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arXiv:2608.02618v1 Announce Type: new Abstract: Recent studies have identified an ``Artificial Hivemind'' effect in Large Language Models (LLMs) causing models to converge on a narrow, homogenized con

researcharxiv-cs-ai

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Safety

CT-HEG: A Bidirectional, Timestamp-Attributed Event Graph for ICU In-Hospital Mortality Prediction - An Architectural Ablation Study

DGX agent

arXiv:2608.02663v1 Announce Type: cross Abstract: Accurate ICU mortality prediction requires modeling irregular clinical observations across heterogeneous entity types. Existing sequence models handle

safetyarxiv-cs-ai

5 Aug 2026

DiagChain: A Diagnostic Benchmark for Evaluating LLM Agents on Evidence-Grounded Attack Chain Reconstruction

arXiv:2608.03591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents offer a promising approach to attack chain reconstruction by retrieving and interpreting heterogeneous telemetry to

Disentangling MLP Neuron Weights in Vocabulary Space

arXiv:2604.06005v2 Announce Type: replace Abstract: Interpreting the information encoded in language model weights remains a fundamental challenge in mechanistic interpretability. In this work, we int

Distractor-Aware Truncation: Disentangling Context-Length Effects from Signal Loss in Long-Context LLM Benchmarks

arXiv:2608.03297v1 Announce Type: new Abstract: A standard claim in the literature on retrieval-augmented and memory-augmented language models is that shorter context is better when the relevant infor

Don't Walk the Line: Boundary Guidance for Filtered Generation

arXiv:2510.11834v3 Announce Type: replace-cross Abstract: Generative models are increasingly paired with safety classifiers that filter harmful or undesirable outputs. A common strategy is to fine-tun

Applications

Enactive Artificial Intelligence: A Decision-Centric Architecture for Complex Systems

arXiv:2608.03413v1 Announce Type: new Abstract: As artificial intelligence (AI) continues to evolve and mature, recent AI practices have moved beyond large language models (LLMs) and text or image gen

applicationsarxiv-cs-ai

Evaluating LLMs in Database Scenarios: A Lifecycle Benchmark for Assessing Their Potential in Core Database Tasks

arXiv:2608.03794v1 Announce Type: cross Abstract: Large Language Models (LLMs) are transforming database interaction paradigms, evolving from simple query translators to autonomous database administra

Externally Validated Breast Ultrasound Segmentation via Multi-task Learning with BI-RADS-Consistent Morphological Priors

arXiv:2511.15968v2 Announce Type: replace-cross Abstract: External validation of breast ultrasound segmentation models remains limited because internal train--test splits do not capture domain shifts

FakeI2V-Bench: Benchmarking the Applicability of Image-level Deepfake Detectors for Deepfake Video Detection

arXiv:2608.03096v1 Announce Type: cross Abstract: Recent advances in video generation models have significantly intensified the deepfake threat, yet the current deepfake video detection benchmarks rem

FLARE: Few-shot Learning-based Adaptive Reflective Engine

arXiv:2608.02919v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in complex, compound AI systems where performance hinges on the quality of prompts. Recent state-

Geo-Embed: Towards Unified Multimodal Embeddings for Urban Understanding

arXiv:2608.03826v1 Announce Type: new Abstract: Geospatial and urban applications increasingly require models to compare heterogeneous evidence across street-view imagery, remote-sensing observations,

KnowHal: A Knowledge-Driven Benchmark for Comprehensive Multimodal Hallucination Evaluation

arXiv:2608.03782v1 Announce Type: new Abstract: Hallucination remains a critical challenge for developing trustworthy Multimodal Large Language Models (MLLMs). While existing benchmarks mainly focus o

LDU-Bench: Multimodal LLM Evaluation for Lithography Defect Understanding under Layout-Varying Circuit Backgrounds

arXiv:2608.03078v1 Announce Type: new Abstract: Multimodal large language models have demonstrated strong defect recognition capability in industrial anomaly detection. However, in lithography review,

Localize, Don't Beautify: Client-Side Control of Image-Editing APIs for Cosmetic Surgery Previews

arXiv:2608.02841v1 Announce Type: new Abstract: Ask a commercial image editor to preview a cosmetic procedure and it will often change more of the face than the request names: a nose edit can also smo

