Search: “reasoning”

arXiv:2606.22870v1 Announce Type: new Abstract: Recent advancements in chain-of-thought (CoT) reasoning have shown promise in enhancing video understanding and reasoning capabilities of multimodal lar

Emad Mostaque, founder of Stability AI, predicts a 18-month timeline for an unspecified AI capability development, citing his track record of accurate capability forecasts. The post appears to be a re

arXiv:2606.04436v1 Announce Type: new Abstract: We propose a 3D-thinking-guided co-training framework that enables vision-language-action (VLA) models to perform 3D spatial reasoning implicitly during

This post describes a user's account suspension by OpenAI shortly after making a payment, resulting in loss of access to multiple Codex agents and client-dependent income, with no explanation provided

Research shows that animals get their minds muddled during heat waves, with birds struggling to learn, dogs biting more often, and chamois picking fights when temperatures rise. When animals can't sta

Jeremy Howard expresses positive views on GPT-5.5, stating he has recently switched to using it as his primary model and finds it nearly comparable to Anthropic's Opus 4.6 and 4.7 while offering signi

arXiv:2605.22897v1 Announce Type: new Abstract: A persistent challenge in machine learning for scientific applications is jointly achieving prediction and understanding. Statistical models excel on st

arXiv:2605.13511v1 Announce Type: cross Abstract: In-context learning (ICL) adapts large language models (LLMs) to new tasks by conditioning on demonstrations in the prompt without parameter updates.

Elon Musk posted on X claiming that Starlink internet connectivity is United Airlines' top feature and significantly enhances the flight experience at no cost to passengers. The post suggests Starlink

arXiv:2509.08150v5 Announce Type: replace Abstract: Reasoning is a fundamentally algorithmic task. Yet current work on LLM-based reasoning relies on free-form generation whose theoretical guarantees (

arXiv:2511.02805v2 Announce Type: replace-cross Abstract: LLM-based search agents often concatenate the full interaction history into the context, producing long and noisy inputs, and increasing compu

Sam Altman expresses satisfaction with progress on OpenAI's priorities, noting that aside from increasing goblin-related features or content, current development aligns well with their stated goals. T

arXiv:2604.27759v1 Announce Type: cross Abstract: Integrating domain knowledge into deep neural networks is a promising way to improve generalization. Existing methods either encode prior knowledge in

This post likely discusses an unexpected or unusual infrastructure discovery, possibly referencing a road that literally terminates at a river without proper connection or explanation. Based on Ethan

In 2016, David (likely referring to David Silver, DeepMind's lead researcher on AlphaGo) wrote in his private journal that the motivation for building AlphaZero was aesthetic—he sought to create a bea

arXiv:2604.23238v1 Announce Type: cross Abstract: Frontier models push the boundaries of what is learnable at extreme computational costs, yet distillation via sampling reasoning traces exposes closed

At #ICLR? Don’t miss @realDanFu 👇 Latent & Implicit Thinking Workshop (LIT) 🕐 Workshop at 1:30 PM BRT (local) Exploring what comes after chain-of-thought and how models may actually reason. 🔗 https://

Clem Delangue expressed his personal motivation for continuing work in the open-source space, stating that enjoyment is his primary driver for persisting in the field. He also expressed confidence tha

arXiv:2602.00931v2 Announce Type: replace-cross Abstract: Large language model reasoning is often treated as a monolithic capability, relying on binary preference supervision that fails to capture par

