Search: “reasoning”

arXiv:2608.20379v1 Announce Type: new Abstract: Advances in large language models (LLMs) have fueled a wave of research into agency: the ability to reason, plan, and act. This effort has produced agen

arXiv:2608.20920v1 Announce Type: new Abstract: Open-web future event prediction requires agents to distill reliable signals from noisy, redundant, and incomplete evidence. Existing retrieval/memory m

arXiv:2608.20549v1 Announce Type: new Abstract: Advances in multimodal large language models (MLLMs) are extending radiological artificial intelligence (AI) beyond task-specific image analysis toward

I was fascinated by Nanbeige's outstanding performance for its size, so I started digging into how much a model can improve its own representation just by looping over itself (for fun). My prototype w

Release: llm 0.33 My highlights from this release: Upgraded to the OpenAI Python library 3.x and switched the HTTP client dependency from httpx to httpx2. #1608, #1631 I shipped a quick 0.32.1 fix for

Hi, I have been using opencode with openrouter for quite a while now. Having read the success stories of using Qwen3.8-27B, I thought of trying it too. But every attempt has been a failure for me so f

Right now standard CoT is a one-way street. You generate forward, dump a pile of scratchpad tokens into the KV cache, and pray the model doesn’t hallucinate halfway through. On phones/laptops that cre

arXiv:2608.20316v1 Announce Type: new Abstract: Heterogeneous AI systems composed of multiple models, architectures, harnesses, or inference-time settings can improve quality and efficiency by routing

arXiv:2608.19737v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) have achieved remarkable progress in video understanding and reasoning. Despite extensive studies on text- and im

arXiv:2608.18103v1 Announce Type: cross Abstract: Background: Mechanistic elucidation of traditional Chinese medicine (TCM) compound formulas remains a central challenge in the modernization of TCM. C

arXiv:2608.18165v1 Announce Type: new Abstract: Knowledge graphs modeled in RDF are powerful for describing static knowledge, but they cannot capture or reason about the dynamic behavior of physical s

arXiv:2608.18682v1 Announce Type: new Abstract: Training multi-turn agentic workflows with reinforcement learning (RL) enables large language models to perform complex reasoning, use external tools, a

arXiv:2608.18628v1 Announce Type: cross Abstract: Aligned vision-language models (VLMs) are designed to balance grounded visual reasoning with safe generation behavior. However, we observe a striking

arXiv:2608.17536v1 Announce Type: cross Abstract: Legal consultation questions exhibit multi-level complexity. A single retrieval strategy often leads to over-reasoning for simple questions and poor i

arXiv:2608.17950v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate remarkable multi-hop reasoning capabilities over long contexts, yet the internal mechanisms enabling these dist

arXiv:2608.17535v1 Announce Type: new Abstract: Simultaneously reconstructing and understanding 3D environments is essential for embodied agents. Toward this goal, feed-forward semantic 3D Gaussian Sp

arXiv:2502.13428v2 Announce Type: replace-cross Abstract: This work investigates how to improve large language model (LLM)-based reasoning for knowledge base question answering (KBQA) via Monte Carlo

arXiv:2608.17741v1 Announce Type: new Abstract: OWL 2 DL ontologies, grounded in the description logic SROIQ, express large knowledge bases in biomedicine and the Semantic Web. Neuro-symbolic (NeSy) l

arXiv:2608.17129v1 Announce Type: new Abstract: Vision-language Models (VLMs) excel at 2D grounding, spatial reasoning and agentic tool-based planning in static scenes. However, consider asking a home

arXiv:2608.14585v1 Announce Type: new Abstract: Euclidean geometry is a compelling testbed for AI reasoning, as it demands the combination of intuitive diagram understanding, axiomatic deduction, and

arXiv:2608.15703v1 Announce Type: new Abstract: Large language model (LLM) agents often perform poorly on complex, long-horizon tasks because their context becomes increasingly cluttered over time. As

arXiv:2509.20357v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) trains language models to use long chain-of-thought reasoning (CoT) in domains like mathematic

arXiv:2608.16072v1 Announce Type: cross Abstract: Reinforcement learning (RL) with group-relative advantages has become the de facto standard for post-training language model reasoners. However, when

