All entries

AutoMine Solution for AV2 2026 Scenario Mining Challenge

arXiv:2606.11874v1 Announce Type: new Abstract: With the development of autonomous driving systems, mining high-value, safety-critical, and planning-relevant scenarios from large-scale driving logs ha

Autoregressive Direct Preference Optimization

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arXiv:2602.09533v2 Announce Type: replace Abstract: Direct preference optimization (DPO) has emerged as a promising approach for aligning large language models (LLMs) with human preferences. However,

researcharxiv-cs-ai

11 Jun 2026

AVIS: Adaptive Test-Time Scaling for Vision-Language Models

arXiv:2606.11576v1 Announce Type: cross Abstract: Modern Vision-Language Models (VLMs) benefit from chain-of-thought prompting and test-time scaling, but these gains often come with prohibitive infere

Bad things apparently come in 4’s; tweet below must be updated with the breaking @wsj news that OpenAI is contemplating drastic price cuts, …

Bad things apparently come in 4’s; tweet below must be updated with the breaking @wsj news that OpenAI is contemplating drastic price cuts, which is surely a sign of weakness. So far today • Banks to

safetygary-marcus--x

Industry

Based Grok 🤣🤣 https://x.com/i/grok/share/32212cc499ae467ebb1f8db2b77d314a

This entry references Grok, xAI's conversational AI assistant, shared through X's integrated Grok feature. The post appears to be a humorous or casual share by Elon Musk, likely demonstrating Grok's c

industryelon-musk--x

Battery detection of XRay images using transfer learning

arXiv:2606.11779v1 Announce Type: new Abstract: The need for detecting and sorting batteries is drastically increasing for many applications. This study proves the potential of transfer learning in pr

researcharxiv-cs-cv

Benchmarking Cross-Domain Audio-Visual Deception Detection

model-releasesarxiv-cs-cv

arXiv:2405.06995v4 Announce Type: replace-cross Abstract: Automated deception detection is crucial for assisting humans in accurately assessing truthfulness and identifying deceptive behavior. Convent

Benchmarking Large Language Models for Safety Data Extraction

arXiv:2606.11204v1 Announce Type: new Abstract: Accurate extraction of structured information from Safety Data Sheets (SDS) remains challenging in industrial safety due to heterogeneous document forma

Bergson: An Open Source Library for Data Attribution

arXiv:2606.11660v1 Announce Type: new Abstract: Data attribution is a promising field in interpretability that aims to explain model behavior through the influence of its training data, with applicati

Bernstein-Schur Kernels: Random Features by Sketched Modulation and Radial Randomization

arXiv:2606.11255v1 Announce Type: new Abstract: Bernstein--Schur kernels are products of a finite-feature kernel (one with an explicit finite-dimensional feature map) and a completely monotone shift-i

Beyond Compaction: Structured Context Eviction for Long-Horizon Agents

arXiv:2606.11213v1 Announce Type: new Abstract: We present Context Window Lifecycle (CWL), a context-management scheme that gives long-horizon LLM agents an effectively unbounded working horizon. As a

Beyond Dark Knowledge: Mixup-Based Distillation for Reliable Predictions

arXiv:2606.12171v1 Announce Type: new Abstract: Knowledge Distillation (KD) and mixup have proven effective at inducing smoothness in class boundaries; KD captures inherent class relationships in prob

researcharxiv-cs-cv

Beyond Fully Random Masking: Attention-Guided Denoising and Optimization for Diffusion Language Models

arXiv:2606.12273v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) offer an efficient alternative to autoregressive models through parallel decoding, yet existing post-training me

researcharxiv-cs-cl

Beyond representational alignment with brain-guided language models for robust reasoning

arXiv:2606.11893v1 Announce Type: cross Abstract: The correspondence between large language models (LLMs) and the neural mechanisms underlying human higher-order cognition remains insufficiently chara

Tutorials

Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching

arXiv:2606.11583v1 Announce Type: new Abstract: Text-attributed graphs (TAGs) underlie real-world applications such as citation networks, social media, and e-commerce. Few-shot graph learning on TAGs

tutorialsarxiv-cs-lg

Beyond Third-Person Audits: Situated Interaction Auditing for User-Centered LLM Bias Research

arXiv:2606.12247v1 Announce Type: cross Abstract: Research on bias in large language models (LLMs) has predominantly focused on third-person audits, which study how models represent or evaluate demogr

safetyarxiv-cs-cl

BioDivergence: A Benchmark and Evaluation Framework for Hidden Contextual Contradictions in Biomedical Abstracts

arXiv:2606.11208v1 Announce Type: cross Abstract: Biomedical findings often seem to conflict across studies, but many of these differences are context-dependent rather than true contradictions. Variat

