Knowledge catalogue
arXiv:2609.36670v1 Announce Type: new Abstract: A critical prerequisite of generative recommendation is designing semantic identifiers (SIDs) that are both scalable to large item sets and efficiently
arXiv:2609.36390v1 Announce Type: cross Abstract: Offline reinforcement learning seeks optimal decision rules from previously collected data. In some applications, a decision can be an entire function
arXiv:2609.36474v1 Announce Type: new Abstract: In regulated industries like consumer finance, seemingly harmless user queries can exploit large language model vulnerabilities, triggering safety failu
arXiv:2609.36458v1 Announce Type: cross Abstract: Semantic-preserving transformations can induce substantial motion in learned representations, while small changes may strongly affect model prediction
arXiv:2609.37314v1 Announce Type: new Abstract: Synthetic anomaly generation helps expand industrial anomaly datasets when real defects are scarce or unavailable. Existing approaches lie at two extrem
arXiv:2609.37642v1 Announce Type: new Abstract: Self-supervised pretraining reshaped prediction in language and vision, and brain foundation models (BFMs) inherited its promise. Representations learne
arXiv:2602.14035v2 Announce Type: replace Abstract: Flowchart-oriented dialogue (FOD) systems aim to guide users through multi-turn decision-making or operational procedures by following a domain-spec
arXiv:2609.36064v1 Announce Type: cross Abstract: Foundation models are powerful generators, but many engineering domains require structured representations that general-purpose systems handle poorly.
arXiv:2609.37851v1 Announce Type: new Abstract: Few-step flow-map generators, including MeanFlow and consistency models, enable efficient sampling through long-range transport, yet their on-policy dis
arXiv:2609.35947v1 Announce Type: new Abstract: Many inference-time tasks for pretrained discrete diffusion models and diffusion language models reduce to drawing samples from a tilted version of the
arXiv:2609.36560v1 Announce Type: cross Abstract: Object re-identification (ReID) faces a recurring challenge: different identities can share highly similar global appearances, while the cues that dis
arXiv:2609.37590v1 Announce Type: new Abstract: LLM agents accumulate interaction histories that grow linearly with task length, causing quadratic inference cost scaling and performance degradation fr
arXiv:2609.36651v1 Announce Type: cross Abstract: Long-context reasoning in large language models incurs substantial computation and memory costs. Visual text compression (VTC) reduces input length by
arXiv:2609.37226v1 Announce Type: cross Abstract: Answering questions and completing tasks over large document collections often requires connecting evidence spread across multiple documents, such as
arXiv:2511.05620v2 Announce Type: replace Abstract: We study worst-case dynamic regret of specific multi-armed bandit algorithms on piecewise-stationary instances with at most one breakpoint. Our cons
arXiv:2609.36531v1 Announce Type: new Abstract: Video world models are largely regarded as predictive models of the physical world and are therefore expected to anticipate the consequences of observed
arXiv:2609.38105v1 Announce Type: new Abstract: When interacting with an unfamiliar deformable material, a robot lacks prior knowledge of its physical properties and how it will respond to applied for
arXiv:2602.12624v2 Announce Type: replace-cross Abstract: Diffusion-based generative models have achieved remarkable performance across various domains, yet their practical deployment is often limited
arXiv:2609.37488v1 Announce Type: cross Abstract: Frozen multimodal large language models (MLLMs) now solve standard referring expression comprehension with a single prompted call, yet on adversarial
arXiv:2609.36640v1 Announce Type: new Abstract: Robot motion planning in everyday environments must satisfy hard geometric constraints while accounting for context-dependent semantic risk. We present
arXiv:2609.37733v1 Announce Type: cross Abstract: Second-quantized neural-network quantum states have achieved accurate molecular energies, but extending them across molecular geometries requires a sh
arXiv:2609.37267v1 Announce Type: new Abstract: Proactive LLM agents can turn idle compute into useful support before users ask. Yet even correct work can misread user context, impose review costs, or
arXiv:2609.37433v1 Announce Type: new Abstract: Robotic foundation models (RFMs) are increasingly capable of general-purpose manipulation, yet reliable physical interaction remains challenging in cont
arXiv:2609.38152v1 Announce Type: new Abstract: We introduce FracGen, a fracture-aware video generation model that produces plausible, controllable fracture dynamics from a single image of an intact o
arXiv:2609.36314v1 Announce Type: cross Abstract: State Space Models (SSMs) compress sequence history into a bounded recurrent state, making the resulting memory law a central architectural choice for
arXiv:2609.36569v1 Announce Type: cross Abstract: Checkpoint selection is a routine decision in supervised fine-tuning (SFT): training produces multiple checkpoints, but only one is retained. Yet fixe
arXiv:2602.08059v2 Announce Type: replace-cross Abstract: Text-to-image diffusion models can reproduce specific artists visual styles at extremely low cost, raising copyright and deployment safety con
arXiv:2609.36161v1 Announce Type: cross Abstract: Can coding agents restore software that no longer runs while preserving its underlying methods, and reconstruct industrial software engines from open
arXiv:2609.37510v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on its own reasoning trajectories using feedback from a stronger teacher. Teacher interventions can improv
arXiv:2609.37145v1 Announce Type: new Abstract: LLM-as-a-Judge is increasingly used to evaluate policy responses on open-ended tasks that lack ground-truth answers. Existing work often directly conver
