Knowledge catalogue
arXiv:2604.11978v1 Announce Type: new Abstract: Large language model (LLM) agents perform strongly on short- and mid-horizon tasks, but often break down on long-horizon tasks that require extended, in
This is becoming a pattern in AI that makes talking about capabilities challenging. First, there are overstated claims (like the flubbed Erdos problems last year), then minor wins (AI helps with disco
arXiv:2604.12282v1 Announce Type: new Abstract: Spreadsheets are central to real-world applications such as enterprise reporting, auditing, and scientific data management. Despite their ubiquity, exis
arXiv:2509.17995v2 Announce Type: replace-cross Abstract: Recent advances have shown that scaling test-time computation enables large language models (LLMs) to solve increasingly complex problems acro
arXiv:2604.13029v1 Announce Type: cross Abstract: The effectiveness of Direct Preference Optimization (DPO) depends on preference data that reflect the quality differences that matter in multimodal ta
This r/ollama thread is a community discussion where users share their preferred cloud-based AI models for coding tasks and compare their reasoning capabilities. The conversation likely highlights pop
This Reddit thread from r/StableDiffusion discusses the community's search for AI tools capable of analyzing an existing video and automatically generating a descriptive text prompt from it — essentia
arXiv:2604.10874v1 Announce Type: cross Abstract: Adverse Outcome Pathways (AOPs) are an important knowledge framework in toxicological research and risk assessment. In recent years, large language mo
arXiv:2604.11304v1 Announce Type: new Abstract: Existing AI benchmarks lack the fidelity to assess economically meaningful progress on professional workflows. To evaluate frontier AI agents in a high-
arXiv:2604.10528v1 Announce Type: new Abstract: While Vision-Language Models (VLMs) demonstrate remarkable zero-shot recognition capabilities across a diverse spectrum of multimodal tasks, it yet rema
arXiv:2604.11424v1 Announce Type: new Abstract: Speech Language Models (SLMs) exhibit strong semantic understanding, yet their generated speech often sounds flat and fails to convey expressive intent,
arXiv:2604.10420v1 Announce Type: new Abstract: Large language models (LLMs) enable waveform-to-text ECG interpretation and interactive clinical questioning, yet most ECG-LLM systems still rely on wea
ClawBench is a benchmark of 153 everyday web tasks spanning 144 live platforms across 15 categories — from completing purchases and booking appointments to submitting job applications. Unlike existing
datasette PR #2689: Replace token-based CSRF with Sec-Fetch-Site header protection Datasette has long protected against CSRF attacks using CSRF tokens, implemented using my asgi-csrf Python library. T
arXiv:2507.03336v4 Announce Type: replace Abstract: Large language models (LLMs) are increasingly tasked with invoking enterprise APIs, yet they routinely falter when near-duplicate tools vie for the
arXiv:2604.10690v1 Announce Type: new Abstract: Foundation models have shown remarkable performance across diverse tasks, yet their ability to construct internal spatial world models for reasoning and
arXiv:2604.11177v1 Announce Type: new Abstract: We benchmark how internal reasoning traces, which we call thought streams, affect video scene understanding in vision-language models. Using four config
arXiv:2508.06445v3 Announce Type: replace-cross Abstract: The rapid rise of Generative AI (GenAI), particularly LLMs, poses concerns for journalistic integrity and authorship. This study examines AI-g
arXiv:2604.11417v1 Announce Type: cross Abstract: Co-speech gestures increase engagement and improve speech understanding. Most data-driven robot systems generate rhythmic beat-like motion, yet few in
arXiv:2604.11462v1 Announce Type: new Abstract: Large Language Models (LLMs) struggle with long-horizon tasks due to the 'context bottleneck' and the 'lost-in-the-middle' phenomenon, where accumulated
arXiv:2604.11104v1 Announce Type: new Abstract: This paper presents an empirical study of a multi-model zero-shot pipeline for knowledge graph construction and exploitation, executed entirely through
arXiv:2601.03416v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have become widely deployed, yet their safety alignment remains fragile under adversarial inputs. Previous
arXiv:2604.09937v1 Announce Type: new Abstract: Healthcare administration accounts for over $1 trillion in annual spending, making it a promising target for LLM-based computer-use agents (CUAs). While
arXiv:2602.15019v3 Announce Type: replace Abstract: Bio-pharmaceutical innovation has shifted: many new drug assets now originate outside the United States and are disclosed primarily via regional, no
arXiv:2503.23514v2 Announce Type: replace-cross Abstract: Large language models (LLMs) can carry out human-like dialogue, but unlike humans, they are stateless due to the superposition property. Howev
This r/ChatGPT Reddit post discusses users encountering image generation failure errors in ChatGPT, a widespread and recurring issue reported across the community. Common causes include server overloa
arXiv:2604.09841v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly adapted through domain-specific fine-tuning, yet it remains unclear whether this improves reasoning bey
arXiv:2604.09970v1 Announce Type: new Abstract: In the decentralized distributed learning, achieving fast convergence and low communication cost is essential for scalability and high efficiency. Adapt
arXiv:2603.22816v3 Announce Type: replace-cross Abstract: Language models increasingly show their work by writing step-by-step reasoning before answering. But are these steps genuinely used, or is the
arXiv:2604.10242v1 Announce Type: new Abstract: Despite recent advances in MLLM-based medical image segmentation, existing LISA-like methods cannot reliably reject false queries and often produce hall
