Knowledge catalogue
arXiv:2609.39859v1 Announce Type: new Abstract: Masked diffusion language models (dLLMs) have shown strong potential for faster inference through parallel token generation when combined with confidenc
arXiv:2609.39305v1 Announce Type: new Abstract: Safe navigation in dynamic environments requires anticipating future environmental states to account for spatiotemporal risks, specifically when and whe
arXiv:2609.39021v1 Announce Type: new Abstract: Reinforcement learning (RL) has substantially improved the reasoning ability of multimodal language models through verifiable rewards and increasingly f
arXiv:2609.38839v1 Announce Type: new Abstract: Long-horizon video generation requires models to effectively leverage an increasingly long generation history. As the generated history grows, retaining
arXiv:2609.38256v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to answer questions about politically contentious issues, yet evaluations typically treat a model's s
arXiv:2609.39634v1 Announce Type: cross Abstract: Common policy improvement methods, including TRPO, PPO, and GRPO, estimate policy improvement under the behavioral policy's state-visitation distribut
arXiv:2609.39257v1 Announce Type: cross Abstract: Accurate forecasting of electricity production is essential for maintaining the operational efficiency and strategic planning of energy utilities. In
arXiv:2609.35618v2 Announce Type: replace Abstract: The question of AI consciousness is one of the most urgent pre-emptive problems in philosophy and computer science, yet progress is hampered by a ca
arXiv:2510.17932v5 Announce Type: replace-cross Abstract: We introduce Chart2Code, a new benchmark for evaluating the chart understanding and code generation capabilities of large multimodal models (L
arXiv:2609.38402v1 Announce Type: cross Abstract: We introduce C2C (From Codebase to Culprit), a framework for precise bug localization that progressively reduces the debugging search space across mul
arXiv:2511.22232v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) are increasingly capable in medical imaging, yet most focus on single-image settings. Clinical interp
arXiv:2509.03122v5 Announce Type: replace-cross Abstract: Reliable model fingerprints are essential for protecting large language models (LLMs) against unauthorized redistribution and commercial misus
arXiv:2602.06940v2 Announce Type: replace Abstract: The unsupervised discovery of features that are both semantically meaningful and stable across runs remains a central challenge in representation le
arXiv:2609.40143v1 Announce Type: new Abstract: Compact regulatory DNA can free up space in vector payloads, reduce synthesis and assay burden, and expose which sequence features drive predicted activ
arXiv:2609.39566v1 Announce Type: new Abstract: Foundation models can serve as clinical agents through tool-use harnesses. However, conventional medical benchmarks assess reasoning over preselected ev
arXiv:2609.38924v1 Announce Type: new Abstract: Major adverse cardiovascular events (MACE) remain the leading cause of mortality worldwide. Opportunistic screening using routinely acquired clinical da
arXiv:2609.39436v1 Announce Type: cross Abstract: Reinforcement learning with a verifiable reward (RLVR) offers a scalable approach to training language-model agents, yet sparse outcome rewards can le
arXiv:2609.39670v1 Announce Type: new Abstract: Vision-language-action (VLA) models enable task-conditioned interaction, but extending them to scene-scale aerial manipulation remains challenging due t
arXiv:2609.39648v1 Announce Type: cross Abstract: Diffusion models are typically viewed as stochastic processes that transform noise into data. We take a complementary perspective: a diffusion model d
arXiv:2609.39383v1 Announce Type: cross Abstract: Large language model (LLM)-based automatic heuristic design (AHD) iteratively proposes and refines heuristics, pairing design rationales with executab
arXiv:2609.38516v1 Announce Type: cross Abstract: Large language models (LLMs) can now improve themselves by revising the instructions they follow, and LLM agents are increasingly orchestrated to work
arXiv:2609.40148v1 Announce Type: new Abstract: Power-law learning curves are often treated as fixed properties of a model and its data, although learning-rate and batch-size schedules can change the
arXiv:2609.39453v1 Announce Type: cross Abstract: Speech emotion recognition (SER) is the task of assigning emotion labels to utterances. Early systems relied on acoustic features, whereas recent appr
arXiv:2606.17010v2 Announce Type: replace Abstract: Heterogeneous Treatment Effect (HTE) identification is essential to explain the impact of an intervention and optimize our policies accordingly. Exi
arXiv:2609.40064v1 Announce Type: new Abstract: Purpose: This study examines whether domain-specific public mood is associated with stock-market dynamics and whether these relationships vary across co
arXiv:2609.39022v1 Announce Type: cross Abstract: Coding agents need to establish that a program satisfies a specification and that the specification captures the requested behavior. We study how expe
arXiv:2609.39486v1 Announce Type: new Abstract: Estimating accident mechanics from real-world crashes is important for vehicle-safety analysis, injury modeling, crash-severity prediction, and operatio
arXiv:2609.39492v1 Announce Type: new Abstract: Matching the same vehicle across front and rear cameras is difficult because the cameras do not share a view and the vehicle's appearance changes substa
arXiv:2511.16030v3 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) enables efficient, high-fidelity novel view synthesis, yet its performance degrades severely under sparse-view supervis
arXiv:2609.39006v1 Announce Type: new Abstract: Cross-embodiment dexterous grasp generation remains challenging because robotic hands differ substantially in geometry, topology, and kinematics. Existi
arXiv:2609.38348v1 Announce Type: new Abstract: Many forms of data, including physical fields, geometric shapes, and visual signals, are naturally described by functions over continuous domains but ar
arXiv:2609.38819v1 Announce Type: cross Abstract: Studying the alignment between the internal representations of vision models and the responses of the visual cortex to the same observed visual stimul
arXiv:2609.39228v1 Announce Type: new Abstract: We present FYAN, a human--AI harness for document-level mathematical formalization. Rather than treating theorems in isolation, FYAN coordinates an end-
arXiv:2609.40137v1 Announce Type: cross Abstract: We present Game-Guided Skill Discovery (GGSD), a framework that uses self-play in games to discover motor skills that are directly playable by humans.
