Knowledge catalogue
arXiv:2606.22769v1 Announce Type: new Abstract: Standard NLP pipelines for occupational clustering discard the 10-15% of job postings that density-based methods assign to noise. We argue this is an er
arXiv:2509.22483v2 Announce Type: replace Abstract: Large language models deployed in sensitive applications increasingly require the ability to unlearn specific knowledge, such as user requests, copy
arXiv:2606.23668v1 Announce Type: new Abstract: Large Language Models (LLMs) are frequently portrayed as general-purpose solvers capable of solving arbitrary tasks. We argue that this view overlooks a
arXiv:2606.20648v1 Announce Type: cross Abstract: Conventionally, existing vehicle platooning approaches are designed for connected vehicles, typically including connected autonomous vehicles and conn
arXiv:2606.23593v1 Announce Type: cross Abstract: Robot-assisted minimally invasive surgery improves surgical precision but introduces complexity, making technical error detection essential for ensuri
arXiv:2606.22873v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly deployed in consumer, medical, financial, and enterprise applications. This broad deployment expands the
New York Times: Sources: the Trump administration is pressing Meta to submit its AI models for voluntary review; Meta is the only major US AI developer without an agreement — Federal officials are urg
arXiv:2506.08026v3 Announce Type: replace-cross Abstract: Real-time market prediction services need correct predictions before a decision deadline; a correct prediction delivered late is not a usable
arXiv:2603.06445v2 Announce Type: replace Abstract: Situated reasoning often relies on active exploration, yet in many real-world scenarios such exploration is infeasible due to physical constraints o
arXiv:2606.16196v2 Announce Type: replace-cross Abstract: Deep neural networks have achieved remarkable performance across medical imaging tasks, yet their tendency to overgeneralize under distributio
This post likely discusses how certain contemporary concepts, practices, or ideologies are actually rooted in or aligned with Buddhist philosophy, possibly challenging common misconceptions about thei
It's crazy how badly the tech industry wants this guy @AlexBores defeated, all because he passed a relatively tame AI transparency law AI billionaires have spent $8,027,347 trying to beat one New York
arXiv:2606.11446v1 Announce Type: new Abstract: This research introduces a framework for incorporating Concept Bottleneck Models (CBMs) into 3D generative architectures to address the inherent 'semant
arXiv:2606.11533v1 Announce Type: cross Abstract: The advancement of AI capabilities compels researchers and the public to be more aware of its potential worldwide impact. A pressing near-term concern
arXiv:2606.12065v1 Announce Type: new Abstract: Automating compliance check for geometry-intensive regulations remains a significant technical bottleneck in Building Information Modeling (BIM), primar
arXiv:2606.11569v1 Announce Type: cross Abstract: Closed-loop planning in complex, real-world driving scenarios presents a critical challenge for autonomous driving systems. While traditional rule-bas
arXiv:2606.11835v1 Announce Type: cross Abstract: Collecting participants' lived experiences is central to design research. Focus groups are uniquely valuable because participants not only share indiv
arXiv:2602.20958v2 Announce Type: replace-cross Abstract: Vision-based Unmanned Aerial Vehicles (UAVs) frameworks aid human search tasks by detecting and recognizing specific individuals, then trackin
arXiv:2606.11195v1 Announce Type: cross Abstract: Large language models (LLMs) have transformed how humans access information, but not how we reason with it. Their fluency accelerates consumption whil
arXiv:2601.14031v2 Announce Type: replace-cross Abstract: Forecasting intermittent time series, which contain zeros, is a crucial challenge in supply chains as inventory policies require probabilistic
arXiv:2606.11474v1 Announce Type: new Abstract: In this paper, we study Mahalanobis-guided latent out-of-distribution (OOD) detection for test-time RL controller switching in nonlinear time-varying sy
arXiv:2606.12066v1 Announce Type: new Abstract: In modern vehicular systems, robust performance under harsh conditions has become a critical problem of autonomous driving. Our study delivers a compreh
arXiv:2606.11409v1 Announce Type: cross Abstract: Adversarial robustness evaluations of large language models (LLMs) typically report attack success rate (ASR) under fixed query budgets, implicitly tr
arXiv:2606.11399v1 Announce Type: new Abstract: Large Language Models (LLMs) are deployed across cultural contexts but often reflect homogenized values inherited from training data. Evaluations of cul
arXiv:2606.12374v1 Announce Type: cross Abstract: Effective multi-human-robot collaboration is essential for expanding human-led operations in the challenging and high-risk underwater environment. For
arXiv:2508.21380v3 Announce Type: replace-cross Abstract: Recent mechanistic work has uncovered learned algorithms within neural networks, from modular arithmetic to search and planning in game-playin
arXiv:2606.12349v1 Announce Type: new Abstract: Accurate identification of hydrodynamic derivatives is essential for control and navigation of Unmanned Surface Vehicles (USVs), but high-fidelity manoe
arXiv:2606.10495v1 Announce Type: new Abstract: Safe social navigation requires robots to distinguish people from ordinary obstacles and to react before danger becomes imminent. We show that pretraine
Anthropic's leadership, through CEO Dario Amodei, has advocated for transparency requirements in frontier AI development as a regulatory approach, arguing that the risks were insufficiently understood
arXiv:2606.09923v1 Announce Type: cross Abstract: Neural operators such as the Fourier Neural Operator (FNO) have emerged as powerful surrogates for solving partial differential equations (PDEs), achi
