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AeroGround: A Comprehensive Benchmark for Aerial-Ground Collaborative Reasoning

arXiv:2608.14721v1 Announce Type: new Abstract: Vision-language models (VLMs) have been widely employed in understanding and reasoning tasks for unmanned aerial vehicles (UAVs). Existing UAV benchmark

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Model Releases

Ask, Condition or Abstain: Reinforcement Learning for Missing-Premise Reasoning

DGX agent

arXiv:2608.16554v1 Announce Type: new Abstract: Answer-only reinforcement learning (RL) trains reasoning models to solve fully specified problems, but many realistic queries omit a premise needed for

model-releasesarxiv-cs-cl

18 Aug 2026

Auxiliary uncertainty signals for LLM-assisted systematic review screening: a benchmark across eight Cohen drug-class reviews

model-releasesarxiv-cs-cl

arXiv:2608.14551v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for title-abstract screening in systematic reviews, but their decisions lack calibrated uncertainty.

Bye-bye, Bluebook? Automating Legal Drudgery With AI-Augmented Rule Following

arXiv:2505.02763v2 Announce Type: replace-cross Abstract: One of the central promises of legal AI is to automate drudgery -- the formal, repetitive tasks of lawyers' work that consume time without cal

Can LLMs Reason Like Automated Theorem Provers for Rust Verification? VCoT-Bench: Evaluating via Verification Chain of Thought

arXiv:2603.18334v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) increasingly assist secure software development, their ability to meet the rigorous demands of Rust program ve

Disentangling Pictorial Cue Understanding from Language Bias in VLMs via Depth Ordering Task

arXiv:2607.01503v2 Announce Type: replace Abstract: In this paper, we study depth perception of vision-language models (VLMs) to isolate the effects of pictorial depth cues and disentangle vision and

Do LLM Agents Negotiate Rationally? A Mechanism-Design Framework for Verifiable Multi-Agent Interaction over A2A/MCP

arXiv:2608.14613v1 Announce Type: new Abstract: Modern LLM-agent frameworks increasingly interoperate through standards such as Anthropic's Model Context Protocol (MCP) for agent-to-tool access and Go

Every Coin Has Two Sides: On the Dual Nature of Generalization in On-Policy Distillation of Large Language Models

arXiv:2608.16647v1 Announce Type: new Abstract: On-policy distillation (OPD) transfers teacher capabilities by supervising trajectories sampled from the student's own policy, yet its generalization be

safetyarxiv-cs-cl

From Errors to Proofs: Minimal-Core-Guided Repair for Neuro-Symbolic Constraint Solving

arXiv:2608.14771v1 Announce Type: new Abstract: Making language models solve constraint problems reliably often means having them translate the problem into a formal specification and delegating the s

Generated Context versus Governed State: Functional Conditions for Accountable Longitudinal Clinical Reasoning

arXiv:2608.14804v1 Announce Type: new Abstract: Large language models (LLMs) have become the dominant interface of clinical artificial intelligence, yet the interface they expose (text in, text out, o

MoE Router-Guided Clustering for Heterogeneous Federated Instruction Tuning

arXiv:2608.15311v1 Announce Type: new Abstract: Federated instruction fine-tuning enables Large Language Models (LLMs) to adapt to decentralized, privacy-sensitive data without requiring data sharing.

PixRestore: Unified Image Restoration via Pixel Diffusion Transformer

arXiv:2608.16793v1 Announce Type: new Abstract: Unified image restoration (UIR) aims to recover high-quality (HQ) content from low-quality (LQ) images with different degradations using a single model.

Prompting is not enough: supervised baselines and leakage control for measuring shared decision-making with LLMs in pediatric encounters

arXiv:2608.14792v1 Announce Type: cross Abstract: Objectives: To determine whether zero-shot prompting of a large language model (LLM) is sufficient to detect shared decision-making (SDM) behaviors in

The Unwritten Benchmark: A New Challenge for Multimodal Machine Learning in Abstract Perceptual Reasoning

arXiv:2608.14558v1 Announce Type: new Abstract: Current multimodal models have demonstrated remarkable proficiency in recognizing static visual and auditory content. However, their capacity for abstra

The Working Set of a Coding Agent: Coherence Debt in Repository-Scale Tasks

model-releasesarxiv-cs-lg

arXiv:2608.16630v1 Announce Type: cross Abstract: Repository-scale coding requires an agent to keep tests, imports, configuration, and migration rules consistent within a bounded context window. We mo

