Knowledge catalogue
arXiv:2608.08168v1 Announce Type: new Abstract: While Large Language Models (LLMs) employing Chain-of-Thought (CoT) exhibit superior reasoning capabilities, the neural mechanisms distinguishing this e
arXiv:2608.08020v1 Announce Type: new Abstract: Test-time compute scaling is a primary driver of performance in large reasoning models (LRMs), but extreme inefficiency bounds current approaches, shift
arXiv:2608.06741v1 Announce Type: new Abstract: Reasoning in large language models (LLMs) is often grounded in human text, human demonstrations, and human-generated rationales. For equilibrium reasoni
arXiv:2511.01295v3 Announce Type: replace Abstract: Recent advances in multi-modal generative models have driven substantial improvements in image editing. However, current generative models still str
arXiv:2608.05757v1 Announce Type: new Abstract: Whole-slide image (WSI) reasoning requires an agent to sequentially acquire visual evidence before answering a diagnostic question. Existing training-fr
arXiv:2608.05631v1 Announce Type: new Abstract: Multimodal large language models excel at passive perception but struggle with complex visual cognitive tasks requiring multi-step temporal reasoning. T
arXiv:2608.05541v1 Announce Type: new Abstract: Evolution Strategy (ES) is a promising alternative to gradient-based fine-tuning for resource-constrained Large Language Model (LLM) reasoning. However,
arXiv:2608.05583v1 Announce Type: cross Abstract: As large language models (LLMs) enter high-stakes domains such as healthcare, understanding their moral reasoning becomes essential. Decisions about s
arXiv:2608.05124v1 Announce Type: cross Abstract: Long context reasoning in large language models (LLMs) is usually constrained by the fact that a single inference trajectory has to simultaneously exp
OpenAI has released GPT‑5.6 Sol, which powers both instant and deep‑reasoning modes for ChatGPT Plus and Pro customers, delivering fact‑centric responses. Free and Go tier users will receive unlimited
arXiv:2604.00513v3 Announce Type: replace-cross Abstract: With the rapid growth of e-commerce, exploring general representations rather than task-specific ones has attracted increasing attention. Alth
arXiv:2608.04575v1 Announce Type: cross Abstract: Reliable physical reasoning from video requires understanding how objects move, interact, and respond to interventions. Existing vision-language model
arXiv:2608.02011v2 Announce Type: replace Abstract: Agentic retrieval-augmented generation (RAG) systems can fail before evidence-conditioned reasoning is tested: an agent may retrieve candidate snipp
Big new release of my LLM CLI tool and Python library for talking to hundreds of different LLMs - reasoning traces, OpenAI Responses support, server-side tools, smarter logging and a whole lot more ht
arXiv:2608.03673v1 Announce Type: new Abstract: Many critical reasoning tasks, including clinical diagnosis, legal judgment, and industrial fault diagnosis, require step-dependent causal chains in whi
arXiv:2608.01755v2 Announce Type: replace Abstract: Recent Vision-Language-Action (VLA) models for autonomous driving (AD) increasingly utilize chain-of-thought (CoT) supervision to enhance the reason
arXiv:2606.19625v2 Announce Type: replace Abstract: We use training-data attribution as an interpretable tool for capability discovery, mapping which regions of the pretraining corpus support social-r
arXiv:2608.01559v1 Announce Type: cross Abstract: Adversarial self-play is an appealing recipe for legal reasoning: have a student model draft an argument, have an adversary attack it, and reward the
arXiv:2608.01664v1 Announce Type: new Abstract: We present our ImageCLEF 2026 Multimodal Reasoning system for the Visual Multiple Choice Question Answering (Visual MCQ) and Visual Open Question Answer
arXiv:2608.01709v1 Announce Type: new Abstract: Vision-language models (VLMs) achieve strong semantic understanding but remain unreliable in metric spatial reasoning, particularly when queries require
arXiv:2607.28677v1 Announce Type: new Abstract: LLM now pass medical licensing examinations and, in curated cases, can rival physicians at diagnostic reasoning. These developments have accelerated the
arXiv:2607.27770v1 Announce Type: new Abstract: A single reinforcement-learning run can produce a strong reasoner yet an incomplete teacher: it often amplifies only a subset of the valid solution mode
arXiv:2607.27564v1 Announce Type: new Abstract: Multi-image medical VQA is not merely a prompt-length problem; it is a fundamental challenge of agentic decision-making. Medical vision-language agents
arXiv:2505.22039v2 Announce Type: replace Abstract: While anomaly detection has made significant progress, generating detailed analyses that incorporate industrial knowledge remains a challenge. To ad
arXiv:2607.25369v1 Announce Type: new Abstract: Agentic systems have rapidly advanced in their ability to interact with real-world environments, leverage external tools, and provide services for users
arXiv:2607.22555v1 Announce Type: new Abstract: Medical diagnosis is a multi-stage process: extract facts, consult knowledge, generate a differential analysis, and select the best diagnosis with expla
arXiv:2602.05547v2 Announce Type: replace-cross Abstract: RL-based post-training with GRPO is widely used to improve large language models on individual reasoning tasks. However, real-world deployment
arXiv:2607.14178v2 Announce Type: replace Abstract: Recent advances in Large Language Models have fueled autonomous AI agents capable of tackling complex scientific tasks, yet existing automated resea
arXiv:2607.23440v1 Announce Type: cross Abstract: In this paper, we push the boundary of LLM reasoning by testing them in a Chinese language game, xiehouyu, with novel xiehouyu created by linguists th
arXiv:2607.22574v1 Announce Type: new Abstract: Evidence-based clinical decision making requires specialists to identify, evaluate and synthesize relevant scientific literature. However, PubMed search