arXiv:2503.12232v3 Announce Type: replace Abstract: Aiming to match pedestrian images captured under varying lighting conditions, visible-infrared person re-identification (VI-ReID) has drawn intensiv
arXiv:2609.37157v1 Announce Type: new Abstract: Learner simulation aims to reproduce how a particular learner behaves on new tasks. Although Large Language Models (LLMs) can generate increasingly fine
arXiv:2609.35806v1 Announce Type: cross Abstract: Automated skill extraction underpins workforce planning, yet most systems represent skills as flat labels with no notion of the responsibility level a
arXiv:2609.36736v1 Announce Type: cross Abstract: Speech brain-computer interfaces (BCIs) aim to restore communication by transforming neural activity related to speech, language, or communicative int
arXiv:2609.36406v1 Announce Type: new Abstract: Cell Painting is a high-content morphological profiling assay widely used for phenotype-based biological inference, with mechanism of action (MOA) predi
arXiv:2609.38111v1 Announce Type: new Abstract: Vision-language models (VLMs) may accept false visual premises, answering questions about a target object's color, count, location, or state even when i
arXiv:2602.06155v2 Announce Type: replace Abstract: Diffusion models generate samples through a sequence of learned denoising steps, and recent work has studied how semantic structure appears along th
arXiv:2609.36145v1 Announce Type: new Abstract: Tampered Text Detection (TTD) is essential for safeguarding document authenticity in security-critical workflows. Existing expert models are effective a
arXiv:2607.11386v2 Announce Type: replace Abstract: UAV swarm for applications, such as indoor inspection, security patrol, and logistics delivery, are often mission-oriented rather than exploration-o
arXiv:2609.37072v1 Announce Type: new Abstract: Aerial seed broadcasting can reach remote restoration sites, but provides limited control over seed placement within the soil. This paper presents a geo
arXiv:2609.35880v1 Announce Type: new Abstract: Offline reinforcement learning (RL) has traditionally focused on learning policies for direct deployment under conservative objectives, where uncertaint
arXiv:2609.36277v1 Announce Type: new Abstract: Existing protein geometry models typically represent molecular surfaces using local geometric features such as sampled points, normals, and curvature. W
arXiv:2609.38010v1 Announce Type: cross Abstract: Modern computer vision models achieve high accuracy when trained on large-scale annotated datasets. In critical domains such as construction safety mo
arXiv:2609.36739v1 Announce Type: cross Abstract: Multi-agent LLM systems are increasingly structured like organizations, with roles, critics and shared memory, yet they are evaluated on tasks that la
arXiv:2609.36106v1 Announce Type: cross Abstract: We study the fundamental limits of transferability in algebraic signal processing through homomorphisms between algebraic signal models. Homomorphisms
arXiv:2609.38116v1 Announce Type: new Abstract: Long-tailed 3D object detection is treated as a class-frequency problem, but LiDAR supervision quality depends on object observability: similar frequenc
arXiv:2609.37694v1 Announce Type: cross Abstract: Diffusion models have recently shown strong potential for probabilistic multivariate time-series forecasting by modeling complex conditional distribut
arXiv:2609.36074v1 Announce Type: new Abstract: Memory-efficient scaling on clustering problems without sacrificing statistical accuracy is of central interest for large-scale data analysis and machin
arXiv:2609.38036v1 Announce Type: cross Abstract: Understanding gender biases in large language models (LLMs) is increasingly important as these systems become embedded in decision-support tools with
arXiv:2609.36748v1 Announce Type: new Abstract: Knowledge editing enables rapid updates of specific factual knowledge in large language models (LLMs) without full retraining. However, more realistic s
arXiv:2609.37911v1 Announce Type: cross Abstract: Scientific queries are often brief, while relevant papers use specialized vocabulary. Generated query expansion can bridge this mismatch, but earlier
arXiv:2609.36617v1 Announce Type: new Abstract: We study the ability of LLMs to generate edit-inducing questions whose answer will improve a paper draft. On a dataset of paired submission and camera-r
arXiv:2609.37708v1 Announce Type: new Abstract: Human social behaviour is not a collection of independent motions, but a jointly organised process in which group dynamics and individual variation cont
arXiv:2609.36663v1 Announce Type: new Abstract: We present GenLimitLib, a source-aligned Lean 4 library for language generation in the limit. Introduced by Kleinberg and Mullainathan at NeurIPS 2024,
arXiv:2609.35882v1 Announce Type: cross Abstract: Genome foundation models are most useful where sequences are generated, yet the largest models need datacenter accelerators and a place to send privat
arXiv:2609.35881v1 Announce Type: cross Abstract: Can a genome model retain a detectable record of the synthetic sequences used to train it? We study watermark inheritance through distillation with Ge
arXiv:2609.36205v1 Announce Type: new Abstract: We study how Gemma 4 31B represents African languages and responds to cultural steering. The first study compares nine African languages and three contr
arXiv:2609.37277v1 Announce Type: cross Abstract: The acquisition of high-dimensional channel state information (CSI) in wireless MIMO-OFDM communications usually requires high pilot overhead, or reli
arXiv:2609.36809v1 Announce Type: new Abstract: Embedding-based retrieval is attractive for long sequence collections because each item can be encoded once and searched by nearest-neighbor ranking. Th
arXiv:2609.37776v1 Announce Type: new Abstract: Monocular RGB video provides an accessible source of human demonstrations for upper-body robot motion, yet video-driven human-to-robot transfer remains