Knowledge catalogue
arXiv:2606.09916v1 Announce Type: cross Abstract: Multi-turn LLM agents fan short queries into long trajectories of tool calls, search results, and intermediate reasoning. Both KV memory and KV read b
arXiv:2606.09940v1 Announce Type: cross Abstract: Dictionary learning methods like Sparse Autoencoders (SAEs) and crosscoders attempt to explain a model by decomposing its activations into independent
arXiv:2606.10088v1 Announce Type: new Abstract: Reduced facial expressivity is a common motor manifestation of Parkinson's disease (PD), often described as hypomimia or facial bradykinesia. This paper
arXiv:2606.11001v1 Announce Type: new Abstract: Zinc-based alloys are indispensable emerging absorbable metallic biomaterials, and their macroscopic performance is governed by microstructural characte
arXiv:2606.10931v1 Announce Type: new Abstract: Warning: This paper contains several toxic and offensive statements. Modern large language models (LLMs) are typically aligned through large-scale post-
arXiv:2606.10531v1 Announce Type: cross Abstract: Quantization-aware training (QAT) is essential for extremely low-bit large language models (LLMs). Current QAT methods are mainly based on scalar quan
The Texas Tribune: Letter: Texas Governor Greg Abbott released regulatory recommendations for state lawmakers to ensure that data centers shoulder the costs of their growth — The recommendations for l
arXiv:2511.10234v3 Announce Type: replace-cross Abstract: While promising, graph reasoners based on Large Language Models (LLMs) lack built-in invariance to symmetries in graph representations. Operat
arXiv:2603.29730v2 Announce Type: replace-cross Abstract: We present mlr3mbo, a modular toolbox for Bayesian optimization in R. mlr3mbo supports single- and multi-objective optimization, multi-point p
arXiv:2606.10806v1 Announce Type: new Abstract: Moonshine is an autonomous agent whose central objective is to generate mathematical conjectures. Its core capability is to extract structure from class
arXiv:2606.09934v1 Announce Type: new Abstract: Feature selection is critical for network intrusion detection systems (NIDS) operating under high-dimensional, highly imbalanced traffic, as found in op
arXiv:2606.10909v1 Announce Type: cross Abstract: Reconstructing local stress fields in heterogeneous microstructures under non-linear, history-dependent loading remains a major computational bottlene
arXiv:2605.05857v2 Announce Type: replace Abstract: Tokamaks remain leading candidates for achieving practical fusion energy, yet many important control problems inside these devices are still difficu
arXiv:2606.09962v1 Announce Type: cross Abstract: Continuous diffusion for categorical data is a framework belonging to the diffusion family and aiming at generating discrete data. The scientific inte
arXiv:2606.10492v1 Announce Type: new Abstract: The growing need for high-resolution image generation in autoregressive text-to-image models has resulted in extended token sequences, significantly inc
OpenAI reported that People's Republic of China (PRC)-linked actors are conducting coordinated influence operations aimed at shaping artificial intelligence policy debates in the United States. These
arXiv:2606.10875v1 Announce Type: new Abstract: Large language models (LLMs) rely on tool use to act as autonomous agents, yet often fail in multi-step execution due to insufficient tool-related knowl
arXiv:2603.08924v2 Announce Type: replace-cross Abstract: AI-powered answer engines are inherently non-deterministic: identical queries submitted at different times can produce different responses and
arXiv:2606.10324v1 Announce Type: new Abstract: The analogy between deep neural network forward passes and renormalization group (RG) flows has been repeatedly noted in the literature, but existing tr
arXiv:2606.10908v1 Announce Type: cross Abstract: We introduce a spoofing countermeasure architecture conditioned on speaker-reference recordings, but observe that it converges to a solution that effe
arXiv:2606.10346v1 Announce Type: new Abstract: Reinforcement learning has become a key paradigm for eliciting reasoning abilities in large language models, where exploration is crucial for discoverin
arXiv:2606.10929v1 Announce Type: cross Abstract: Task vectors, LoRA, activation steering, and random search around pretrained weights all suggest that learned behaviour can be controlled by linear di
arXiv:2602.01179v2 Announce Type: replace Abstract: Gradual domain adaptation (GDA) aims to mitigate domain shift by progressively adapting models from the source domain to the target domain via inter
arXiv:2510.07061v2 Announce Type: replace Abstract: While automatic metrics drive progress in Machine Translation (MT) and Text Summarization (TS), existing metrics have been developed and validated a
DiffusionGemma is an experimental open model built for exceptionally fast text generation that NVIDIA has optimized to run on GeForce RTX GPUs, RTX PRO, and DGX Spark systems. Rather than generating t
arXiv:2512.17629v4 Announce Type: replace-cross Abstract: Prescriptive Process Monitoring (PresPM) recommends interventions during running business processes to optimize key performance indicators (KP
arXiv:2606.10228v1 Announce Type: cross Abstract: Safe exploration is a prerequisite for deploying reinforcement learning (RL) agents in safety-critical domains. In this paper, we approach safe explor
arXiv:2606.10388v1 Announce Type: cross Abstract: Agent skill libraries are becoming routable software assets: a retrieved skill can contribute instructions, scripts, resource bindings, and execution
arXiv:2606.10227v1 Announce Type: new Abstract: Metal additive manufacturing enables the fabrication of complex parts, but achieving consistent build quality remains challenging due to interactions in
arXiv:2606.10368v1 Announce Type: cross Abstract: Speech-to-text (S2T) systems for recognition (ASR) and translation (S2TT) typically generate discrete text tokens. In contrast, continuous-target lang
