Search: “models”

Pairwise matrices for sparse autoencoders: single-feature inspection mislabels causal axes

arXiv:2605.03160v1 Announce Type: new Abstract: The standard sparse-autoencoder (SAE) interpretability protocol labels each feature from its top-activating contexts and validates by single-feature ste

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PhysicianBench: Evaluating LLM Agents in Real-World EHR Environments

DGX agent

arXiv:2605.02240v1 Announce Type: new Abstract: We introduce PhysicianBench, a benchmark for evaluating LLM agents on physician tasks grounded in real clinical setting within electronic health record

model-releasesarxiv-cs-ai

6 May 2026

Prediction horizon shapes representations in predictive learning

arXiv:2511.09290v2 Announce Type: replace Abstract: Predictive learning has emerged as a central paradigm for training models across diverse data domains and is increasingly viewed as a foundation for

researcharxiv-cs-lg

RAG over Thinking Traces Can Improve Reasoning Tasks

model-releasesarxiv-cs-cl

arXiv:2605.03344v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) has proven effective for knowledge-intensive tasks, but is widely believed to offer limited benefit for reasoning

Real Image Denoising with Knowledge Distillation for High-Performance Mobile NPUs

arXiv:2605.03680v1 Announce Type: new Abstract: While deep-learning-based image restoration has achieved unprecedented fidelity, deployment on mobile Neural Processing Units (NPUs) remains bottlenecke

Rose-SQL: Role-State Evolution Guided Structured Reasoning for Multi-Turn Text-to-SQL

arXiv:2605.03720v1 Announce Type: new Abstract: Recent advances in Large Reasoning Models (LRMs) trained with Long Chain-of-Thought have demonstrated remarkable capabilities in code generation and mat

researcharxiv-cs-cl

Applications

SCALER:Synthetic Scalable Adaptive Learning Environment for Reasoning

arXiv:2601.04809v5 Announce Type: replace Abstract: Reinforcement learning (RL) offers a principled way to enhance the reasoning capabilities of large language models, yet its effectiveness hinges on

applicationsarxiv-cs-ai

SCGNN: Semantic Consistency enhanced Graph Neural Network Guided by Granular-ball Computing

arXiv:2605.02617v2 Announce Type: new Abstract: Capturing semantic consistency among nodes is crucial for effective graph representation learning. Existing approaches typically rely on k-nearest neigh

Task-Aware Scanning Parameter Configuration for Robotic Inspection Using Vision Language Embeddings and Hyperdimensional Computing

arXiv:2605.03909v1 Announce Type: cross Abstract: Robotic laser profiling is widely used for dimensional verification and surface inspection, yet measurement fidelity is often dominated by sensor conf

The Productivity-Reliability Paradox: Specification-Driven Governance for AI-Augmented Software Development

arXiv:2605.01160v1 Announce Type: cross Abstract: Since 2022, AI-powered coding assistants have produced contradictory evidence: controlled studies report 20-56% productivity gains on well-scoped task

TMD-Bench: A Multi-Level Evaluation Paradigm for Music-Dance Co-Generation

arXiv:2605.01809v1 Announce Type: cross Abstract: Unified audio-visual generation is rapidly gaining industrial and creative relevance, enabling applications in virtual production and interactive medi

Toward a Science of Intent: Closure Gaps and Delegation Envelopes for Open-World AI Agents

arXiv:2604.25000v2 Announce Type: replace Abstract: Recent work has framed intelligence in verifiable tasks as reducing time-to-solution through learned structure and test-time search, while systems w

Trojan Hippo: Weaponizing Agent Memory for Data Exfiltration

arXiv:2605.01970v2 Announce Type: cross Abstract: Memory systems enable otherwise-stateless LLM agents to persist user information across sessions, but also introduce a new attack surface. We characte

Safety

Uni-OPD: Unifying On-Policy Distillation with a Dual-Perspective Recipe

arXiv:2605.03677v1 Announce Type: new Abstract: On-policy distillation (OPD) has recently emerged as an effective post-training paradigm for consolidating the capabilities of specialized expert models

safetyarxiv-cs-lg

Voice Mapping of Text-to-Speech Systems: A Metric-Based Approach for Voice Quality Assessment

arXiv:2605.00861v1 Announce Type: cross Abstract: This study investigates voice mapping as an evaluation framework for text-to-speech (TTS) synthesis quality. The study analyzes six TTS models, includ

researcharxiv-cs-ai

Where to Bind Matters: Hebbian Fast Weights in Vision Transformers for Few-Shot Character Recognition

arXiv:2605.02920v1 Announce Type: cross Abstract: Standard transformer architectures learn fixed slow-weight representations during training and lack mechanisms for rapid adaptation within an episode.

