Search: “reasoning”

Prune the Summary, Not the History: Source-Judged Compaction for Long-Horizon Agents

arXiv:2610.20927v1 Announce Type: new Abstract: Long-horizon LLM agents rely on context compaction: when the interaction history outgrows the context window, a compressor replaces it with a natural-la

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Research

Recursive Reward Improvement via Adversarial Meta-Prompt Evolution

DGX agent

arXiv:2610.21040v1 Announce Type: new Abstract: Recursive Self-Improvement (RSI) has gained wide attention for its ability to work with limited data. In a proposer-solver loop, a proposer model writes

researcharxiv-cs-ai

16 Oct 2026

Reinforcement Learning-driven Diffusion Models: Language, Vision, and Beyond

arXiv:2610.21539v1 Announce Type: new Abstract: Diffusion models (DMs) have steadily demonstrated outstanding performance in high-fidelity, controllable generation across diverse scenarios, following

Reliable Knowledge Acquisition for Continual Learning Agents

arXiv:2610.21739v1 Announce Type: cross Abstract: As agents powered by foundation models move into open-ended environments, continual learning from experience becomes essential. For agents built on mo

Retrofitting Recurrent Vision-Language Models from Pretrained Backbones

model-releasesarxiv-cs-cv

arXiv:2610.22075v1 Announce Type: new Abstract: Recent work shows that depth recurrence can be retrofitted into pretrained language models. In this work, we adapt this paradigm for visual understandin

RIGen: Beyond Action Following with Reciprocal Interaction in Generative Gameplay

arXiv:2610.20811v1 Announce Type: new Abstract: Generative gameplay requires more than immediate responses to player actions.Actions should have persistent consequences, while the generated world shou

researcharxiv-cs-cv

Robust Multi-Agent Pursuit with Preemptive Communication via Reachability-Based Control Barrier Functions

arXiv:2610.22198v1 Announce Type: cross Abstract: Safe multi-agent coordination typically assumes what real-world, deployed, resource-constrained robots cannot provide: continuous, perfect state shari

safetyarxiv-cs-ro

Router Drift Control: Mitigating Off-Policy Degradation in Mixture-of-Experts Reinforcement Learning

arXiv:2610.21682v1 Announce Type: cross Abstract: Reinforcement learning (RL) can become off-policy due to training-inference mismatch, mini-batch updates, and asynchronous training.The resulting mism

Applications

SCRIBE: Scalable Contextual Representations of Interpretable Brain-Grounded Semantics

arXiv:2610.21518v1 Announce Type: new Abstract: Understanding how the human brain constructs meaning from real-world language remains a fundamental challenge, as semantics depend on context that varie

applicationsarxiv-cs-ai

Selection Is Not Learning: Choosing Model Futures

arXiv:2610.22029v1 Announce Type: cross Abstract: Self-evolving language models train on tasks they generate, so every round must decide which updated model the next round inherits. Current loops such

SHRED: Self-Play for Heterogeneous Road-Users without Expert Demonstrations

arXiv:2610.20866v1 Announce Type: cross Abstract: Realistic traffic simulation and autonomous planning both depend on modeling interactions among heterogeneous road users whose dynamics, constraints,

SOMA: Stack-of-Objects Manipulation using a Quadruped Robot

arXiv:2610.20968v1 Announce Type: cross Abstract: Executing heavy-duty physical manipulation in unpredictable environments remains a major challenge for quadrupedal robots lacking dedicated manipulato

SUPN: Shallow Universal Polynomial Networks for Efficient Function Approximation

model-releasesarxiv-cs-lg

arXiv:2511.21414v2 Announce Type: replace Abstract: Deep neural networks (DNNs) and Kolmogorov-Arnold networks (KANs) are flexible function approximators, but their accuracy can require substantial ne

Terminal-RSI: Recursive Self-Improving Terminal Agents via Recursive Self-Rewrite and Policy-Refine

arXiv:2610.22192v1 Announce Type: new Abstract: Converting verifier outcomes into useful policy updates and partial progress into complete task resolution is crucial for terminal agents. We present Te

Applications

TGrasp6D: Task-Informed Vision-Language Priors and Pose Refinement enable Zero-Shot Task-Oriented 6DoF Grasping

arXiv:2610.20901v1 Announce Type: cross Abstract: Task-Oriented Grasping (TOG) has emerged as an important skill for purposeful robotic manipulation. Given the task of using a knife to 'slice bread,'

applicationsarxiv-cs-ai

Tool-Augmented Large Language Models for Reliable Medical Calculation from Patient Notes

arXiv:2610.21477v1 Announce Type: new Abstract: Medical calculators translate raw patient observations into clinically actionable values and scores. However, applying these calculators directly to fre

