Knowledge catalogue
arXiv:2606.27251v1 Announce Type: cross Abstract: Building persistent embodied agents in unstructured environments demands unified orchestration of heterogeneous tools spanning both cyber (APIs, IoT)
arXiv:2606.27268v1 Announce Type: cross Abstract: Recently, a few works have made early attempts to study test-time scaling for embodied tasks. However, two major challenges remain unsolved: (1) reaso
arXiv:2606.26432v1 Announce Type: new Abstract: Tabular foundation models achieve strong accuracy on choice prediction tasks, but their predictions often violate the economic logic those tasks require
arXiv:2606.27282v1 Announce Type: new Abstract: Time-series forecasting research has been moving steadily toward larger architectures, from specialized transformers to general-purpose foundation model
arXiv:2606.27275v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly critical to digital library workflows, yet their ability to process historical language remains poorly und
arXiv:2606.26806v1 Announce Type: new Abstract: Long-running language agents need more than memory access. Retrieval systems can fetch past facts at query time, but they do not decide which experience
arXiv:2606.26790v1 Announce Type: new Abstract: Outcome-based reinforcement learning provides a stable optimization backbone for language agents, but its sparse trajectory-level rewards provide little
arXiv:2606.26839v1 Announce Type: cross Abstract: Learning robust navigation policies directly from visual observations remains a fundamental challenge in vision-based robotic navigation. In end-to-en
arXiv:2606.26631v1 Announce Type: new Abstract: Interleaved multimodal reasoning improves visual grounding by revisiting visual evidence during multi-step generation, yet existing methods typically re
arXiv:2602.18446v2 Announce Type: replace-cross Abstract: Users increasingly rely on Large Language Models (LLMs) for Deep Research, using them to synthesize diverse sources into structured reports th
arXiv:2606.26151v1 Announce Type: cross Abstract: While autonomous rovers have become indispensable to precision farming, achieving consistent operational safety remains a critical challenge. Conventi
arXiv:2606.22902v2 Announce Type: replace Abstract: Real-world users typically have access to multiple Large Language Models (LLMs) from different providers, and these LLMs often excel at distinct dom
arXiv:2606.25010v1 Announce Type: cross Abstract: Neural scaling laws for transformer language models predict smooth improvements in pretraining loss with increasing parameters, but downstream capabil
arXiv:2606.25317v1 Announce Type: new Abstract: An efficient and accurate system for detecting errors in procedural tasks is crucial for supporting human needs in daily life, as it can provide instant
arXiv:2510.23842v2 Announce Type: replace Abstract: Most state-of-the-art sign language models are trained on interpreter or isolated vocabulary data, which overlooks the variability that characterize
arXiv:2606.25383v1 Announce Type: new Abstract: As previous research on annotator disagreement in discourse phenomena has shown, understanding text coherence varies considerably from one individual to
arXiv:2606.25270v1 Announce Type: new Abstract: Keystroke dynamics have been explored extensively as a passive digital biomarker for Parkinson's disease (PD), typically by extracting summary statistic
arXiv:2604.25345v2 Announce Type: replace Abstract: Agentic AI systems are increasingly being integrated into scientific workflows, yet their behavior under realistic conditions remains insufficiently
arXiv:2606.25533v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as a dominant paradigm for enhancing large language models with external knowledge. By coupling retri
arXiv:2602.06566v3 Announce Type: replace-cross Abstract: Despite recent successes, test-time scaling -- i.e., dynamically expanding the token budget during inference as needed -- remains brittle for
arXiv:2606.24940v1 Announce Type: cross Abstract: Predicting transcriptional responses to genetic perturbations could reduce the experimental burden of functional genomics, but extrapolation to genes
arXiv:2606.24392v1 Announce Type: new Abstract: Existing ECG report generation is tightly coupled -- interpretation and reporting fused end-to-end, so errors propagate without stage-level recourse --
arXiv:2606.24286v1 Announce Type: new Abstract: Multimodal Large Language Models have achieved remarkable progress in short-form audio-video understanding, yet long-form audio-video comprehension rema
arXiv:2605.06177v2 Announce Type: replace Abstract: Reproducing and comparing deep research agents today is hard: the same backbone evaluated on the same benchmark can report different accuracies acro
arXiv:2606.24506v1 Announce Type: cross Abstract: Emerging LLM services increasingly host many sparse MoE models, yet most models receive sparse requests and remain cold. This creates a GPU memory pro
arXiv:2606.24422v1 Announce Type: new Abstract: We introduce EgoSAT, the first comprehensive benchmark for egocentric video reasoning in streaming settings, designed to evaluate the capabilities of mo
arXiv:2606.24546v1 Announce Type: new Abstract: Modern autonomous Cyber-Physical Systems (CPSs), such as self-driving cars, face increasingly complex demands, and yet are expected to act reliably. The
arXiv:2606.24335v1 Announce Type: new Abstract: Counterfactual analysis is widely used to study evidence use in vision-language models, but its diagnostic value is limited on well-posed tasks: when se
arXiv:2606.23957v1 Announce Type: new Abstract: Learning Koopman operators with autoencoders enables linear prediction in a latent space, but long-horizon rollouts often drift off the learned manifold