A Coupled Fourth Order Telegraph Diffusion Framework Using Grayscale Indicators for Image Despeckling

arXiv:2605.00881v1 Announce Type: cross Abstract: Speckle noise severely limits the quality of images acquired from coherent imaging systems such as Synthetic Aperture Radar (SAR) and medical ultrasou

researcharxiv-cs-cv

A tutorial on learning from preferences and choices with Gaussian Processes

arXiv:2403.11782v5 Announce Type: replace Abstract: Preference modelling lies at the intersection of economics, decision theory, machine learning and statistics. By understanding individuals' preferen

tutorialsarxiv-cs-lg

Action Agent: Agentic Video Generation Meets Flow-Constrained Diffusion

model-releasesarxiv-cs-ro

arXiv:2605.01477v1 Announce Type: new Abstract: We present Action Agent, a two-stage framework that unifies agentic navigation video generation with flow-constrained diffusion control for multi-embodi

Hardware

AgriKD: Cross-Architecture Knowledge Distillation for Efficient Leaf Disease Classification

arXiv:2605.01355v1 Announce Type: new Abstract: Automated leaf disease classification is critical for early disease detection in resource-constrained field environments. Vision Transformers (ViTs) pro

hardwarearxiv-cs-cv

Aligning LLMs with Biomedical Knowledge using Balanced Fine-Tuning

arXiv:2511.21075v3 Announce Type: replace Abstract: Engineering LLMs to accelerate life sciences research requires a robust alignment with biomedical knowledge. We observe that biomedical text exhibit

Almost for Free: Crafting Adversarial Examples with Convolutional Image Filters

arXiv:2605.01098v1 Announce Type: cross Abstract: Adversarial examples in machine learning are typically generated using gradients, obtained either directly through access to the model or approximated

researcharxiv-cs-cv

Assistance Without Interruption: A Benchmark and LLM-based Framework for Non-Intrusive Human-Robot Assistance

model-releasesarxiv-cs-ro

arXiv:2605.01368v1 Announce Type: new Abstract: Human-robot interaction (HRI) has long studied how agents and people coordinate to achieve shared goals. In this work, we formalize and benchmark the no

Applications

Automatic Reflection Level Classification in Hungarian Student Essays

arXiv:2605.02402v1 Announce Type: new Abstract: Reflective thinking is a key competency in education, but assessing reflective writing remains a time-consuming and subjective task for education expert

applicationsarxiv-cs-cl

AutoSpatial: Visual-Language Reasoning for Social Robot Navigation through Efficient Spatial Reasoning Learning

model-releasesarxiv-cs-ro

arXiv:2503.07557v2 Announce Type: replace Abstract: We present a novel method, AutoSpatial, an efficient approach with structured spatial grounding to enhance VLMs' spatial reasoning. By combining min

CEZSAR: A Contrastive Embedding Method for Zero-Shot Action Recognition

arXiv:2605.01165v1 Announce Type: new Abstract: This paper proposes a novel Zero-Shot Action Recognition~(ZSAR) method based on contrastive learning. In ZSAR, we aim to classify examples from classes

CLaC at SemEval-2026 Task 6: Response Clarity Detection in Political Discourse

model-releasesarxiv-cs-cl

arXiv:2605.02170v1 Announce Type: new Abstract: In this paper, we present our system for SemEval-2026 Task 6 (CLARITY) on response clarity and evasion detection in question-answer pairs from U.S. pres

Class-Aware Adaptive Differential Privacy in Deep Learning for Sensor-Based Fall Detection

arXiv:2605.01679v1 Announce Type: cross Abstract: Fall detection is a critical task in healthcare, particularly for elderly people. Timely fall detection and treatment can prevent severe injuries. Sen

CoAction: Cross-task Correlation-aware Pareto Set Learning

arXiv:2605.01712v1 Announce Type: new Abstract: Pareto set learning (PSL) is an emerging paradigm in multi-objective optimization that trains neural networks to map preference vectors to Pareto optima

Concepts Whisper While Syntax Shouts: Spectral Anti-Concentration and the Dual Geometry of Transformer Representations

arXiv:2605.01609v1 Announce Type: new Abstract: We test whether the causal inner product of itet{park2024linear} -- defined by the unembedding covariance Sigma -- enables cross-lingual concept transpo

Controllable Logical Hypothesis Generation for Abductive Reasoning in Knowledge Graphs

arXiv:2505.20948v3 Announce Type: replace Abstract: Abductive reasoning in knowledge graphs aims to generate plausible logical hypotheses from observed entities, with broad applications in areas such

