Knowledge catalogue
arXiv:2601.19926v2 Announce Type: replace-cross Abstract: We present a systematic review of 337 articles evaluating the syntactic abilities of Transformer-based language models (TLMs), reporting on ov
arXiv:2510.20665v3 Announce Type: replace Abstract: Evaluating the quality of reasoning traces from large language models remains understudied, labor-intensive, and unreliable: current practice relies
arXiv:2602.14862v2 Announce Type: replace-cross Abstract: Temperature scaling is a simple method that allows to control the uncertainty of probabilistic models. It is mostly used in two contexts: impr
arXiv:2605.28713v1 Announce Type: new Abstract: Context compression aims to shorten long context inputs with minimal information loss for LLM inference acceleration. While existing methods have shown
arXiv:2605.28589v1 Announce Type: new Abstract: Several important learning tasks can be formulated as minimizing an entropy-regularized objective over an appropriate space of probability distributions
This week alone: DOJ opens an investigation into the woman Trump raped. The White House is caught steering a 620 million contract to Don Jr.’s firm. The Pentagon hands out a 10 billion contract after
arXiv:2512.09786v2 Announce Type: replace Abstract: Examples of embedded intelligence include a wide variety of tiny neural networks used on-board wireless sensors and actuators, which are expected to
arXiv:2605.28557v1 Announce Type: cross Abstract: LLMs are increasingly used for software modernization, code translation, and database migration. However, LLM-based Oracle2PostgreSQL migration remain
arXiv:2605.28710v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for the automatic evaluation of generated text, yet most prior work focuses on English. Despite the
arXiv:2605.28331v1 Announce Type: new Abstract: Transfer learning makes it possible to use large vision networks on a variety of domains, by specializing their models' general filters to new tasks. Ho
arXiv:2601.21309v4 Announce Type: replace Abstract: The increasing scale of graph datasets has significantly improved the performance of graph representation learning methods, but it has also introduc
arXiv:2605.28566v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities, yet their standard generation process -- auto-regressive token predict
arXiv:2605.27478v1 Announce Type: cross Abstract: We introduce Triangular-Reference Schrodinger Bridges for Time Series (TR-SBTS), a conservative extension of the SBTS framework in which the Brownian
arXiv:2605.28165v1 Announce Type: new Abstract: The literature has proposed various robust alternatives to empirical risk minimisation to address failure modes such as distribution shift, label noise
arXiv:2404.06106v3 Announce Type: replace Abstract: Low dimensional structures appear ubiquitously in the eigenspectra of deep learning matrices in classification networks trained in the overparameter
arXiv:2605.27740v1 Announce Type: new Abstract: Long-context inference in large language models (LLMs) is bottlenecked by the linear growth of the self-attention key-value (KV) cache. Top-k sparse att
arXiv:2605.28507v1 Announce Type: new Abstract: Recent work on the sequence universality of State Space Models (SSMs) has introduced efficient, maximally expressive continuous-time approaches for time
arXiv:2605.28364v1 Announce Type: cross Abstract: Reinforcement learning with multinomial logistic (MNL) function approximation has become an important framework due to its flexibility and broad appli
arXiv:2602.07574v2 Announce Type: replace-cross Abstract: Modern multimodal large language models (MLLMs) adopt a unified self-attention design that processes visual and textual tokens at every Transf
arXiv:2605.28229v1 Announce Type: cross Abstract: With the rapid development of pre-training technologies, adapting large-scale Vision-Language Models (VLMs) for video understanding ie image-to-video
arXiv:2605.28181v1 Announce Type: new Abstract: Diffusion language models decode text by iteratively denoising masked token sequences, making the choice of which positions to decode a central inferenc
arXiv:2605.28346v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly evaluated for whether they identify the right visual content, but little is known about whether they expr
arXiv:2605.28211v1 Announce Type: new Abstract: SpeechLLMs are increasingly deployed in professional settings where domain customisation is standard practice: users supply context in prompts with sens
arXiv:2605.27463v1 Announce Type: cross Abstract: Generative surveying -- where collections of LLM-based personas provide feedback on messages -- has emerged as a cheap and scalable alternative to tra
arXiv:2605.28228v1 Announce Type: new Abstract: Emotional Support Dialogue Systems (ESDSes) are increasingly evaluated and trained with LLM-simulated seekers. However, such simulated seekers often beh
arXiv:2605.27913v1 Announce Type: new Abstract: Node classification on graphs often requires labeled nodes, yet obtaining labels at graph scale is expensive. When node attributes contain semantic cont
arXiv:2510.08525v3 Announce Type: replace Abstract: Reasoning large language models exhibit complex reasoning behaviors via extended chain-of-thought generation that are highly fragile to information
arXiv:2605.28087v1 Announce Type: new Abstract: Service robots must infer object ownership to correctly interpret instructions such as 'bring me my cup.' However, ownership is a latent attribute that
arXiv:2605.28227v1 Announce Type: new Abstract: Speech translation models are increasingly capable of preserving speech-specific information (e.g., speaker gender, prosody, and emphasis), yet evaluati
arXiv:2605.27739v1 Announce Type: cross Abstract: Deep neural network training often exhibits highly anisotropic loss geometry, where a few sharp dominant Hessian directions coexist with a large flatt
