Search: “reasoning”

An Open Recipe for IMO Gold: Training Nemotron for Olympiad Mathematics

arXiv:2609.10712v1 Announce Type: new Abstract: We study how model post-training and test-time inference design affect natural-language proof generation for hard olympiad mathematics. Starting from Ne

12 Sept 2026

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Extending SMT Solving with Non-Ground Clause Learning

DGX agent

arXiv:2609.11509v1 Announce Type: new Abstract: Quantifier instantiation is currently the main approach to non-ground SMT solving: solvers generate ground instances and solve the resulting ground SMT

researcharxiv-cs-ai

12 Sept 2026

Towards a Deterministic Math Solver for Clinical Language Models

arXiv:2609.10728v1 Announce Type: new Abstract: Large language models are unreliable at arithmetic, which is a problem for clinical calculators where a single numerical error changes the recommendatio

12 Sept 2026

HALDETECT at ImageEval 2026 Shared Tasks: Answer-First Contrastive Grounding with QLoRA

model-releasesarxiv-cs-cv

arXiv:2609.11236v1 Announce Type: new Abstract: Large multimodal models tend to hallucinate visual detail fluently, which limits their deployment for fine-grained interpretation. We present HALDETECT,

11 Sept 2026

New Evidence, Same Choice: Testing Physical Experiment Selection in Vision Language Models

model-releasesarxiv-cs-cl

arXiv:2609.11022v1 Announce Type: cross Abstract: A model first sees an image from one physical measurement experiment, such as how far a block coasted, and must answer a question about a new trial, s

11 Sept 2026

SenseNova-U1.5: Towards Native Unified Visual Intelligence

arXiv:2609.11929v1 Announce Type: new Abstract: We launch SenseNova-U1.5, an 8B-MoT native unified multimodal model that understands, reasons about, and generates visual content within an encoder-free

safetyarxiv-cs-cv

11 Sept 2026

Beyond One-Shot Expansion: Contrastive Evidence Exploration for Multi-Hop Retrieval

arXiv:2609.07050v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) critically depends on retrieving the evidence necessary for effective reasoning. However, this remains particularly

Bilevel Planning with Learned Symbolic Abstractions from Interaction Data

arXiv:2603.08599v2 Announce Type: replace Abstract: Intelligent agents must reason over both continuous dynamics and discrete representations to generate effective plans in complex environments. Previ

researcharxiv-cs-ro

CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification

arXiv:2604.25512v3 Announce Type: replace Abstract: In phishing detection, machine learning classifiers act as a first line of defense, but the false positives they produce are triaged by human analys

Deep and shallow biases in language models

arXiv:2609.09901v1 Announce Type: new Abstract: Large language models often repeatedly select the same answer even when many alternatives are plausible. Prior work treats this concentration as bias, b

safetyarxiv-cs-cl

Distilling Vision-Language Models for On-Device Fire Understanding

arXiv:2609.05782v1 Announce Type: new Abstract: Vision-language models (VLMs) offer a promising alternative to conventional fire detection systems by reasoning about the semantic context of a scene an

local-aiarxiv-cs-ai

From Representation to Enactment: The ABC Framework of the Translating Mind

arXiv:2511.16811v2 Announce Type: replace Abstract: Building on the third-wave Extended Mind (EM) theory and radical enactivism, this article suggests an alternative to representation-based models of

researcharxiv-cs-cl

Agents

Inference-Time Graph Engineering for Multi-Agent LLM Workflows

arXiv:2609.05774v1 Announce Type: new Abstract: Recent multi-agent LLM systems increasingly rely on graph-structured communication to coordinate specialized agents. We revisit multi-agent orchestratio

agentsarxiv-cs-ai

ObGynLongBench: Revealing the Evidence-to-EHR Gap in Longitudinal EHR Decision-Making

arXiv:2609.07601v1 Announce Type: cross Abstract: The application of large language models (LLMs) to personalized medical assistants has garnered growing interest. However, existing medical benchmarks

DART: Distributional Adversarial Recurrent Training for Algorithm Learning

arXiv:2609.05988v1 Announce Type: new Abstract: Recurrent reasoning models (RRMs) can solve structured problems, achieving easy-to-hard generalization through iterative computation in hidden space. Th

local-aiarxiv-cs-lg

Generalist Open-World Temporal Perception

arXiv:2609.06823v1 Announce Type: new Abstract: The next generation of artificial intelligence systems will likely be natively temporal and multimodal in both inputs and outputs: able to converse, per

researcharxiv-cs-cv

HealthLoopQA: A Context-Aware Question Answering Benchmark for Interpreting Wearable Monitoring Data in Diabetes Care

model-releasesarxiv-cs-lg

arXiv:2609.06976v1 Announce Type: new Abstract: As medical wearables become integrated into daily chronic disease care, effectively interpreting longitudinal monitoring data is essential for patients

