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ASMR-Bench: Auditing for Sabotage in ML Research

arXiv:2604.16286v1 Announce Type: new Abstract: As AI systems are increasingly used to conduct research autonomously, misaligned systems could introduce subtle flaws that produce misleading results wh

AST: Adaptive, Seamless, and Training-Free Precise Speech Editing

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Human DGX agent

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DGX agent

arXiv:2604.16056v1 Announce Type: cross Abstract: Text-based speech editing aims to modify specific segments while preserving speaker identity and acoustic context. Existing methods rely on task-speci

researcharxiv-cs-ai

20 Apr 2026

AstroVLM: Expert Multi-agent Collaborative Reasoning for Astronomical Imaging Quality Diagnosis

arXiv:2604.16024v1 Announce Type: cross Abstract: Vision Language Models (VLMs) have been applied to several specific domains and have shown strong problem-solving capabilities. However, astronomical

agentsarxiv-cs-cv

AtManRL: Towards Faithful Reasoning via Differentiable Attention Saliency

arXiv:2604.16158v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly rely on chain-of-thought (CoT) reasoning to solve complex tasks. Yet ensuring that the reasoning trace both

Attention Sinks Are Provably Necessary in Softmax Transformers: Evidence from Trigger-Conditional Tasks

arXiv:2603.11487v5 Announce Type: replace Abstract: Transformers often display an attention sink: probability mass concentrates on a fixed, content-agnostic position. Are sinks a byproduct of the opti

researcharxiv-cs-lg

ATTNPO: Attention-Guided Process Supervision for Efficient Reasoning

arXiv:2602.09953v2 Announce Type: replace Abstract: Large reasoning models trained with reinforcement learning and verifiable rewards (RLVR) achieve strong performance on complex reasoning tasks, yet

Author-in-the-Loop Response Generation and Evaluation: Integrating Author Expertise and Intent in Responses to Peer Review

arXiv:2602.11173v2 Announce Type: replace Abstract: Author response (rebuttal) writing is a critical stage of scientific peer review that demands substantial author effort. In practice, authors posses

AutoDrive-R^2: Incentivizing Reasoning and Self-Reflection Capacity for VLA Model in Autonomous Driving

arXiv:2509.01944v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models in autonomous driving systems have recently demonstrated transformative potential by integrating multimoda

safetyarxiv-cs-cv

AutoFed: Personalized Federated Traffic Prediction via Adaptive Prompt

arXiv:2512.24625v2 Announce Type: replace-cross Abstract: Accurate traffic prediction is essential for Intelligent Transportation Systems, including ride-hailing, urban road planning, and vehicle flee

Hardware

AutoFlows++: Hierarchical Message Flow Mining for System on Chip Designs

arXiv:2604.15359v1 Announce Type: cross Abstract: Understanding communication behavior in modern system-on-chip (SoC) designs is critical for functional verification, performance analysis, and post-si

hardwarearxiv-cs-lg

Automatic Combination of Sample Selection Strategies for Few-Shot Learning

arXiv:2402.03038v2 Announce Type: replace-cross Abstract: In few-shot learning, the selection of samples has a significant impact on the performance of the model. While effective sample selection stra

Automating Crash Diagram Generation Using Vision-Language Models: A Case Study on Multi-Lane Roundabouts

arXiv:2604.15332v1 Announce Type: cross Abstract: Crash diagrams are essential tools in transportation safety analysis, yet their manual preparation remains time-consuming and prone to human variabili

Applications

AutoNFS: Automatic Neural Feature Selection

arXiv:2503.13304v2 Announce Type: replace Abstract: Feature selection (FS) is a fundamental challenge in machine learning, particularly for high-dimensional tabular data, where interpretability and co

applicationsarxiv-cs-lg

BAGEL: Benchmarking Animal Knowledge Expertise in Language Models

arXiv:2604.16241v1 Announce Type: cross Abstract: Large language models have shown strong performance on broad-domain knowledge and reasoning benchmarks, but it remains unclear how well language model

Beyond a Single Frame: Multi-Frame Spatially Grounded Reasoning Across Volumetric MRI

arXiv:2604.15808v1 Announce Type: cross Abstract: Spatial reasoning and visual grounding are core capabilities for vision-language models (VLMs), yet most medical VLMs produce predictions without tran

Beyond Distribution Sharpening: The Importance of Task Rewards

arXiv:2604.16259v1 Announce Type: cross Abstract: Frontier models have demonstrated exceptional capabilities following the integration of task-reward-based reinforcement learning (RL) into their train

