Knowledge catalogue
arXiv:2610.17059v1 Announce Type: new Abstract: Clinical decisions depend on anticipating how a patient's condition will evolve as treatment is adjusted and new evidence becomes available. Existing me
arXiv:2610.20548v1 Announce Type: new Abstract: Understanding the social intelligence of large language models (LLMs) is increasingly important for applications involving human-AI interaction and mult
arXiv:2412.12877v3 Announce Type: replace Abstract: Recent Artificial Intelligence (AI)-based video editing has enabled users to edit videos through simple text prompts, significantly simplifying the
Cybersecurity company Proofpoint Inc. today unveiled new Microsoft 365 security features meant to get managed service providers and the businesses they look after ready to adopt artificial intelligenc
arXiv:2610.14570v1 Announce Type: new Abstract: Large language models (LLMs) support social simulation, but faithfully reproducing real users requires grounding both structured platform decisions and
arXiv:2610.16443v1 Announce Type: new Abstract: Recent world action models draw on video generation priors to jointly predict future observations and robot actions. Adapting these priors to control re
arXiv:2610.19872v1 Announce Type: new Abstract: A reasoning prompt can improve one deployment and degrade another. RCCM (Reasoning-Control Capability Mismatch) examines these reversals by pairing answ
arXiv:2610.17665v2 Announce Type: new Abstract: Can vision-language models turn 2D observations into 3D understanding that supports precise action? This requires inferring object geometry, anticipatin
arXiv:2610.19705v1 Announce Type: new Abstract: LLM routing dynamically selects and coordinates heterogeneous language models across diverse tasks. However, existing approaches are largely built aroun
arXiv:2508.06149v2 Announce Type: replace Abstract: We present Big5-Scaler, a prompt-based framework for conditioning large language models (LLMs) with controllable Big Five personality traits. By emb
Maria Curi / Axios: Source: the US FTC issues ~50-page civil investigative demands to OpenAI and Anthropic over safety incidents, safeguarding practices, and consumer protection — - The FTC sought doc
arXiv:2610.17907v1 Announce Type: cross Abstract: The code generation capabilities of large language models (LLMs) can equally be misused to implement harmful functionality, underscoring the need to u
arXiv:2610.14443v1 Announce Type: cross Abstract: Model cards, system cards, and safety reports increasingly document LLM safety behaviors, yet safety evaluations typically start from predefined bench
Artificial intelligence governance startup nFOX Lab today launched a platform for managing owned large language models and announced plans to open-source Blockhouse, a security sandbox designed to res
arXiv:2610.17085v1 Announce Type: cross Abstract: On many estimates, artificial general intelligence (AGI) is likely to emerge within the next few decades or even sooner. Once it does, it may quickly
arXiv:2610.20277v1 Announce Type: new Abstract: Designing RNA sequences that satisfy target tertiary-structure objectives is an important problem in RNA engineering, with broad applications in synthet
arXiv:2610.17158v1 Announce Type: new Abstract: When an LLM judge filters instruction-tuning data, its scoring format (binary verdict, 1--5 rating, 0--100 score, or comparison) is rarely justified or
The Nordic countries have become the European leader in unicorn startup density, producing the most emerging companies valued at $1 billion or more per capita on the continent. Now, a new chapter for
arXiv:2610.18883v1 Announce Type: new Abstract: Token-selection methods for on-policy distillation (OPD) prioritize student-visited positions using diverse signals, including uncertainty, teacher-stud
arXiv:2610.15136v1 Announce Type: new Abstract: Trust calibration remains the dominant framing of AI-assisted decision making: the goal is to close the gap between perceived and actual trustworthiness
arXiv:2610.20516v1 Announce Type: new Abstract: On-policy self-distillation provides dense supervision for reasoning models via a privileged self-teacher. However, this supervision reflects local toke
arXiv:2610.14709v1 Announce Type: cross Abstract: Large language models are increasingly deployed in applications that act on users' long-term behavior histories, from personal assistants to recommend
What makes NVIDIA Nemotron 3.5 Super VL different? → Text, image, document, and video understanding in one model → Reasoning and tool use with visual context preserved across steps → 120B hybrid MoE (
arXiv:2610.14360v1 Announce Type: new Abstract: Large language model (LLM) alignment often assumes that the user interacting with a model is also the primary beneficiary of its behavior. Yet in many c
arXiv:2610.14334v1 Announce Type: new Abstract: In Model Context Protocol (MCP) systems, LLMs select tools from metadata rather than from their implementations. Names and descriptions summarize what a
arXiv:2610.15666v1 Announce Type: new Abstract: Converting natural-language instructions into executable action plans conditioned on a scene image is a core capability for embodied human-robot interac
arXiv:2610.18305v1 Announce Type: new Abstract: The world encompasses both physical environments and human actors. Predicting its evolution requires understanding both physical laws and how emotions s
arXiv:2610.16555v1 Announce Type: cross Abstract: We introduce qtBench, a benchmark of 29 open problems in algebraic combinatorics. These problems ask for a simple combinatorial algorithm that explain
AI is mass decompiling games. That's both awesome and scary. Indie game developer Popcar talks about the recent wave of AI-powered decompilation of existing games, enabling weird mashups between exist
And extra thanks to @swyx for another example of where taste meets vision. It's nice to see true community-building vs. transactional behavior happening again We hosted the AI Engineer NYC speaker din
