Knowledge catalogue
arXiv:2609.12871v1 Announce Type: cross Abstract: Datasets are a crucial element in the development of perception algorithms. They relate sensor measurement data to annotated reference information and
OverviewAs enterprises scale their AI and agentic workflows, they require serverless platforms that make data preparation for model training, evaluation, and inference effortless and efficient. Datafl
arXiv:2609.12162v1 Announce Type: cross Abstract: Draft-verify-revise is a common LLM orchestration pattern for scaling inference-time compute. One LLM drafts, a second critiques the draft and provide
arXiv:2609.12929v1 Announce Type: new Abstract: Dementia affects an estimated 57 million people worldwide, and for most families the hardest part of care is not memory loss but the behavioral and psyc
arXiv:2609.12268v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) can improve knowledge-intensive question answering, but the first design choice is easy to overlook: how should the
arXiv:2412.09378v4 Announce Type: replace-cross Abstract: Clinical trials bridge basic research and clinical application, serving as essential steps in drug development. This review examines clinical
We are excited to share that Google Cloud was named a Leader and received the highest score in the ‘current offering’ category in the Forrester Wave™: Public Cloud Platforms, Q3 2026 report, which exa
arXiv:2609.12412v1 Announce Type: cross Abstract: Foundation models, including large language models, vision-language models, and time-series foundation models, are increasingly deployed on embedded a
I have conducted an audit of Anthropic's finances. What I have found is so shocking that I am calling for a Congressional investigation. Anthropic is not just seeking regulatory capture. It has built
arXiv:2609.12495v1 Announce Type: cross Abstract: Large language models are being organized into multi-agent systems with specialized roles, but whether such specialization produces distinct forecasts
Intelligence was yours. A founder’s intellectual property, business logic, differentiation, their intelligence, was once theirs alone. You built it, you owned it, and no tools/platform vendor stood be
arXiv:2609.12364v1 Announce Type: new Abstract: Latent embeddings have become a central data abstraction in modern machine learning, especially in biomedicine, where foundation models are increasingly
arXiv:2609.12024v1 Announce Type: cross Abstract: Optimal transport gives structured data a geometry, but exact evaluation is costly in large pairwise analyses that exploit relationships among distanc
arXiv:2602.21223v2 Announce Type: replace Abstract: It is not only what we ask large language models (LLMs) to do that matters, but also how we ask them. Phrases like ``This is urgent'' or ``As your s
arXiv:2609.12544v1 Announce Type: new Abstract: Clinical de-identification relies on accurately identifying personally identifiable information (PII). However, manually annotated datasets are costly t
arXiv:2609.12884v1 Announce Type: new Abstract: A medical claim's correctness often depends not on the claim alone, but on the clinical structure around it. A claim may require a lab reference range,
arXiv:2609.12752v1 Announce Type: new Abstract: Objective Prediction models are commonly trained using objectives such as Bernoulli negative log-likelihood (NLL), although downstream clinical decision
arXiv:2609.12366v1 Announce Type: new Abstract: We present ORQA, a method for testing occupation-level knowledge in large language models. Prior methods either map abstract LLM skills to occupations v
arXiv:2602.07181v4 Announce Type: replace Abstract: User preferences are increasingly used to personalize Large Language Model (LLM) responses, yet reliably leveraging preference signals remains under
arXiv:2609.12913v1 Announce Type: new Abstract: Dense retrieval systems increasingly rely on multi-billion-parameter language models, whose memory and computational requirements make large-scale index
arXiv:2609.12223v1 Announce Type: new Abstract: Collision cross section (CCS), derived from ion mobility mass spectrometry, is a common descriptor for molecular annotation. Prediction is challenging f
arXiv:2609.12356v1 Announce Type: new Abstract: SNAP-KG is a framework for assigning newly arriving entities to semantic communities in a growing knowledge graph (KG) using only their raw features, wi
Style transfer capabilities of different open-source methods 2026 Update This is the updated version of the study published at 2025.09.12. Link: https://www.reddit.com/r/StableDiffusion/comments/1nfoz
I’ve been building Catalyst around a capability I want engineering agents to have: An AI agent can read a program and make a good guess about what matters. Catalyst lets it measure what actually matte
arXiv:2609.11224v1 Announce Type: cross Abstract: Sports data analysts translate domain questions into insights by combining computation with sport-specific domain expertise. Large language models eas
arXiv:2609.09957v1 Announce Type: cross Abstract: Modern large language models (LLMs) can translate natural-language descriptions into operations research (OR) formulations. Post-training techniques i
arXiv:2609.11234v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in peer review at major AI conferences, yet novelty remains a persistent weak point. Existing benchma
arXiv:2607.09770v2 Announce Type: replace Abstract: Industrial agentic AI systems increasingly exhibit a gap between prototype capability and production deployment. In particular, adaptive agents may
arXiv:2609.11282v1 Announce Type: new Abstract: Multimodal forecasting models that combine time series with text annotations promise richer prediction through textual context, but how do we know wheth
Comparing models on dollars per million tokens misses what production workloads actually pay for: outcomes. This post shares an open-source benchmarking harness that measures cost per correct answer,
arXiv:2609.11877v1 Announce Type: cross Abstract: Many biological discovery problems require experiments to be selected sequentially under constrained budgets. CRISPR screening is a prominent example,
Hi everyone, I was interested in learning LoRA fine-tuning, and ended up building CodeFinetuner over the past few months, a full pipeline that fine-tunes a small code autocomplete model (e.g. Qwen2.5-
arXiv:2609.11380v1 Announce Type: new Abstract: Adapting natural-image foundation models like DINOv3 to multi-modal medical imaging is challenging due to the significant domain gap between natural col
arXiv:2511.17036v2 Announce Type: replace Abstract: Visual persuasion uses images to shape cognition, emotion, and behavior, with its effects depending on both visual attributes and semantic context.
