Search: “reasoning”

One of the premier journals in my field... I think there are very valid reasons to set rules on AI in peer review (including disclosure), but the idea that all AI models steal your data is very 2023.

Pew Research Center: A survey of US teens: ~90% say entertainment is a reason they use TikTok, Instagram, or Snapchat, 57% Snapchat message daily, and 37% say TikTok impacts sleep — Teens largely turn

Long-horizon AI research agents are mostly a state-management problem. It is not enough for an agent to reason well in the next turn. ML research requires task setup, implementation, experiments, debu

AI keeps getting better but the last time the shape of the jagged frontier changed radically was o1 & the Reasoner A good mental model of the coming months is that models get very good at the things t

I see every week on X an announcement or demo which implies that robotic manipulation has been solved. The only reason I don't believe it is because manipulation had already been solved last week by s

The reason symmetry is so important in physics is because symmetry is a highly effective compression operator. If a system is invariant under some symmetry, you only need to explain one axis of it. Sc

Someone recently suggested to me that the reason OpenClaw moment was so big is because it's the first time a large group of non-technical people (who otherwise only knew AI as synonymous with ChatGPT

We gradually transitioned from a democratic welfare state to full-on race communism between the 1960s and today for two reasons: 1. The beneficiaries of our race laws went from 10 percent of the popul

arXiv:2610.18583v1 Announce Type: new Abstract: Sampling-based test-time scaling enhances LLM reasoning by aggregating multiple reasoning paths for the same query. Existing methods use answer frequenc

arXiv:2610.16769v1 Announce Type: new Abstract: Geospatial reasoning links VLMs to real-world action and decision making. Reliable reasoning requires accurate spatial information and effective spatial

arXiv:2610.18161v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated strong zero-shot reasoning across diverse domains, but their potential for Human-Object Inter

arXiv:2510.22833v2 Announce Type: replace-cross Abstract: While reinforcement learning agents can achieve superhuman performance in many complex tasks, they typically do not become more computationall

arXiv:2610.12961v1 Announce Type: cross Abstract: We introduce MVGraph, a benchmark for evaluating large vision-language models (LVLMs) on multi-graph reasoning where the evidence required by a query

arXiv:2610.05013v1 Announce Type: new Abstract: Lifelong physical agents must reason over extended interactions where past events continue to shape the world long after they disappear from view. Beyon

arXiv:2610.04737v1 Announce Type: cross Abstract: Reasoning models can remain capable of solving a task while still defaulting to cheaper but misleading shortcuts. This creates a central oversight pro

arXiv:2610.04156v1 Announce Type: cross Abstract: LLM agents for clinical text-to-SQL applications reason autonomously over multiple steps but cannot assess whether their own reasoning or outputs can

arXiv:2610.00724v1 Announce Type: cross Abstract: Language models learn content and style jointly, making stylistic variation in their outputs difficult to identify and control. We study whether recur

arXiv:2606.23595v2 Announce Type: replace Abstract: Language model reasoning can be substantially improved at test time via scaffolds that scale inference compute across different primitives -- sequen

arXiv:2610.00910v1 Announce Type: cross Abstract: Human reasoning depends on how objects are related within propositions. extit{How do relations organize the language representations of contextual con

arXiv:2609.39478v1 Announce Type: new Abstract: This study systematically evaluates self-evaluation-based uncertainty quantification across different language models of varying sizes on question-answe

arXiv:2609.31456v1 Announce Type: new Abstract: Vision-language models (VLMs) often struggle with compositional reasoning tasks, but the reasons for this underperformance remain unclear. A common hypo

You can simply run any GGUF with llama.cpp with n_predict=1 and n_probs=10, disable reasoning, and prompt it such as 'If the following email is spam, respond with 1, if not spam, respond with 0. Do no

arXiv:2609.14699v1 Announce Type: new Abstract: Visual content shapes audience perception and opinion on social media, and computational social science increasingly relies on automated tools to analyz

arXiv:2504.19023v2 Announce Type: replace-cross Abstract: Semantic reasoning aims to infer new knowledge from existing knowledge, with OWL ontologies serving as a standardized framework for organizing