Beyond MCQ: An Open-Ended Arabic Cultural QA Benchmark with Dialect Variants

arXiv:2510.24328v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used to answer everyday questions, yet their performance on culturally grounded and dialectal co

Applications

Beyond Passive Viewing: A Pilot Study of a Hybrid Learning Platform Augmenting Video Lectures with Conversational AI

arXiv:2604.15334v1 Announce Type: cross Abstract: The exponential growth of AI education has brought millions of learners to online platforms, yet this massive scale has simultaneously exposed critica

applicationsarxiv-cs-ai

Local Ai

Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning

arXiv:2604.15414v1 Announce Type: cross Abstract: Continual reinforcement learning must balance retention with adaptation, yet many methods still rely on single-model preservation, committing to one e

local-aiarxiv-cs-ai

Beyond Surface Statistics: Robust Conformal Prediction for LLMs via Internal Representations

arXiv:2604.16217v1 Announce Type: cross Abstract: Large language models are increasingly deployed in settings where reliability matters, yet output-level uncertainty signals such as token probabilitie

Beyond Text Prompts: Precise Concept Erasure through Text-Image Collaboration

arXiv:2604.15829v1 Announce Type: new Abstract: Text-to-image generative models have achieved impressive fidelity and diversity, but can inadvertently produce unsafe or undesirable content due to impl

researcharxiv-cs-cv

Bilevel Optimization of Agent Skills via Monte Carlo Tree Search

arXiv:2604.15709v1 Announce Type: new Abstract: Agent exttt{skills} are structured collections of instructions, tools, and supporting resources that help large language model (LLM) agents perform part

agentsarxiv-cs-ai

BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels

arXiv:2604.15591v1 Announce Type: cross Abstract: Effective biomedical information retrieval requires modeling domain semantics and hierarchical relationships among biomedical texts. Existing biomedic

Brain Score Tracks Shared Properties of Languages: Evidence from Many Natural Languages and Structured Sequences

arXiv:2604.15503v1 Announce Type: new Abstract: Recent breakthroughs in language models (LMs) using neural networks have raised the question: how similar are these models' processing to human language

Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials

model-releasesarxiv-cs-lg

arXiv:2604.15821v1 Announce Type: cross Abstract: Universal Machine Learning Interatomic Potentials (uMLIPs), pre-trained on massively diverse datasets encompassing inorganic materials and organic mol

Breakout-picker: Reducing false positives in deep learning-based borehole breakout characterization from acoustic image logs

arXiv:2604.16011v1 Announce Type: new Abstract: Borehole breakouts are stress-induced spalling on the borehole wall, which are identifiable in acoustic image logs as paired zones with near-symmetry az

researcharxiv-cs-cv

Tutorials

Bridging the phenotype-target gap for molecular generation via multi-objective reinforcement learning

arXiv:2509.21010v2 Announce Type: replace-cross Abstract: The de novo generation of drug-like molecules capable of inducing desirable phenotypic changes is receiving increasing attention. However, pre

tutorialsarxiv-cs-ai

Bureaucratic Silences: What the Canadian AI Register Reveals, Omits, and Obscures

arXiv:2604.15514v1 Announce Type: new Abstract: In November 2025, the Government of Canada operationalized its commitment to transparency by releasing its first Federal AI Register. In this paper, we

C-Mining: Unsupervised Discovery of Seeds for Cultural Data Synthesis via Geometric Misalignment

arXiv:2604.15675v1 Announce Type: new Abstract: Achieving cultural alignment in Large Language Models (LLMs) increasingly depends on synthetic data generation. For such synthesis, the most vital initi

safetyarxiv-cs-cl

Can LLMs Understand the Impact of Trauma? Costs and Benefits of LLMs Coding the Interviews of Firearm Violence Survivors

arXiv:2604.16132v1 Announce Type: cross Abstract: Firearm violence is a pressing public health issue, yet research into survivors' lived experiences remains underfunded and difficult to scale. Qualita

