Knowledge catalogue
arXiv:2610.19640v1 Announce Type: new Abstract: The scarcity of observed biodegradation reactions constrains the training of product and pathway predictors. We present enviGraph, a framework that gene
arXiv:2610.20320v1 Announce Type: new Abstract: Large-scale robot learning increasingly relies on diverse human demonstrations, yet tactile manipulation data remain difficult to scale, and existing mu
arXiv:2610.19362v1 Announce Type: new Abstract: Best-of-N sampling is a simple inference-time scaling strategy for large language models, yet its behavior under incomplete reward models remains poorly
arXiv:2610.19344v1 Announce Type: new Abstract: Layer-ablation studies often perturb early or late layers of a language model, compare the damage to prediction on two corpora, and read a larger early-
arXiv:2610.19860v1 Announce Type: cross Abstract: In sectors such as energy, aquaculture and chemical processing, the effectiveness of both mechanistic and data-driven models is frequently limited by
arXiv:2610.20439v1 Announce Type: new Abstract: We study classification dynamics under small initialization and identify a pronounced non-monotone behavior of accuracy that we call the accuracy well:
arXiv:2610.15938v1 Announce Type: new Abstract: Evaluation of text generated by large language models (LLMs) is increasingly delegated to LLMs acting as judges, yet what such a judge must be shown rem
arXiv:2610.18520v1 Announce Type: cross Abstract: Autoregressive language models produce, at every prediction step, a vocabulary-sized logit vector whose structure determines the conditional next-toke
arXiv:2610.15499v1 Announce Type: cross Abstract: Digital technologies can preserve records of wrongdoing indefinitely, and AI can give those records continuing influence over a person's future. We de
arXiv:2610.20229v1 Announce Type: cross Abstract: We investigate the minimal width of deep neural networks that can approximate all continuous functions from R^n to R^m in the topology of uniform conv
arXiv:2610.19387v1 Announce Type: new Abstract: Identification and ranking of salient propositions in documents is indirectly involved in many AI tasks, but few papers define proposition salience scor
arXiv:2610.17324v1 Announce Type: cross Abstract: AI-generated outputs are frequently perceived as highly creative, but also tend to be more similar to each other (homogenized) than human ideas. Does
arXiv:2610.19934v1 Announce Type: new Abstract: Weight pruning can degrade the accuracy of large reasoning models (LRMs), yet it remains unclear whether it damages reasoning itself. We find that much
arXiv:2610.14984v1 Announce Type: cross Abstract: Generative AI makes options cheap and shifts the bottleneck of creative work from generating options to screening them. We argue that AI's value depen
arXiv:2507.21137v2 Announce Type: replace Abstract: In this paper we try to answer the question 'What constitutes Sudoku difficulty rating across different Sudoku websites?' Using two distinct methods
arXiv:2610.20442v1 Announce Type: new Abstract: Foundation models are driving a paradigm shift in robotic intelligence, moving beyond low-level action prediction to goal-directed orchestration of data
arXiv:2610.19041v1 Announce Type: cross Abstract: Understanding the dynamics of language acquisition in Large Language Models (LLMs) is crucial for uncovering how statistical representations evolve in
arXiv:2610.20416v1 Announce Type: new Abstract: For the fundamental problem of prediction from expert advice, Freund asked whether one could compete with a comparator that switches k times among only
arXiv:2512.10309v3 Announce Type: replace-cross Abstract: Chemical reaction networks are widely used to model stochastic dynamics in biophysical processes. Their exact probabilistic description via th
arXiv:2610.19401v1 Announce Type: cross Abstract: As large, powerful pre-trained models become more prevalent, there is growing interest in developing methods that allow different neural networks to b
arXiv:2610.16887v1 Announce Type: new Abstract: Transparency in AI-supported decision systems is widely required but poorly defined. Existing approaches treat transparency as a property of systems or
arXiv:2610.19067v1 Announce Type: new Abstract: A wearable assistive robot must continuously estimate terrain, locomotion mode, gait phase, and walking speed. These quantities are commonly addressed b
arXiv:2504.07170v3 Announce Type: replace Abstract: Trustworthy AI encompasses many aspirational aspects for aligning AI systems with human values, including fairness, privacy, robustness, explainabil
arXiv:2610.19536v1 Announce Type: new Abstract: Typed decision models are language models that answer a question with a fixed set of possible answers by returning a probability for each answer instead
arXiv:2610.15028v1 Announce Type: cross Abstract: Recent bidirectional encoders have been modernized with innovations from causal large language models (LLMs). However, the pretraining recipe itself r
arXiv:2502.12713v2 Announce Type: replace Abstract: Echocardiography plays a fundamental role in the extraction of clinical parameters (e.g. left ventricular volume and ejection fraction) required to
arXiv:2610.15227v1 Announce Type: new Abstract: Unified vision-language models (VLMs) perform both text-to-image (T2I) and image-to-text (I2T) generation with one shared Transformer backbone, yet how
arXiv:2507.15585v2 Announce Type: replace-cross Abstract: One way social groups are marginalized in discourse is that the narratives told about them often default to a narrow, stereotyped range of top
arXiv:2610.19899v1 Announce Type: new Abstract: Neural networks operate naturally on finite-dimensional vectors, yet many problems require reasoning about higher-order objects such as distributions, f
arXiv:2511.09568v3 Announce Type: replace-cross Abstract: Diffusion models show promise for 3D molecular generation, but face a fundamental trade-off between sampling efficiency and conformational acc
