Search: “reasoning”

Learning at the Right Pace: Adaptive Data Scheduling Improves LLM Reinforcement Learning

arXiv:2606.22305v2 Announce Type: replace Abstract: Large Language Models (LLMs) achieve remarkable reasoning capabilities through reinforcement learning (RL) post-training. However, existing RL post-

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Recursive Offline Distillation via Entropy Gain Sampling and Answer Mixing

DGX agent

arXiv:2610.20914v1 Announce Type: new Abstract: Distillation of reasoning chains from Large Language Models (LLMs) into their smaller siblings is a standard training approach. Frequently, researchers

researcharxiv-cs-lg

16 Oct 2026

ReFlow: Replay-Driven Decoupling for Efficient RL Post-Training

arXiv:2610.21640v1 Announce Type: new Abstract: Reinforcement learning (RL) has been a critical paradigm for improving large language model (LLM) reasoning and agentic capabilities, but its efficiency

Uncovering Grounding IDs: How External Cues Shape Multi-Modal Binding

arXiv:2509.24072v5 Announce Type: replace-cross Abstract: Large vision-language models (LVLMs) perform well on multimodal tasks, but their ability to reason and precisely align visual and textual info

safetyarxiv-cs-ai

X-RIC: An Agentic RAN-Intelligent Controller Benchmark for Enhancing Reinforcement Learning

arXiv:2610.22269v1 Announce Type: new Abstract: The O-RAN ALLIANCE introduces architectural flexibility through disaggregation and AI-driven control. While the RAN Intelligent Controller (RIC) enables

A Counterfactual Graph Approach to Oppositional Narrative Analysis

arXiv:2603.06135v3 Announce Type: replace-cross Abstract: Distinguishing legitimate critical speech from coordinated conspiracy narratives is a pressing challenge for the integrity of the online infor

Agent-Environment Alignment via Automated Interface Generation

arXiv:2505.21055v2 Announce Type: replace Abstract: Large language model (LLM) agents have shown impressive reasoning capabilities in interactive decision-making tasks. These agents interact with envi

safetyarxiv-cs-ai

Beyond Benchmark Accuracy: A Diagnostic Test for LLM Chemical Prediction

model-releasesarxiv-cs-cl

arXiv:2610.19906v1 Announce Type: new Abstract: LLMs have achieved strong performance on chemical prediction benchmarks. However, existing evaluation methods typically provide target entity identity i

Beyond Black Boxes: Visual Grounding for Text Embeddings

arXiv:2610.18047v1 Announce Type: new Abstract: In this paper, our goal is to represent text embeddings as sketches for three main reasons. First, to visualize text representation and to explore their

Frustratingly Easy Majority Voting Using Length Conditioning

arXiv:2610.19890v1 Announce Type: new Abstract: Majority Voting (MV) is a simple and widely used strategy which improves the reasoning accuracy of Large Language Models (LLMs) by allocating additional

Function-Space Temporal Self-Distillation for Policy Optimization

arXiv:2610.19687v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) improves reasoning in language models, but scalar outcomes provide coarse supervision over respo

Generating Synthetic Doctor-Patient Conversations for Long-form Audio Summarization

arXiv:2604.06138v2 Announce Type: replace-cross Abstract: Long-context audio reasoning is underserved in both training data and evaluation. Existing benchmarks target short-context tasks, and the open

applicationsarxiv-cs-ai

Keep Your Receipts: Trustworthy Cleanup of Agent-Built Knowledge Graphs

arXiv:2610.17719v1 Announce Type: new Abstract: Agents build knowledge graphs as memory, and building them has become easy. Maintaining them has not. When a source is retracted, overruled, or erased,

Knowledge transfer between simple discrete worlds

arXiv:2610.15081v1 Announce Type: new Abstract: Adapting to a new world and reasoning correctly about which outcomes are possible under rules one has not met before is a basic capacity underlying gene

Learning Adaptive Deliberation Control for Reliable Recognition with Multi-Agent Language Models

arXiv:2610.17840v1 Announce Type: new Abstract: Multi-agent deliberation can improve recognition by criticizing and revising candidate answers, but fixed controls cannot respond to changes in disagree

safetyarxiv-cs-ai

MADDIE: Multi-Agent Drug Discovery Engine

arXiv:2610.16975v1 Announce Type: new Abstract: Drug discovery remains a resource-intensive and time-consuming process, involving billions of dollars and a decade of development to advance a drug to m