Separating Capability from Confidence: Grounded Dual-State Calibration for GRPO-Trained Medical Vision-Language Models

arXiv:2609.06419v1 Announce Type: new Abstract: Medical vision-language models (VLMs) require confidence that reflects both answer correctness and patient-specific visual evidence. Recent GRPO-based m

researcharxiv-cs-cv

Evidence Integration in Large Language Models

arXiv:2609.04290v1 Announce Type: cross Abstract: Despite increasing reliance on LLMs that reason with external evidence supplied by tools, retrieval-augmented generation, other agents, and users, how

safetyarxiv-cs-ai

Agents

First Things First: Teaching LLM-Based Agents to Prioritize Must-Haves before Nice-to-Haves

arXiv:2609.05224v1 Announce Type: new Abstract: Recent progress in multimodal large language models (MLLMs) has fueled significant enthusiasm in their potential to act as autonomous agents for real-wo

agentsarxiv-cs-cv

SciDocBench: A Workflow-Centered Benchmark and Data Pipeline for Scientific Document Understanding

arXiv:2609.05141v1 Announce Type: new Abstract: Scientific papers require models to reason jointly over text, equations, figures, tables, code, and datasets while preserving the provenance of supporti

Unifying ICL, SFT, KL-Regularized RL Through a Bayesian Lens

arXiv:2609.05111v1 Announce Type: new Abstract: Large language models are now trained and evaluated under a diverse set of paradigms: supervised fine-tuning (SFT), few-shot in-context learning (ICL),

What Matters in On-Policy Distillation? A Perspective on Data Efficiency and Data Selection

arXiv:2609.05198v1 Announce Type: new Abstract: On-Policy Distillation (OPD) has emerged as a widely adopted post-training paradigm for enhancing large language models in reasoning domains. However, t

safetyarxiv-cs-ai

Epistemic Warrant for LLM Recommendations: Characterizing the Basis for Reliance When Ground Truth Is Unavailable

arXiv:2609.04127v1 Announce Type: new Abstract: Large language models are increasingly used to support organizational decisions, yet users often lack a principled basis for assessing whether to rely o

local-aiarxiv-cs-ai

Ex-Omni: Enabling 3D Facial Animation Generation for Omni-modal Large Language Models

arXiv:2602.07106v3 Announce Type: replace-cross Abstract: Omni-modal large language models (OLLMs) aim to unify multimodal understanding and generation, yet extending them to jointly produce speech an

Imagine-then-Plan: Agent Learning from Adaptive Lookahead with World Models

arXiv:2601.08955v3 Announce Type: replace-cross Abstract: Recent advances in world models have shown promise for modeling future dynamics of environmental states, enabling agents to reason and act wit

safetyarxiv-cs-ai

The Geometry of Ignorance: LLMs Know When to Temper Bayesian Priors

arXiv:2609.02959v1 Announce Type: cross Abstract: What does a language model predict when it has few clues? The answer lurks in its unembedding geometry: a single direction of the unembedding matrix e

The Gradient Does Not See Rank: Rank-Indifference in Matrix-CODI on ProsQA

arXiv:2609.03090v1 Announce Type: new Abstract: Continuous chain-of-thought models compress reasoning into latent tokens. Matrix-valued variants, which route each latent token through a d x d matrix b

researcharxiv-cs-lg

Towards Numerical TOHTN Planning with SMT-based HTN-SAT Encoding

arXiv:2609.03938v1 Announce Type: new Abstract: While HTN planning has received significant attention in recent years, support for numerical reasoning remains very limited. In this paper, we investiga

APEx: Distillation of Agent Procedural Experience for Adaptive Deep Research Question Answering

arXiv:2609.02253v1 Announce Type: new Abstract: Deep research agents augment large language models with external tools to answer complex, long-horizon questions through multi-turn reasoning. Learning

Applications

Architecting Conversational Data Systems for Stateless LLM APIs: The Hydration Proxy Pattern

arXiv:2609.01834v1 Announce Type: new Abstract: As enterprise platforms transition to conversational reasoning interfaces, the stateless nature of LLM APIs creates an architectural gap. While stateles

applicationsarxiv-cs-ai

CHisAgent: A Multi-Agent Framework for Event Taxonomy Construction in Ancient Chinese Cultural Systems

arXiv:2601.05520v2 Announce Type: replace Abstract: Despite strong performance on many tasks, large language models (LLMs) show limited ability in historical and cultural reasoning, particularly in no