Would you like to join the research effort on JEPA and World Models easily? After a full year of hard work, we’re excited to finally release stable-worldmodel: an open-source, scalable platform built
Yann LeCun discusses his perspectives on large language models (LLMs) and reinforcement learning (RL) as approaches for developing world models, presenting arguments he frequently advocates for in the
arXiv:2605.27410v1 Announce Type: cross Abstract: Variational Quantum Algorithms (VQAs) are a leading approach to exploiting near-term quantum hardware, leveraging parameterized quantum circuits and c
arXiv:2605.28008v1 Announce Type: new Abstract: Large language models (LLMs) can now solve complex problems through long chain-of-thought (CoT) reasoning, but the trade-off between performance and tok
arXiv:2605.27338v1 Announce Type: new Abstract: ASP(Q) extends Answer Set Programming (ASP) with Quantifiers over answer sets. In this paper we focus on the class of ASP(Q) programs with two quantifie
arXiv:2605.26936v1 Announce Type: new Abstract: Underwater robotics has advanced significantly over recent decades. however, the development of miniaturized underwater robots remains limited by low en
arXiv:2605.27287v1 Announce Type: new Abstract: Multilevel Image thresholding is an important preprocessing algorithm in computer vision applications nowadays. Since most common thresholding methods t
arXiv:2510.01168v3 Announce Type: replace-cross Abstract: We study a class of constrained nonconvex-nonconcave minimax optimization problems in which the inner maximization involves potentially comple
arXiv:2512.19332v2 Announce Type: replace Abstract: We study the numerical and Boolean expressiveness of MPLang, a declarative language that captures the computation of graph neural networks (GNNs) th
arXiv:2603.12754v2 Announce Type: replace Abstract: We present a method for learning large-scale, broad-coverage construction grammars from corpora of language use. Starting from utterances annotated
arXiv:2605.26287v1 Announce Type: new Abstract: Masked autoencoders (MAE) have shown great promise in medical image classification. However, the random masking strategy employed by traditional MAEs ma
arXiv:2512.08371v4 Announce Type: replace Abstract: Datasets may contain observations with multiple labels. If the labels are not mutually exclusive, and if the labels vary greatly in frequency, obtai
arXiv:2605.26341v1 Announce Type: new Abstract: Physics-informed machine learning (PIML) integrates mechanistic knowledge, typically in the form of partial differential equations (PDE), into data-driv
arXiv:2508.03774v5 Announce Type: replace-cross Abstract: Accurate modeling of scattering from three-dimensional (3D) perfectly electrically conducting (PEC) targets at microwave frequencies constitut
arXiv:2509.21167v2 Announce Type: replace-cross Abstract: Most existing methods for concept unlearning in text-to-image diffusion models minimize a mean squared error (MSE) loss between the denoiser o
arXiv:2605.26940v1 Announce Type: new Abstract: Large language models (LLMs) can support democratic deliberation at scales previously constrained by turn-taking and facilitation bandwidth. Recent work
arXiv:2601.07284v2 Announce Type: replace Abstract: Retargeting human motion to heterogeneous robots is a fundamental challenge in robotics, primarily due to the severe kinematic and dynamic discrepan
arXiv:2605.26925v1 Announce Type: cross Abstract: We present a Multi-task Soft Actor-Critic (SAC) Reinforcement Learning framework designed for open-system quantum control across diverse Hamiltonians,
arXiv:2509.18919v2 Announce Type: replace Abstract: The pretraining-finetuning paradigm is a crucial strategy in metallic surface defect detection for mitigating the challenges posed by data scarcity.
arXiv:2512.14561v2 Announce Type: replace Abstract: Despite the growing promise of large language models (LLMs) in automated essay scoring (AES), empirical findings regarding their reliability compare
Alex Rives discusses advanced AI models for protein biology on the Latent Space podcast, including ESMFold2 (an improved protein structure prediction tool), ESMC (a protein language model), and ESM At
arXiv:2605.27371v1 Announce Type: cross Abstract: Many employers screen job applicants with algorithms built by the same few algorithm vendors. We hypothesize that algorithmic monoculture leads to the
arXiv:2605.26419v1 Announce Type: new Abstract: Amortized inference promises fast test-time Bayesian inference, but existing methods are inherently tied to fixed models. Extending amortization to unse
arXiv:2605.26683v1 Announce Type: cross Abstract: Cross-lingual transfer in language models is difficult to study in natural corpora because lexical overlap, morphology, data imbalance, and tokenizati
arXiv:2605.26460v1 Announce Type: cross Abstract: Multi-Modal Diffusion Transformers (MM-DiTs) encode rich representations for training-free concept grounding, but existing attention-based methods oft
arXiv:2602.07120v2 Announce Type: replace Abstract: Language models (LMs) tend to memorize portions of their training data and emit verbatim spans. When the underlying sources are sensitive or copyrig
arXiv:2605.26149v1 Announce Type: cross Abstract: Open surface components prevail in real industrial 3D content and support rendering, physical simulation and geometric editing. Garments serve as a ty
arXiv:2605.26732v1 Announce Type: new Abstract: Learning-based surrogates have become increasingly effective for wave-field prediction, and neural operators in particular have shown strong performance
arXiv:2602.15919v2 Announce Type: replace-cross Abstract: Recent work in the privacy literature shows that sample-targeted membership inference attacks (MIAs) significantly outperform untargeted appro
arXiv:2605.26630v1 Announce Type: new Abstract: Liver surface landmark detection is a fundamental prerequisite for anatomical guidance in laparoscopic liver surgery. However, it remains unreliable in