Knowledge catalogue
arXiv:2604.09181v1 Announce Type: new Abstract: Diffusion models and their variations, such as rectified flows, generate diverse and high-quality images, but they are still hindered by slow iterative
arXiv:2604.09253v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are powerful but remain vulnerable to multimodal jailbreak attacks. Existing attacks mainly rely on either explicit visu
This Japanese blog post explores whether signs and symbols can emerge within a single neural network, drawing on analysis of the 'Neural Computers' paper. The discussion likely examines the intersecti
arXiv:2604.08572v1 Announce Type: cross Abstract: State-of-the-art post-hoc out-of-distribution detection methods rely on intermediate layer activation editing. However, they exhibit inconsistent perf
arXiv:2604.08557v1 Announce Type: cross Abstract: Diffusion-based language models (dLLMs) generate text by iteratively denoising masked token sequences. We show that their safety alignment rests on a
arXiv:2604.08566v1 Announce Type: new Abstract: This study examines how different artificial intelligence architectures interpret sentiment in conflict-related media discourse, using the 2023 Gaza War
arXiv:2604.08573v1 Announce Type: cross Abstract: Learning discriminative representations is a central goal of supervised deep learning. While cross-entropy (CE) remains the dominant objective for cla
arXiv:2604.09038v1 Announce Type: cross Abstract: Robust geo-localization in changing environmental conditions is critical for long-term aerial autonomy. While visual place recognition (VPR) models pe
arXiv:2604.03058v2 Announce Type: replace-cross Abstract: LLMs can be socially sycophantic, affirming users when they ask questions like 'am I in the wrong?' rather than providing genuine assessment.
arXiv:2508.06869v4 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) demonstrate exceptional performance in vision-language tasks, yet their processing of long videos is
arXiv:2604.09501v1 Announce Type: new Abstract: Norm, the formal theoretical linguist, and Claudette, the computational language scientist, have a lovely time discussing whether modern language models
From this perspective, Gemini is also worse than the original Bard. Sydney was the original sin of LLMs anthropormism, but also got the idea that AIs can sometimes be better with personalities right.
Automated lint: 34 errors, 0 warnings, 3 info
This r/MachineLearning post presents educational PyTorch implementations of FlashAttention versions 1 through 4, designed to highlight the key algorithmic differences across each iteration rather than
arXiv:2604.07615v1 Announce Type: new Abstract: In language model interpretability research, extbf{circuit tracing} aims to identify which internal features causally contributed to a particular outp
arXiv:2604.08479v1 Announce Type: new Abstract: Recent research shows that greater numbers of people are turning to Large Language Models (LLMs) for emotional support, and that people rate LLM respons
arXiv:2604.07749v1 Announce Type: new Abstract: Large language models (LLMs) can shift their answers under pressure in ways that reflect accommodation rather than reasoning. Prior work on sycophancy h
arXiv:2603.17812v2 Announce Type: replace-cross Abstract: Recent video diffusion models achieve high-quality generation through recurrent frame processing where each frame generation depends on previo
arXiv:2604.07717v1 Announce Type: new Abstract: Human immunodeficiency virus (HIV)-related stigma is a critical psychosocial determinant of health for people living with HIV (PLWH), influencing mental
arXiv:2411.18084v2 Announce Type: replace-cross Abstract: Mobile apps are essential in daily life but frequently employ deceptive patterns, such as visual emphasis or linguistic nudging, to manipulate
arXiv:2604.07569v1 Announce Type: cross Abstract: Despite the increasing prevalence of large language models (LLMs), we still have a limited understanding of how their representational spaces are stru
arXiv:2509.07673v4 Announce Type: replace Abstract: Deep neural networks have exhibited impressive performance in image classification tasks but remain vulnerable to adversarial examples. Standard adv
One great outcome of PaperWiki is personalized surveys. Survey papers continue to be one of the best ways to track a field. My agents are now generating personalized surveys on topics using my paper L
arXiv:2508.21618v2 Announce Type: replace-cross Abstract: We present PhISM, a physics-informed deep learning architecture that learns without supervision to explicitly disentangle hyperspectral observ
arXiv:2604.06424v1 Announce Type: cross Abstract: This paper presents a transformer-based approach to solving the SympTEMIST named entity recognition (NER) and entity linking (EL) tasks. For NER, we f
arXiv:2604.07801v1 Announce Type: new Abstract: Large language models are trained and evaluated on quantitative reasoning tasks written in clean, emotionally neutral language. However, real-world quer
arXiv:2604.08510v1 Announce Type: new Abstract: Large language models (LLMs) can perform remarkably complex tasks, yet the fine-grained details of how these capabilities emerge during pretraining rema
arXiv:2604.07834v1 Announce Type: new Abstract: This paper presents an LLM-driven approach for constructing diverse social media datasets to measure and compare loneliness in the caregiver and non-car
As part of the evaluation of its Claude Mythos model, Anthropic engaged a clinical psychiatrist for approximately 20 hours of evaluation sessions, describing Mythos as 'the most psychologically se...
The search results did not return the specific Reddit post content. Based on what was retrieved, I'm unable to produce a fully sourced factual summary of that particular Reddit thread about the LQS...