Towards Better Aligned LLM Judges for Radiology Report Evaluation across Modalities and Anatomies

arXiv:2604.03376v2 Announce Type: replace Abstract: Current work on radiology report evaluation has focused primarily on LLM-based metrics for chest X-rays (CXR), leaving open whether such approaches

researcharxiv-cs-ai

Towards Unbiased On-Policy Distillation for Block Diffusion Language Models

arXiv:2610.05373v2 Announce Type: replace Abstract: On-policy distillation (OPD) has emerged as an effective post-training paradigm for language models, with recent efforts extending it to block diffu

safetyarxiv-cs-cl

VELD-QA: A Large-Scale Dataset for Visual Evidence Grounding and Question Answering in Long Documents

arXiv:2610.20964v1 Announce Type: cross Abstract: Existing long-document QA benchmarks rarely combine large-scale human-authored questions with detailed visual evidence annotations, limiting diagnosis

Tutorials

VLA-Delta: Learning Physical State Transition for Vision-Language-Action Models

arXiv:2610.21306v1 Announce Type: new Abstract: Vision-language-action (VLA) models transfer semantic knowledge acquired through vision-language pretraining to robot control, achieving strong performa

tutorialsarxiv-cs-cv

When Correctness Feedback Erases Evidence: Phase-Separated Completeness Auditing in Verus

arXiv:2610.20676v1 Announce Type: new Abstract: Correctness feedback suppresses the very evidence that specification-completeness auditing is designed to uncover: programs that satisfy a written contr

researcharxiv-cs-ai

Where Is the 'Self' of a Large Language Model? -- From Self-Index to Self-Reference

arXiv:2610.16717v2 Announce Type: replace Abstract: Does a large language model have a self, and if so, of what kind? We separate two functional levels familiar from biology: a self-index, which disti

researcharxiv-cs-cl

Local Ai

Wolof-Adapted Industry-Ready Open-Weight Language Models

arXiv:2610.20824v1 Announce Type: cross Abstract: Wolof is spoken by 23M people, mostly in Senegal, but remains weakly supported by open-weight language models, while API models raise cost, governance

local-aiarxiv-cs-ai

A Generalist Hanabi Agent

arXiv:2503.14555v2 Announce Type: replace-cross Abstract: Traditional multi-agent reinforcement learning (MARL) systems can develop cooperative strategies through repeated interactions. However, these

Agent Sociodrama: Social-Dynamics-Guided Persona Recasting for LLM Agent Teams

arXiv:2610.15788v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed as teams, yet they fail in characteristically social ways: information holders stay silent,

Agentic Architect: An Agentic AI Framework for Architecture Design Exploration and Optimization

arXiv:2604.25083v2 Announce Type: replace Abstract: Computer Architecture research has traditionally required the exploration of vast, highly complex design spaces through manual human effort. Large L

Hardware

AgriJev: Efficient Agricultural Candidate Scoring with Multi-Scale Adapted Models

arXiv:2610.19478v1 Announce Type: new Abstract: Agricultural systems make decisions over heterogeneous entity types, textual judgments, and knowledge-question options. Generating a textual answer adds

hardwarearxiv-cs-cl

AlignMerge - Alignment-Preserving Large Language Model Merging via Fisher-Guided Geometric Constraints

arXiv:2512.16245v2 Announce Type: replace Abstract: Merging large language models (LLMs) is a practical way to compose capabilities from multiple fine-tuned checkpoints without retraining. Yet standar

Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generation

model-releasesarxiv-cs-cv

arXiv:2610.20571v1 Announce Type: new Abstract: Long-form agentic video generation must turn a screenplay into shots that remain spatially and temporally consistent across nonconsecutive requests. Pas

AuthGateBench: Benchmarking LLMs as Authorization Gate

arXiv:2610.16426v1 Announce Type: new Abstract: Automated approval systems let coding agents execute high-risk actions with minimal human oversight by delegating the authorization decision to a classi

AutoMARS: Automatic and Scalable Multi-Agent Large Language Model Framework for Supply Chain Risk Assessment

arXiv:2610.17206v1 Announce Type: new Abstract: We propose AutoMARS (Automatic Multi-Agent Risk Assessment), an agentic framework for automatic and scalable comparative risk assessment in supply chain