Applications

LogitScope: A Framework for Analyzing LLM Uncertainty Through Information Metrics

arXiv:2603.24929v2 Announce Type: replace Abstract: Understanding and quantifying uncertainty in large language model (LLM) outputs is critical for reliable deployment. However, traditional evaluation

applicationsarxiv-cs-ai

PACE: Adaptive Budget Allocation for Time-Efficient Embodied Planning

arXiv:2608.03034v1 Announce Type: cross Abstract: Reasoning-enhanced large language models have achieved remarkable improvements in planning tasks, yet their deployment in embodied systems remains imp

Pattern over Pixels: Measuring Pattern Completion Bias in Multimodal Code Generation

arXiv:2608.03691v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are increasingly used to translate webpage screenshots into front-end code, but repeated UI patterns may sway

Rethinking Self-Evolving Agent Skills: Feedback Dynamics over Multiple Rounds

arXiv:2608.02636v1 Announce Type: cross Abstract: Self-evolving skill systems promise to improve agents by turning execution feedback into persistent skill updates without changing the underlying mode

Rubrics as Privileged Information for Open-Ended Generation

arXiv:2608.02948v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD), where a single model acts as both student and teacher with different contexts, has shown promise in verifiable dom

Screenshots or Tools? Eliciting Tool Use and Managing Multimodal Context in Hybrid GUI-MCP Computer-Use Agents

arXiv:2608.03327v1 Announce Type: new Abstract: Hybrid computer-use agents can act through screenshots or call text tools. We find that having a tool available does not settle which way the effect goe

Research

Separating quantum circuits from classical LLMs

arXiv:2608.03962v1 Announce Type: cross Abstract: Modern large language models - transformers and diffusion language models - are built around two canonical algorithmic tasks: prediction and generatio

researcharxiv-cs-ai

Tutorials

Soft Guidance Starts to Outperform CoT Prompting as LLMs Improve

arXiv:2608.03550v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting remains the standard baseline for evaluating models' reasoning abilities. Originally, this technique was introduced to

tutorialsarxiv-cs-ai

SpatialCLI: Learning to Reason With Spatial Tools, Then Without Them

arXiv:2607.27703v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly used in embodied agents to interpret visual inputs, reason about spatial relationships, and make task

StereoVGGT: A Training-Free Visual Geometry Transformer for Stereo Vision

arXiv:2603.29368v2 Announce Type: replace Abstract: Driven by the advancement of 3D devices, stereo vision tasks including stereo matching and stereo conversion have emerged as a critical research fro

SUV: Future Scene Understanding as Video Generation for End-to-End Driving

arXiv:2608.03084v1 Announce Type: new Abstract: End-to-end driving requires a coherent understanding of future scenes, yet existing methods model these scenes using task-specific heads and output form

Unequal Verdicts: Investigating Gender Bias in LLM-Based Fake News Detection

arXiv:2608.03627v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used for automated fact-checking, yet their susceptibility to gender bias in this context remains underexp

AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification

arXiv:2509.24560v2 Announce Type: replace Abstract: Extended Chain-of-Thought (CoT) reasoning has significantly bolstered the capabilities of medical large language models (LLMs). However, current mod

ChaosProbe: A Neurochaotic Lens on Frozen Transformer Input-Embedding Spaces

arXiv:2608.01968v1 Announce Type: new Abstract: Transformer models are most often understood through what they do: their benchmark performance, generation quality, or behavior on downstream tasks. Yet

Control Under Compression: Reliability Frontiers for Tool-Using Agents

arXiv:2608.01056v1 Announce Type: cross Abstract: Tool-using language-model agents are governed not only by task prompts but also by persistent system-side instructions that specify tools, arguments,

DeBERTa-Sentinel: Toward Transparent and Trustworthy Detection of AI-Generated Text

arXiv:2608.01046v1 Announce Type: new Abstract: The rapid spread of large language models (LLMs) across the web raises concerns about misinformation, academic integrity, automated content manipulation

Decoupling semantics from vision: A framework for faithful visual-text compression evaluation

arXiv:2608.01848v1 Announce Type: new Abstract: Recent visual-text compression (VTC) methods, typified by DeepSeek-OCR, report impressive high token compression ratios for long-context modeling tasks

Defending Membership Inference Attacks via Privacy-aware Sparsity Tuning

arXiv:2410.06814v2 Announce Type: replace Abstract: Over-parameterized models are typically vulnerable to membership inference attacks, which aim to determine whether a specific sample is included in

