Search: “reasoning”

arXiv:2609.20843v1 Announce Type: new Abstract: Knowledge graph question answering (KGQA) enables models to answer natural-language questions through structured graph reasoning and has achieved substa

arXiv:2609.19883v1 Announce Type: cross Abstract: Characterizing LLM reasoning remains an open challenge, as many existing benchmarks isolate specific reasoning skills, rely on external knowledge, or

arXiv:2602.03994v3 Announce Type: replace-cross Abstract: Chain-of-thought (CoT) prompting is widely used as a reasoning aid and is often treated as a transparency mechanism. Yet behavioral gains unde

arXiv:2609.18581v1 Announce Type: new Abstract: Although vision-language models (VLMs) possess strong visual understanding and reasoning capabilities, existing vision-and-language navigation (VLN) age

arXiv:2603.21676v2 Announce Type: replace-cross Abstract: Standard Transformers have a fixed computational depth, limiting their ability to generalize to tasks that require variable-depth reasoning. T

arXiv:2601.19847v3 Announce Type: replace Abstract: Despite the strong reasoning capabilities of recent large language models (LLMs), achieving reliable performance on challenging tasks often requires

arXiv:2603.18614v2 Announce Type: replace Abstract: Tool-augmented large language models (LLMs) must tightly couple multi-step reasoning with external actions, yet existing benchmarks often confound t

arXiv:2609.12509v1 Announce Type: new Abstract: Evaluation of vision-language models (VLMs) for multimodal mathematical reasoning remains limited for low-resource languages and for geometry problems t

arXiv:2609.07483v1 Announce Type: new Abstract: Modus Tollens (MT) is a classical logical inference rule, while counterfactuals are hypothetical statements that are contrary to facts, and counterfactu

arXiv:2609.09030v1 Announce Type: cross Abstract: Chain-of-thought reasoning provides a structured computation between a model's input and final answer. Yet it is often evaluated through endpoint accu

arXiv:2609.05902v1 Announce Type: new Abstract: Recent image generation systems increasingly combine multimodal understanding, reasoning, and synthesis, suggesting that they may do more than render pl

arXiv:2609.06004v1 Announce Type: new Abstract: Quantitative spatial reasoning in visual-language models (VLMs) aims to infer spatial distances and directional relationships among objects in 3D space

Its chat template has this line: {%- set resolved_reasoning_effort = reasoning_effort|default('xhigh') %} xhigh is the most expensive of its three settings (low / medium / xhigh). If you never set one

arXiv:2609.05090v1 Announce Type: new Abstract: Evaluation of medical artificial intelligence agents remains predominantly answer-centric, assessing only the correctness of final outputs while overloo

arXiv:2609.04963v1 Announce Type: new Abstract: Reasoning allows artificial intelligence models to revisit and correct their mistakes, enabling recent frontier advances in mathematical theorem solving

TL;DR My TAK quant of Qwen 3.8 27b scored 82.81% on reasoning, comparted with 77.34% for the byte matched Unsloth UD IQ2_S and 83.59% for BF16. Edit: Some of you have tried coding with this reasoning-

arXiv:2609.03892v1 Announce Type: cross Abstract: 3D spatial reasoning underpins understanding and acting in the physical world, yet it remains unreliable in current multimodal large language models (

arXiv:2508.00669v2 Announce Type: replace-cross Abstract: The proliferation of Large Language Models (LLMs) in medicine has enabled impressive capabilities, yet a critical gap remains in their ability

arXiv:2609.03729v1 Announce Type: new Abstract: Despite the remarkable prowess of Vision-Language Models (VLMs) in general multimodal tasks, they remain fundamentally ``flat'' when reasoning about the

arXiv:2609.02336v1 Announce Type: new Abstract: Effective in-context learning (ICL) for complex reasoning relies on selecting the right demonstrations. Traditional retrieval methods based on surface s

arXiv:2505.23713v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in socially grounded applications, where success requires interpreting context, inferring oth

arXiv:2603.26737v2 Announce Type: replace-cross Abstract: Current multimodal LLMs encode images as static visual prefixes and rely on text-based reasoning, lacking goal-driven and adaptive visual acce

arXiv:2606.23181v2 Announce Type: replace Abstract: Hybrid reasoning models can answer directly or spend extra tokens on extended thinking. A practical router should choose between these modes for eac