arXiv:2606.11019v1 Announce Type: cross Abstract: Learning-based motion planners, despite recent progress, often suffer from temporal inconsistency. Small perturbations across frames can accumulate in
arXiv:2606.11046v1 Announce Type: new Abstract: Instruction-tuned LLMs are increasingly converted into reasoning models through post-training to improve multi-step task performance. This conversion is
arXiv:2602.12542v2 Announce Type: replace-cross Abstract: Deep learning models for clinical event prediction on electronic health records (EHR) often suffer performance degradation when deployed under
arXiv:2606.09635v1 Announce Type: cross Abstract: Ensuring the reliability of Large Language Models (LLMs) under distribution drift requires inference-time adaptation. While inference-time alignment m
arXiv:2606.09848v1 Announce Type: cross Abstract: As generative and agentic AI becomes embedded in everyday products, practitioners face a persistent challenge: how to design human-AI coordination --
arXiv:2606.10818v1 Announce Type: cross Abstract: Real-world robotic manipulation tasks often involve forceful interactions with the environment, such as using tools of varying weights, transporting o
arXiv:2606.10288v1 Announce Type: new Abstract: Perceptive bipedal locomotion over sparse terrain remains a difficult challenge: model-based methods are precise but brittle to uncertainty, while model
arXiv:2606.09850v1 Announce Type: cross Abstract: Post-training alignment algorithms are predominantly evaluated as black boxes, obscuring how they reshape language models' internal computations. We p
arXiv:2606.10581v1 Announce Type: new Abstract: Speech carries more information than just words: a child's voice, a fearful tone, or a noisy background should all lead a sufficiently competent spoken-
arXiv:2606.09844v1 Announce Type: cross Abstract: Large Language Models (LLMs) alter their privacy behavior based on the perceived identity of their interlocutor. While safety mechanisms typically pre
arXiv:2606.10501v1 Announce Type: new Abstract: Deploying Vision-Language-Action (VLA) models in real robotic systems requires robustness not only to semantic and perceptual variations, but also to em
arXiv:2606.10740v1 Announce Type: new Abstract: Failures in multi-turn reasoning models are largely invisible to terminal-score evaluation. A model can lock onto an unsafe stance early in a long dialo
arXiv:2602.12246v2 Announce Type: replace-cross Abstract: The convergence of robotics and next-generation communication is a critical driver of technological advancement. As the world transitions from
arXiv:2606.09253v1 Announce Type: new Abstract: Deformable image registration (DIR) is widely used in radiotherapy for dose propagation and accumulation, but uncertainty in the underlying deformation
arXiv:2606.08247v1 Announce Type: cross Abstract: Acute asthma risk assessment requires rapid interpretation of respiratory sounds, oxygenation, airflow limitation, speech ability, work of breathing,
alienating your customers before you IPO is maybe not the best idea, @AnthropicAI Brilliant idea! Next up: Apple randomly reboots your Mac if you're building competing tech, Gmail silently edits your
arXiv:2606.08982v1 Announce Type: new Abstract: Baichuan-M4 is Baichuan Intelligence's clinical-grade medical large model, designed for continuous care rather than single-turn medical question answeri
arXiv:2606.07714v1 Announce Type: cross Abstract: Suicide ideation detection models are typically evaluated using aggregate performance metrics, yet little is known about how they internally represent
arXiv:2606.08454v1 Announce Type: new Abstract: Activation steering provides a lightweight inference-time mechanism for controlling large language models (LLMs) by modifying their internal activation
Brilliant idea! Next up: Apple randomly reboots your Mac if you're building competing tech, Gmail silently edits your email if you mention rival platforms, and Tesla Autopilot swerves if it detects yo
arXiv:2606.08596v1 Announce Type: new Abstract: Constructing efficient and reliable policies to assist humans is indispensable for human-AI collaboration. Existing methods mainly follow two lines of w
arXiv:2606.09388v1 Announce Type: new Abstract: Deploying safe large language models (LLMs) on resource-constrained edge devices presents a critical challenge: while dual-model systems combining LLMs
arXiv:2606.09701v1 Announce Type: cross Abstract: AI red teaming must continually adapt to evolving attackers and defenders. Reinforcement learning offers a promising approach to discovering novel att
arXiv:2606.07802v1 Announce Type: cross Abstract: Culture is the most insidious vector of gradual human disempowerment by AI: unlike economic or political displacement, cultural displacement attacks t
arXiv:2606.08312v1 Announce Type: new Abstract: In this work we study offline reinforcement learning (RL) under temporally extended task constraints expressed in Linear Temporal Logic over finite trac
arXiv:2606.07866v1 Announce Type: new Abstract: Regulatory review of advanced nuclear reactor designs routinely spans more than three years and consumes hundreds of millions of dollars in combined reg
arXiv:2606.07612v1 Announce Type: cross Abstract: We argue that many Anthropomorphic Misalignment Research (AMR) studies need stronger evidence to ensure that they can provide a robust foundation for
arXiv:2606.08296v1 Announce Type: new Abstract: A key premise in leading arguments for existential risk from artificial intelligence is that malfunctioning artificial agents could not be easily shut d
arXiv:2606.09255v1 Announce Type: new Abstract: We present RPO-PDT: a retrieval-grounded, role-play-based dialogue system for adaptive student support in higher education. RPO-PDT is: (1) able to prov
arXiv:2511.05355v3 Announce Type: replace Abstract: Flow matching (FM) has shown promising results in data-driven planning. However, it inherently lacks formal guarantees for ensuring state and action