On Friday, Chinese AI firm DeepSeek released a preview of V4, its long-awaited new flagship model. Notably, the model can process much longer prompts than its last generation, thanks to a new design t

arXiv:2604.20486v1 Announce Type: new Abstract: Training multimodal agents via reinforcement learning for knowledge-intensive visual reasoning is fundamentally hindered by the extreme sparsity of outc

arXiv:2604.17458v2 Announce Type: replace Abstract: Graph-based Retrieval-Augmented Generation (GraphRAG) enhances LLMs by structuring corpus into graphs to facilitate multi-hop reasoning. While recen

arXiv:2511.17171v4 Announce Type: replace Abstract: Predicting wildfire risk is a reasoning-intensive spatial problem that requires the integration of visual, climatic, and geographic factors to infer

arXiv:2604.17892v1 Announce Type: new Abstract: Recently, latent reasoning has been introduced into large language models (LLMs) to leverage rich information within a continuous space. However, withou

arXiv:2604.16995v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a promising paradigm for training reasoning-oriented models by leveraging rule-based reward signals. However,

arXiv:2601.11374v2 Announce Type: replace Abstract: Scientific writing is an expert-domain task that demands deep domain knowledge, task-specific requirements and reasoning capabilities that leverage

arXiv:2510.08480v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have demonstrated remarkable potential in bridging visual and textual reasoning, yet their reliance on text

Ethan Mollick observes that an AI system (likely Claude or another large language model) still declines to write sestinas, suggesting that certain behavioral constraints or limitations persist despite

arXiv:2604.12167v1 Announce Type: new Abstract: We present (Experience-Modulated Biologically-inspired Emergent Reasoning), a hybrid cognitive architecture that reorganises the relationship between la

arXiv:2512.10226v2 Announce Type: replace Abstract: Recent Vision-Language-Action (VLA) models for autonomous driving explore inference-time reasoning as a way to improve driving performance and safet

arXiv:2604.11600v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress but continue to struggle with geometric reasoning, primarily due to the perce

arXiv:2604.10087v1 Announce Type: new Abstract: We present EL-DRUIN, an ontological reasoning system for geopolitical intelligence analysis that combines formal ontology, finite semigroup algebra, and

arXiv:2604.09029v1 Announce Type: cross Abstract: Large language models have been widely explored as decision-support tools in high-stakes domains due to their contextual understanding and reasoning c

A Reddit post on r/ChatGPT in which the author shares their findings after a year of testing and comparing LLM subscription services across different price tiers (e.g., 20/month, higher-cost tiers). T

Auto-generated index of all models mentioned across the wiki.

Auto-generated index of all tools mentioned across the wiki.

arXiv:2509.23322v2 Announce Type: replace Abstract: With the continuous expansion of Large Language Models (LLMs) and advances in reinforcement learning, LLMs have demonstrated exceptional reasoning c

arXiv:2604.06374v1 Announce Type: cross Abstract: Latent reasoning via continuous chain-of-thoughts (Latent CoT) has emerged as a promising alternative to discrete CoT reasoning. Operating in continuo

arXiv:2610.17309v1 Announce Type: cross Abstract: We introduce StartLux-Decision, decision models from 0.8B to 27B parameters that combine efficient single-pass prediction with adaptive, confidence-gu

arXiv:2409.19979v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated prominent reasoning capabilities in recommendation tasks by transforming them into text-generat

arXiv:2510.12680v2 Announce Type: replace Abstract: Hybrid thinking enables LLMs to switch between reasoning and direct answering, offering a balance between efficiency and reasoning capability. Yet o

arXiv:2603.13856v3 Announce Type: replace-cross Abstract: Building AI systems that can plan, act, and create in the physical world requires more than pattern recognition. Such systems must reason abou

arXiv:2610.02684v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly explored for clinical reasoning, but whether they appropriately revise judgments as patient evidence evolv

arXiv:2610.02632v1 Announce Type: new Abstract: As large language models are increasingly deployed for multi-step reasoning, verifying the correctness of their outputs has become essential for maintai

arXiv:2610.00333v1 Announce Type: cross Abstract: Multimodal on-policy distillation (OPD) aims to improve visual grounding while preserving the strong reasoning capabilities of language models. Recent

arXiv:2609.37326v1 Announce Type: new Abstract: On-policy distillation, where a student learns from a stronger teacher's feedback on its own outputs, is a common way to pass reasoning to smaller model