Search: “reasoning”

arXiv:2610.04950v1 Announce Type: new Abstract: On-policy distillation (OPD) improves the reasoning capabilities of small language models through token-level teacher supervision on student-generated t

arXiv:2610.03792v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) has produced large reasoning gains in language models, and verifiable video benchmarks make it app

arXiv:2610.03176v1 Announce Type: new Abstract: We study hindsight-guided distillation for rare disease diagnosis on ZebraMap: a 1.5B student is fine-tuned on chain-of-thought traces from a 8B teacher

arXiv:2610.03296v1 Announce Type: new Abstract: Complex reasoning queries can be decomposed into directed acyclic task graphs and distributed across heterogeneous LLMs, reducing latency through parall

arXiv:2610.02979v1 Announce Type: new Abstract: Unstructured discharge notes in Electronic Health Records (EHRs) often carry signal complementary to structured medical codes, holding patient-specific

arXiv:2610.02876v1 Announce Type: new Abstract: A multimodal large language model that correctly reports a spatial fact does not necessarily use that fact in subsequent reasoning. To study this distin

arXiv:2610.01834v1 Announce Type: new Abstract: Judgment models such as jev{} return, in a single call and without reasoning text, a probability for each described option. This makes them attractive a

arXiv:2610.01236v1 Announce Type: new Abstract: Collaboration between a small language model (SLM) and a large language model (LLM) offers an opportunity to combine the efficiency of smaller models wi

arXiv:2610.00872v1 Announce Type: new Abstract: Long-term memory systems for large language models (LLMs) have gained popularity for extending reasoning capabilities across applications. Current memor

arXiv:2609.38342v1 Announce Type: new Abstract: On-policy self-distillation uses a model as its own teacher to provide dense supervision for reasoning, often through reference-solution conditioning. P

arXiv:2609.38931v1 Announce Type: cross Abstract: Self-consistency samples many reasoning trajectories and aggregates their final answers, treating the LLM as a black box that returns one answer per t

arXiv:2609.38406v1 Announce Type: new Abstract: Access to real-world information is often noisy and fragmented. Constructing a coherent narrative from such fragments requires models to reconstruct mis

arXiv:2609.39363v1 Announce Type: cross Abstract: Multi-image understanding requires MLLMs not only to recognise the content of individual images, but also to organise visual evidence distributed acro

arXiv:2609.35779v1 Announce Type: cross Abstract: Given recent achievements of large language models (LLMs), frontier models are expected to perform well on Bayesian reasoning tasks, at least as well

arXiv:2609.37351v1 Announce Type: cross Abstract: Test-time compute scaling has emerged as a cornerstone of advanced machine reasoning, yet performing iterative deliberation directly within continuous

arXiv:2609.35699v1 Announce Type: new Abstract: Distillation attacks copy the reasoning capabilities of closed-source large language models, allowing bad actors to replicate state-of-the-art performan

arXiv:2609.30489v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated historic breakthroughs in general reasoning with early successes in biomedical science. However, existing

arXiv:2409.18486v5 Announce Type: replace Abstract: This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, sp

arXiv:2609.30130v1 Announce Type: cross Abstract: Current vision-language models (VLMs) excel at visual content understanding and text-based reasoning, yet their structure limits the advancement of in

arXiv:2609.22158v1 Announce Type: new Abstract: Key-value (KV) caching is essential for efficient autoregressive large language model (LLM) inference, but the cache grows linearly with context length,

arXiv:2409.09253v2 Announce Type: replace-cross Abstract: Owing to the unprecedented capability in semantic understanding and logical reasoning, large language models (LLMs) have shown fantastic poten

arXiv:2609.16919v1 Announce Type: new Abstract: This paper introduces NeuroSymbEAD, a large-scale neuro-symbolic caption dataset featuring an ego-centric knowledge graph (KG) of static and dynamic obj

arXiv:2609.15849v1 Announce Type: cross Abstract: Can LLMs reason through new information like humans, or do they merely retrieve cached opinions? This is critical for silicon sampling, where LLM pers