When State Becomes an Attack Surface: State-Semantic Injection in LLM-Driven Embodied Agents

arXiv:2608.16806v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated capabilities in in-context learning, task decomposition, step-by-step reasoning, and code generation, d

safetyarxiv-cs-ai

When Stories Evolve: Benchmarking LLM Storytelling Across Agent Architectures in Open-Ended World Simulations

arXiv:2608.15654v1 Announce Type: cross Abstract: Large language models can write fluent stories, but open-ended storytelling requires more than local fluency. In evolving world simulations and AI-nat

A Data-Driven Algorithm for Model-Free Control Synthesis

arXiv:2602.13157v2 Announce Type: replace-cross Abstract: Presented is an algorithm to synthesize the optimal infinite-horizon LQR feedback controller for continuous-time systems. The algorithm does n

researcharxiv-cs-ro

A Graph-Based Reinforcement Learning Framework for Structured Drift Diagnosis and Recovery in Autonomous LLM Agents

arXiv:2608.14109v1 Announce Type: new Abstract: Autonomous LLM agents are increasingly deployed in complex real-world workflows, yet they remain vulnerable to runtime behavioral drift, a silent deviat

Detecting Contaminated Code-Generation Prompt Batches via Influence Functions

model-releasesarxiv-cs-lg

arXiv:2608.14303v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for code generation, yet they remain vulnerable to prompts that elicit insecure implementations. Exis

Non-Shattering at and Above the Dynamical Temperature in the Spherical Pure p-Spin Model

arXiv:2608.14369v1 Announce Type: cross Abstract: We consider the notion of shattering introduced by Ben Arous and Jagannath for spherical pure p-spin glasses with overlap q. For every pgeq 3 and 0sqr

researcharxiv-cs-lg

S2Dialog: Multimodal Dialogue Retrieval with Semantic and Acoustic-Style Modeling

arXiv:2608.14029v1 Announce Type: new Abstract: Multimodal dialogue retrieval aims to retrieve dialogues from multimodal dialogue banks that are similar to a target dialogue in terms of both textual s

researcharxiv-cs-cl

Agent Behavioral Contracts II: Certifying Compositional Reliability Without Assuming Independence

arXiv:2608.12895v1 Announce Type: new Abstract: Compositional reliability bounds for multi-agent systems multiply component reliabilities, a step licensed by a conditional-independence assumption that

Learning Under Treatment-Induced Label Indeterminacy with Expert Annotations of Counterfactual Outcomes: A Case Study in Neurological Prognostication

arXiv:2608.12477v1 Announce Type: new Abstract: Clinical prediction models are often developed as if the outcome of interest were cleanly observed for every patient. This assumption fails when treatme

safetyarxiv-cs-lg

Applications

RealMat: Realistic Materials with Diffusion and Reinforcement Learning

arXiv:2509.01134v2 Announce Type: replace-cross Abstract: Generative models for high-quality materials are particularly desirable to make 3D content authoring more accessible. However, the majority of

applicationsarxiv-cs-cv

Rules or Character? Scaling Laws for AI Safety Design

arXiv:2608.13345v1 Announce Type: new Abstract: Artificial Intelligence (AI) safety systems combine character shaping (e.g., Reinforcement Learning from Human Feedback [RLHF], Constitutional AI), whic

Scaling Representation Diversity: Modulated Attention and Reconstructive Regularization for Visual Grounding

arXiv:2608.12748v1 Announce Type: new Abstract: Referring Expression Comprehension (REC) is commonly studied under dataset-specific fine-tuning, resulting in specialist models with limited cross-datas

Agents

SynAct: A Reasoning-Acting Large Language Model Agent for Adaptive Synthesis Optimization

arXiv:2608.12751v1 Announce Type: cross Abstract: Logic synthesis transforms RTL designs into gate-level netlists, where PPA results are highly sensitive to the choice of optimization commands, making

agentsarxiv-cs-ai

DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation

arXiv:2608.11889v1 Announce Type: cross Abstract: Prompting-based (extit{i}.extit{e}., non-fine-tuning) Text-to-SQL methods, where underlying large language model parameters are not changed for the ta

FrontierFinance: A Challenging Benchmark for Measuring Frontier Intelligence of Finance Agents

arXiv:2608.11683v1 Announce Type: new Abstract: AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow. Existi

G0.5: One Autoregressive Stream for Robot Reasoning and Action

arXiv:2608.11739v1 Announce Type: cross Abstract: The prevailing recipe for Vision-Language-Action (VLA) models couples a pretrained VLM with a separately trained flow-matching action expert. This mak

