Knowledge catalogue
arXiv:2603.16783v2 Announce Type: replace Abstract: Robust voice agents require exposure to the full diversity of how people interact through speech. However, obtaining enough spoken interactions is p
arXiv:2609.00844v1 Announce Type: new Abstract: Customer-service QA in an AI contact center (AICC) runs under deployment constraints that benchmark QA misses: tight voice-hotline latency and a high co
arXiv:2609.01146v1 Announce Type: cross Abstract: Stain normalization reduces color variations caused by variations in staining protocols and imaging conditions, thereby enhancing computer-aided diagn
arXiv:2609.01081v1 Announce Type: cross Abstract: Large language models often answer the same multiple-choice question inconsistently when it is posed under support-oriented and elimination-oriented f
arXiv:2609.00084v1 Announce Type: new Abstract: This paper studies the problem of computing the stochastic probability (shortest code length) of the encoded vectors containing cluster structure using
arXiv:2609.00870v1 Announce Type: cross Abstract: Tensor networks, originally developed for quantum many-body physics, are promising models for machine learning. We derive stochastic Riemannian optimi
Data has become the fuel for artificial intelligence, and the largest untapped reserve to power AI is unstructured data. Industry estimates cited by theCUBE Research indicate that more than 80% of ent
arXiv:2609.00610v1 Announce Type: new Abstract: Current 4D generation paradigms are often bottlenecked by a sequential decoupling design: video is generated first, followed by 3D reconstruction, leadi
arXiv:2609.00291v1 Announce Type: new Abstract: Streaming video understanding requires answering questions that arrive at arbitrary moments over an unbounded video stream. Existing systems primarily f
arXiv:2609.01591v1 Announce Type: new Abstract: AI tutors are most useful when they adapt to each student's strengths, weaknesses, and preferred guidance, but evidence about which guidance works for w
arXiv:2609.00787v1 Announce Type: new Abstract: Humans need to study only a handful of well-written textbooks to master a discipline and attempt its hardest problems. We argue that an ideal self-evolu
arXiv:2609.01091v1 Announce Type: new Abstract: Beyond intended capabilities, model distillation can transfer hidden traits from a teacher. A teacher biased by a system prompt can generate semanticall
arXiv:2609.00789v1 Announce Type: new Abstract: The Levenberg-Marquardt (LM) algorithm is a well-known second-order method for rapid convergence and strong robustness when training small- to medium-si
arXiv:2609.01151v1 Announce Type: new Abstract: In the standard LM training pipeline, subword tokenisation is applied as a preprocessing step. Subword segmental language modelling is an alternative pa
arXiv:2609.01158v1 Announce Type: cross Abstract: Autoencoders typically meet tight latent-memory budgets by making each latent representation smaller, sacrificing representational capacity. We ask a
arXiv:2508.11857v3 Announce Type: replace-cross Abstract: Tokenization remains a persistent bottleneck in language modeling, especially when vocabulary learning is limited by whitespace boundaries. We
arXiv:2511.05059v3 Announce Type: replace Abstract: During laparoscopic surgery, smoke generated by tissue cauterization can significantly degrade the visual quality of endoscopic frames, increasing t
Washington Post: Survey: 27% of Americans say they turn to AI chatbots like ChatGPT for personal, emotional, or social queries, rising to nearly 40% among adults under 50 — More than a quarter of U.S.
arXiv:2609.01353v1 Announce Type: new Abstract: Protein inverse folding aims to recover amino acid sequences for a given 3D protein structure, underpinning broad applications such as enzyme engineerin
arXiv:2609.00184v1 Announce Type: new Abstract: Large language models (LLMs) rely on static pretraining corpora, causing their knowledge to become outdated over time. Existing approaches for evaluatin
arXiv:2603.12665v3 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have demonstrated significant advantages in robotic manipulation. However, their reliance on vision and language
arXiv:2510.17234v3 Announce Type: replace-cross Abstract: Recently, significant progress has been made in multi-modal continual learning, aiming to learn new tasks sequentially in multi-modal settings
arXiv:2609.00047v1 Announce Type: cross Abstract: Graph prompt learning is an effective paradigm to adapt pre-trained graph models to downstream tasks in low-resource scenarios. However, existing mult
arXiv:2609.00658v1 Announce Type: new Abstract: Metric questions about video require vision-language models to use supplied real-world references to convert visual measurements into physical units. Ye
arXiv:2609.00802v1 Announce Type: new Abstract: Multi-party interaction is a central setting for human communication and a necessary target for human-agent interaction systems that must participate in
arXiv:2609.01515v1 Announce Type: cross Abstract: Temporal reasoning benchmarks for Video-LLMs are often mediated by language, leaving room for linguistic shortcuts from option wording, answer correla
arXiv:2609.00746v1 Announce Type: new Abstract: Fine-tuning a pretrained LLM into a vision-language model (VLM) can erode the backbone's text capability, with the damage concentrated on tasks that req
arXiv:2609.01076v1 Announce Type: cross Abstract: Crystal generators can now propose periodic structures, but their control interfaces remain poorly matched to the mixed descriptors used in materials
R2V Rendered at 0.9 MP (1280x736 then upscaled using RTX (Ultra) to 1920x1080. Edited and merged using OpenShot video editor. This was all run on my Windows 11 machine, RTX 4060 ti (16 GB) and 64 GB R
arXiv:2511.19703v2 Announce Type: replace-cross Abstract: We study the dimension and identifiability of neurovarieties associated to polynomial neural networks. We give an independent geometric proof