arXiv:2606.10021v1 Announce Type: new Abstract: Lumbar spine conditions are a leading cause of disability worldwide, yet reliable quantification of degeneration from MRI remains challenging. In clinic
arXiv:2606.09877v1 Announce Type: cross Abstract: LLM wiki systems compile knowledge into pre-filled KV caches for efficient inference, but assume a static corpus -- an assumption that fails whenever
arXiv:2602.04119v2 Announce Type: replace Abstract: The application of generative models for experimental drug discovery campaigns is severely limited by the difficulty of designing molecules de novo
arXiv:2602.23499v4 Announce Type: replace-cross Abstract: Collecting a high-quality dataset is a critical task that demands meticulous attention to detail, as overlooking certain aspects can render th
This Twitter broadcast demonstrates a workflow for exporting and importing 3D character models from ComfyUI, a node-based generative AI tool, into Blender for further refinement and animation. The ses
arXiv:2606.11087v1 Announce Type: cross Abstract: Expressive continuous control policies, such as diffusion and flow models, form the backbone of recent advances in scaling imitation learning for simu
arXiv:2606.09922v1 Announce Type: cross Abstract: Bioelectrical signals are increasingly acquired at scales that challenge the bandwidth of brain-computer interfaces. However, their compression is sti
arXiv:2601.20970v3 Announce Type: replace-cross Abstract: The maximum-entropy remote sampling problem (MERSP) is to select a subset of s random variables from a set of n random variables, so as to max
the paper is long (30 pages), but have your AI read it: https://arxiv.org/abs/2606.10241 here's a simple interactive tutorial on the topic that claude made: https://claude.ai/public/artifacts/038db6cf
“They have been running literally the same play for seven years: scare, hype (evoking media interest), and (eventually) release. Scare, hype, release. And repeat. Is it that hard to see?” 👇🏼 - @GaryMa
arXiv:2606.11119v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models. H
arXiv:2509.25017v2 Announce Type: replace-cross Abstract: Wildfires are among the most severe natural hazards, posing a significant threat to both humans and natural ecosystems. The growing risk of wi
arXiv:2606.09958v1 Announce Type: cross Abstract: In mixed-traffic environments where autonomous and human-driven vehicles may co-exist, motion planning for autonomous vehicles requires anticipating t
Want to learn more about the Far-Field ASR benchmark? Join Treble's webinar tomorrow, June 11th, with @shinjiw_at_cmu, Cohere's @Julianfmack, and other industry leaders discussing the future of far-fi
We need more real time data on how AI may be impacting the economy - this is a really useful addition. Today, the Stanford @DigEconLab launches the AI Economic Indicators, a new platform for tracking
arXiv:2606.10229v1 Announce Type: cross Abstract: We study whether demonstration-curation metrics that detect defective training episodes also improve the downstream behavior-cloning policy that train
arXiv:2606.09899v1 Announce Type: cross Abstract: A central goal of mechanistic interpretability is to identify which internal components causally drive a language model's behavior. Because these impo
Claude Fable efficiently processes tokens at high speed when initiating workflows, demonstrating rapid token consumption during execution. This observation from Ethan Mollick highlights the computatio
When you hear AI 'safety' you should hear 'censorship' and 'control' instead. All of us surveilled and spied by safeguards of loving grace. Today it's intelligent Terms of Service control. You can't d
arXiv:2606.10302v1 Announce Type: new Abstract: Open-ended generation tasks often require a set of meaningfully different outputs, yet large language models often produce similar generations. Existing
👇@zerohedge confirms what I said here and in my May 29 Marcus on AI newsletter. Tokenmaxxing RIP. This morning @zerohedge out with a report on the death of Tokenmaxxing 'Microsoft’s AI Chief added to
arXiv:2603.04125v2 Announce Type: replace Abstract: Few-Shot Action Recognition (FS-AR) has shown promising results but is often limited by a closed-set assumption that fails in real-world open-set sc
arXiv:2512.07355v2 Announce Type: replace Abstract: Two traditions of interpretability have evolved side by side but seldom spoken to each other: Concept Bottleneck Models (CBMs), which prescribe what
arXiv:2606.08093v1 Announce Type: new Abstract: Pathology is the cornerstone of modern medicine, where accurate decision-making relies heavily on evidence-based practices. While artificial intelligenc
arXiv:2606.09073v1 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) is bottlenecked by reward hacking, where the policy exploits errors in a proxy reward model (RM) and
arXiv:2606.08105v1 Announce Type: new Abstract: When attention concentrates on a single token, a sink, what is the model actually computing? Attention sinks are ubiquitous in softmax transformers, yet
arXiv:2606.09734v1 Announce Type: cross Abstract: Training parameterised quantum circuits (PQCs) on quantum hardware is bottlenecked by the measurement cost of gradient estimation, which under the par
arXiv:2606.09104v1 Announce Type: cross Abstract: Deep reinforcement learning (DRL) frameworks for portfolio optimization have shown promise for their ability to learn allocation rules dynamically fro
arXiv:2606.08696v1 Announce Type: cross Abstract: Counterfactual recourse aims to provide actionable feature changes that would alter an unfavorable decision made by a predictive model. In practice, a
arXiv:2606.09100v1 Announce Type: cross Abstract: Community detection is a fundamental problem in the analysis of complex networks. It has applications across social, biological, and financial domains