Search: “reasoning”

Skip a Layer or Loop It? Learning Program-of-Layers in LLMs

arXiv:2606.06574v1 Announce Type: new Abstract: Large language models (LLMs) perform inference by following a fixed depth and order, non-recurrent execution of all layers. We reveal the wide existence

researcharxiv-cs-lg

8 Jun 2026

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Agent-Orchestrated Adaptive RAG: A Comparative Study on Structured and Multi-Hop Retrieval

DGX agent

arXiv:2606.05658v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by grounding their responses in external knowledge, but conventional pipeli

model-releasesarxiv-cs-ai

6 Jun 2026

Benchmark Everything Everywhere All at Once

arXiv:2606.06462v1 Announce Type: new Abstract: Benchmarks are fundamental for evaluating and advancing LLMs and MLLMs by providing standardized and explicit measures of performance. However, their co

Beyond Output Matching: Preserving Internal Geometry in NVFP4 LLM Distillatio

arXiv:2606.05682v1 Announce Type: new Abstract: Demand for low-precision inference, including NVFP4-based approaches, has grown as large language models are increasingly deployed in latency and cost c

Edit-R2: Context-Aware Reinforcement Learning for Multi-Turn Image Editing

arXiv:2606.05950v1 Announce Type: new Abstract: Text-guided image editing has advanced rapidly with diffusion models and unified multimodal foundation models. However, most existing methods remain con

Reward-Decomposed Reinforcement Learning for Immersive Video Role-Playing

arXiv:2605.04733v2 Announce Type: replace Abstract: Text-based role-playing models can imitate character styles, but often fail to capture scene atmosphere and evolving tension, which are crucial for

The End of Software Engineering: How AI Agents Are Fundamentally Restructuring the Software Paradigm

arXiv:2606.05608v1 Announce Type: cross Abstract: For over half a century, software engineering has operated on a foundational premise: human engineers decompose problems, encode decision logic into s

Aligning Tree-Search Policies with Fixed Token Budgets in Test-Time Scaling of LLMs

model-releasesarxiv-cs-cl

arXiv:2602.09574v2 Announce Type: replace Abstract: Tree-search decoding is an effective form of test-time scaling for large language models (LLMs), but real-world deployment often imposes a fixed per

EGTR-Review: Efficient Evidence-Grounded Scientific Peer Review Generation via Multi-Agent Teacher Distillation

arXiv:2606.06025v1 Announce Type: new Abstract: Scientific peer review generation has attracted increasing attention for reducing reviewing burdens and providing timely feedback. However, existing Lar

agentsarxiv-cs-cl

Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations

arXiv:2510.17256v2 Announce Type: replace Abstract: Large language models have exhibited impressive performance across a broad range of downstream tasks in natural language processing. However, how a

local-aiarxiv-cs-cl

LongSpace: Exploring Long-Horizon Spatial Memory from Perception to Recall in Video

model-releasesarxiv-cs-cl

arXiv:2606.05677v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have advanced image and video understanding and can increasingly handle longer visual inputs. Long-horizon ta

MCBench: A Multicontext Safety Assessment Benchmark for Omni Large Language Models

model-releasesarxiv-cs-cl

arXiv:2606.05177v1 Announce Type: new Abstract: Existing multimodal safety benchmarks focus solely on visual inputs and cannot assess Omni Large Language Models (LLMs) that process vision, audio, and

Token costs are why there will be no saas apocalypse / good dev tools are cached intelligence for agents! The popular theory goes: agents ca…

model-releasesclem-delangue--x

Token costs are why there will be no saas apocalypse / good dev tools are cached intelligence for agents! The popular theory goes: agents can write code, so they'll just rebuild every tool from scratc

Visual Commonsense Driven Knowledge Refinements for Scene Graph Generation

arXiv:2606.06369v1 Announce Type: new Abstract: Learning-driven Scene Graph Generation (SGG) models excel on frequent relation types but degrade sharply under annotation sparsity, failing to capture r

researcharxiv-cs-cv

We've made a breakthrough in self-evolving AI scientists moving from 'search' to 'principled discovery': Scientific discovery requires that …

model-releasesgary-marcus--x

We've made a breakthrough in self-evolving AI scientists moving from 'search' to 'principled discovery': Scientific discovery requires that the search space itself changes, and an AI scientist must pe