Capture the Flags: Family-Based Evaluation of Agentic LLMs via Semantics-Preserving Transformations

arXiv:2602.05523v2 Announce Type: replace-cross Abstract: Agentic large language models (LLMs) are increasingly evaluated on cybersecurity tasks using capture-the-flag (CTF) benchmarks, yet existing p

agentsarxiv-cs-ai

CASR: A Robust Cyclic Framework for Arbitrary Large-Scale Super-Resolution with Distribution Alignment and Self-Similarity Awareness

arXiv:2602.22159v2 Announce Type: replace Abstract: Arbitrary-Scale SR (ASISR) remains fundamentally limited by cross-scale distribution shift: once the inference scale leaves the training range, nois

safetyarxiv-cs-cv

Causal Bootstrapped Alignment for Unsupervised Video-Based Visible-Infrared Person Re-Identification

arXiv:2604.15631v1 Announce Type: new Abstract: VVI-ReID is a critical technique for all-day surveillance, where temporal information provides additional cues beyond static images. However, existing a

safetyarxiv-cs-cv

Chain-of-Thought Degrades Visual Spatial Reasoning Capabilities of Multimodal LLMs

arXiv:2604.16060v1 Announce Type: cross Abstract: Multimodal Reasoning Models (MRMs) leveraging Chain-of-Thought (CoT) based thinking have revolutionized mathematical and logical problem-solving. Howe

Characterising LLM-Generated Competency Questions: a Cross-Domain Empirical Study using Open and Closed Models

arXiv:2604.16258v1 Announce Type: new Abstract: Competency Questions (CQs) are a cornerstone of requirement elicitation in ontology engineering. CQs represent requirements as a set of natural language

ChatENV: An Interactive Vision-Language Model for Sensor-Guided Environmental Monitoring and Scenario Simulation

model-releasesarxiv-cs-cv

arXiv:2508.10635v3 Announce Type: replace Abstract: Understanding environmental changes from remote sensing imagery is vital for climate resilience, urban planning, and ecosystem monitoring. Yet, curr

ChemAmp: Amplified Chemistry Tools via Composable Agents

arXiv:2505.21569v3 Announce Type: replace-cross Abstract: Although LLM-based agents are proven to master tool orchestration in scientific fields, particularly chemistry, their single-task performance

agentsarxiv-cs-ai

ChemGraph-XANES: An Agentic Framework for XANES Simulation and Analysis

arXiv:2604.16205v1 Announce Type: cross Abstract: Computational X-ray absorption near-edge structure (XANES) is widely used to probe local coordination environments, oxidation states, and electronic s

CHOP: Chunkwise Context-Preserving Framework for RAG on Multi Documents

model-releasesarxiv-cs-cl

arXiv:2604.15802v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems lose retrieval accuracy when similar documents coexist in the vector database, causing unnecessary informat

CIG: Measuring Conversational Information Gain in Deliberative Dialogues with Semantic Memory Dynamics

arXiv:2604.15647v1 Announce Type: new Abstract: Measuring the quality of public deliberation requires evaluating not only civility or argument structure, but also the informational progress of a conve

CiPO: Counterfactual Unlearning for Large Reasoning Models through Iterative Preference Optimization

arXiv:2604.15847v1 Announce Type: new Abstract: Machine unlearning has gained increasing attention in recent years, as a promising technique to selectively remove unwanted privacy or copyrighted infor

CLewR: Curriculum Learning with Restarts for Machine Translation Preference Learning

arXiv:2601.05858v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated competitive performance in zero-shot multilingual machine translation (MT). Some follow-up work

CLIMB: Controllable Longitudinal Brain Image Generation using Mamba-based Latent Diffusion Model and Gaussian-aligned Autoencoder

arXiv:2604.15611v1 Announce Type: cross Abstract: Latent diffusion models have emerged as powerful generative models in medical imaging, enabling the synthesis of high quality brain magnetic resonance

Closing the Theory-Practice Gap in Spiking Transformers via Effective Dimension

arXiv:2604.15769v1 Announce Type: cross Abstract: Spiking transformers achieve competitive accuracy with conventional transformers while offering 38-57imes energy efficiency on neuromorphic hardware,

CLOTH-HUGS: Cloth Aware Human Gaussian Splatting

arXiv:2604.15875v1 Announce Type: new Abstract: We present Cloth-HUGS, a Gaussian Splatting based neural rendering framework for photorealistic clothed human reconstruction that explicitly disentangle

researcharxiv-cs-cv

CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval

arXiv:2604.15663v1 Announce Type: cross Abstract: Code search, framed as information retrieval (IR), underpins modern software engineering and increasingly powers retrieval-augmented generation (RAG),

COEVO: Co-Evolutionary Framework for Joint Functional Correctness and PPA Optimization in LLM-Based RTL Generation

arXiv:2604.15001v2 Announce Type: replace Abstract: LLM-based RTL code generation methods increasingly target both functional correctness and PPA quality, yet existing approaches universally decouple

CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution

arXiv:2604.15840v1 Announce Type: new Abstract: Reinforcement learning for LLM agents is typically conducted on a static data distribution, which fails to adapt to the agent's evolving behavior and le

agentsarxiv-cs-cl