Knowledge catalogue
arXiv:2609.24384v1 Announce Type: new Abstract: Recognizing hand-drawn geometric shapes is a foundational sub-problem of sketch recognition, with applications in education, human-computer interaction,
arXiv:2602.02940v2 Announce Type: replace-cross Abstract: Formal semantics and distributional semantics are distinct approaches to linguistic meaning: the former models meaning as reference via model-
arXiv:2609.22562v1 Announce Type: new Abstract: Multi-vector visual document retrieval (VDR) models such as ColPali and ColNomic achieve strong accuracy by representing each document with hundreds to
arXiv:2609.24170v1 Announce Type: new Abstract: Embodied AI systems are often organized into System 1 and System 2. System 1 is typically a pretrained policy that generates actions at high frequency,
David Gewirtz / ZDNET: Anthropic raises its five-hour usage limits by 20% on Pro, Max, and Team plans, and gives subscription users a rate limit reset — Anthropic's newest Claude model aims to make fr
arXiv:2609.23133v1 Announce Type: new Abstract: Recent advances in vision-language-action models have stimulated growing interest in underwater embodied intelligence. However, their reliance on large-
arXiv:2609.24650v1 Announce Type: new Abstract: Readability assessment is essential for tailoring texts to intended audiences across educational, healthcare, and information retrieval domains. However
arXiv:2609.22124v1 Announce Type: new Abstract: This paper describes the system submitted to the UNLP 2026 Shared Task on Multi-Domain Document Understanding. The challenge required extracting precise
arXiv:2609.23383v1 Announce Type: cross Abstract: Bayesian filtering provides a principled framework for online state estimation under uncertainty, yet its application to systems with high-dimensional
arXiv:2609.23789v1 Announce Type: new Abstract: Modern conditional generative models face significant challenges when learning complex covariate dependencies. While sufficient dimension reduction (SDR
arXiv:2609.22977v1 Announce Type: cross Abstract: Similarity-based retrieval is the dominant rule for conditioning large language models (LLMs) in in-context learning, retrieval-augmented generation,
arXiv:2609.22111v1 Announce Type: new Abstract: Large language model agents are increasingly capable of conducting research autonomously, producing research documents alongside the code and experiment
arXiv:2609.22235v1 Announce Type: new Abstract: While multi-agent systems based on large language models (LLMs) have shown promise in automating the progressive workflow of academic research, extendin
arXiv:2510.17904v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are widely used because they process structures, syntax and code well, but this same ability also makes them para
arXiv:2609.24088v1 Announce Type: new Abstract: In latent generative models, reconstruction quality is often assumed to correlate with generative performance. However, reconstruction FID (rFID) can ex
arXiv:2609.22866v1 Announce Type: new Abstract: We introduce Causilo, a tabular foundation model (TFM) that combines frontier predictive performance with exceptionally fast inference. On TabArena, Cau
arXiv:2609.23567v1 Announce Type: cross Abstract: We reconsider the theory of probabilistic formal languages generated by n-gram models and by probabilistic context-free grammars (PCFGs). The expected
arXiv:2609.24651v1 Announce Type: new Abstract: Diffusion and flow models, as promising generative paradigms for speech enhancement, face a training--inference mismatch: training uses analytical path
arXiv:2609.24625v1 Announce Type: cross Abstract: How should natural language processing models be selected and adapted for global health literature in environments where annotated data and computatio
arXiv:2609.22510v1 Announce Type: cross Abstract: As LLM applications integrate with external tools, they are increasingly exposed to indirect prompt injection (IPI), where adversarial instructions ar
arXiv:2609.22225v1 Announce Type: new Abstract: Large language models (LLMs) exhibit a range of human-like decision-making behaviors, but whether these reflect similar underlying mechanisms or surface
arXiv:2609.23065v1 Announce Type: new Abstract: Chain-of-thought (CoT) can sound plausible yet be unfaithful to the model's underlying reasoning. Most prior work probes CoT faithfulness through input-
arXiv:2609.22308v1 Announce Type: new Abstract: Coding-agent benchmarks usually evaluate implementation after the target behavior has been specified in text, code, or demonstrations. Existing research
arXiv:2603.14910v2 Announce Type: replace-cross Abstract: Classical optimal control designs a separate controller for each plant. Even for the Linear Quadratic Regulator (LQR), every new model must be
arXiv:2609.23726v1 Announce Type: new Abstract: Large language models have shown strong performance across a range of legal tasks, but existing benchmarks rarely evaluate the ability to take and defen
arXiv:2609.24564v1 Announce Type: new Abstract: CLIP, a foundational vision-language model, has emerged as a powerful tool for open-vocabulary semantic segmentation. While freezing CLIP's text encoder
arXiv:2609.24882v1 Announce Type: new Abstract: Hybrid AI-physics climate modeling aims to improve coarse (~100km-resolution) Earth system models by learning to parameterize subgrid processes from hig
arXiv:2609.24172v1 Announce Type: new Abstract: Legends are fundamental to chart understanding, as reliable interpretation requires correctly binding legend entries to corresponding visual marks. Whil
arXiv:2609.22700v1 Announce Type: new Abstract: Step-level reasoning evaluators are commonly based on autoregressive language models, whose causal attention restricts each step representation to the p
