

Tracing Target Answers in Poisoned Retrieval Corpora via Token Influence Attribution

arXiv:2606.25721v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems are vulnerable to corpus poisoning attacks that manipulate model outputs through malicious retrieved docu

researcharxiv-cs-cl

UC-Search: Risk-Aware Test-Time Search for Delayed Constrained Time-Series Control

arXiv:2606.25274v1 Announce Type: new Abstract: Time-series models are usually scored as forecasters, yet deployed systems often require delayed decisions under uncertainty and hard feasibility constr

Agents

UniTeD: Unified Temporal Diffusion for Joint Perception and Planning in Autonomous Driving

arXiv:2606.25736v1 Announce Type: new Abstract: Diffusion models have shown strong potential for multi-modal planning in end-to-end autonomous driving. However, most existing methods confine diffusion

agentsarxiv-cs-cv

Wear-Clearance-Impact Coupling in the Jansen Linkage: A Gait-Durability-Optimized Design Slows Joint Loosening

arXiv:2606.25208v1 Announce Type: new Abstract: A companion study introduced joint durability into the dimensional design of the Theo Jansen walking linkage and found its classical 'holy numbers' Pare

local-aiarxiv-cs-ro

What Actually Works for Spacecraft Fault-Tolerant Control: An Honest Settled-Gate Benchmark of Learned and Classical Methods

arXiv:2606.25374v1 Announce Type: new Abstract: Recent learned fault-tolerant-control (FTC) work reports high success on spacecraft actuator faults, but often in simulation, on narrow fault sets, and

3D Masked Autoencoders are Robust Learners of Volumetric and Multimodal Cellular Representations for Microscopy

arXiv:2606.23964v1 Announce Type: cross Abstract: Self-supervised learning in fluorescence microscopy often relies on 2D projections, despite the inherently three-dimensional nature of cells. We prese

local-aiarxiv-cs-cv

Applications

A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning

arXiv:2307.13127v3 Announce Type: replace-cross Abstract: Data used to train predictive models via empirical risk minimization (ERM) often contain sensitive personal information. While differential pr

applicationsarxiv-cs-lg

A global log for medical AI

arXiv:2510.04033v2 Announce Type: replace Abstract: Modern computer systems rely on syslog, a universal protocol that records critical events across heterogeneous infrastructure. Medicine's rapidly gr

A Synthetic Reliability-Aware PINN Benchmark for Offshore Wind Turbine Support-Structure Monitoring with Bayesian Inverse Identification

arXiv:2606.24176v1 Announce Type: new Abstract: Reliable structural health monitoring (SHM) of offshore wind turbine (OWT) support structures requires fast state estimation from sparse measurements. R

ActiveScope: Actively Seeking and Correcting Perception for MLLMs

arXiv:2606.24292v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated impressive vision-language understanding, yet still struggle with fine-grained perception in

local-aiarxiv-cs-cv

Adaptive Hebbian Memory Routing in Vision Transformers for Few-Shot Learning

arXiv:2606.24756v1 Announce Type: new Abstract: Few-shot image recognition requires models to adapt to new classes from a small labeled support set. Hebbian fast-weight memory can provide temporary as

researcharxiv-cs-cv

Adversarial dynamical systems characterize when data-driven learning succeeds or fails

arXiv:2407.06312v2 Announce Type: replace-cross Abstract: Many systems resist analytical modeling, making data-driven inference of dynamics important. Yet data-driven methods can fail to converge or g

An LMM for Precisely Grounding Elements in Documents

arXiv:2606.24118v1 Announce Type: new Abstract: Visual grounding in documents is a crucial ability for Large Multimodal Models (LMMs) in areas such as document understanding, deep research and documen

local-aiarxiv-cs-cv

Are LLM Evaluators Really Narcissists? Sanity Checking Self-Preference Evaluations

arXiv:2601.22548v4 Announce Type: replace-cross Abstract: Recent research has shown that large language models (LLMs) favor their own outputs when acting as judges, undermining the integrity of automa

ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning

arXiv:2606.24601v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) addresses the problem of training multiple agents that pursue collaborative, competitive, or mixed objectives.

Beyond U-Net: A Latent-Representation-Aligned Skip-Free Backbone for Flow-Matching Speech Enhancement

arXiv:2606.24745v1 Announce Type: cross Abstract: Generative models, particularly diffusion and score-based approaches, have recently achieved strong performance in speech enhancement, but their itera

Bilevel Data Curation for LLM Fine-tuning: Offline Selection and Online Self-Refining Generation

arXiv:2511.21056v2 Announce Type: replace-cross Abstract: Supervised fine-tuning (SFT) datasets are critical to the downstream performance of large language models, yet they often contain low-quality

safetyarxiv-cs-cl

Blockwise Policy-Drift Gating for On-Policy Distillation

arXiv:2606.24084v1 Announce Type: cross Abstract: On-policy distillation (OPD) trains a student policy using teacher signals computed on trajectories sampled by the student itself. Recent work shows t