Output Discipline Sets the Floor: A Falsification-Guided, Model-Agnostic Agent Protocol for Auditable Hypothesis-Driven Decision Making

arXiv:2610.18298v1 Announce Type: new Abstract: What binds large language models on auditable, hypothesis-driven decision tasks, where every candidate can be checked against the evidence? On the publi

Overtaught, Underlearned: Learner-Centered Distillation from Multi-Agent Discussion

arXiv:2610.16543v1 Announce Type: new Abstract: Multi-agent discussion produces reasoning collaboratively, while deployment still relies on a smaller student that must answer alone. Full-graph distill

PANINI-DR: Controlled Deep Research through Generative Semantic Workspaces

arXiv:2610.15208v1 Announce Type: cross Abstract: Deep research requires connecting multiple steps of search, reasoning, and computation, with intermediate findings guiding what to investigate next. A

PatientWM: Towards Generalizable Clinical World Models via Unified Patient States

arXiv:2610.17059v1 Announce Type: new Abstract: Clinical decisions depend on anticipating how a patient's condition will evolve as treatment is adjusted and new evidence becomes available. Existing me

ProCARE: Real-World Study Automation via Profile-Grounded Evidence Contracts

arXiv:2610.16175v1 Announce Type: new Abstract: Real-world studies (RWS) analyze routinely collected healthcare data, such as electronic health records and insurance claims, to estimate treatment effe

Progress-Aware Distillation for Mitigating Stagnation in Small Language Model Agents

arXiv:2610.19439v1 Announce Type: new Abstract: While LLM-based agent systems have demonstrated remarkable proficiency in reasoning and solving complex real-world tasks, they also come with substantia

agentsarxiv-cs-lg

PROSE: Training-Free Egocentric Scene Registration with Vision-Language Models

arXiv:2606.16569v2 Announce Type: replace Abstract: Registering two captures of the same indoor space taken at different times underpins persistent spatial memory for robots and AR systems, yet the re

researcharxiv-cs-cv

QORH: Learning Query-Specific Ordered Rule Hypergraphs for Inductive Knowledge Graph Link Prediction

arXiv:2610.18198v1 Announce Type: new Abstract: Knowledge graph link prediction involving unseen entities and relations requires learning transferable reasoning patterns from observed graph structure.

tutorialsarxiv-cs-ai

QuantEval: A Benchmark for Financial Quantitative Tasks in Large Language Models

model-releasesarxiv-cs-cl

arXiv:2601.08689v3 Announce Type: replace Abstract: Large Language Models (LLMs) have shown strong capabilities in finance-oriented question answering, yet their evaluation on professional quantitativ

RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory

arXiv:2508.04903v4 Announce Type: replace-cross Abstract: Large language model (LLM) agents are increasingly organized into multi-agent systems for complex reasoning and decision-making tasks. However

ReferFlow: Beyond Final-Target Localization for Complex Referring Expressions

arXiv:2610.20352v1 Announce Type: new Abstract: Multimodal Large Language Models have made significant progress in referring expression comprehension (REC), yet most REC benchmarks still focus on fina

tutorialsarxiv-cs-cv

Test-Time Scaling via Budgeted Multi-Attribute Verification

arXiv:2609.34322v2 Announce Type: replace Abstract: Verifying LLM-generated answers under a shared computational budget requires jointly deciding which candidates to inspect and which verification att

tutorialsarxiv-cs-ai

Typed Decision Models on Tabular Data

arXiv:2610.19536v1 Announce Type: new Abstract: Typed decision models are language models that answer a question with a fixed set of possible answers by returning a probability for each answer instead

researcharxiv-cs-cl

VASPO: Self-Distillation with Privileged Value Estimation

arXiv:2610.20516v1 Announce Type: new Abstract: On-policy self-distillation provides dense supervision for reasoning models via a privileged self-teacher. However, this supervision reflects local toke

What the Machine Invented: Mining New Mathematical Instruments from OpenAI's October 2026 Release of AI-Generated Mathematics

arXiv:2610.17090v1 Announce Type: cross Abstract: On 6 October 2026 OpenAI published an initial 722 AI-written mathematical manuscripts, organized into 372 problem families, in the public repository g

