Search: “reasoning”

arXiv:2609.17921v1 Announce Type: new Abstract: Vision-language model (VLM) agents combine specialized perception, tools, and reasoning to address complex visual tasks. In multi-agent settings, differ

GoBench evaluates LLMs on 9x9 Go games against a ladder of KataGo opponents, from random to superhuman. It measures general reasoning ability, strongly correlates with ARC-AGI 2 (r=0.83 correlation),

arXiv:2609.10221v2 Announce Type: new Abstract: Reinforcement learning over a frozen reasoner has become a common recipe for teaching a policy which external tools to invoke. We show that this recipe

Spent about two weeks convinced I had a prompting problem. My local Qwen3 agent would just stop. No error, no crash, finish_reason came back as 'stop' like the model had decided it was done. Except it

arXiv:2504.00285v2 Announce Type: replace Abstract: Large Language Models (LLMs) are effective at deceiving when prompted to do so. Models that demonstrate better performance on reasoning tasks are al

arXiv:2609.08896v1 Announce Type: cross Abstract: The rapid growth of multimodal data has intensified the need for question answering (QA) systems capable of reasoning across heterogeneous sources suc

I put together a mega write-up on GPT-6 Astra & looped transformers. How looped transformers / recurrent depth works, cost-tradeoffs, whether it hides reasoning traces, with lots of figures and a tour

arXiv:2609.04947v1 Announce Type: cross Abstract: Recently, multimodal large-scale reasoning models have demonstrated remarkable capabilities in solving complex tasks through long Chains-of-Thought (M

Qwen3.8-Flash-Next (UD-Q4_K_XL) on a single RTX 3090 24GB + 128GB DDR4 — is this config optimal? Hardware CPU: Intel Core i5-12600K RAM: 128 GB DDR4 @ 3600 MHz GPU: NVIDIA RTX 3090, 24 GB VRAM OS: Win

arXiv:2602.10009v3 Announce Type: replace Abstract: Large Language Models (LLMs) are unable to reliably reason about specific physical systems. Attempts to imbue LLMs with knowledge of the necessary p

arXiv:2609.03511v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate strong multilingual reasoning performance, yet their robustness to semantics-preserving structural variation re

arXiv:2606.19264v2 Announce Type: replace-cross Abstract: The knowledge encoded in large language models (LLMs) can serve as a substrate for structured reasoning over variables describing a complex wo

I got access to GPT-6 Astra this afternoon, so naturally I used it to generate SVGs of pelicans riding bicycles - at low, medium, high, xhigh and max reasoning levels (Astra doesn't support reasoning=

arXiv:2608.29632v1 Announce Type: cross Abstract: Competitive programming is increasingly being used to evaluate the algorithmic reasoning capabilities of large language models (LLMs). However, existi

arXiv:2511.04093v2 Announce Type: replace Abstract: Large language models (LLMs) excel at reasoning but struggle with knowledge-intensive questions due to limited context and parametric knowledge. How

arXiv:2609.00012v1 Announce Type: new Abstract: Long-horizon tasks remain uncommon in large language model (LLM) evaluation, and for a reason: when each step depends on the last, per-step accuracy tha

arXiv:2608.30556v1 Announce Type: new Abstract: Path-finding over knowledge graphs has become an effective way to ground LLM reasoning on multi-hop questions. However, biomedical QA introduces two dis

arXiv:2608.30959v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) still struggle on tasks requiring complex visual understanding. We argue that the core issue is not high-level reasoning

arXiv:2608.28660v1 Announce Type: cross Abstract: Test-time scaling (TTS) improves language model reasoning by allocating additional test-time compute, but prior work mainly studies closed-ended tasks

arXiv:2608.29411v1 Announce Type: new Abstract: Integer sequences from the On-Line Encyclopedia of Integer Sequences (OEIS) are increasingly used to benchmark mathematical reasoning in language models

arXiv:2608.28481v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have demonstrated strong capabilities in natural language understanding and mathematical reasoning. Howe

arXiv:2608.25630v1 Announce Type: new Abstract: Volumetric medical VQA requires reasoning over long and redundant 3D visual token sequences, especially in multi-sequence MRI where complementary modali

arXiv:2602.19001v2 Announce Type: replace Abstract: As large language models increasingly power personal assistants, users expect them to reason over multimodal life histories, from recognizing people

arXiv:2606.18699v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown impressive capabilities across diverse tasks, yet their performance on jurisdiction-specific legal rea

