Knowledge catalogue
arXiv:2605.18191v1 Announce Type: new Abstract: Current reinforcement learning(RL) methods are broadly applicable and powerful in verifiable settings where scalar rewards can be provided. However, in
arXiv:2605.18080v1 Announce Type: cross Abstract: We present a novel parameterized 4-qubit Eisert-Wilkens-Lewenstein (EWL) quantum game circuit for recommender systems in quadruple helix innovation ec
arXiv:2605.18038v1 Announce Type: new Abstract: Salmon re-identification in commercial net-pens is challenging due to large populations, which impose strict accuracy requirements and make large-scale
arXiv:2605.16452v1 Announce Type: cross Abstract: Accurate peak detection across diverse cardiac physiological signals, including the Electrocardiogram (ECG), Photoplethysmogram (PPG), Ballistocardiog
arXiv:2605.16858v1 Announce Type: cross Abstract: Autonomous Vehicles (AVs) must make reliable decisions in dense urban environments where pedestrian behavior is variable, sometimes abnormal, and ofte
arXiv:2605.17671v1 Announce Type: cross Abstract: Non-contrastive self-supervised learning (SSL) is an effective framework for predictive representation learning, but popular (and in practice effectiv
arXiv:2505.00409v3 Announce Type: replace-cross Abstract: Automatic anonymization is increasingly used to enable ethical sharing of clinical speech, yet its perceptual and clinical consequences remain
arXiv:2605.18303v1 Announce Type: cross Abstract: World models built on recurrent state space architectures enable efficient latent imagination, yet remain physically unstructured, producing dynamics
arXiv:2605.16325v1 Announce Type: cross Abstract: Phase-transition phenomena in deep learning (grokking, emergent capabilities, and ontological reorganization under context shift) have been studied th
arXiv:2605.18606v1 Announce Type: new Abstract: Neural operators approximate PDE solution maps, but they need not respect the symmetries of the governing equation. In out-of-distribution (OOD) regimes
arXiv:2605.16860v1 Announce Type: cross Abstract: Accurate long-horizon glucose forecasting is critical for automated insulin delivery systems, which help people with type 1 diabetes (T1D) manage thei
arXiv:2605.16351v1 Announce Type: cross Abstract: Scientific foundation models are expected to reuse representations under changes in dataset, acquisition protocol, and deployment domain, yet many seq
arXiv:2605.18598v1 Announce Type: new Abstract: We address the fundamental question of why deep neural networks generalize by establishing a pointwise generalization theory for fully connected network
arXiv:2605.16479v1 Announce Type: cross Abstract: Job seekers often initiate search with short, underspecified queries. At LinkedIn, over 80% of job-related queries contain three or fewer keywords, ma
arXiv:2605.16588v1 Announce Type: new Abstract: Safety-critical autonomy in unstructured environments poses significant challenges for online safety certification under evolving constraints. We propos
Politicians who boost AI do not represent the views of their constituents. New polling in the UK shows: - Most people think AI will destroy more jobs than it creates (57% v 17%) - 7 in 10 are worried
arXiv:2605.18672v1 Announce Type: new Abstract: This position paper argues that enforcing LLM agent safety within a single abstraction layer is not merely suboptimal but categorically insufficient for
arXiv:2605.17347v1 Announce Type: cross Abstract: When a neural network estimates someone's age from a photograph, does it process biometric data? The answer depends on whether identity-discriminative
arXiv:2605.16551v1 Announce Type: new Abstract: Evaluating LLM-based agents remains challenging because identifying meaningful failure cases often requires substantial human effort to design realistic
arXiv:2605.17618v1 Announce Type: new Abstract: Autism Spectrum Disorder (ASD) is characterized by challenges with social interaction and communication and by restricted or repetitive patterns of thou
arXiv:2605.18352v1 Announce Type: new Abstract: Presupposition projection in conditionals is central to theories of meaning and pragmatics, yet it remains largely unevaluated in large language models.
arXiv:2605.16793v1 Announce Type: new Abstract: Time series forecasting under non-stationarity faces a fundamental tension between capturing stable representations and adapting to distribution shifts.
