All entries

arXiv:2606.02679v1 Announce Type: new Abstract: Multimodal systems often benefit from combining information across language, sound, and visual streams, but this benefit is not guaranteed. A modality t

local-aiarxiv-cs-lg

BehaviorBench: Modeling Real-World User Decisions from Behavioral Traces

DGX agent

arXiv:2606.02798v1 Announce Type: new Abstract: Many decision-support settings require systems that adapt to individual users, but evaluation data for this problem remain limited. Existing benchmarks

model-releasesarxiv-cs-ai

3 Jun 2026

Benchmarking Speech-to-Speech Translation Models

arXiv:2606.03241v1 Announce Type: new Abstract: Speech-to-speech translation (S2ST) has advanced rapidly, but offline evaluation lacks a unified protocol: studies report non-overlapping metric subsets

applicationsarxiv-cs-cl

Benchmarking Visual State Tracking in Multimodal Video Understanding

model-releasesarxiv-cs-cv

arXiv:2606.03920v1 Announce Type: new Abstract: Understanding a video requires more than recognizing isolated moments, as humans continuously track entities, states, and events over time. This capacit

Local Ai

Best ai lipsync for animated characters in 2026?

local-air-stablediffusion

In 2026, leading AI lip-sync tools for animated characters include platforms like Magic Hour, Hedra, Sync.so, and Higgsfield, each optimizing for different approaches such as accurate mouth alignment

Best of Both Worlds: Multimodal Reasoning and Generation via Unified Discrete Flow Matching

arXiv:2602.12221v2 Announce Type: replace Abstract: We propose UniDFlow, a unified discrete flow-matching framework for multimodal understanding, generation, and editing. It decouples understanding an

safetyarxiv-cs-cv

BEV-ODOM2: Enhanced BEV-based Monocular Visual Odometry with PV-BEV Fusion and Dense Flow Supervision for Ground Robots

model-releasesarxiv-cs-ro

arXiv:2509.14636v2 Announce Type: replace Abstract: Scale-consistent ego-motion estimation is fundamental for autonomous ground robots. Bird's-Eye-View (BEV) representation naturally addresses the sca

Beyond Compression: Quantifying Spectral Accessibility in Vision Representations

arXiv:2606.03795v1 Announce Type: new Abstract: Vision-language models map visual features into a shared embedding space through learned projection layers, yet it remains unclear how these transformat

Beyond Encoder Accumulation: Measuring Encoder Roles in Multi-Encoder VLMs

model-releasesarxiv-cs-ai

arXiv:2606.03879v1 Announce Type: cross Abstract: As foundation models scale toward fusing more heterogeneous visual streams, understanding how diverse encoders interact under joint training becomes a

Beyond False Stability: High-Noise Drift Gating for Test-Time Adversarial Defenses in Vision-Language Models

arXiv:2606.03730v1 Announce Type: new Abstract: Vision-language models (VLMs) such as CLIP show strong zero-shot generalization but remain highly vulnerable to adversarial attacks. Adversarial trainin

Beyond Gradient Descent: Adam for Analog Ising Machines

arXiv:2606.03917v1 Announce Type: cross Abstract: As Moore's law reaches its limits, Ising machines offer a promising alternative computing approach for difficult optimization problems. However, many

Beyond Ideal Instruction: A Comprehensive Framework for Evaluating LLMs in Realistic Interactions

model-releasesarxiv-cs-cl

arXiv:2606.03318v1 Announce Type: new Abstract: Despite great advances in tool-use capabilities of large language models (LLMs), existing evaluation benchmarks struggle to fully align with real-world

Beyond Semantics: Modeling Factual and Affective Perceptual Experiences from Vision-Language Data

arXiv:2606.03345v1 Announce Type: cross Abstract: We present P-Topics (Perception Topics) modeling, a novel problem for understanding how images are perceived affectively and across cultures. The goal

Beyond Single Solution: Multi-Hypothesis Collaborative Deep Unfolding Network for Image Compressive Sensing

arXiv:2606.03666v1 Announce Type: new Abstract: Recent deep unfolding networks (DUNs) have advanced Compressive Sensing (CS) by effectively integrating iterative optimization with deep learning archit

Beyond the Literal: Decomposing Pragmatic Intent in Multimodal Meme Understanding

arXiv:2606.03604v1 Announce Type: new Abstract: When asked what a meme or sarcastic post means, Large Vision Language Models (LVLMs) tend to describe what the image shows rather than what the author i

Beyond 'To whom it may concern': Tailoring Machine Translation to Audience and Intent

arXiv:2606.03259v1 Announce Type: new Abstract: Translation quality depends on purpose: the same source text demands different translations depending on audience, tone, and communicative intent. Yet M