Which Knowledge Should LLMs Trust? Uncovering a Generalizable Steering Direction Across Knowledge Conflicts

arXiv:2610.20067v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently encounter conflicts between internal parametric knowledge and external contextual knowledge, yet the mechanisms

researcharxiv-cs-cl

Local Ai

Why Agentic Workflows Fail: A Taxonomy Derived from Literature and Empirical Data

arXiv:2610.17418v1 Announce Type: cross Abstract: Agentic workflows combine large language models with planning, memory, tools, execution environments, and iterative feedback. Their failures are diffi

local-aiarxiv-cs-ai

World Models Need a Physical Registry: Learning Internal Predictive States for Physically Plausible World Models

arXiv:2610.16365v1 Announce Type: new Abstract: Video world models must generate physically plausible futures, not just realistic frames. Existing approaches typically use semantic, physical, or predi

tutorialsarxiv-cs-ai

A Production-Grade, High-Efficiency Agentic RL Framework for Frontier LLMs with Validated Recipes

arXiv:2610.16352v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a critical phase in the post-training pipeline of large language models (LLMs). As the frontier of LLM capabi

Articulated 3D Scene Graphs for Open-World Mobile Manipulation

arXiv:2602.16356v2 Announce Type: replace-cross Abstract: Semantic scene understanding has enabled robots to reason and perform affordance-driven object interaction. Despite this, real-world manipulat

applicationsarxiv-cs-cv

Code2Worlds-2: Physically Plausible Agent Harness for Embodied World Simulation

arXiv:2610.17651v1 Announce Type: cross Abstract: Recent coding large language models (LLMs) can synthesize dynamic 4D worlds from natural language, but the resulting content remains a visual artifact

Local Ai

DyJR: Preserving Diversity in Reinforcement Learning with Verifiable Rewards via Dynamic Jensen-Shannon Replay

arXiv:2603.16157v2 Announce Type: replace Abstract: While Reinforcement Learning (RL) enhances Large Language Model reasoning, on-policy algorithms like GRPO are sample-inefficient as they discard pas

local-aiarxiv-cs-lg

From Classification to Cross-Modal Understanding: Leveraging Vision-Language Models for Fine-Grained Renal Pathology

arXiv:2511.11984v2 Announce Type: replace Abstract: Fine-grained glomerular subtyping is essential for kidney biopsy interpretation, yet it presents a harder setting than the broad organ- or disease-l

Is Complex Query Answering Really Complex?

arXiv:2410.12537v4 Announce Type: replace Abstract: Complex query answering (CQA) on knowledge graphs (KGs) is gaining momentum as a challenging reasoning task. In this paper, we show that the current

applicationsarxiv-cs-lg

Look Back to Move Forward: Learning to Revisit for Agentic Compositional Image Generation

arXiv:2610.16224v1 Announce Type: new Abstract: Text-to-image models have made remarkable progress in visual fidelity and instruction following, yet complex compositional generation remains challengin

Make {pi}0.5 Learn to Call Astra for Reliable Robot Manipulation

arXiv:2610.14117v1 Announce Type: new Abstract: GPT-6 Astra provides robust embodied reasoning, but frequent external queries increase inference time and token costs. We equip a frozen pi_{0.5} policy

safetyarxiv-cs-ro

METIS: Multi-Source Egocentric Training for Integrated Dexterous Vision-Language-Action Model

arXiv:2511.17366v2 Announce Type: replace-cross Abstract: Building a generalist robot that can perceive, reason, and act across diverse tasks remains an open challenge, especially for dexterous manipu

applicationsarxiv-cs-cv

Model-Based Online Decision Making via Generative Trajectory Planning

arXiv:2610.18677v1 Announce Type: new Abstract: Online decision-making faces a tension between efficient inference and long-horizon reasoning. Value-based methods rely on short-horizon bootstrapping t

PINT: A Unified Model with Shared Predictive Intent for Robot Manipulation

arXiv:2610.16355v1 Announce Type: new Abstract: Generalist robot manipulation can benefit from pretrained vision-language models (VLMs) for semantic reasoning and video generation models (VGMs) for wo

applicationsarxiv-cs-ro

Proactive Physical AI through Continuous Observation and Timely Intervention

arXiv:2610.16967v1 Announce Type: new Abstract: Physical AI is commonly formulated around an explicitly specified task: given a goal or instruction, a system perceives, reasons, and acts. In many real

applicationsarxiv-cs-cv

Probing and Inducing Clinical Modality Grounding in Brain Tumor Segmentation

arXiv:2603.19844v3 Announce Type: replace Abstract: Multimodal MRI provides complementary information for brain tumor segmentation, but high accuracy alone does not reveal whether a model relies on cl

Reward-Aligned Distillation

arXiv:2610.14572v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards improves the reasoning capabilities of large language models, but remains computationally expensive and s