BioMamba: Domain-Adaptive Biomedical Language Models

arXiv:2408.02600v3 Announce Type: replace Abstract: Background. Biomedical language models should improve performance on biomedical text while retaining general-language-modeling fluency. For Mamba-ba

Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learning

arXiv:2606.11767v1 Announce Type: cross Abstract: Blind grasping with a dexterous hand is a crucial manipulation capability. Nevertheless, learning such tactile-only policies for real robots remains c

Agents

Bootstrapped Monitoring: Leveraging Transparent Reasoning to Oversee Stronger AI Agents

arXiv:2606.11998v1 Announce Type: new Abstract: Trusted monitoring is a cornerstone of AI control. However, as frontier models grow more capable, the increasing capabilities gap between trusted and un

agentsarxiv-cs-lg

Agents

Breaking Entropy Bounds: Accelerating RL Training via MTP with Rejection Sampling

arXiv:2606.12370v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a key component in modern large language models, yet the rollout stage remains the key bottleneck in RL trainin

agentsarxiv-cs-cl

🚨 Breaking neuroscience/evolution news! New Nature paper provides further evidence for a central hypothesis (with a long lineage) that I de…

🚨 Breaking neuroscience/evolution news! New Nature paper provides further evidence for a central hypothesis (with a long lineage) that I defended in my 2004 book, The Birth of the Mind: the evolutiona

safetygary-marcus--x

Bridging Day and Night: Unsupervised Cross-Domain Re-Identification with Synergistic Prompt and Prototype Learning

arXiv:2606.12258v1 Announce Type: new Abstract: Cross-domain day-night re-identification (ReID) is fundamentally challenged by the substantial visual appearance discrepancies between daytime and night

safetyarxiv-cs-cv

Applications

Bridging the Modality Gap in Forensic Image Retrieval

arXiv:2606.12294v1 Announce Type: new Abstract: Automated image retrieval plays an increasingly critical role in modern forensic analysis, supporting investigative workflows that rely on efficient com

applicationsarxiv-cs-cv

Bridging the Morphology Gap: Adapting VLA Models to Dexterous Manipulation via Intent-Conditioned Fine-Tuning

arXiv:2606.12109v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable zero-shot generalization in robotic manipulation, yet the vast majority of pre-traine

Bridging the sim2real gap in the table tennis robot with a transformer-based ball states predictor

model-releasesarxiv-cs-ro

arXiv:2606.11464v1 Announce Type: new Abstract: Robotic table tennis is a representative benchmark for high-speed, closed-loop robotic control in dynamic environments, where accurate and fast predicti

Building Social World Models with Large Language Models

arXiv:2606.11482v1 Announce Type: cross Abstract: Understanding and predicting how social beliefs evolve in response to events -- from policy changes to scientific breakthroughs -- remains a fundament

By the way, public service announcement: if you're one of the numerous people posting about Anthropic's dystopian ways and you're thinking a…

model-releasesjeremy-howard--x

By the way, public service announcement: if you're one of the numerous people posting about Anthropic's dystopian ways and you're thinking about getting Claude to help you write that post... don't! An

Calibrating Decision Robustness via Inverse Conformal Risk Control

arXiv:2510.07750v3 Announce Type: replace-cross Abstract: Robust optimization safeguards decisions against uncertainty by optimizing against worst-case scenarios, yet their effectiveness hinges on a p

Calibration Drift Under Reasoning: How Chain-of-Thought Budgets Induce Overconfidence in Large Language Models

arXiv:2606.11211v1 Announce Type: cross Abstract: The ability of large language models (LLMs) to express calibrated uncertainty is important for safe deployment. Chain-of-thought (CoT) reasoning is wi

Called the LLM price wars – which are about to heat up — over two years ago. See especially predictions 3, 4, and 7, which laid out the no m…

Called the LLM price wars – which are about to heat up — over two years ago. See especially predictions 3, 4, and 7, which laid out the no moat, small profit, price war regime we have seen ever since.

safetygary-marcus--x

Can AI Agents Synthesize Scientific Conclusions?

arXiv:2606.11337v1 Announce Type: new Abstract: Scientific AI agents increasingly retrieve evidence, reason across sources, and synthesize conclusions used in consequential decisions. Yet, their abili

Can AI Reason Like an Urban Planner? Benchmarking Large Language Models Against Professional Judgment

arXiv:2606.11678v1 Announce Type: new Abstract: Problem, Research Strategy, and Findings: The rise of large language models (LLMs) raises a key question for urban planning: which forms of professional

safetyarxiv-cs-cl

Tutorials

Can confirm we saw a strong spike in growth of token consumption for Codex over last 48 hours. Unusual when we don't launch something.