arXiv:2607.22314v1 Announce Type: new Abstract: Multiple Sequence Alignments (MSAs) provide protein language models with explicit evolutionary context, but their large depth makes subsampling unavoida
arXiv:2607.21185v1 Announce Type: new Abstract: Neurosymbolic (NeSy) systems integrate neural networks with logical reasoning to achieve both generalization and interpretability, but recent work has s
arXiv:2607.21412v1 Announce Type: new Abstract: Large Language Models (LLMs) excel at natural language understanding and generation but remain unreliable for multi-step logical reasoning, especially i
arXiv:2605.11936v2 Announce Type: replace Abstract: Recent soft prompt research has tried to improve reasoning by inserting trained vectors into LLM inputs, yet whether the gain comes from the learned
arXiv:2607.09999v2 Announce Type: replace Abstract: We show that post-training quantization can silently alter how large language models reason even when task accuracy is preserved. Using a six-catego
arXiv:2607.19194v2 Announce Type: cross Abstract: High-level planning for autonomous driving is a knowledge-intensive engineering decision task that requires accurate scene understanding, timely infer
arXiv:2607.18664v1 Announce Type: new Abstract: Video generation models achieve high visual quality but often struggle to generate physics-aware videos. Unlike rigid-body motion, which can be describe
arXiv:2607.19358v1 Announce Type: new Abstract: Recent advances in long chain-of-thought reasoning models such as DeepSeek-R1 have led to increasingly longer inference context lengths under the test-t
arXiv:2607.20268v1 Announce Type: new Abstract: While Large Language Models (LLMs) excel at many tasks, they frequently struggle with complex reasoning that requires long-horizon planning and iterativ
arXiv:2510.13903v2 Announce Type: replace-cross Abstract: Chain-of-thought prompting has popularized step-by-step reasoning in large language models, yet model performance still degrades as problem co
arXiv:2607.13558v1 Announce Type: new Abstract: Urban region profiling constitutes a core problem in urban computing, supporting applications such as population estimation, economic assessment, and en
arXiv:2607.11192v2 Announce Type: replace Abstract: A large share of day-to-day work in professional domains happens inside PDF files: benefits packets, leases, datasheets, clinical guidelines, constr
arXiv:2607.12177v1 Announce Type: new Abstract: The analysis of satellite and aerial imagery has entered a new era with the advent of foundation models. This paper describes the concept of Geospatial
arXiv:2607.08724v1 Announce Type: new Abstract: Human decision-making is highly flexible -- some actions are taken immediately; others require longer deliberation. Language models have exhibited a sim
arXiv:2607.07708v1 Announce Type: cross Abstract: Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge
arXiv:2607.07507v1 Announce Type: cross Abstract: Hallucinations in vision language models (VLMs) are commonly treated as semantic errors, yet they often arise from partial or ambiguous visual evidenc
arXiv:2607.06974v1 Announce Type: new Abstract: Large language models (LLMs) increasingly improve their reasoning at test time via additional computation, yet most existing works treat each problem in
arXiv:2511.13726v2 Announce Type: replace-cross Abstract: We propose RT (Refine Thought), a method that can enhance the semantic reasoning ability of text embedding models. The method obtains the fina
arXiv:2607.07117v1 Announce Type: cross Abstract: In text-to-image in-context learning (T2I-ICL), a model has to infer a latent compositional pattern from fewshot demonstrations for generating a query
arXiv:2607.06720v1 Announce Type: new Abstract: Training large language models (LLMs) with extended reasoning has enabled in-context search, in which models iteratively generate, critique, and revise
arXiv:2607.06447v1 Announce Type: new Abstract: Recent LLM-based mathematical reasoning agents have begun to tackle research-level problems and, in several cases, have contributed to the resolution of
arXiv:2607.02607v1 Announce Type: cross Abstract: Video reasoning requires Large Multimodal Models (LMMs) to remain grounded in dense evidence, yet existing systems largely adopt 'read-once, generate-
arXiv:2512.22867v2 Announce Type: replace Abstract: Socially compliant navigation requires structured reasoning about dynamic pedestrians and physical constraints to ensure safe and interpretable deci
arXiv:2607.02983v1 Announce Type: new Abstract: Recent reasoning-centric Large Language Models (LLMs) have made significant strides, yet they predominantly operate on a passive-inference pattern that
arXiv:2602.13517v2 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated impressive reasoning capabilities by scaling test-time compute via long Chain-of-Thought (CoT). Howev
arXiv:2607.02927v1 Announce Type: cross Abstract: Video understanding is moving beyond closed-context perception toward open-world evidence exploration, a paradigm formalized as Video Deep Research (V
arXiv:2607.01581v1 Announce Type: new Abstract: The capacity of Large Language Models (LLMs) to reason about pedagogical intent within instructional communication remains underexplored, particularly i
arXiv:2607.01440v1 Announce Type: new Abstract: Faithful reasoning is essential in medicine, where clinical decisions require transparent justification grounded in reliable evidence. Current medical L
arXiv:2512.14157v2 Announce Type: replace Abstract: Recent medical MLLMs have made significant progress in generating step-by-step textual reasoning chains. However, they still struggle with complex c
arXiv:2607.02509v1 Announce Type: new Abstract: Understanding and reasoning over long contexts has become a key requirement for deploying large language models (LLMs) in realistic applications. Althou