arXiv:2607.09835v2 Announce Type: replace Abstract: The recently introduced YOLO26 architecture incorporates NMS-free end-to-end inference and is optimized for deployment on resource-constrained CPU-b
arXiv:2609.11910v1 Announce Type: new Abstract: Artificial Intelligence does more than create a governance problem. It can also reveal where institutions have already failed to provide responsiveness,
arXiv:2609.11851v1 Announce Type: new Abstract: Language identification in code-mixed text, largely observed in social media, is highly essential when users frequently switch between multiple language
arXiv:2609.11842v1 Announce Type: new Abstract: Diffusion and flow-matching schedules control the signal and noise coefficients that mix data and noise along affine probability paths. Minimizing a kin
arXiv:2512.18041v2 Announce Type: replace Abstract: Processing overlapping narrative documents, such as legal testimonies or historical accounts, often aims not for compression but for a unified, cohe
arXiv:2609.11022v1 Announce Type: cross Abstract: A model first sees an image from one physical measurement experiment, such as how far a block coasted, and must answer a question about a new trial, s
arXiv:2609.11117v1 Announce Type: new Abstract: Reproducibility is essential to scientific progress, yet the growing volume and complexity of scientific publications make exhaustive manual verificatio
arXiv:2609.11477v1 Announce Type: new Abstract: Brain metastases exhibit high inter-lesion variability in size, enhancement pattern, and post-treatment appearance, making volumetric segmentation of bo
arXiv:2609.10895v1 Announce Type: new Abstract: Reacting to sudden physical hazards (catching a slipping plate, dodging a falling knife) is both a meaningful test of embodied intelligence and a hard r
arXiv:2609.09657v1 Announce Type: new Abstract: Existing emotional support conversation systems mainly focus on one-on-one seeker-supporter interactions and individual emotional states, leaving interp
arXiv:2609.11864v1 Announce Type: cross Abstract: Speech large language models (SpeechLLMs) offer reduced latency and retain paralinguistic nuances that are typically lost in cascaded automatic speech
arXiv:2609.11355v1 Announce Type: new Abstract: This paper describes our system for Task~2 of the second Multilingual Conversational Speech Language Model (MLC-SLM) Challenge. We adapt Qwen3-Omni-30B-
arXiv:2609.10901v1 Announce Type: new Abstract: LLM search agents are often evaluated on final-answer accuracy, overlooking the process. Analyzing a search strategy requires understanding how credible
arXiv:2609.10824v1 Announce Type: cross Abstract: Before an LLM agent tackles tasks in a new environment, it can inspect available corpora and tools and construct reusable resources such as indices, s
arXiv:2609.10742v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) are powerful models for handling attributed graphs in tasks such as classification, link prediction, and community detect
arXiv:2609.11838v1 Announce Type: new Abstract: Cardiovascular screening models trained on national health surveys routinely report areas under the receiver operating characteristic curve (AUROC) near
arXiv:2509.18052v4 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to simulate human collective behavior, yet claims that such simulations are human-like remain lar
arXiv:2504.20007v4 Announce Type: replace-cross Abstract: This paper proposes a novel interdisciplinary framework for analyzing police body-worn camera (BWC) footage from the Rochester Police Departme
arXiv:2609.03247v1 Announce Type: cross Abstract: Large language model (LLM) security has largely focused on role-playing jailbreaks, with less attention to what happens when a user asks an LLM to ver
arXiv:2609.11166v1 Announce Type: new Abstract: Rapid evaluation of many simultaneous road-link disruptions requires a practical compromise between exact spectral recomputation and local approximation
arXiv:2609.09707v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) applies a uniform cross-entropy loss to all target tokens, even though different tokens provide unequal learning signals fo
arXiv:2609.10357v1 Announce Type: new Abstract: Time-series foundation models are evaluated almost exclusively on public archives that predate them, so a strong score cannot be separated from having s
arXiv:2602.14488v3 Announce Type: replace-cross Abstract: IR in low-resource languages remains limited by the scarcity of high-quality, task-specific annotated datasets. Manual annotation is expensive
arXiv:2609.10026v1 Announce Type: new Abstract: Heart rate is a critical biomarker of health, and remote photoplethysmography (rPPG) enables its contactless estimation from video data for telemedicine
arXiv:2609.07379v1 Announce Type: cross Abstract: Large language models (LLMs) have recently shown promise for historical entity linking, but preference optimization for this task is often formulated
arXiv:2609.07944v1 Announce Type: new Abstract: Existing causal-inference benchmarks for LLMs mostly score method descriptions or whether generated code runs, not whether the executed workflow recover