safetyarxiv-cs-cl

GMTRouter: Personalized LLM Router over Multi-turn User Interactions

arXiv:2511.08590v2 Announce Type: replace Abstract: Large Language Model (LLM) routing has demonstrated strong capability in balancing response quality with computational cost. As users exhibit divers

researcharxiv-cs-cl

Applications

Using Large Language Models for Legal Decision-Making in Austrian Value-Added Tax Law: A Comparative Study

arXiv:2507.08468v2 Announce Type: replace Abstract: This paper provides an experimental evaluation of the capability of large language models (LLMs) to assist in legal decision-making within the frame

applicationsarxiv-cs-cl

BrainDiff: Longitudinal Report Generation for Multimodal Brain MRI

arXiv:2609.00593v1 Announce Type: new Abstract: Neuroradiologists rarely read a brain MRI in isolation, yet automated brain-MRI report generation has been built almost entirely for single studies. Tem

researcharxiv-cs-cv

RePro: Proof-Verified Benchmark Rewriting for Reliable Evaluation of LLM Mathematical Problem Solving

arXiv:2609.00062v1 Announce Type: cross Abstract: Data contamination undermines the reliable evaluation of large language models (LLMs) on mathematical problem solving. While rewriting-based evaluatio

Safin-1: Safety from Within through Memory-Native State Evolution

arXiv:2609.00092v1 Announce Type: new Abstract: Long-horizon complex tasks require foundation models to accumulate information, maintain internal states, and adapt over extended interactions. Safety s

safetyarxiv-cs-lg

Synthetic Worlds for Temporal Evaluation and Knowledge Updating in LLMs

model-releasesarxiv-cs-cl

arXiv:2609.00184v1 Announce Type: new Abstract: Large language models (LLMs) rely on static pretraining corpora, causing their knowledge to become outdated over time. Existing approaches for evaluatin

Think Like a Doctor: Conversational Diagnosis through the Exploration of Diagnostic Knowledge Graphs

arXiv:2602.01995v2 Announce Type: replace Abstract: Conversational diagnosis requires multi-turn history-taking, where an agent asks clarifying questions to refine differential diagnoses under incompl

VoiceLongMemEval: Do Assistants Remember How You Sounded?

arXiv:2609.00570v1 Announce Type: new Abstract: With the growing scale of multi-agent architectures and large language models, deployed AI assistants are increasingly tasked with reasoning over long,

A homotopy-type-theoretic generalization of neurosymbolic inference

arXiv:2606.17851v2 Announce Type: replace Abstract: A wide range of neurosymbolic (NeSy) systems compute one functional: a belief-weighted sum of a logical quantity over a space of sigma-structures, o

A Model with No Head and Many Thoughts

model-releasesarxiv-cs-cl

arXiv:2608.31069v1 Announce Type: cross Abstract: Large language models decode by projecting hidden states through a large vocabulary head at every step. This operation is computationally costly and f

Beyond Polarization: The Generative Constraint of Chain-of-Thought in Pointwise Reranking

arXiv:2608.30398v1 Announce Type: new Abstract: In pointwise document reranking, Chain-of-Thought models typically underperform direct scoring models. While existing diagnostics attribute this to infe

safetyarxiv-cs-cl

Can Agentic Trading Systems Pay for Their Own Intelligence?

arXiv:2607.10286v2 Announce Type: replace Abstract: Large language model (LLM) agents are increasingly used in trading systems, where model reasoning, tool use, and continual decisions incur costs tha

Agents

Can LLM Agents Discover? Evaluating Creativity on ML Engineering Tasks

arXiv:2608.30047v1 Announce Type: new Abstract: Recent AI systems promise autonomous scientific discovery, claiming to discover algorithms and produce research papers, yet understanding whether they e

agentsarxiv-cs-ai

CoMPASS: Collaborative Molecular Property Prediction via Adaptive Small-Large Model Synergy

arXiv:2608.30674v1 Announce Type: cross Abstract: Accurate molecular property prediction requires both statistical reliability and chemical reasoning. Graph neural networks can be calibrated directly

local-aiarxiv-cs-ai

Applications

Cross Lingual Transfer in Tulu Legal Comprehension: Script-Dependent Improvement and RAG-Induced Knowledge Conflict

arXiv:2608.28645v1 Announce Type: cross Abstract: Low-resource languages without an adequate training corpus often use a related, higher-resource language as a scaffold for comprehension. Still, there

applicationsarxiv-cs-ai

Do Large Language Models Possess a Theory of Mind? A Comparative Evaluation Using the Strange Stories Paradigm

arXiv:2603.18007v2 Announce Type: replace-cross Abstract: The study explores whether current Large Language Models (LLMs) exhibit Theory of Mind (ToM) capabilities -- specifically, the ability to infe