arXiv:2606.24479v1 Announce Type: new Abstract: In-camera JPEG previews are ubiquitous in raw image formats and provide an sRGB reference at negligible storage cost. Although existing metadata-based r
arXiv:2503.05226v2 Announce Type: replace-cross Abstract: Monte Carlo tree search is attractive for robotic manipulation because it can improve action selection through simulation without requiring a
arXiv:2606.24033v1 Announce Type: cross Abstract: Existing low-bit KV-cache quantizers often treat each cached key as a flat vector. Under RoPE, however, a key's contribution to a future attention log
arXiv:2606.23697v1 Announce Type: cross Abstract: Semantic segmentation of code written in a C-family language remains a challenging problem, due to the language's complex syntax, macro expansion, and
What is actually limiting model routers for coding tasks? Most routers treat picking a model as a static, one-off classification. This paper identifies the real bottleneck as information deficit. Simp
arXiv:2606.22171v1 Announce Type: new Abstract: Epidemic forecasting models typically rely on surveillance data reported over administrative regions, treating them as atomic units, thereby obscuring s
arXiv:2606.21109v1 Announce Type: new Abstract: Virtual-cell and perturbation models are increasingly used to predict cellular responses for biomedical discovery, but chemical and genetic perturbation
Release: datasette 1.0a35 I'll write more about this one tomorrow, but it's a big release. Three highlights from the release notes: New 'Create table' interface in the database actions menu, backed by
arXiv:2010.14694v4 Announce Type: replace-cross Abstract: This paper integrates deep neural networks (DNNs) into structural models to increase flexibility and capture rich heterogeneity while preservi
arXiv:2606.21956v1 Announce Type: new Abstract: Infrared small target detection (IRSTD) in high-resolution images is crucial for many practical applications, such as surveillance of unmanned aerial ve
arXiv:2509.21489v3 Announce Type: replace Abstract: Graph foundation models face several fundamental challenges including transferability across diverse domains and data scarcity, which calls into que
arXiv:2606.20189v2 Announce Type: replace Abstract: Leveraging Vision Foundation Models (VFMs) for camera-to-LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data
arXiv:2606.20676v1 Announce Type: new Abstract: MLLM-as-a-Judge is conventionally validated by agreement with human annotations, but this metric is undefined when the human pool is culturally heteroge
arXiv:2606.23682v1 Announce Type: new Abstract: Reference-based diffusion models enable highly controllable image generation by leveraging elements from input images to guide prompt-driven synthesis.
arXiv:2606.22261v1 Announce Type: new Abstract: Abnormality detection in complex systems faces two practical barriers: abnormal labels are scarce, and binary labels do not quantify how far an event ha
arXiv:2606.22076v1 Announce Type: new Abstract: Single-reference unseen object 6D pose estimation reduces object onboarding by estimating poses of arbitrary novel objects from only one reference view.
arXiv:2606.22946v1 Announce Type: new Abstract: Neural operators learn mappings between function spaces, but are typically developed with dense input-output training fields and fully observed inputs a
arXiv:2606.21113v1 Announce Type: new Abstract: Object-centric image generation is important in settings with few labeled examples, including pedestrian analysis in smart-city scenes, traffic-sign ins
One thing we’ve seen pretty consistently: shipping the first version is only a small part of the work A key part of building reliable agents is having a repeatable lifecycle for improving them over ti
arXiv:2606.21596v1 Announce Type: new Abstract: Reconstructing compositional 3D scenes from a single image is a fundamental challenge in 3D world modeling. Recent methods can recover high-fidelity, co
arXiv:2606.20724v1 Announce Type: cross Abstract: Long-horizon web agents often fail in ways hidden by final-answer evaluation: they may visit useful pages, produce a well-formed answer, and terminate
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
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
arXiv:2606.12400v1 Announce Type: new Abstract: Long input sequences are central to document understanding and multi-step reasoning in Large Language Models, yet the quadratic cost of attention makes
arXiv:2511.19314v2 Announce Type: replace Abstract: Information-seeking is a core capability for AI agents, requiring them to gather and reason over tool-generated information across long trajectories
arXiv:2606.12329v1 Announce Type: new Abstract: AI coding assistants now support a growing share of software work, from quick scripts to production applications. Yet these agents remain largely statel
arXiv:2606.11466v1 Announce Type: new Abstract: Point cloud semantic segmentation requires architectures that capture both fine-grained local geometry and broad global scene structure. Transformer-bas
arXiv:2606.11275v1 Announce Type: cross Abstract: Rotary Position Embeddings (RoPE) make attention scores position-relative but leave the value pathway position-blind: the message sent by a value toke
arXiv:2606.11846v1 Announce Type: new Abstract: Current virtual staining approaches offer the potential for time- and cost-efficient biomarker quantification in cancer diagnostics and prognostics. How
arXiv:2412.09023v3 Announce Type: replace Abstract: Channel and spatial attention mechanisms introduced in earlier work enhance the representational capabilities of deep convolutional neural networks
arXiv:2606.11990v1 Announce Type: cross Abstract: Remaining Useful Life (RUL) prediction is essential for industrial predictive maintenance, yet many learning-based approaches rely on extensive featur