Astra's launch has produced a strange discourse. The reporting that broke the story framed the model's use of recurrent depth primarily as a safety regression, because it means the model reveals less

arXiv:2509.25160v2 Announce Type: replace-cross Abstract: Mathematical reasoning is a key capability for vision-language models (VLMs), yet current benchmarks mainly evaluate text-based or explicitly

arXiv:2608.19611v1 Announce Type: cross Abstract: LLM reasoning is stochastic, and so understanding a model requires grappling with the distribution of reasoning chains that it might produce for a giv

arXiv:2607.16094v2 Announce Type: replace Abstract: Compositional visual question answering requires Vision-Language Models (VLMs) to execute multiple reasoning operations like object selection, spati

arXiv:2608.18579v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have shown strong potential for image quality assessment (IQA) by improving consistency between quality ratin

arXiv:2608.16977v1 Announce Type: new Abstract: AI systems are increasingly capable of contributing to mathematical research. In research practice, frontier-model reasoning is a limited resource, and

arXiv:2608.14179v1 Announce Type: new Abstract: Large language models (LLMs) have shown remarkable reasoning and generative capabilities, motivating their use as universal reasoning engines for percep

arXiv:2608.00422v2 Announce Type: replace Abstract: Large language models (LLMs) can generate fluent reasoning traces that nevertheless lead to incorrect answers, making response-level uncertainty est

arXiv:2607.27700v1 Announce Type: new Abstract: Large Vision-Language Models (VLMs) suffer from prohibitive inference overhead due to long sequences of visual tokens. However, existing visual token re

DigitalOcean launched Kimi K3 on day 0. It’s already one of the most popular models on the platform and across the market: second most likes on Hugging Face, sixth most traffic on OpenCode. Getting a

arXiv:2607.22859v1 Announce Type: new Abstract: Mathematical Word Problems (MWPs) are an important benchmark for evaluating natural language understanding and quantitative reasoning. Despite recent pr

arXiv:2508.12265v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable progress in reasoning across diverse domains. However, effective reasoning in real-world t

arXiv:2607.04232v1 Announce Type: cross Abstract: Unlike natural-language specifications, executable formal specifications provide machine-checkable constraints for verifying, debugging, and repairing

arXiv:2606.27999v1 Announce Type: new Abstract: Despite the rapid advance of Multimodal Large Language Models (MLLMs) in high-level video understanding, a fundamental bottleneck remains: these models

arXiv:2606.11678v1 Announce Type: new Abstract: Problem, Research Strategy, and Findings: The rise of large language models (LLMs) raises a key question for urban planning: which forms of professional

arXiv:2606.08511v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) face a significant inference bottleneck due to the quadratic computational cost of self-attention over long vis

arXiv:2606.08405v1 Announce Type: new Abstract: While data-intensive deep reinforcement learning can optimize complex control policies, scientific discovery in physical systems fundamentally requires

arXiv:2606.03217v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning has become a widely used mechanism for eliciting multi-step reasoning in large language models by generating intermed

arXiv:2605.25603v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning improves the problem-solving ability of large language models (LLMs), but generated reasoning traces may not faithfully

arXiv:2509.12194v2 Announce Type: replace Abstract: Differential diagnosis is an iterative process that integrates patient information with broader medical knowledge. Clinical case series such as the

This post contrasts 'wordcels' (those who work primarily with language) with 'tensor rotators' (those working with mathematical/numerical abstractions), arguing that language-focused AI practitioners

arXiv:2601.16312v2 Announce Type: replace-cross Abstract: Research in AI4Science has shown promise in many science applications, including polymer design. However, current LLMs are ineffective in this