I ran 11 uncensored variants of Gemma 4 12B that I grabbed from huggingface, sorting by downloads. 10 full abliterations plus 2 LoRA adapters which were requested to be added in the comparison, agains

arXiv:2608.21883v1 Announce Type: new Abstract: Multimodal large reasoning models often rely on long Chain-of-Thought (CoT) traces in which a substantial fraction of tokens, such as repeated visual de

New/Old benchmark that provides a lot of answers for local LLM. I present to you a new test that I developed somewhat by accident: https://huggingface.co/cHunter789/Qwen3.8-27B-i1-IQ4_KS_KT-GGUF/tree/

Row-Bot v4.8.0 is now available. This release adds provider-aware reasoning controls, letting each chat keep a valid reasoning choice for the exact model in use. Depending on model support, you can se

arXiv:2608.19029v1 Announce Type: new Abstract: Accurate and responsible medical question answering (QA) is important in healthcare, where complex cases require factual knowledge and nuanced reasoning

arXiv:2608.18671v1 Announce Type: new Abstract: Egocentric video captures activities from the wearer's perspective, providing a direct view of human attention, hand--object interaction, and goal-direc

arXiv:2608.17744v1 Announce Type: new Abstract: Take three frontier mixture-of-experts models (Alibaba, OpenAI, NVIDIA; 3.6-4.0B active parameters each) and fine-tune them to reason in a low-resource

arXiv:2608.14465v1 Announce Type: new Abstract: A frozen language model on reasoning tasks has two coupled weaknesses: it under-uses evidence its own residual stream already encodes, and it fails to d

arXiv:2512.19799v2 Announce Type: replace Abstract: Advances in LLM reasoning and tool use have enabled agentic science, yet frontier theoretical and computational physics remains challenging because

arXiv:2601.11729v2 Announce Type: replace Abstract: Visual Foundation Models (VFMs), such as DINO and CLIP, excel in semantic understanding of images but exhibit limited spatial reasoning capabilities

arXiv:2608.12743v1 Announce Type: new Abstract: Spatial intelligence is becoming a foundation for embodied agents, robotic planning, and multimodal assistants. To improve the spatial reasoning ability

arXiv:2608.11838v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have advanced image geolocalization mainly by improving how they reason about geographic cues. How that reasoni

arXiv:2604.02621v2 Announce Type: replace Abstract: Reinforcement Learning (RL) substantially improves the reasoning capabilities of language models, but most existing RL fine-tuning approaches rely e

arXiv:2608.10725v1 Announce Type: new Abstract: Large language models (LLMs) often rely on shortcuts rather than systematic reasoning, raising safety concerns in medical applications. Allowing models

arXiv:2608.09638v1 Announce Type: new Abstract: Theory of Mind (ToM) is essential for agent interactions, yet existing evaluations either rely on static scenarios that oversimplify mental-state reason

arXiv:2608.07424v1 Announce Type: new Abstract: Test-time scaling is often implemented by spending more compute along one axis: sampling more solutions, extending a chain of thought, or applying a str

arXiv:2608.04928v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning offers a window into the decision-making of large language models (LLMs), which can be monitored for target behaviors b

arXiv:2608.03204v1 Announce Type: cross Abstract: Post-training reinforcement learning (RL) algorithms are commonly used to align large vision-language models (LVLMs) with human intent and the require

arXiv:2608.03450v1 Announce Type: cross Abstract: Reasoning in Multimodal Large Language Models (MLLMs) requires both fine-grained visual perception and rigorous logical deduction. Explicit text-based

arXiv:2608.03034v1 Announce Type: cross Abstract: Reasoning-enhanced large language models have achieved remarkable improvements in planning tasks, yet their deployment in embodied systems remains imp

First of all, obviously I took some help from AI to type this post and this is the topic that enabled me to accomplish all that: https://old.reddit.com/r/LocalLLaMA/comments/1veow4b/deepseek_v4flash_2

arXiv:2608.00110v1 Announce Type: new Abstract: 3D scene understanding requires reasoning about entity existence, spatial layout, and object relations, yet RGB images alone often provide insufficient