Google Research introduced two AI agents to streamline academic workflows: **PaperVizAgent**, a visualizer agent for drawing academic figures, and **ScholarPeer**, a reviewer agent that automatica...
arXiv:2603.25370v2 Announce Type: replace-cross Abstract: Probabilistic forecasts provide a principled representation of forecast uncertainty through predictive distributions. Directly propagating pre
arXiv:2610.21270v1 Announce Type: new Abstract: Trustworthy biomedical AI requires reliable action selection when evidence is missing, stale, conflicting, or late, not only accurate answers on static
arXiv:2610.22296v1 Announce Type: cross Abstract: Neural watermarking systems are typically trained as tightly coupled embedder-detector pairs and implicitly assume static deployment. In real-world de
arXiv:2610.22127v1 Announce Type: cross Abstract: Personalization of Large Language Models (LLMs) is increasingly important for user-centric applications, yet prior methods mainly adapt dense LLMs, wh
arXiv:2504.00070v2 Announce Type: replace Abstract: This paper introduces Fuzzy Attention Network-Based Transformer (FANTF), an approach for time series forecasting, classification, and anomaly detect
arXiv:2610.20638v1 Announce Type: new Abstract: Research on deceptive alignment is becoming increasingly urgent as frontier-model capabilities advance and their risk of causing catastrophic harm incre
arXiv:2603.15270v3 Announce Type: replace-cross Abstract: Medical coding, which assigns standardized ICD codes to clinical documents, is a fundamental yet challenging task in modern healthcare. It req
arXiv:2610.22295v1 Announce Type: new Abstract: Understanding characteristics and activities of individuals in complex multi-person environments is crucial for real-world applications. However, existi
arXiv:2610.21520v1 Announce Type: cross Abstract: Joint Acoustic Scene Classification (ASC) and Audio Tagging (AT) models traditionally rely on unconstrained multihead architectures. However, this des
arXiv:2610.22324v1 Announce Type: new Abstract: One of the first steps of a research pipeline is generating ideas. Large Language Models (LLMs) are increasingly used to automate this step, but most of
arXiv:2610.20979v1 Announce Type: cross Abstract: Generative modeling across related distributions raises a question: how can shared structure support accurate generation for a target distribution wit
arXiv:2509.24072v5 Announce Type: replace-cross Abstract: Large vision-language models (LVLMs) perform well on multimodal tasks, but their ability to reason and precisely align visual and textual info
TL;DR -- Interpretability might be way more tractable than we thought, and the guy who helped convince everyone otherwise is now saying we can understand models by directly studying neurons and their
arXiv:2610.20764v1 Announce Type: new Abstract: This paper challenges the understanding capabilities of existing AI paradigms and emerging world models by asking a fundamental question: How quickly ca
arXiv:2610.18515v2 Announce Type: cross Abstract: Large language models (LLMs) increasingly act as personal assistants; however, little is known about how they capture national-level preferences on a
arXiv:2610.19896v1 Announce Type: new Abstract: The reintegration of people in the workplace after an upper-limb injury is often a long process. Collaborative robots and humanoids operating in shared
arXiv:2610.19613v1 Announce Type: new Abstract: As machine unlearning for multimodal large language models (MLLMs) rapidly advances, rigorous evaluation of its effectiveness becomes increasingly impor
arXiv:2610.18047v1 Announce Type: new Abstract: In this paper, our goal is to represent text embeddings as sketches for three main reasons. First, to visualize text representation and to explore their
arXiv:2610.19778v1 Announce Type: cross Abstract: Computational compressive imaging, such as single-pixel imaging and lensless imaging, is a promising imaging technique, enabling the reconstruction of
arXiv:2610.17880v1 Announce Type: cross Abstract: Persona vectors are linear directions in the residual-stream activations of LLMs that encode behavioral traits and enable fine-grained control over mo
arXiv:2610.15503v1 Announce Type: new Abstract: This Article critically examines the contemporary regulatory framework and discourse surrounding data portability in the United States. Using recent reg
arXiv:2610.19120v1 Announce Type: cross Abstract: Purpose: Free-breathing abdominal MRI is limited by respiratory motion, which introduces image artifacts and k-space inconsistencies. Existing reconst
arXiv:2610.19113v1 Announce Type: new Abstract: In-context learning (ICL) allows language models to adapt their behavior from instructions and demonstrations in a prompt, but accuracy alone does not r
arXiv:2610.19576v1 Announce Type: new Abstract: Temporal graph data are recorded in different representations, but whether these differences also determine the scope of learned knowledge reuse remains
arXiv:2601.02978v3 Announce Type: replace-cross Abstract: Recent work in Mechanistic Interpretability (MI) has enabled the identification and intervention of internal features in Large Language Models
arXiv:2610.06170v2 Announce Type: replace Abstract: Biomedical large language model (LLM) evaluation requires auditable assessment of narrow, evolving, source-grounded subspecialty knowledge. Multiple
arXiv:2610.19123v1 Announce Type: new Abstract: Generating novel molecules with desired properties is a central challenge in drug discovery. Although foundational work focused on de novo generation, p
arXiv:2610.19795v1 Announce Type: new Abstract: Video world models aim to simulate complex environments with realistic appearance and dynamics. Maintaining consistency over long interactions requires
arXiv:2610.20083v1 Announce Type: new Abstract: Visual world knowledge is a core capability of multimodal large language models (MLLMs), underpinning knowledge-intensive visual question answering and