arXiv:2512.09944v4 Announce Type: replace Abstract: Echocardiography interpretation requires integrating multi-view temporal evidence with quantitative measurements and guideline-grounded reasoning, y

arXiv:2609.15972v1 Announce Type: new Abstract: As language models become more capable, long-term collaboration in learning, reasoning, and decision-making calls for a deeper understanding of the peop

arXiv:2609.14533v1 Announce Type: cross Abstract: Automated theorem proving seeks to use computational systems to prove or disprove mathematical and logical statements [1, 2]. It underpins a wide rang

arXiv:2609.14473v1 Announce Type: new Abstract: Personal AI assistants hold the potential to evolve from digital interfaces into embodied companions capable of guiding users through complex physical a

arXiv:2609.14412v1 Announce Type: new Abstract: Deep research agents answer complex questions through iterative loops of searching, reading, and reasoning. Recent work on reasoning-intensive benchmark

arXiv:2609.13997v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has shown remarkable success in improving the mathematical reasoning of large language models. Yet

arXiv:2609.13422v1 Announce Type: new Abstract: LLM judges are increasingly used to evaluate and improve AI-generated outputs, yet their reliability for complex professional work remains unclear. We s

arXiv:2609.12162v1 Announce Type: cross Abstract: Draft-verify-revise is a common LLM orchestration pattern for scaling inference-time compute. One LLM drafts, a second critiques the draft and provide

arXiv:2609.13060v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) offer promising parallel generation capabilities but lag behind autoregressive models in complex reasoning and tool-use

arXiv:2609.12243v1 Announce Type: new Abstract: Inference-time power sampling via Sequential Monte Carlo (SMC) can substantially improve large language model (LLM) reasoning without requiring post-tra

arXiv:2602.15329v2 Announce Type: replace Abstract: Online video understanding requires models to perform continuous perception and long-range reasoning within potentially infinite visual streams. Its

arXiv:2609.11085v1 Announce Type: cross Abstract: Neurosymbolic systems rely on mathematical solvers to guarantee reasoning correctness, yet solvers are fundamentally blind to whether a formal transla

arXiv:2609.09203v1 Announce Type: new Abstract: Existing benchmarks for autonomous AI scientists evaluate only final outputs---generated code, hypotheses, or papers---yet discard the reasoning process

arXiv:2602.00276v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated strong reasoning capabilities on math and coding, but frequently fail on symbolic classical planning

arXiv:2609.08271v1 Announce Type: new Abstract: In various data models, the classical triple is a typical semantic data model. However, due to the design of the triple as a simple structure for repres

arXiv:2511.09057v4 Announce Type: replace Abstract: A world model is a cognitive simulator of the real-world environment allowing biological agents to reason about how the world evolves, whether spont

arXiv:2609.07937v1 Announce Type: new Abstract: Structured visual reasoning, such as image puzzles, demands fine-grained visual perception, an ability current Vision Language Models (VLMs) lack. VLMs

arXiv:2609.03619v1 Announce Type: cross Abstract: Multi-agent debate (MAD) improves the reasoning capabilities of large language models by having multiple agents iteratively refine their responses thr

arXiv:2609.03395v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong performance on table question answering, yet their accuracy often degrades as table size increases. We

arXiv:2505.22451v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) have made significant progress in mathematical capabilities in recent times. However, these successes have been primar

arXiv:2607.10110v2 Announce Type: replace Abstract: Recent work on looped language models suggests that many reasoning problems benefit from greater computational depth rather than from additional ind

arXiv:2603.18579v2 Announce Type: replace-cross Abstract: Evaluating whether explanations faithfully reflect a model's reasoning remains an open problem. Existing benchmarks use single interventions w

arXiv:2609.01658v1 Announce Type: cross Abstract: Retrieval-Augmented Generation enhances Large Language Models by grounding responses in external knowledge, but multi-hop reasoning remains vulnerable