Information Abundance Paradox: Long-Context Training Undermines Parametric Knowledge

arXiv:2608.12218v1 Announce Type: cross Abstract: Large language models are increasingly trained and deployed with long contexts that span documents, code repositories, and interaction histories. This

Inverse Theory of Mind Modeling for Content Recommendation: From Web Browsing to Dynamic Intelligent Interfaces

arXiv:2608.11354v1 Announce Type: new Abstract: Modern recommender systems treat observed actions as reliable proxies for user preferences, yet interactions often reflect exploration or comparison rat

safetyarxiv-cs-ai

Measure, Don't Optimize: Forecasting Recovery in LLM Unlearning

arXiv:2608.11408v1 Announce Type: new Abstract: Prior white-box studies show that large language models can retain latent traces of target knowledge after unlearning, even when the knowledge is no lon

researcharxiv-cs-cl

Representation Finetuning for Continual Learning

arXiv:2603.11201v3 Announce Type: replace-cross Abstract: The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams. While pre-trained models

Surfsvr: 2D Surface Priors as 3D Geometric Regularizers for Sparse Voxel Reconstruction

arXiv:2608.11938v1 Announce Type: new Abstract: Sparse voxel reconstruction offers an efficient representation for high-fidelity 3D modeling, yet its geometry is commonly optimized from local photomet

The Edge-based Contiguous p-median Problem with Connections to Logistics Districting

arXiv:2608.11230v1 Announce Type: new Abstract: This paper introduces the edge-based contiguous p-median (ECpM) problem to partition the roads in a network into a given number of compact and contiguou

researcharxiv-cs-ai

The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy

arXiv:2607.11175v2 Announce Type: replace Abstract: The growing ability of large language models and vision-language models to jointly interpret and reason over images and text is reshaping medical im

Compositional Benchmark Synthesis for Hierarchical Human Action Recognition

arXiv:2608.10765v1 Announce Type: new Abstract: Recognizing human behavior across levels of abstraction, from atomic actions to long-horizon intentions, requires data annotated along a semantic hierar

Agents

Cross-View Sequential Visual Localization with Spatio-Temporal Context Modeling for Autonomous Driving

arXiv:2608.10660v1 Announce Type: cross Abstract: Continuous and reliable localization is essential for autonomous driving. Cross-view visual localization matches ground images with satellite maps, pr

agentsarxiv-cs-ai

DashArena: Benchmarking LLMs on Interactive Analytic Dashboard Generation

arXiv:2608.10567v1 Announce Type: new Abstract: Analytic dashboards combine coordinated views and interactions for data exploration and decision-making. Recent models can generate them from data and n

FoR-SALE: Frame of Reference-guided Spatial Adjustment in LLM-based Diffusion Editing

arXiv:2509.23452v2 Announce Type: replace-cross Abstract: Current text-to-image generation models, even state-of-the-art models, exhibit a significant performance gap when spatial expressions are desc

safetyarxiv-cs-cl

Generating Attacks for LLMs with GFlowNets

arXiv:2608.10171v1 Announce Type: new Abstract: The rapid advancement of Large Language Models (LLMs) has facilitated their ubiquitous integration into various domains, leading to widespread adoption.

researcharxiv-cs-ai

SQuaT: Self-Supervised Knowledge Distillation via Student-Aware Quantized Teacher Features

arXiv:2608.10709v1 Announce Type: new Abstract: Quantization-Aware Training (QAT) enables the deployment of quantized models with minimal accuracy degradation. However, in practical scenarios, trainin

researcharxiv-cs-lg

VibeLifeBench: Can Your Life Agent Be Proactive and Persistent in a Living World?

arXiv:2608.10875v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly deployed as personal assistants. Existing evaluations, however, mostly use short, self-contained re

Where To Look? : Causal Tracing of Vision Encoders in VLM

arXiv:2608.10758v1 Announce Type: new Abstract: Vision-language models can describe an image with remarkable accuracy, yet a more fundamental question remains unanswered: what visual information actua

researcharxiv-cs-cv

An Expectation-Maximization Perspective on Reinforcement Learning for LLM Reasoning

arXiv:2504.18587v2 Announce Type: replace-cross Abstract: Reinforcement learning has emerged as a powerful approach for improving the reasoning capabilities of large language models, as demonstrated b

11 Aug 2026

BASIS: Breach-Aware Selective Prompt Injection Shielding with Prefill Attention Probes