arXiv:2605.17382v1 Announce Type: new Abstract: The rapid progress of generative artificial intelligence has exposed fundamental limitations in existing evaluation methodologies, particularly for open
arXiv:2605.16429v1 Announce Type: cross Abstract: We introduce extbf{QuantFPFlow}, a reinforcement learning framework that integrates quantum amplitude estimation into the Fokker--Planck~(FP) formulat
arXiv:2605.18570v1 Announce Type: new Abstract: Cross-domain knowledge alignment is essential for integrating heterogeneous medical systems, yet existing approaches typically treat entity alignment as
arXiv:2605.18130v1 Announce Type: new Abstract: Medical image segmentation is more clinically valuable when it supports diagnosis rather than merely producing lesion masks. However, diagnostically rel
arXiv:2605.18591v1 Announce Type: cross Abstract: Natural policy gradients improve optimization by accounting for the geometry of distribution space, but their practical use is limited by the cost of
arXiv:2605.16999v1 Announce Type: new Abstract: Reinforcement learning post-training has substantially improved the reasoning accuracy of vision-language models, yet the resulting policies remain poor
arXiv:2605.18359v1 Announce Type: new Abstract: Large multimodal models (LMMs) inherit the self-attention mechanism of pretrained language backbones, yet standard attention can exhibit suboptimal allo
arXiv:2601.22664v4 Announce Type: replace Abstract: Reinforcement Learning from Human Feedback (RLHF) is a pivotal technique for aligning large language models (LLMs) with human preferences, yet it is
arXiv:2601.08454v2 Announce Type: replace Abstract: Constructing physically accurate simulation environments (Real2Sim) traditionally relies on manual system identification or rigid, exhaustive explor
arXiv:2605.17308v1 Announce Type: new Abstract: Electrocardiogram (ECG) diagnosis in clinical practice relies on structured reasoning over multiple hierarchical aspects, including cardiac rhythm, cond
arXiv:2605.16460v1 Announce Type: new Abstract: Referring expression counting (REC) is an intention-driven task that requires context-aware visual reasoning. While recent vision-language models incorp
arXiv:2605.16302v1 Announce Type: cross Abstract: Reinforcement learning for multi-step reasoning with large language models (LLMs) often relies on sparse terminal rewards, leading to poor credit assi
arXiv:2605.17036v1 Announce Type: new Abstract: This paper studies autonomous generative AI agents in multi-echelon supply chains using the MIT Beer Game. We identify four inference-time levers that s
arXiv:2605.17830v1 Announce Type: new Abstract: Safety evaluations of memory-equipped LLM agents typically measure within-task safety: whether an agent completes a single scenario safely, often under
arXiv:2502.07027v2 Announce Type: replace-cross Abstract: Molecular Relational Learning (MRL) is widely applied in natural sciences to predict relationships between molecular pairs by extracting struc
arXiv:2605.18599v1 Announce Type: new Abstract: Transformer-based models have advanced feedforward novel view synthesis (NVS). Current architectures such as GS-LRM and LVSM mix semantic information (e
arXiv:2502.16691v2 Announce Type: replace Abstract: Recent research has increasingly focused on training large language models (LLMs) using federated learning, known as FedLLM. However, responsible AI
arXiv:2605.17548v1 Announce Type: cross Abstract: Code review has evolved for decades, from informal peer checking to today's pull request (PR) workflows, yet it remains a largely manual, uneven, and
arXiv:2605.16331v1 Announce Type: cross Abstract: Protein language models are increasingly used to guide experimental and clinical decisions, yet it is often unclear whether a confident prediction ref
arXiv:2605.16651v1 Announce Type: new Abstract: Explanation mechanisms are increasingly used to support transparency and trust in vision-language models (VLMs), particularly in settings where model de
arXiv:2605.18004v1 Announce Type: new Abstract: Randomized linear algebra (RLA) algorithms are a modern class of numerical linear algebra techniques that play an essential role in scientific computing
Running the same CNA search on base models (before instruction tuning) yields a structurally similar set of neurons, but ablating them produces almost no behavioral change. We read this as evidence th
arXiv:2605.18579v1 Announce Type: new Abstract: Pre-training on text-attributed graphs (TAGs) is central to building transferable graph foundation models, where LLM-as-Aligner methods align graph and
arXiv:2605.16871v1 Announce Type: new Abstract: Explainable robots require not only successful task execution but also the ability to expose internal decision-making process in a user-friendly manner.
arXiv:2605.18719v1 Announce Type: new Abstract: Diffusion models have been widely studied for removing unsafe content learned during pre-training. Existing methods require expensive supervised data, e
arXiv:2605.18104v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) often fail to transfer safety capabilities learned in the text modality to semantically equivalent non-text inp
arXiv:2605.17648v1 Announce Type: new Abstract: Generative recommendation treats next-item prediction as autoregressive item-identifier generation. Specifically, items are encoded as semantic identifi
arXiv:2605.17271v1 Announce Type: cross Abstract: Bi-causal optimal transport (OT) is a natural framework for comparing and coupling stochastic processes under nonanticipative information constraints,
arXiv:2605.17084v1 Announce Type: cross Abstract: In language models, what a representation encodes is determined by the geometry of its representation space: distances, not activations, carry meaning
arXiv:2605.18026v1 Announce Type: new Abstract: Roundabouts, characterized by frequent merging and yielding interactions, remain a safety-critical corner case for the development and testing of intell
arXiv:2605.18299v1 Announce Type: new Abstract: Search-augmented reasoning agents interleave internal reasoning with calls to an external retriever, and their performance relies on the quality of each
arXiv:2605.18162v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have made striking progress, yet their spatial reasoning remains fragile: models that answer an original input correctly
arXiv:2605.16440v1 Announce Type: cross Abstract: Deep neural networks are vulnerable to adversarial perturbations, limiting deployment in safety-critical applications such as synthetic aperture radar
arXiv:2601.18442v3 Announce Type: replace Abstract: Autonomous Vehicle (AV) requires rigorous testing in safety-critical scenarios for safety validation, yet its validation is hindered by the high cos
arXiv:2605.18039v1 Announce Type: new Abstract: Learning dense correspondences across deformable 3D shapes remains a long-standing challenge due to structural variability, non-isometric deformation, a
arXiv:2605.17999v1 Announce Type: new Abstract: This paper proposes a Shared Backbone Proximal Policy Optimization (Shared Backbone PPO) algorithm. By sharing the base module between the Actor and Cri
arXiv:2605.16973v1 Announce Type: new Abstract: Domain generalization aims to enhance model robustness against unseen domains with embedding distribution shifts. While large-scale vision-language mode
This post appears to be a community prompt-sharing discussion where Swyx invites people to share their '/goal' prompts (likely system prompts or directives designed to optimize AI behavior toward spec