Decompose and Recompose: Reasoning New Skills from Existing Abilities for Cross-Task Robotic Manipulation

arXiv:2605.01448v1 Announce Type: cross Abstract: Cross-task generalization is a core challenge in open-world robotic manipulation, and the key lies in extracting transferable manipulation knowledge f

Demographic-Aware Transfer Learning for Sleep Stage Classification in Clinical Polysomnography

arXiv:2605.02245v1 Announce Type: new Abstract: Automated sleep stage classification typically employs a single population-agnostic model, disregarding established demographic variations in sleep arch

researcharxiv-cs-lg

Efficient Parameter Estimation of Truncated Boolean Product Distributions

arXiv:2007.02392v3 Announce Type: replace Abstract: We study the problem of estimating the parameters of a Boolean product distribution in d dimensions, when the samples are truncated by a set S subse

EGAD: Entropy-Guided Adaptive Distillation for Token-Level Knowledge Transfer

arXiv:2605.01732v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable performance across diverse domains, yet their enormous computational and memory requirements hinde

tutorialsarxiv-cs-cl

Exploring Accuracy Law for Deep Time Series Forecasters: An Empirical Study

arXiv:2510.02729v2 Announce Type: replace Abstract: Deep time series forecasting has emerged as a rapidly growing field in recent years. Despite the exponential growth of community interests, progress

tutorialsarxiv-cs-lg

Federated Reinforcement Learning for Efficient Mobile Crowdsensing under Incomplete Information

arXiv:2605.02705v1 Announce Type: new Abstract: Mobile crowdsensing (MCS) is a distributed sensing architecture that utilizes existing sensors on mobile units (MUs) to perform sensing tasks. A mobile

FoR-Net: Learning to Focus on Hard Regions for Efficient Semantic Segmentation

arXiv:2605.02764v1 Announce Type: new Abstract: We present FoR-Net, a lightweight architecture for semantic segmentation that focuses on identifying and enhancing hard regions. Instead of relying on h

From Where Things Are to What They Are For: Benchmarking Spatial-Functional Intelligence in Multimodal LLMs

arXiv:2605.02130v1 Announce Type: new Abstract: Human-level agentic intelligence extends beyond low-level geometric perception, evolving from recognizing where things are to understanding what they ar

GameScope: A Multi-Attribute, Multi-Codec Benchmark Dataset for Gaming Video Quality Assessment

arXiv:2605.01272v1 Announce Type: new Abstract: The development of video game streaming has grown rapidly, with major platforms such as YouTube and Twitch using different codecs. To support quality as

InfantAgent-Next: A Multimodal Generalist Agent for Automated Computer Interaction

arXiv:2505.10887v3 Announce Type: replace Abstract: This paper introduces extsc{InfantAgent-Next}, a generalist agent capable of interacting with computers in a multimodal manner, encompassing text, i

Safety

Logit-Gap Steering: A Forward-Pass Diagnostic for Alignment Robustness

arXiv:2506.24056v2 Announce Type: replace-cross Abstract: RLHF-style alignment trains language models to refuse unsafe requests, but how much operational margin does this refusal rest on? We introduce

safetyarxiv-cs-cl

MSMixer: Learned Multi-Scale Temporal Mixing with Complementary Linear Shortcut for Long-Term Time Series Forecasting

arXiv:2605.02689v1 Announce Type: new Abstract: Long-term time series forecasting requires models that simultaneously capture rapid oscillations, medium-range periodicities, and slowly evolving macro-

researcharxiv-cs-lg

Hardware

MusicInfuser: Making Video Diffusion Listen and Dance

arXiv:2503.14505v3 Announce Type: replace Abstract: We introduce MusicInfuser, an approach that aligns pre-trained text-to-video diffusion models to generate high-quality dance videos synchronized wit

hardwarearxiv-cs-cv

Only Say What You Know: Calibration-Aware Generation for Long-Form Factuality

arXiv:2605.01749v1 Announce Type: new Abstract: Large Reasoning Models achieve strong performance on complex tasks but remain prone to hallucinations, particularly in long-form generation where errors

researcharxiv-cs-cl

PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies

arXiv:2605.01625v1 Announce Type: new Abstract: Proteins are inherently multiscale physical systems whose functional properties emerge from coordinated structural organization across multiple spatial

Provably Learning Attention with Queries

arXiv:2601.16873v2 Announce Type: replace Abstract: We study the problem of learning Transformer-based sequence models with black-box access to their outputs. In this setting, a learner may adaptively

tutorialsarxiv-cs-lg

Safety

RECAP: Transparent Inference-Time Emotion Alignment for Medical Dialogue Systems

arXiv:2509.10746v3 Announce Type: replace Abstract: Large language models in healthcare often produce emotionally flat or opaque responses, failing to provide the transparent reasoning required for cl

safetyarxiv-cs-cl