arXiv:2609.38690v1 Announce Type: new Abstract: Spatial transcriptomics enables spatially resolved gene expression analysis from slide-level images while preserving morphological features, providing v
arXiv:2609.40091v1 Announce Type: cross Abstract: Automated report generation can ease the burden radiolo gists face when interpreting multi-sequence MRI studies. Unlike CT, MRI examinations comprise
arXiv:2609.38285v1 Announce Type: cross Abstract: Vision-language models (VLMs) can contradict themselves across views of the same spatial relation and fail to respond when that relation changes. Addr
arXiv:2609.38488v1 Announce Type: cross Abstract: Conventional 3D Gaussian Splatting (3DGS) requires depth sorting and ordered alpha blending to correctly render overlapping Gaussian primitives. Stoch
arXiv:2609.38519v1 Announce Type: new Abstract: Egocentric gaze prediction enables many downstream applications but remains challenging, as human gaze is inherently stochastic. This stochasticity is c
arXiv:2609.39558v1 Announce Type: cross Abstract: Accurate human torque estimation is crucial for enabling task-agnostic control in robotic exoskeleton systems. However, estimation errors may cause mi
arXiv:2504.04711v2 Announce Type: replace Abstract: Current large language models (LLMs) are constrained by human-derived training data and limited by a single level of abstraction that impedes defini
arXiv:2609.39301v1 Announce Type: cross Abstract: This paper considers a class of multiblock nonconvex and nonsmooth optimization problems arising in many applications. Existing methods construct prox
arXiv:2609.38911v1 Announce Type: new Abstract: Learning must acquire new capabilities while preserving both prior responsibilities and the capacity to learn again. We introduce Generalized Residual C
arXiv:2511.11689v4 Announce Type: replace-cross Abstract: Generative AI chatbots built for mental health could extend access to care, but evidence from real-world use is limited. We report a single-ar
arXiv:2609.38524v1 Announce Type: cross Abstract: We consider estimating the one-step-ahead conditional distribution of a multivariate stochastic process. Many existing approaches rely on assumptions
arXiv:2609.39128v1 Announce Type: new Abstract: Global video geo-localization aims to infer the geographic location of a video worldwide, evaluating performance across four geographic hierarchies: cit
arXiv:2609.38623v1 Announce Type: new Abstract: Learning partial differential equation (PDE) dynamics across varying domains is central to predictive modelling and data-driven discovery of governing e
arXiv:2609.38863v1 Announce Type: cross Abstract: The two-dimensional irregular knapsack problem in a fixed circular container is an important combinatorial optimization problem for maximizing materia
arXiv:2609.38400v1 Announce Type: cross Abstract: Co-speech gestures for robots must adapt not only to speech and embodiment, but also to the workspace available for performing the motion. Since the s
arXiv:2609.38312v1 Announce Type: cross Abstract: The Platonic Representation Hypothesis predicts that sufficiently scaled foundation models converge on a shared representation of the world. As each n
arXiv:2609.39692v1 Announce Type: cross Abstract: On-policy distillation (OPD) has demonstrated two important capabilities in language models: compressing large teachers into smaller students and merg
arXiv:2609.39552v1 Announce Type: cross Abstract: Text-to-song generation models can be prompted to imitate specific artists or regurgitate entire songs from their training data. Although these phenom
arXiv:2609.40317v1 Announce Type: new Abstract: While talking head generation has advanced rapidly, generating natural listener behavior in dyadic conversations, which know when to react, how to react
arXiv:2609.40297v1 Announce Type: new Abstract: Autonomous exploration demands that robots continuously evaluate candidate viewpoints based on their expected information gain and execution cost. Sampl
arXiv:2609.39869v1 Announce Type: new Abstract: Grammar-constrained generation guarantees syntactic validity, but can substantially degrade semantic quality when the model's preferred outputs are poor
arXiv:2609.38424v1 Announce Type: new Abstract: Node-level graph anomaly detection (GAD) identifies nodes whose attributes and interactions deviate from dominant graph regularities. Existing GAD model
arXiv:2609.39658v1 Announce Type: new Abstract: Diffusion models generate data by reversing a forward corruption process that typically approaches a simple Gaussian prior. Recent work has extended thi
arXiv:2609.38798v1 Announce Type: new Abstract: Graph agents extend large language models (LLMs) with the ability to actively explore and reason over knowledge graphs through multi-step interactions w
arXiv:2609.38923v1 Announce Type: new Abstract: Working agents need to read diverse files, coordinate tools, and produce deliverables. Training such agents requires tasks built on many real files with
arXiv:2609.39777v1 Announce Type: new Abstract: LLM-based multi-agent systems coordinate specialized reasoning through aggregation, interaction, and adaptive control, yet their potential for graph lea