arXiv:2610.14005v1 Announce Type: cross Abstract: Under the accelerated pace of AI development, any benchmark whose difficulty is calibrated with humans or current AI systems becomes circular, or satu
arXiv:2610.14913v1 Announce Type: cross Abstract: Reinforcement learning (RL) is essential for adaptive decision-making, e.g., robust Meta-RL and domain randomization (DR), and efficient post-training
arXiv:2610.17183v1 Announce Type: cross Abstract: Current chemistry benchmarks primarily evaluate outcome prediction or reaction pathway generation, but rarely test whether a model can explain why an
arXiv:2610.18038v1 Announce Type: cross Abstract: World action models (WAMs) built on pretrained video generation models adapt quickly to new robotic tasks, but they forget previously learned tasks un
arXiv:2610.17214v1 Announce Type: new Abstract: What information does a reinforcement learning policy omit? At each state, a standard policy stores one probability per action. It has no place to recor
arXiv:2601.00978v2 Announce Type: replace Abstract: Human-robot collaboration (HRC) in structured assembly requires reliable state estimation and adaptive task planning under noisy perception and huma
arXiv:2610.16558v1 Announce Type: cross Abstract: World models capture environment dynamics and support counterfactual reasoning for planning. In image-goal navigation, where the target often lies bey
Every local setup I tried for Qwen-Image 2.1 meant a Python env, a node graph and an evening of picking quantization, offloading and resolution for my card. Kokon is a free, open-source desktop app th
arXiv:2607.18360v2 Announce Type: replace-cross Abstract: GPTZero found 53 NeurIPS 2025 accepted papers with hallucinated citations, and venues now screen submissions with automated reference checkers
arXiv:2610.14679v1 Announce Type: new Abstract: We introduce HumanPhys, a human-centric benchmark for real-world physical perception, and a training corpus emitted by the same pipeline. Each question
arXiv:2610.18053v1 Announce Type: cross Abstract: General-purpose robot manipulation policies learn short-horizon skills effectively, but degrade rapidly on long-horizon tasks. Existing benchmarks can
arXiv:2610.17804v1 Announce Type: cross Abstract: How can language models learn to optimize physical designs through experience? We introduce FracLearn, an experience-abstraction-generalization framew
arXiv:2610.18352v1 Announce Type: new Abstract: A robot following a multi-step instruction needs to act on the right objects in the right order, even as the scene changes. It must also maintain awaren
arXiv:2610.18478v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have shown strong potential for embodied vision-language navigation (VLN), yet their performance degrades on long-h
arXiv:2610.17007v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) learn a policy that samples compositional objects in proportion to an unnormalized reward R(x). This makes them use
arXiv:2511.19548v2 Announce Type: replace Abstract: Neuroeconomics appears to offer welfare economics an evidential supplement, since neural data may help distinguish choices that express genuine pref
OpenAI is in the news again. This time, it’s due to a recent incident involving corporate espionage. According to a statement the AI lab released on 13th October, the company discovered a “sophisticat
arXiv:2610.14001v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) uses a self-teacher conditioned on privileged information to turn the sparse outcome signal of reinforcement learning
arXiv:2610.16967v1 Announce Type: new Abstract: Physical AI is commonly formulated around an explicitly specified task: given a goal or instruction, a system perceives, reasons, and acts. In many real
arXiv:2610.14635v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable progress across a wide range of multimodal tasks. However, object hallucination remains
arXiv:2610.16632v1 Announce Type: new Abstract: Memory-based long-video generation aims to combine user control with long-range visual consistency. We organize this process into three functional compo
arXiv:2610.14043v1 Announce Type: cross Abstract: Frontier language models reliably refuse to explain how to cause physical harm. We ask whether this refusal survives when the same models control a ro
arXiv:2610.17426v1 Announce Type: new Abstract: Clinical decision-making is a sequential process of information collection, clinical inference, and forecasting under partial observability, often witho
arXiv:2610.17867v1 Announce Type: new Abstract: Foundation models for tables and relational databases are increasingly used for predictive tasks across various scientific and industrial domains. Howev
arXiv:2610.16338v1 Announce Type: new Abstract: Recent years have witnessed rapid progress in joint audio--visual generation. However, audio--visual synchronization remains a challenge, as generated s
arXiv:2610.18256v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) has emerged as a popular training paradigm for LLMs, providing dense, token-level supervision by aligning a model's o
arXiv:2610.15721v1 Announce Type: new Abstract: Cross-model KV cache sharing reduces prefill computation by allowing a receiver model to reuse prefix KV states generated by a related sender model. Exi
arXiv:2610.15961v1 Announce Type: cross Abstract: Emergent misalignment (EM) occurs when fine-tuning a language model to a narrow task makes it misaligned on unrelated ones. Because organizations now
arXiv:2610.15888v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards spends much of its rollout budget on states that yield no learning signal. We recast this as a question a
arXiv:2610.16735v1 Announce Type: new Abstract: Multimodal reinforcement learning with verifiable rewards commonly assigns one sequence advantage to an entire response. This outcome-level signal disti