Knowledge catalogue
arXiv:2605.08384v1 Announce Type: new Abstract: In this work, we introduce frozen-encoder model composition, a novel approach to multimodal embedding models. We build on the VLM-style architecture, in
arXiv:2605.10159v1 Announce Type: new Abstract: jNO (jax Neural Operators) is a JAX-native library for neural operators and foundation models with unified support for both data-driven and physics-info
arXiv:2605.09954v1 Announce Type: cross Abstract: Articulated objects used in simulation and embodied AI are typically specified by geometry and kinematic structure, but lack the fine-grained dynamica
arXiv:2605.09635v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in K-12 education, yet existing benchmarks such as C-Eval, CMMLU, GaokaoBench, and EduEval mainly eva
arXiv:2605.09572v1 Announce Type: cross Abstract: Sign language production from symbolic notation offers a scalable route to accessible sign animation. We present KANMultiSign, a multi-scale sequence
arXiv:2605.08175v1 Announce Type: cross Abstract: While significant progress has been made in Video Question Answering and cross-modal understanding, causal reasoning about how visual dynamics drive m
arXiv:2605.09132v1 Announce Type: new Abstract: Vision--language models (VLMs) show promise for clinical decision support in radiology because they enable joint reasoning over radiological images and
arXiv:2601.11258v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) face the 'knowledge cutoff' challenge, where their frozen parametric memory prevents direct internalization of ne
arXiv:2605.09735v1 Announce Type: cross Abstract: Static-graph LLM decoders provide predictable launches, fixed tensor shapes, and low submission overhead, but online decoding exposes highly irregular
arXiv:2605.09751v1 Announce Type: new Abstract: Trainable input embedding tables are a standard component of modern language models. We ask whether they are actually necessary at the input interface.
arXiv:2602.01015v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly embedded in AI-based tutoring systems. Can they faithfully model novice reasoning and metacognitive ju
arXiv:2605.08732v1 Announce Type: cross Abstract: Modern vision-based world models can represent observations as compact yet expressive latent manifolds, but fast goal-oriented planning in these space
arXiv:2605.08605v1 Announce Type: cross Abstract: We introduce the Lattice Deduction Transformer (LDT), a recurrent transformer that approximates logically sound deduction by projecting its latent sta
arXiv:2605.06366v2 Announce Type: replace Abstract: Diffusion language models (DLMs) have recently emerged as competitive alternatives to autoregressive (AR) language models, yet differences in their
arXiv:2605.09806v1 Announce Type: cross Abstract: Large reasoning models, such as OpenAI o1 and DeepSeek-R1, tend to become increasingly verbose as their reasoning capabilities improve. These inflated
arXiv:2605.09295v1 Announce Type: new Abstract: Text-to-SQL translates natural language questions into executable SQL queries, enabling intuitive database access for non-experts. While large language
arXiv:2512.06571v3 Announce Type: replace Abstract: Learning fast and robust ball-kicking skills is a critical capability for humanoid soccer robots, yet it remains a challenging problem due to the ne
arXiv:2512.16875v4 Announce Type: replace-cross Abstract: We study the problem of finding confidence ellipsoids for an arbitrary distribution in high dimensions. Given samples from a distribution D an
arXiv:2602.21198v2 Announce Type: replace-cross Abstract: Embodied LLMs endow robots with high-level task reasoning, but they cannot reflect on what went wrong or why, turning deployment into a sequen
arXiv:2605.10504v1 Announce Type: new Abstract: A causal-decoder block is hierarchical: lower layers build the residual basis that upper layers attend over. We identify a failure mode in GPT pretraini
arXiv:2605.09665v1 Announce Type: cross Abstract: Data selection is a key component of efficient instruction tuning for large language models, as recent work has shown that data quality often matters
arXiv:2605.09963v1 Announce Type: new Abstract: Existing self-supervised learning (SSL) methods primarily learn object-invariant representations but often neglect the spatial structure and relationshi
arXiv:2605.10151v1 Announce Type: new Abstract: This paper addresses the problem of learning to sparsify stochastic linear bandits, where a decision-maker sequentially selects actions from a high-dime
arXiv:2605.10186v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into legal drafting and research workflows, where incorrect citations or fabricated precedent
arXiv:2510.08592v3 Announce Type: replace-cross Abstract: Test-Time Scaling (TTS) improves LLM reasoning by exploring multiple candidate responses and then operating over this set to find the best out
arXiv:2605.09764v1 Announce Type: cross Abstract: LLM-guided evolutionary methods such as AlphaEvolve have proven effective in domains like math, systems research, and algorithmic discovery, but their
arXiv:2605.08805v1 Announce Type: new Abstract: Audio-Visual Segmentation (AVS) targets pixel level localization of sounding emitting objects in videos. However, existing models rely on dense cross-mo
arXiv:2605.10810v1 Announce Type: new Abstract: We introduce an automatically generated benchmark for predicting hidden text in technical papers. A paper supplies visible context X and a hidden contin
arXiv:2605.10319v1 Announce Type: new Abstract: Layered image assets are widely used in real-world creative workflows, enabling non-destructive iteration and flexible re-composition. Recent advances i
arXiv:2605.09384v1 Announce Type: cross Abstract: The reasoning gap between large and compact vision-language models (VLMs) limits the deployment of medical AI on portable clinical devices. Compact VL