**llm-anthropic 0.29** is a software package that offers programmatic access to Anthropic’s large language models, including the Claude series. It handles authentication, API requests, and response pa
arXiv:2609.23340v1 Announce Type: cross Abstract: Pitch estimation on an edge device is constrained in three ways at once. The model must be small, it must stay accurate when the input is noisy, and o
arXiv:2609.24259v1 Announce Type: new Abstract: The effectiveness of agent memory ultimately depends on whether the underlying LLM gives each memory in context an appropriate degree of influence over
arXiv:2609.22886v1 Announce Type: new Abstract: Model merging consolidates fine-tuned experts into one multi-task model without retraining. All existing data-free methods approach this problem entirel
arXiv:2609.24391v1 Announce Type: new Abstract: Voice-controlled interaction in industrial settings is hampered by acoustic noise, which severely degrades audio-only speech recognition. Audio-visual s
arXiv:2609.24377v1 Announce Type: cross Abstract: We study latent-space watermarking through a pretrained generator using a prescribed latent-to-output stochastic mapping, called the renderer. A water
arXiv:2609.24128v1 Announce Type: cross Abstract: Judge-specific sensitivity is useful for aggregating pairwise LLM evaluations, but its interpretation depends on which systematic presentation effects
arXiv:2609.23463v1 Announce Type: new Abstract: Federated learning lets multiple parties train a shared model without pooling their data, but a client with far less data than the others can end up poo
arXiv:2609.24768v1 Announce Type: new Abstract: Professional photo finishing relies on both global adjustments and region-specific local edits guided by semantic masks, yet current automated methods h
arXiv:2609.24538v1 Announce Type: new Abstract: Functional annotation of newly sequenced proteins remains a bottleneck in molecular biology: the number of sequences in public repositories grows far fa
This LoRA can do a lot of good things such as detail enhancement, creative upscaling, photo restoration and quality improvements. It's a Image-to-Image(I2I) edit LoRA, so it will require image input,
arXiv:2609.22959v1 Announce Type: cross Abstract: The medications prescribed to a patient often change during a hospital admission as clinicians start, stop, or continue therapies. We study whether mo
arXiv:2609.23655v1 Announce Type: new Abstract: Imbalanced gradient magnitudes between the visual and language pathways of a vision-language model are often treated as a defect to be corrected. We tes
Tibo said Tuesday, GPT-6 Sol is suddenly everywhere, and now Claude Opus 5.5 leaks are blowing up too. Yet… nothing so far. 😂 At this point I’m just refreshing X like it’s a live event. What do you th
arXiv:2510.03095v4 Announce Type: replace Abstract: Diffusion- and flow-based generative models have recently demonstrated strong performance in protein backbone generation tasks, offering unprecedent
arXiv:2609.23596v1 Announce Type: new Abstract: With face-recognition models now embedded in everyday authentication and surveillance, recent works have pinpointed a critical weakness: these models re
arXiv:2609.24280v1 Announce Type: cross Abstract: Malware detection is a critical task in cybersecurity, and graph neural networks over control flow graphs have shown promising results for it. However
arXiv:2609.24885v1 Announce Type: new Abstract: When a language model answers from a curated corpus via graph-based retrieval, a large grounding uplift does not establish reasoning over the retrieved
arXiv:2609.22246v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to forecast real-world events by retrieving and reasoning over news. We show that this dependence on
arXiv:2609.24055v1 Announce Type: new Abstract: Robots can recover from failures by asking bystanders for help, but effective human-in-the-loop recovery requires communication that accounts for differ
arXiv:2609.24289v1 Announce Type: new Abstract: As Large Language Model (LLM) agents are applied in continuously interactive environments, driving the evolution of their own capabilities becomes a cor
arXiv:2609.22261v1 Announce Type: new Abstract: Lexicon-driven misogyny detectors cannot, by construction, distinguish a slur used against a woman from the same slur mentioned in counter-speech ('don'
arXiv:2609.22145v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) offer a compelling alternative to autoregressive models, yet they may expose sensitive training data during de
arXiv:2604.04720v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) still exhibit large performance gaps between English and other languages, yet much current work assumes these gaps can
arXiv:2609.22631v1 Announce Type: new Abstract: Accurate automated interpretation of electrocardio- grams (ECGs) is essential for early detection of cardiac condi- tions such as myocardial infarction
https://preview.redd.it/ybwqqbrst0rh1.png?width=811&format=png&auto=webp&s=1e40c5b53e304caf2a10efa2baa6f998b9a9a0bb MiMo-V2.6-Distill-Qwen-9B is a 9B agentic model developed by Xiaomi MiMo through sup
arXiv:2609.21176v1 Announce Type: new Abstract: This paper addresses the challenges of dynamic scene synthesis from sparse-view videos. Existing methods employ geometric priors, adaptive optimization,
arXiv:2609.20981v1 Announce Type: new Abstract: Autoregressive (AR) models suffer from local greediness, while diffusion language models (DLMs) often lack the strict causal structure required for reas
arXiv:2609.21157v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly being explored for chip design, but most existing approaches operate directly at RTL. We ask whether
arXiv:2609.21562v1 Announce Type: cross Abstract: Coding agents can modify and test code across large software projects. Game development is a domain where agents must implement gameplay rules. A game
# the What An engine to run Gemma 4 31B on blackwell under massive concurrency and rather specific workload patterns. I've been waiting for someone to do ninfer but for gemma, and, well, ended up havi