tutorialsjeremy-howard--x

Can confirm we saw a strong spike in growth of token consumption for Codex over last 48 hours. Unusual when we don't launch something. Usage share of OpenAI grew vs Anthropic yesterday despite Mythos

Can News Predict the Market? Limits of Zero-Shot Financial NLP and the Role of Explainable AI

arXiv:2606.12210v1 Announce Type: new Abstract: Can financial news reliably predict short-term stock movements? Despite advances in large language models, this question remains unresolved. We revisit

researcharxiv-cs-cl

Local Ai

Can Open-Source LLM Agents Replace Static Application Security Testing Tools? An Empirical Assessment

arXiv:2606.11672v1 Announce Type: cross Abstract: This paper explores the value of agentic AI tools for cybersecurity purposes. We evaluate the efficacy of a general-purpose GenAI Large Language Model

local-aiarxiv-cs-ai

Capacity-Constrained Online Convex Optimization with Delayed Feedback

arXiv:2606.11711v1 Announce Type: new Abstract: Online learning with delayed feedback typically assumes that the learner can track all pending rounds until their feedback arrives. In practice, trackin

Carbon-Aware Governance Gates: An Architecture for Sustainable GenAI Development

arXiv:2602.19718v2 Announce Type: replace-cross Abstract: The rapid adoption of Generative AI (GenAI) in the software development life cycle (SDLC) increases computational demand, which can raise the

researcharxiv-cs-ai

Applications

CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting

arXiv:2511.09789v2 Announce Type: replace Abstract: Recent advances in deep forecasting models have achieved remarkable performance, yet most approaches still struggle to provide both accurate predict

applicationsarxiv-cs-lg

Categorical Prior Lock-in: Why In-Context Learning Fails for Structured Data

arXiv:2606.11961v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as conditional generators for structured data, relying on in-context learning (ICL) to adapt to new

Applications

Categorical Robustness Assessment for Machine Learning based Network Intrusion Detection Systems

arXiv:2606.12075v1 Announce Type: cross Abstract: Network Intrusion Detection Systems (NIDS) heavily utlize Machine Learning (ML) but ML models can be manipulated via adversarial attacks. These attack

applicationsarxiv-cs-lg

Tutorials

Causal Clothes-Invariant Feature Learning for Cloth-Changing Person Re-ID

arXiv:2305.06145v2 Announce Type: replace Abstract: In cloth-changing person re-identification (CCReID), it is critical to learn clothes-invariant feature, which can provide discriminative ID features

tutorialsarxiv-cs-cv

Causal Emotion Recognition in Conversation: Context Saturation and Discourse-Marker Evidence

arXiv:2601.00181v3 Announce Type: replace-cross Abstract: We address two persistent gaps in Emotion Recognition in Conversation: which modeling choices materially affect performance, and how recogniti

researcharxiv-cs-ai

Agents

CCKS: Consensus-based Communication and Knowledge Sharing

arXiv:2606.12281v1 Announce Type: cross Abstract: In Decentralized Training and Decentralized Execution (DTDE) for cooperative Multi-Agent Reinforcement Learning (MARL), action-advising-based knowledg

agentsarxiv-cs-ai

CellNet -- Localizing Cells using Sparse and Noisy Point Annotations

arXiv:2606.12286v1 Announce Type: new Abstract: Counting living cells is an important step in many biological research workflows. Our collaborators at the Wellcome Sanger Institute study vital genes i

researcharxiv-cs-cv

Certifiable Safe RLHF: Semantic Grounding and Fixed Penalty Constraint Optimization for Safer LLM Alignment

arXiv:2510.03520v2 Announce Type: replace-cross Abstract: Ensuring safety is a foundational requirement for large language models (LLMs). Achieving an appropriate balance between enhancing the utility

CFCamo: A Counterfactual Detect-or-Abstain Framework for Camouflaged Object Detection

model-releasesarxiv-cs-cv

arXiv:2606.11231v1 Announce Type: new Abstract: Vision-language reinforcement learning has recently shown strong target-present localization for camouflaged object detection (COD). Yet localization is

Hardware

Characterizing Software Aging in GPU-Based LLM Serving Systems

arXiv:2606.11916v1 Announce Type: cross Abstract: This paper proposes an empirical methodology to study software aging in GPU-based LLM serving systems. Traditional aging studies focus on CPU-centric

hardwarearxiv-cs-ai