arXiv:2601.22361v2 Announce Type: replace-cross Abstract: Assessing the veracity of online content has become increasingly critical. Large language models (LLMs) have recently enabled substantial prog
Most AI assistants wait for you to ask. But a truly useful agent should notice you need help before you say anything. New research takes a serious shot at building proactive agents that work in real t
arXiv:2509.13047v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across many domains, yet their application to specialized fields remain
arXiv:2604.09967v1 Announce Type: cross Abstract: Muon has emerged as a promising optimizer for large-scale foundation model pre-training by exploiting the matrix structure of neural network updates t
arXiv:2604.10866v1 Announce Type: new Abstract: AI agents are expected to perform professional work across hundreds of occupational domains (from emergency department triage to nuclear reactor safety
arXiv:2601.12104v2 Announce Type: replace-cross Abstract: Fine-tuned language models pose significant privacy risks, as they may memorize and expose sensitive information from their training data. Mem
arXiv:2502.19731v2 Announce Type: replace Abstract: Applying large language models (LLMs) to assist in psycho-counseling is an emerging and meaningful approach, driven by the significant gap between p
arXiv:2604.11501v1 Announce Type: cross Abstract: We compare two strategies for compressing the KV cache in transformer inference: rank reduction (discard dimensions) and quantization (keep all dimens
arXiv:2603.01692v3 Announce Type: replace-cross Abstract: LLM-based agents for machine learning engineering (MLE) predominantly rely on tree search, a form of gradient-free optimization that uses scal
arXiv:2604.11407v1 Announce Type: cross Abstract: We revisit retrieval-augmented generation (RAG) by embedding retrieval control directly into generation. Instead of treating retrieval as an external
arXiv:2604.09749v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) frequently hallucinate objects that are absent from the visual input, often because attention during decoding i
arXiv:2504.06313v5 Announce Type: replace Abstract: This paper investigates how popular text-to-image (T2I) models, DALL-E 3 and Gemini 3 Pro Preview, depict people from 206 nationalities when prompte
arXiv:2604.11309v1 Announce Type: cross Abstract: Large Language Models (LLMs) face prominent security risks from jailbreaking, a practice that manipulates models to bypass built-in security constrain
arXiv:2604.10386v1 Announce Type: new Abstract: Accurate estimation of cancer risk from longitudinal electronic health records (EHRs) could support earlier detection and improved care, but modeling su
Two major shifts will be seen in Agentic AI after Harness and YOU MUST KNOW. 1. Workflow design of your agents matters a lot more than any frontier model selection. Till now we have mostly focused on
arXiv:2604.11557v1 Announce Type: new Abstract: Tool-use capability is a fundamental component of LLM agents, enabling them to interact with external systems through structured function calls. However
arXiv:2604.11611v1 Announce Type: new Abstract: To overcome the sparse reward challenge in reinforcement learning (RL) for agents based on large language models (LLMs), we propose Mutual Information S
arXiv:2601.06165v2 Announce Type: replace-cross Abstract: Current vision-language benchmarks predominantly feature well-structured questions with clear, explicit prompts. However, real user queries ar
arXiv:2601.17172v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly capable of generating personalized, persuasive text at scale, raising new questions about bias a
This r/ChatGPT Reddit post titled 'AI Race' likely reflects community discussion around the intensifying competition among major AI developers and their models. The post probably covers user perspecti
arXiv:2604.08590v1 Announce Type: cross Abstract: We present AlphaLab, an autonomous research harness that leverages frontier LLM agentic capabilities to automate the full experimental cycle in quanti
arXiv:2604.08556v1 Announce Type: cross Abstract: What exactly do efficient sequence models gain over simple temporal averaging? We use exponential moving average (EMA) traces, the simplest recurrent
arXiv:2604.09174v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) aims to reduce hallucination by grounding answers in retrieved evidence, yet hallucinated answers remain common eve
General Purpose Technologies have downstream effects everywhere, good and bad. Those outcomes can be mitigated, or encouraged, with the right sorts of policy choices If the only options are being for
This Reddit post from r/ChatGPT uses an archived screenshot of OpenAI's website as it appeared in 2015 to highlight the dramatic transformation the company has undergone over the past decade. When Ope
arXiv:2603.28013v3 Announce Type: replace-cross Abstract: Multi-agent LLM systems are entering production -- processing documents, managing workflows, acting on behalf of users -- yet their resilience
arXiv:2604.08797v1 Announce Type: cross Abstract: Stories are key to transmitting values across cultures, but their interpretation varies across linguistic and cultural contexts. Thus, we introduce mu
arXiv:2604.09338v1 Announce Type: new Abstract: Spatial reasoning is central to navigation and robotics, yet measuring model capabilities on these tasks remains difficult. Existing benchmarks evaluate
arXiv:2602.15313v2 Announce Type: replace Abstract: AI Memory, specifically how models organizes and retrieves historical messages, becomes increasingly valuable to Large Language Models (LLMs), yet e
arXiv:2604.08923v1 Announce Type: new Abstract: Dimensional Aspect-Based Sentiment Analysis (DimABSA) extends traditional ABSA from categorical polarity labels to continuous valence-arousal (VA) regre