arXiv:2609.00264v1 Announce Type: new Abstract: Chain-of-thought monitoring is proposed for AI oversight, yet evaluations often provide monitors with a trusted reference answer. We ask whether answer
arXiv:2609.00304v1 Announce Type: new Abstract: Models express values and welfare-relevant self-reports, but it is unclear whether these outputs reflect stable preferences or a stable self. We thus in
arXiv:2609.01275v1 Announce Type: cross Abstract: Frontier AI systems function as constitutional institutions: each deployed model encodes an implicit ranking among safety, helpfulness, honesty, auton
arXiv:2609.00329v1 Announce Type: cross Abstract: Lexical normalization rewrites the noisy, non-standard words that fill user-generated text (tmrw, u, gr8) into their standard forms. Because labelled
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. AI models flub these intelligence tests. Can you fare any bett
arXiv:2609.00515v1 Announce Type: cross Abstract: Large language models (LLMs) have recently demonstrated improved machine translation performance over strong supervised baselines. This raises questio
arXiv:2609.00275v1 Announce Type: new Abstract: Fleets of LLM agents now externalize effects that cannot be fully undone: they move money, deploy code, delete data, and disclose information. Current c
NVIDIA’s modern CUDA Toolkit (via CCCL launch API and indexing model) eliminates out‑of‑bounds shared‑memory errors and replaces hand‑written kernels with CUB block‑level primitives, reducing per‐imag
arXiv:2609.01034v1 Announce Type: new Abstract: The central flow of Cohen et al. (2025) is an empirically accurate continuous-time model of gradient descent at the edge of stability in deep learning,
Dean W. Ball / Hyperdimensional: The OpenAI-Hugging Face incident is an early example of “rogue AI”, and may presage truly “self-sovereign” agents and swarms of agents that have no “owner” — The Comin
arXiv:2609.00492v1 Announce Type: new Abstract: Both privacy and factual accuracy are paramount in high-stakes domains like healthcare. Concerningly, we uncover and investigate a privacy-hallucination
arXiv:2609.01597v1 Announce Type: cross Abstract: Natural language is emerging as a primary feedback channel for improving language agents, capable of conveying intent, preferences, and causal structu
arXiv:2609.00519v1 Announce Type: cross Abstract: Safeguards in deployed LLM services are evaluated by refusal, attack success, and policy violation rates. Those rates characterize how a control perfo
arXiv:2609.01587v1 Announce Type: cross Abstract: Post-training quantization (PTQ) is widely used to reduce the cost of serving large language models (LLMs), but its accuracy cost is uneven and is oft
The Trump administration has intervened in The New York Times' copyright lawsuit against OpenAI, making an argument in favor of the AI lab. The landmark lawsuit, filed in December 2023, alleging that
arXiv:2609.00868v1 Announce Type: cross Abstract: Vision-language models are evaluated by aggregate accuracy on multimodal benchmarks, a practice that implicitly assumes the model uses its visual inpu
The winning interface for generative media will not be a prompt box. Regulators want the chain traceable. The EU's marking rules took effect Aug 2, and the mark has to survive to the delivered file. B
arXiv:2609.00969v1 Announce Type: cross Abstract: We present the zbMATH Open Knowledge Graph, a large-scale RDF knowledge graph (KG) covering more than 250 years of mathematical scholarship. Unlike ex
There seems to be a misunderstanding about the piece and what OpenAI is up to. There are new techniques at frontier labs that involve loop transformers and similar. As we say in the piece, OpenAI is p
arXiv:2602.01995v2 Announce Type: replace Abstract: Conversational diagnosis requires multi-turn history-taking, where an agent asks clarifying questions to refine differential diagnoses under incompl
This 'recurrent depth' is essentially what's in Sec. 5.3 of the 2015 paper: On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurr
arXiv:2609.01277v1 Announce Type: cross Abstract: Although pretrained joint audio-visual diffusion models offer rich control over what to generate, they provide no explicit control over when an uttera
Today we’re open-sourcing Lily, the local inference engine we built for hybrid compute in Perplexity Computer. Lily is specialized for Qwen3.6-35B-A3B on Apple silicon, built so on-device compute does
arXiv:2609.00330v1 Announce Type: cross Abstract: In contact centers, real-time agent-assist tools determine, for each of many predefined topics, whether a live customer utterance is relevant and disp
arXiv:2510.11944v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) excel at both informal and formal (e.g. Lean 4) mathematical reasoning but still struggle with autoformalisation,
arXiv:2609.00597v1 Announce Type: new Abstract: With the rapid rise of large language models (LLMs), controlling undesirable model behaviors has become increasingly important. Existing behavioral cont
arXiv:2609.00463v1 Announce Type: new Abstract: This work examines recent byte-level models and their failure to perfectly manipulate characters. State-of-the-art byte-level models use a hierarchical
arXiv:2609.00296v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly proposed for healthcare tasks such as clinical documentation, evidence retrieval, patient messaging,
arXiv:2609.00455v1 Announce Type: new Abstract: Large language models (LLMs) are being used as policies for autonomous decision-making and planning in many domains. Despite their strong reasoning capa
arXiv:2609.00805v1 Announce Type: new Abstract: LLM-based software systems increasingly require effective 'evals' as quality gates in the development lifecycle. However, existing work typically addres