arXiv:2512.23044v3 Announce Type: replace Abstract: Autonomous agents are shifting information seeking from passive retrieval toward proactive, open-ended research, yet this progress has largely bypas
arXiv:2501.12375v4 Announce Type: replace Abstract: Depth Anything has achieved remarkable success in monocular depth estimation with strong generalization ability. However, it suffers from temporal i
arXiv:2610.19847v1 Announce Type: new Abstract: We address inverse rendering with 3D Gaussian Splatting (3DGS) by introducing explicit visibility modeling for relighting as well as material and light
arXiv:2610.20604v1 Announce Type: new Abstract: World-action models aim to turn visual anticipation into driving decisions, yet typically train action experts on trajectory representations defined ind
arXiv:2610.19411v1 Announce Type: new Abstract: A major challenge in wearable device activity recognition is the heterogeneity across datasets in sensor placement, channel count, sampling rate and sub
arXiv:2610.16620v1 Announce Type: new Abstract: Editing the color of images in a realistic way finds many applications such as changing the perception of an image, data augmentation or film post proce
arXiv:2610.20035v1 Announce Type: cross Abstract: We introduce Weighted Adjoint Matching, a method to finetune a pretrained diffusion or flow matching model given a differentiable reward. We derive it
arXiv:2610.19611v1 Announce Type: new Abstract: Modern vision-language models can accept multi-image contexts, but aggregate accuracy rarely reveals where a multi-image reasoning pipeline fails. We st
arXiv:2610.20450v1 Announce Type: cross Abstract: Pairwise comparison data are a standard supervision signal in preference learning, but disagreement in such data can reflect stable latent heterogenei
arXiv:2610.20052v1 Announce Type: new Abstract: Ambiguous inputs can admit multiple interpretations, each potentially yielding a different answer. Existing work largely treats this ambiguity as a prop
arXiv:2610.17090v1 Announce Type: cross Abstract: On 6 October 2026 OpenAI published an initial 722 AI-written mathematical manuscripts, organized into 372 problem families, in the public repository g
arXiv:2610.16325v1 Announce Type: new Abstract: When a steering intervention changes a language model's behavior, which part of the intervention deserves the credit? We test this question for a target
arXiv:2610.20429v1 Announce Type: new Abstract: Though modern AI models like LLMs predict discrete tokens, today's robotic foundation models represent actions as continuous vectors. In this paper, we
arXiv:2610.20342v1 Announce Type: new Abstract: Missing data is a pervasive challenge in machine learning, often degrading predictive performance and complicating model deployment. Most existing appro
arXiv:2610.19769v1 Announce Type: cross Abstract: Architectural technical debt arises when a system's implementation drifts from its inten ded modular decomposition, while the mapping between source f
arXiv:2610.18786v1 Announce Type: cross Abstract: LLM ensemble gains are often measured against a single baseline, but the value attributed to additional candidates can change with the comparison used
arXiv:2610.16102v1 Announce Type: cross Abstract: We argue that tacit knowing is better modelled as an operating regime of one memory substrate than as a kind of content. The substrate is VaCoAl, an a
arXiv:2610.20067v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently encounter conflicts between internal parametric knowledge and external contextual knowledge, yet the mechanisms
arXiv:2610.20107v1 Announce Type: new Abstract: Web agents built on vision language models decide every action by generating text such as click(12), although most of that text encodes a choice among a
arXiv:2610.10226v2 Announce Type: replace-cross Abstract: Can AI make Software Engineering (SE) -- the discipline -- obsolete? And can it make software engineers -- the professionals -- redundant? Thi
arXiv:2610.19419v1 Announce Type: new Abstract: MLLM-based detectors formulate vision-language grounding as autoregressive coordinate generation, yet in dense scenes, they often collapse into a box re
arXiv:2610.16292v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has been becoming a promising 3D reconstruction technique because of its advantage of high-fidelity rendering effect with h
arXiv:2602.10516v4 Announce Type: replace Abstract: Audio-driven 3D talking avatar generation is increasingly important in virtual communication, digital humans, and interactive media, where avatars m
arXiv:2610.17625v1 Announce Type: new Abstract: For video generation in dynamic world modeling, observed static and dynamic content should be efficiently reused to provide conditions for repeated gene
arXiv:2610.15998v1 Announce Type: new Abstract: DNA methylation is a critical epigenetic modification that regulates gene expression and genome stability, primarily catalyzed by the DNA methyltransfer
arXiv:2602.07496v2 Announce Type: replace Abstract: Inverse Reinforcement Learning (IRL) seeks to infer reward functions from expert demonstrations. When demonstrations originate from multiple experts
arXiv:2610.16781v1 Announce Type: cross Abstract: Parallel token proposals in language-model decoding need a joint law, not merely accurate token marginals. We construct a family of coordinate-Bethe-a
arXiv:2610.18227v1 Announce Type: cross Abstract: Domain-specific curvature is an appealing idea for domain generalization (DG), but curvature learning can denote substantially different computations.
arXiv:2610.17705v1 Announce Type: new Abstract: 3D scene graph prediction is commonly supervised with local object and predicate classification losses. Although effective for slot-wise label predictio
arXiv:2610.17159v1 Announce Type: cross Abstract: We develop a novel generalization of McDiarmid's concentration inequality and use it to obtain a uniform law of large numbers result and error bounds