AutoSec-RAN: Specification-Grounded LLM Fine-Tuning for Automated Security Analysis of O-RAN

arXiv:2610.15995v1 Announce Type: cross Abstract: This paper presents AutoSec-RAN, a specification-grounded large language model (LLM) framework for automated security triage of Open Radio Access Netw

Behavioral History Outperforms Descriptions of the Person for LLM Synthetic Personas

arXiv:2610.03998v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as synthetic personas representing survey respondents. Their validity as substitutes for particul

Benchmarking and Training Reward Models for Cross-Source Evidence Grounding in Search Agents

arXiv:2610.17827v1 Announce Type: new Abstract: Existing approaches to training and evaluating search agents focus heavily on final-answer quality, largely neglecting how generated answers map back to

BenchPreS: A Benchmark for Context-Aware Personalized Preference Selectivity of Persistent-Memory LLMs

arXiv:2603.16557v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly store user preferences in persistent memory to support personalization across interactions. However,

Beyond Distribution Matching: Selective Margin Preservation for Supervised Fine-Tuning

arXiv:2610.19685v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) changes logit margins among non-target tokens at teacher-forced prefixes. Matching a local reference distribution keeps t

researcharxiv-cs-cl

Beyond Perfect Teammates: Multi-Agent Collaboration with Unreliable Colleagues

arXiv:2610.16117v2 Announce Type: new Abstract: LLM-based multi-agent collaboration is increasingly used to solve complex tasks by dividing work among specialized agents and integrating complementary

Beyond Size: How Gradients Shape Pruning Decisions in Large Language Models

arXiv:2311.04902v3 Announce Type: replace Abstract: Large Language Models (LLMs) with billions of parameters are prime targets for network pruning, which removes some model weights without hurting per

Big-Big-SycoBench: A Benchmark to Detect Sycophancy Through Model Responses to Valid vs. Invalid Arguments

arXiv:2610.15634v1 Announce Type: new Abstract: Sycophancy in large language models risks amplifying unsupported claims or beliefs across the knowledge ecosystem as these systems increasingly contribu

Can LLM Agents Efficiently Identify Appliance Faults? Insights From Diagnosing a Simulated Appliance

arXiv:2610.19291v1 Announce Type: new Abstract: In so-called 'Industry 4.0,' integrated sensors, analytics tools, and other digital technologies supplement industrial workflows, potentiating enhanced

Can LLMs Access their World Knowledge for Event Prediction?

arXiv:2610.19021v1 Announce Type: new Abstract: Recent work has questioned whether large language models (LLMs) can be useful for temporal event prediction. Despite vast pretrained world knowledge, LL

Choosing LLMs for Strategic Agent Interactions

arXiv:2610.18354v1 Announce Type: new Abstract: Game-theoretic benchmarks claim that large language model (LLM) strategic decision-making varies across game types, interaction structures, and capabili

Applications

Cognifying Education: Mapping AI's transformative role in emotional, creative, and collaborative learning

arXiv:2509.25266v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is rapidly reshaping educational practice, challenging long held assumptions about teaching and learning. This ar

applicationsarxiv-cs-ai

Contrastive uncertainty learning for robust multi-structure segmentation in lower-pelvic MR

arXiv:2610.20251v1 Announce Type: new Abstract: Boundary delineation of anatomical structures in prostate MRI is often compromised by regional ambiguity. Automated segmentation methods relying on glob

safetyarxiv-cs-cv

CORE: Contrast-and-Reflect for Self-Supervised Prompt Optimization in Anomaly Synthesis

arXiv:2610.19610v1 Announce Type: new Abstract: Industrial anomaly detection (AD) is bottlenecked by the scarcity of anomalous samples, making high-fidelity anomaly synthesis crucial for improving dow

researcharxiv-cs-cv

Correct Choices, Failed Sequences: Choice2Seq Benchmarks Correctness Retention Across Output Interfaces

arXiv:2610.16191v1 Announce Type: new Abstract: A model may select the correct answer yet fail to construct it through a sequence of local decisions. We introduce Choice2Seq, a diagnostic benchmark th

Correction Training for Diffusion Models

arXiv:2610.14136v2 Announce Type: new Abstract: Iterative sampling in diffusion models can lead to error accumulation due to exposure bias. The denoiser is trained on real images interpolated with noi

DivAlgo: A systematic evaluation of algorithmic diversity for code generation models

arXiv:2610.19157v1 Announce Type: cross Abstract: Code generation models are actively being used in professional and educational settings. However, the competence of large language models (LLMs) to ge