Where, What, Why, and Importance: Structured Defect Grounding for Text-to-Image Feedback

arXiv:2606.06113v1 Announce Type: new Abstract: Despite generating increasingly photorealistic images, text-to-image (T2I) models still exhibit localized, subtle, and structurally complex failures. Di

local-aiarxiv-cs-cv

AgenticDiffusion: Agentic Diffusion-based Path Planning for Vision-Based UAV Navigation

arXiv:2606.04111v1 Announce Type: cross Abstract: Indoor UAV navigation requires efficient exploration, scene understanding, and reliable trajectory execution under limited field-of-view observations.

local-aiarxiv-cs-ai

An Empirical Audit of Input Encoders for Multi-Channel Signal Transformers

arXiv:2606.04752v1 Announce Type: cross Abstract: Transformers consuming multi-channel scalar signals must embed C simultaneous values into one d_{ext{model}}-dimensional vector per time step. We empi

Can I Take Another Dose? Evaluating LLM Decision-Making Under Temporal Uncertainty in OTC Dosing QA

arXiv:2606.04262v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for everyday health questions, including whether a user can safely take another dose of an over-the

Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning

arXiv:2507.21892v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) mitigates hallucination in LLMs by incorporating external knowledge, but relies on chunk-based retrieval that l

agentsarxiv-cs-cl

Inclusion-of-Thoughts: Mitigating Preference Instability via Purifying the Decision Space

arXiv:2604.04944v2 Announce Type: replace-cross Abstract: Multiple-choice questions (MCQs) are widely used to evaluate large language models (LLMs). However, LLMs remain vulnerable to the presence of

researcharxiv-cs-ai

Safety Under Scaffolding: How Evaluation Conditions Shape Measured Safety

arXiv:2603.10044v2 Announce Type: replace-cross Abstract: A safety score earned on a benchmark need not predict how the same model behaves once it is wrapped in an agentic scaffold the benchmark never

Synthetic Personalities: How Well Can LLMs Mimic Individual Respondents Using Socio-Economic Microdata?

arXiv:2606.04592v1 Announce Type: cross Abstract: LLM-based digital twins promise to scale and accelerate market research, but most published twins are either coarse persona bots conditioned on a few

researcharxiv-cs-ai

UniCanvas: A Diffusion-base Unified Model for Text-in-Image Joint Generation

arXiv:2606.04264v1 Announce Type: new Abstract: Recent years have seen remarkable progress in unified vision-language models handling both multimodal understanding and generation within a single archi

researcharxiv-cs-cv

VentAgent: When LLMs Learn to Breathe -- Multi-Objective Arbitration for ARDS Ventilation

arXiv:2606.04632v1 Announce Type: cross Abstract: Mechanical ventilation for Acute Respiratory Distress Syndrome (ARDS) requires balancing competing physiological goals, including oxygenation, lung pr

safetyarxiv-cs-cl

A Negative Result on Cross-Model Activation Transfer in a Pythia Multi-Hop Setting

arXiv:2606.03280v1 Announce Type: new Abstract: Recent work shows that language models can transmit behavioural traits through hidden signals in generated data during training. We ask whether a more d

AI Agents Enable Adaptive Computer Worms

arXiv:2606.03811v1 Announce Type: cross Abstract: A computer worm is malware that spreads on a network by replicating itself from one machine to another. Traditional worms, like WannaCry, exploited pr

Constitutional On-Policy Safe Distillation

arXiv:2606.03089v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) has emerged as an efficient post-training paradigm by using a teacher conditioned on privileged information to prov

Dynamics of Cognitive Heterogeneity: Investigating Behavioral Biases in Multi-Stage Supply Chains with LLM-Based Simulation

arXiv:2604.17220v2 Announce Type: replace-cross Abstract: Modeling coordination among generative agents in complex multi-round decision-making presents a core challenge for AI and operations managemen

Economy of Minds: Emerging Multi-Agent Intelligence with Economic Interactions

arXiv:2606.02859v1 Announce Type: cross Abstract: How can a population of agents self-orchestrate and self-adapt into stronger collective intelligence without centralized control? Inspired by Friedric

agentsarxiv-cs-ai

FGRPO: Federated GRPO with Adaptive Aggregation on Non-IID Data

arXiv:2606.03094v1 Announce Type: new Abstract: Recent advances in language models have established reinforcement learning as the primary paradigm for eliciting self-correction and long-chain reasonin

safetyarxiv-cs-lg

Framing Migration News with LLMs: Structured CoT as a Support for Human Interpretation

arXiv:2606.03761v1 Announce Type: new Abstract: Frame analysis of migration news is a socially consequential task: media scholars and researchers who study how migration is narrated need tools that ar

local-aiarxiv-cs-cl

Learning Self-Interpretation from Interpretability Artifacts: Training Lightweight Adapters on Vector-Label Pairs

arXiv:2602.10352v2 Announce Type: replace-cross Abstract: Self-interpretation methods prompt language models to describe their own internal states, but remain unreliable due to hyperparameter sensitiv