BigFinanceBench: A Workflow-Grounded Benchmark for Financial-Research Agents

model-releasesarxiv-cs-ai

arXiv:2606.03829v1 Announce Type: new Abstract: Financial-research answers are decision-relevant only when another analyst can audit how they were produced: which source was chosen, which period and a

Binary Road Surface Classification Using Machine Learning on Production Vehicle Signals During Cruising

arXiv:2606.02762v1 Announce Type: new Abstract: Knowledge of real-time road slipperiness, or even better, a refined estimate of peak grip potential, is a critical input for vehicle warning and interve

applicationsarxiv-cs-lg

Bionic Human-Motion Style Transfer for Physically Executable Whole-Body Control of Humanoid Robots

arXiv:2606.03536v1 Announce Type: new Abstract: Expressive whole-body motion is important for humanoid robots operating in human environments, where robots are expected to move stably while presenting

safetyarxiv-cs-ro

Black-box, Adaptive, Efficient, Transferable, Harmful, Applicable... Attacks Are All You Need to Break LLMs

arXiv:2606.03647v1 Announce Type: cross Abstract: Accurately evaluating adversarial robustness is a longstanding challenge. A flawed attack design can inflate robustness estimates, making deployment r

safetyarxiv-cs-ai

Bootstrap Your Generator: Unpaired Visual Editing with Flow Matching

arXiv:2606.03911v1 Announce Type: new Abstract: Modern generative models possess a deep understanding of visual content, yet training them for image editing typically requires massive datasets of pair

BotDirector: Robot Storytelling Across the Symmetrical Reality with Multi-modal Interactions

arXiv:2606.03223v1 Announce Type: cross Abstract: Robot storytelling offers a unique blend of technological innovation and creative expression that engages children in unprecedented ways. However, the

agentsarxiv-cs-ai

🚨BREAKING: Hampshire police claim they felt controlled and manipulated to 'think a certain way' after mandatory DEI training, the Times hav…

🚨BREAKING: Hampshire police claim they felt controlled and manipulated to 'think a certain way' after mandatory DEI training, the Times have exposed It's confirmed: they're brainwashing the police to

industryelon-musk--x

Breaking the Self-Confirming Loop: Diagnosing and Mitigating Systemic Reward Bias in Self-Rewarding RL

arXiv:2510.08977v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) efficiently scales the reasoning ability of large language models (LLMs) but is bottlene

safetyarxiv-cs-cl

Bregman meets Levy: Stochastic mirror descent with heavy-tailed noise in continuous and discrete time

arXiv:2606.03769v1 Announce Type: cross Abstract: We study the robustness of stochastic mirror descent (SMD) under heavy-tailed noise, focusing on whether the method retains its convergence guarantees

Bridging Auxiliary Constraints to Resolve Instruction Following in Large Reasoning Models

arXiv:2606.03624v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have demonstrated impressive capabilities in many tasks, yet they struggle with reliably following multiple instructions,

researcharxiv-cs-ai

Bridging Predictive Uncertainty and Safe Action: Sample-Conditioned Differentiable Planning for Autonomous Driving

arXiv:2606.03296v1 Announce Type: new Abstract: Complex, dynamic, and interactive driving environments pose significant challenges for autonomous driving, primarily due to the pervasive uncertainty of

safetyarxiv-cs-ro

Brief Announcement: Generative Markov Model for Distributed Computing Systems

arXiv:2606.03061v1 Announce Type: cross Abstract: Emerging distributed computing paradigms, such as the computing continuum, are inherently heterogeneous, stochastic, and complex. Efficiently and effe

safetyarxiv-cs-ai

Bring Databricks into Kiro IDE with the AI Dev Kit Power

This article describes how to integrate Databricks with Kiro IDE using the AI Dev Kit, enabling developers to leverage Databricks' data and AI capabilities directly within their integrated development

industrydatabricks

Bring your favorite agent into Devin Desktop using ACP: https://docs.devin.ai/desktop/acp

Devin Desktop now supports Agent Control Protocol (ACP), allowing users to integrate their preferred AI agents into the platform. This feature enables users to bring custom or third-party agents into

agentscognition-ai--x

Broadcom reports Q2 revenue up 48% YoY to 22.19B, vs. 22.27B est., and forecasts Q3 semiconductor revenue from AI below estimates; AVGO drops 12%+ after hours (Reuters)