This post was made with AI. I tried to remove as much slop as possible and keep it straight to the point to save your time as I know how annoying AI slop posts can be, but I still wanted to retain all

arXiv:2608.13698v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR), often optimized with Group Relative Policy Optimization (GRPO), has become a central recipe for i

arXiv:2608.14094v1 Announce Type: cross Abstract: Cloud-local LLM inference systems have the potential to use the reasoning capability of large cloud models while protecting sensitive user data on per

arXiv:2506.01793v2 Announce Type: replace Abstract: Most current evaluations of foundation models focus on objective benchmarks, such as knowledge coverage and reasoning accuracy, often overlooking us

arXiv:2608.14104v1 Announce Type: cross Abstract: The Shapes Constraint Language (SHACL) is a W3C recommendation to express syntactic constraints, called shapes, on RDF graphs. SHACL validators are us

arXiv:2608.14498v1 Announce Type: new Abstract: Vision-language models (VLMs) enable embodied agents to reason and act from visual observations and language instructions. Reinforcement learning (RL) p

DeepSeek V4 Pro (Aug 2026) has been released on the Together AI platform. The model employs a 1.6‑terabyte Mixture‑of‑Experts architecture with a 1‑million‑token context window and supports three dist

arXiv:2608.13441v1 Announce Type: new Abstract: Although multimodal large language models (MLLMs) have shown substantial potential in visual understanding and graphic code generation, editing scientif

arXiv:2608.13113v1 Announce Type: cross Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have led to substantial progress in video understanding, accompanied by a growing number o

arXiv:2608.13420v1 Announce Type: new Abstract: Embodied intelligent virtual agents are expected to operate as persistent, adaptive, and context-aware entities within complex virtual and Metaverse wor

arXiv:2608.13006v1 Announce Type: new Abstract: Multi-hop retrieval must recover passages that provide sufficient evidence together. An initial passage often resolves an entity or relation implicit in

arXiv:2608.12762v1 Announce Type: new Abstract: Schedulability analysis is essential for certifying real-time systems, but existing tests are often developed through pen-and-paper proofs that are diff

arXiv:2608.11420v1 Announce Type: new Abstract: Medical diagnostic reasoning is a high-impact use case for LLMs that carries significant implications for the health and wellbeing of users. When OpenAI

arXiv:2604.02512v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly exhibit human-like patterns of pragmatic and social reasoning. This paper addresses two related ques

arXiv:2608.11829v1 Announce Type: cross Abstract: On-policy distillation (OPD) has emerged as a promising post-training technique for enhancing LLM reasoning. It is commonly believed to enable the stu

arXiv:2608.10916v1 Announce Type: cross Abstract: Autoformalisation (AF) systems map natural language reasoning steps into formal statements in a proof assistant such as Lean. We consider how to asses

arXiv:2608.10315v1 Announce Type: cross Abstract: Large language models (LLMs) are powerful black-box systems, making it difficult to discern whether their answers reflect stable internal beliefs or s

arXiv:2608.10448v1 Announce Type: new Abstract: Multimodal emotion recognition in conversation (MERC) requires understanding complex interactions between verbal and non-verbal cues. However, most exis

arXiv:2608.10810v1 Announce Type: cross Abstract: Emotion understanding in discourse requires reasoning beyond surface sentiment because speakers often convey affect through indirect, implicit, polite

arXiv:2608.08814v1 Announce Type: cross Abstract: We present 360CityArena, a benchmark for evaluating the urban exploration capabilities of embodied agents within a photorealistic environment construc

arXiv:2608.07637v1 Announce Type: new Abstract: Long-running molecular simulation campaigns require repeated continuation from saved states, provenance-aware progression, adaptive assessment, and occa

arXiv:2608.08281v1 Announce Type: new Abstract: Recently, Large Language Models (LLMs) have shown considerable capability for situational understanding, reasoning, and decision making in different dom

arXiv:2608.08764v1 Announce Type: cross Abstract: On-policy self-distillation improves language-model reasoning by querying a teacher on states actually visited by the student. Recent methods create a