MemoGen: Can Past Experience Improve Future Text-to-Image Generation?

model-releasesarxiv-cs-cv

arXiv:2606.03243v1 Announce Type: new Abstract: Modern text-to-image models have achieved strong visual synthesis, yet remain unreliable when prompts require implicit visual constraints, relational re

QUBRIC: Co-Designing Queries and Rubrics for RL Beyond Verifiable Rewards

arXiv:2606.03968v1 Announce Type: cross Abstract: Rubric-based RL is a promising route for extending reinforcement learning beyond verifiable rewards, yet existing methods optimize rubrics while treat

Rethinking Molecular Text Representations for LLMs: An Empirical Study

arXiv:2606.03057v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for molecular tasks, but it remains unclear which molecular representation to use. We present a sys

Test-Time Optimization of Physical Query Plans with LLMs

arXiv:2602.10387v2 Announce Type: replace-cross Abstract: Traditional query optimization relies on cost-based optimizers that estimate execution cost (e.g., runtime, memory, and I/O) using predefined

researcharxiv-cs-ai

Trans GAN-WT: A Feature Extraction and Interactive Learning-Based Anomaly Detection Model for Wind Turbine Time Series Data

arXiv:2606.03112v1 Announce Type: cross Abstract: With the increasing scale and number of wind farms, wind turbines' daily operation and maintenance costs are increasing. To reduce operation and maint

researcharxiv-cs-lg

A Communication-Centric 6G-LLM Architecture for Scalable Tactical Autonomous Defense Vehicle Networks

arXiv:2606.01312v1 Announce Type: cross Abstract: The integration of Artificial Intelligence (AI) and emerging 6G networks introduces new opportunities for scalable coordination in tactical autonomous

agentsarxiv-cs-ai

Agent Skills Should Go Beyond Text: The Case for Visual Skills

arXiv:2606.01414v1 Announce Type: new Abstract: Reusable skills are a key mechanism for extending agent capabilities, allowing agents to accumulate experience and solve increasingly complex tasks. Yet

agentsarxiv-cs-cv

An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation

arXiv:2606.00987v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temp

APEX-SQL: Talking to the data via Agentic Exploration for Text-to-SQL

arXiv:2602.16720v2 Announce Type: replace-cross Abstract: Text-to-SQL systems powered by Large Language Models have excelled on academic benchmarks but struggle in complex enterprise environments. The

Baton: Explicit Semantic Blueprints for Joint Video-Audio Generation

arXiv:2605.25195v2 Announce Type: replace Abstract: Current open-source diffusion models struggle to generate stable and synchronized audio-visual content, particularly in scenarios demanding complex

safetyarxiv-cs-cv

Beyond One-shot: AI Agents for Learning in Field Experiments

arXiv:2606.02458v1 Announce Type: new Abstract: Organizations routinely run experiments for A/B testing, yet the data generated from one experiment is underutilized to inform subsequent intervention d

agentsarxiv-cs-ai

Business Utility of Large Language Models as Exploratory Data Analysis Agents

arXiv:2606.00051v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in analytical workflows, but their suitability as exploratory data analysis (EDA) agents in busines

Code2Math: Can Your Code Agent Effectively Evolve Math Problems Through Exploration?

arXiv:2603.03202v3 Announce Type: replace Abstract: As large language models (LLMs) advance their mathematical capabilities toward the IMO and research level, the scarcity of challenging, high-quality

agentsarxiv-cs-cl

CodeCytos: AI-assisted spatial molecular imaging analysis via code-augmented agent action space

arXiv:2606.00472v1 Announce Type: cross Abstract: Conventional tissue image analysis software provides foundational capabilities for cellular analysis, including segmentation, basic morphological feat

Community-Aware Assessment of Social Textual Engagement and Resonance: A Human-Centric Perspective on User-Generated Content Evaluation

arXiv:2606.01897v1 Announce Type: new Abstract: Traditional Video Quality Assessment (VQA) focuses narrowly on aesthetic fidelity, overlooking the complex social dynamics that define quality in User-G