arXiv:2606.15872v2 Announce Type: replace Abstract: Frontier scientific reasoning remains a major challenge for large language models (LLMs), where even the strongest commercial systems fall short of

arXiv:2608.27501v1 Announce Type: new Abstract: Mathematical reasoning has seen rapid progress in large language models (LLMs), yet existing methods optimize predominantly for final-answer correctness

arXiv:2511.14684v2 Announce Type: replace Abstract: Large language models (LLMs) often make reasoning errors when solving mathematical problems, and how to automatically detect and correct these error

arXiv:2608.23956v1 Announce Type: new Abstract: Test-time reasoning methods such as iterative refinement, decomposition, and repeated sampling are often evaluated in isolation, making their gains diff

arXiv:2412.10390v2 Announce Type: replace Abstract: Knowledge graph reasoning is pivotal in various domains such as data mining, artificial intelligence, the Web, and social sciences. These knowledge

arXiv:2506.10903v2 Announce Type: replace Abstract: Statement autoformalization plays a crucial role in formal mathematical reasoning by enabling the automatic translation of natural language statemen

arXiv:2608.21170v1 Announce Type: cross Abstract: Vision-language models (VLMs) have advanced rapidly in multimodal reasoning, yet recent work shows that their failures often reflect an interaction be

arXiv:2608.19794v1 Announce Type: new Abstract: The convergence of large language models (LLMs), structured knowledge bases (KBs), and reasoning ability (RA) presents a promising trajectory toward gen

arXiv:2608.18734v1 Announce Type: new Abstract: 4D understanding and reasoning is a fundamental capability for embodied AI agents operating in dynamic physical environments. However, existing vision e

arXiv:2608.18534v1 Announce Type: new Abstract: Large language models are increasingly used to support financial operations, but their apparent reasoning performance can depend on whether they receive

arXiv:2608.17253v1 Announce Type: cross Abstract: Reinforcement learning (RL) has emerged as a powerful approach for improving reasoning in language and vision-language models, yet its strongest succe

arXiv:2608.14610v1 Announce Type: new Abstract: Legal reasoning tasks such as legal judgment prediction (LJP) require identifying the temporally correct version of the law governing a case -- a capabi

arXiv:2608.14003v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) achieve strong performance on complex tasks through extended chain-of-thought generation, but incur substantial computatio

arXiv:2608.14015v1 Announce Type: cross Abstract: Understanding tens-of-minutes surgical videos requires long-horizon temporal reasoning, answering what happens before, after, or across stages of a pr

arXiv:2608.12374v1 Announce Type: cross Abstract: Language models (LMs) struggle with logical tasks like reasoning on syllogisms. It has been shown that Knowledge Representation (KR) plays a crucial r

arXiv:2608.13344v1 Announce Type: new Abstract: Long-horizon Earth observation reasoning requires models to organize multi-stage geographic evolution, localize spatial changes, detect temporal anomali

arXiv:2508.17905v2 Announce Type: replace Abstract: Unified Structured Knowledge Reasoning (USKR) aims to answer natural language questions by using structured sources such as tables, databases, and k

arXiv:2608.11691v1 Announce Type: cross Abstract: Reinforcement-learning (RL) post-training equips multimodal large reasoning models (MLRMs) with exploratory chains of thought (CoT), substantially imp

Just downloaded the model, UD-Q5_K_XL quant, asked it to generate a long story to test out reasoning and speed with dflash (super fast btw, ~ 90 to 160 tok/s on a 5090 depending on task) and was surpr

arXiv:2608.04771v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) excel on complex tasks through long chain-of-thought (CoT) reasoning, but their lengthy intermediate steps cause severe ov

arXiv:2608.04759v1 Announce Type: cross Abstract: Although Multimodal Large Language Models (MLLMs) have made substantial progress, their spatial reasoning may still produce intermediate judgments inc

arXiv:2608.01292v1 Announce Type: new Abstract: Legal reasoning is inherently jurisdiction-dependent: the same facts can call for different legal rules and yield different conclusions across legal sys

arXiv:2608.00533v1 Announce Type: new Abstract: Large Language Models have achieved substantial progress in reasoning capabilities. Yet in low-resource native settings, many suffer from cross-lingual