LiteParse is the best open-source, model-free document parser for AI agents. Run it over over 50+ document types, and it will parse dense pages with complex text layouts and tables, and it will extrac
arXiv:2605.10779v1 Announce Type: cross Abstract: The rapid proliferation of LLM-based autonomous agents in real operating system environments introduces a new category of safety risk beyond content s
arXiv:2605.08985v1 Announce Type: new Abstract: Visual encoding constitutes a major computational bottleneck in Multimodal Large Language Models (MLLMs), especially for high-resolution image inputs. T
arXiv:2605.09015v1 Announce Type: new Abstract: Sardinian, a Romance language with roughly one million speakers, has minimal presence in modern NLP. Commercial services do not support it, and current
Release: llm 0.32a2 A bunch of useful stuff in this LLM alpha, but the most important detail is this one: Most reasoning-capable OpenAI models now use the /v1/responses endpoint instead of /v1/chat/co
arXiv:2605.09252v1 Announce Type: new Abstract: Tool-augmented LLM agents tend to call tools indiscriminately, even when the model can answer directly. Each unnecessary call wastes API fees and latenc
arXiv:2605.10574v1 Announce Type: new Abstract: As artificial intelligence advances, models are not improving uniformly. Instead, progress unfolds in a jagged fashion, with capabilities growing uneven
arXiv:2605.08247v1 Announce Type: cross Abstract: GCC and LLVM underpin much of modern software infrastructure, relying on distinct Intermediate Representations (IRs) to drive optimizations and code g
arXiv:2605.08321v1 Announce Type: cross Abstract: LLMs are increasingly capable of persuasion, which raises the question of how to protect users against manipulation. In a preregistered user study (N=
arXiv:2605.10401v1 Announce Type: new Abstract: Efficient branching policies are essential for accelerating Mixed Integer Linear Programming (MILP) solvers. Their design has long relied on hand-crafte
arXiv:2605.08212v1 Announce Type: cross Abstract: There is an increasing number of algorithmic computations in theoretical physics. These, while conceptually simple, can nevertheless be time-consuming
arXiv:2605.08305v1 Announce Type: cross Abstract: Large Language Model (LLM) systems have been the frontier of AI in many application domains, leading to new challenges and opportunities for hyperpara
arXiv:2605.10777v1 Announce Type: new Abstract: The quality of open-weight language models has dramatically improved in recent years. Sharing weights greatly facilitates model adoption by enabling the
arXiv:2605.08545v1 Announce Type: new Abstract: Agent benchmarks typically report only final outcomes: pass or fail. This threatens evaluation credibility in three ways. First, scores may be inflated
arXiv:2509.20909v2 Announce Type: replace Abstract: Large language models (LLMs) are commonly evaluated on challenging benchmarks such as AIME and Math500, where benchmark contamination can make memor
arXiv:2605.08787v1 Announce Type: new Abstract: Recent advances in 3D medical vision-language models have enabled joint reasoning over volumetric images and text, showing strong performance in medical
arXiv:2605.09879v1 Announce Type: new Abstract: While reasoning has become a central capability of large language models (LLMs), the reasoning patterns required for different scenarios are often misal
arXiv:2605.10496v1 Announce Type: new Abstract: Tiny UAV detection from an onboard event camera is difficult when the observer and target move at the same time. In this motion-on-motion regime, ego-mo
arXiv:2605.10820v1 Announce Type: new Abstract: Scientific discovery is fundamentally a resource-constrained process that requires navigating complex trade-offs between the quality and quantity of mea
arXiv:2605.08437v1 Announce Type: cross Abstract: Existing benchmarks for legal AI focus primarily on tasks where LLMs must produce legal arguments or documents, yet the capacity to judge such argumen
arXiv:2605.10760v1 Announce Type: new Abstract: Collaborative photorealistic 3D reconstruction from multiple agents enables rapid large-scale scene capture for virtual production and cooperative multi
arXiv:2605.08498v1 Announce Type: cross Abstract: We introduce MathConstraint, a hard, adaptive benchmark for evaluating the combinatorial reasoning capabilities of LLMs. We combine constraint satisfa
arXiv:2602.02561v2 Announce Type: replace-cross Abstract: While the ecosystem of Lean and Mathlib has enjoyed celebrated success in formal mathematical reasoning with the help of large language models
arXiv:2605.08697v1 Announce Type: new Abstract: The emerging collaborative information-based knowledge tracing (KT) has been a promising way to enhance modeling of learners' knowledge states. The core
arXiv:2605.08557v1 Announce Type: cross Abstract: Parameter-efficient adaptation of pretrained vision models is commonly performed through linear probes, prompts, low-rank updates, or lightweight resi
arXiv:2605.09288v1 Announce Type: cross Abstract: Partial differential equation (PDE) solvers underpin scientific computing, but real-world deployment is bounded by compute. Classical Monte Carlo solv
arXiv:2605.09131v1 Announce Type: new Abstract: The Model Context Protocol (MCP) has unified the interface between Large Language Models (LLMs) and external tools, yet a fundamental gap remains in how
arXiv:2605.08941v1 Announce Type: new Abstract: The promise of AI-driven scientific discovery hinges on whether AI agents can autonomously design and execute the computational workflows that underpin
arXiv:2605.10904v1 Announce Type: new Abstract: Vehicle-to-Everything (V2X) communication has emerged as a promising paradigm for autonomous driving, enabling connected agents to share complementary p
arXiv:2510.22170v2 Announce Type: replace Abstract: Persona conditioning is widely used to steer large language model (LLM) behavior, but it is unclear whether it induces stable behavioral structure o