DeltaFlow: Noise-Adaptive Bidirectional Gated Delta Networks for Embedded Language Flows

arXiv:2608.01240v1 Announce Type: new Abstract: Embedded Language Flows (ELF) rely primarily on full non-causal attention for iterative denoising, repeatedly incurring quadratic sequence-mixing cost a

ET-Prune: Evidence-Aware Dynamic Budgeting for Visual Token Pruning in Text-Rich MLLMs

arXiv:2608.01979v1 Announce Type: cross Abstract: Visual token pruning reduces the inference cost of multimodal large language models, but a fixed token ratio is poorly matched to text-rich inputs. In

Feed-Forward Steering in Transformer Residual Dynamics

arXiv:2608.02071v1 Announce Type: new Abstract: Attention-only dynamical theories model Transformer residual directions as particles aggregating on a sphere. We extend this framework by incorporating

Grounding Agentic VLMs with Dedicated Segmentation for Fine-Grained Vehicle Damage Assessment

arXiv:2608.02470v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly deployed as reasoning agents in real-world visual assessment pipelines, yet their spatial grounding remai

HarMoE: Multi-Source Chest Radiograph Pretraining with Dataset-Disentangled Experts

arXiv:2608.02252v1 Announce Type: new Abstract: Recent vision-language models for chest X-ray understanding are largely built on image-report alignment and therefore rely heavily on MIMIC-CXR as the d

Humans Are More Diverse: Frontier LLMs Show Extreme Policies in Idealised AI Development Races

arXiv:2608.01193v1 Announce Type: cross Abstract: An AI development race creates a multi-agent safety dilemma. Each company can develop slowly and safely, or move faster while taking a risk that may r

InteracVid: Building a Real Interactive Audio-Visual Response Dataset from Live-Chat Videos

arXiv:2608.01157v1 Announce Type: new Abstract: Large language models have made text the default medium for human--AI interaction, buttext alone cannot express the full range of responses required by

Research

KING: Embodiment-Aware Kinematic Graph Neural Network for Unified Motion Representation of Legged and Wheeled Robots

arXiv:2608.01015v1 Announce Type: new Abstract: Kinematic models provide reliable motion constraints for odometry estimation in featureless environments, where exteroceptive sensing degrades and IMU i

researcharxiv-cs-ro

LangFIR: Discovering Sparse Language-Specific Features from Monolingual Data for Language Steering

arXiv:2604.03532v2 Announce Type: replace Abstract: Large language models (LLMs) show strong multilingual capabilities, yet reliably controlling the language of their outputs remains difficult. Repres

Local Ai

Language Equality has a Price: A Systematic Investigation of Multi-turn LLM Performance for EU-24+

arXiv:2608.01395v1 Announce Type: new Abstract: We evaluate large language models (LLMs) as language agents playing goal-directed dialogue games in self-play across 30 languages: the 24 official EU la

local-aiarxiv-cs-cl

LongHorizon-Harness: Advancing Long-Horizon Agents for Real-World Tasks

arXiv:2608.01964v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly undertake long-horizon tasks that require sustained reasoning, tool use, and revision across many interde

Machine-Precision Prediction of Low-Dimensional Chaotic Systems from Noise-Free Data

arXiv:2507.09652v2 Announce Type: replace-cross Abstract: Low-dimensional chaotic systems such as the Lorenz-63 model are commonly used to benchmark system-agnostic methods for learning dynamics from

onepot-Bench 0: towards lab-aware in silico chemistry benchmarks

arXiv:2608.02595v1 Announce Type: new Abstract: Language models are playing an increasingly important role in laboratory science, performing tasks such as experiment planning, execution, and post-hoc

OpenART: Scaling Agent Red Teaming via Open-Ended Environment Evolution

arXiv:2608.00677v1 Announce Type: new Abstract: AI agents operate in persistent environments where early state changes can influence decisions far into the future. Unlike conventional language-model i

Passing Coarse Marginal Checks Can Be Cheap: Persona Mixtures and Imprecise Treatment-Response Estimates in an LLM Persona Panel

arXiv:2608.00979v1 Announce Type: cross Abstract: Large language models are increasingly used as synthetic research participants and are often validated by whether their marginal responses resemble hu