Reuters: Broadcom reports Q2 revenue up 48% YoY to 22.19B, vs. 22.27B est., and forecasts Q3 semiconductor revenue from AI below estimates; AVGO drops 12%+ after hours — Broadcom (AVGO.O) missed Wall

industrytechmeme

Broadcom revenue miss stuns Wall Street, and its stock sinks after-hours

Shares of the chipmaker Broadcom Inc. came crashing down to Earth in late trading today after it reported weaker-than-expected second-quarter revenue. The stock fell a bit initially, and then much mor

industrysiliconangle

Build 2026: From observability to ROI for AI agents on any framework

9 min read · June 3, 2026 · Sebastian Kohlmeier Shipping an AI agent is the easy part. Keeping it accurate, safe, and accountable in production is where teams get stuck. Agents are non-deterministic.

agentsmicrosoft-foundry

Build smarter document workflows: What’s new in Azure Content Understanding at Build 2026

Azure Content Understanding (CU) in Foundry Tools is Microsoft’s comprehensive content AI service. It ingests diverse data types — documents, audio, images, and video — and extracts the most critical

agentsmicrosoft-foundry

Building Better Activation Oracles

arXiv:2606.02609v1 Announce Type: cross Abstract: Activation Oracles (AOs) are promising methods for interpreting residual stream activations. However, current AOs face important issues, such as hallu

safetyarxiv-cs-ai

Building Reliable Long-Form Generation via Hallucination Rejection Sampling

arXiv:2606.03628v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable progress in open-ended text generation, yet they remain prone to hallucinating incorrect or unsu

researcharxiv-cs-ai

Building Trust in Black-box Optimization: A Comprehensive Framework for Explainability

arXiv:2410.14573v2 Announce Type: replace-cross Abstract: Optimizing costly black-box functions within a constrained evaluation budget presents significant challenges in many real-world applications.

applicationsarxiv-cs-ai

Built with Grok

'Built with Grok' is a post from Elon Musk on X platform promoting or highlighting something created using Grok, xAI's large language model. The post likely showcases an application, feature, or capab

industryelon-musk--x

Buzz, Choose, Forget: A Meta-Bandit Framework for Bee-Like Decision Making

arXiv:2510.16462v3 Announce Type: replace Abstract: This work introduces MAYA, a sequential imitation learning model based on multi-armed bandits, designed to reproduce and predict individual bees' de

BYORn: Bootstrap Your Own Responses to Defend Large Vision-Language Models Against Backdoor Attacks

arXiv:2606.02947v1 Announce Type: cross Abstract: Supervised fine-tuning is the predominant approach for adapting autoregressive vision-language models to downstream tasks. Recent work has shown that

CAD-to-CT Registration of Cylindrical Objects via Ellipse-Based Axis Estimation

arXiv:2606.02935v1 Announce Type: new Abstract: Accurate registration of CAD models to CT scans is essential for establishing ground truth geometry in volumetric imaging. Obtaining reliable object mas

Calibrating Urban Traffic Simulation from Sparse Road Observations via Genetic Optimization

arXiv:2606.03823v1 Announce Type: new Abstract: Urban traffic simulation is a critical tool for infrastructure planning, including the placement of electric vehicle charging stations. However, realist

applicationsarxiv-cs-ai

Calibration Data Trade-offs Across Capability Dimensions: Why Multi-Source Mixing Matters for High-Sparsity LLM Pruning

model-releasesarxiv-cs-ai

arXiv:2606.03328v1 Announce Type: cross Abstract: Post-training pruning compresses large language models to high sparsity using a small unlabelled calibration set, and recent work has concluded that t

Can Factual Opinions Be Edited (Manipulated) in Large Language Models?

model-releasesarxiv-cs-cl

arXiv:2606.03096v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly integrated into various domains, making knowledge editing techniques crucial yet potentially hazardous. Cu

Can LLM Rerankers Predict Their Own Ranking Performance?

arXiv:2606.03535v1 Announce Type: cross Abstract: Retrieval effectiveness varies substantially across queries, making it important to estimate ranking quality before relevance judgments are available.

Can Local Learning Match Self-Supervised Backpropagation?

arXiv:2601.21683v2 Announce Type: replace Abstract: While end-to-end self-supervised learning with backpropagation (global BP-SSL) has become central for training modern AI systems, theories of local

Can Structural Cues Save LLMs? Evaluating Language Models in Massive Document Streams

model-releasesarxiv-cs-cl

arXiv:2603.19250v2 Announce Type: replace Abstract: Evaluating language models in streaming environments is critical, yet underexplored. Existing benchmarks either focus on single complex events or pr

CANMOT: Class-Aware Noise Modeling for Multi-Object Tracking in Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2606.03590v1 Announce Type: new Abstract: Kalman filter (KF)-based multi-object tracking (MOT) remains a strong baseline for autonomous driving due to its strong performance, computational effic