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arXiv:2608.13394v1 Announce Type: cross Abstract: 6G networks will not be serving as communication infrastructures only; rather, they are expected to evolve into intelligent systems, where thousands o

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Research

How Significant Are the Real Performance Gains? An Unbiased Evaluation Framework for GraphRAG

DGX agent

arXiv:2506.06331v2 Announce Type: replace-cross Abstract: By retrieving contexts from knowledge graphs, graph-based retrieval-augmented generation (GraphRAG) enhances large language models (LLMs) to g

researcharxiv-cs-ai

14 Aug 2026

Labels Are Not Endpoints: Treatment Leakage and Construct Validity in MCP Agent Security Evaluation

arXiv:2608.12880v1 Announce Type: cross Abstract: Security evaluations of tool-using agents often equate stored labels with behavioral facts. We audit a preserved campaign by tracing 10,200 execution

Lines and Ladders: A Context-Aware Multi-Agent Framework for Large-Scale Retail Price Taxonomy

arXiv:2608.12674v1 Announce Type: new Abstract: Maintaining price consistency and executing an Every Day Low Price strategy is critical for global retailers. However, with catalogs spanning millions o

Measuring Curriculum-Labor Market Alignment at the Scale of a Program Portfolio

arXiv:2608.12356v1 Announce Type: cross Abstract: A college offering several overlapping computing degrees implicitly assumes that its programs are differentiated in line with how the labor market seg

Mimicry without understanding: the origins of decision bias in large language models

arXiv:2608.12339v1 Announce Type: cross Abstract: Large Language models (LLMs) were found to be susceptible to a host of social, affective, and cognitive biases. We examined two mechanisms through whi

MOSAIC: Unveiling the Moral, Social and Individual Dimensions of Large Language Models

arXiv:2603.00048v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed in sensitive applications including psychological support, healthcare, and high-stakes

Non-Degenerate Risk Certification for Automated Security Decisions: A Decision-Contract Theory with ATT&CK-Aligned Triage as a Worked Instance

arXiv:2608.12444v1 Announce Type: cross Abstract: An unconditional risk bound on automated decisions can be satisfied without automating anything, since a selector that never acts drives the bound to

researcharxiv-cs-lg

On the Structural Limits of Machine Learning Decision Systems: An Information-Theoretic, Interaction-Based, and Stochastic-Dynamical Perspective

arXiv:2608.13510v1 Announce Type: cross Abstract: Machine learning procedures are commonly evaluated in terms of predictive accuracy and computational efficiency. However, their achievable performance

agentsarxiv-cs-lg

Reasoning Jury: Multi-Model Consensus for Evaluating Reasoning Traces

arXiv:2608.12585v1 Announce Type: new Abstract: Improving reasoning LLMs requires the ability to judge the quality of long reasoning traces for effective reasoning data curation, strong training signa

Reliability-Aware Sexism Detection: Combining DPO with Annotator Agreement and Token-Level Confidence Scoring

arXiv:2608.12330v1 Announce Type: new Abstract: The detection of online sexism remains an open problem. Sexism detection is inherently subjective, yet most existing systems reduce multi-annotator labe

SoK: From Generation to Consumption of Privacy Documents in Software Systems

arXiv:2608.12511v1 Announce Type: cross Abstract: Privacy documents (e.g., privacy policies) are a central mechanism through which digital services disclose data practices and seek user consent. Over

safetyarxiv-cs-cl

StreamReason-Bench: Can Large Language Models Reason about Event-Time Stream-Processing Semantics?

arXiv:2608.12348v1 Announce Type: cross Abstract: Streaming systems increasingly hand work to large language models (LLMs) -- writing pipelines, triaging alerts, reading logs -- and all of it assumes

The AI Accountability Ecosystem in the Era of Language Models

arXiv:2608.12320v1 Announce Type: cross Abstract: This article reviews and updates the framework for accountability in AI based on account- ability ecosystems. We update the framework in light of the

Training and Benchmarking Code Generation for Physics-Inspired Animations

arXiv:2602.10840v2 Announce Type: replace Abstract: Large language models (LLMs) have been widely studied in areas such as mathematical reasoning, complex coding, and scientific problem solving. Howev

researcharxiv-cs-lg

When Should Multi-Round RAG Stop? Structured Stopping Judgments and Retrieval Reduction in Search-R1

arXiv:2608.13237v1 Announce Type: cross Abstract: Multi-round retrieval-augmented generation (RAG) must decide when to stop searching as evidence accumulates. Because the deployed policy is determined

safetyarxiv-cs-cl

When Your Agent Opens the Chat App: Agent-Controlled Search over Raw Chat Logs Rivals Structured Memory

arXiv:2608.12888v1 Announce Type: new Abstract: Agent-memory systems increasingly buy retrieval quality with structure, transforming raw conversation histories into summaries, embeddings, trees, or kn

A Modular Agentic Framework for Synthetically Constrained Multi-Objective Hit-to-Lead Optimization

arXiv:2608.11483v1 Announce Type: new Abstract: Hit-to-lead optimization requires iterative design of hit analogs across competing potency, selectivity, physicochemical, pharmacokinetic, safety, and s

Accuracy and Order Sensitivity Diverge Under Label-Free Strategies

arXiv:2608.11947v1 Announce Type: cross Abstract: Multiple-choice benchmarks are widely used to evaluate large language models, but MCQ scores conflate knowledge with sensitivity to option order, whic

Agent Safety Should Be a Runtime Contract

arXiv:2608.11274v1 Announce Type: cross Abstract: The dominant paradigm treats AI safety as a property to be instilled during model training via RLHF, DPO, or Constitutional AI. We argue this is struc

AI Guardrail Survival under Single-Cycle Agentic Self-Summarization

arXiv:2608.11392v1 Announce Type: cross Abstract: Long-running agents periodically compact their context, replacing the transcript with a model-generated summary.Recent work shows that dropping a stan

Behavior and Representation in Open-Weight Large Language Models for Combinatorial Optimization: From Feature Extraction to Algorithm Selection

arXiv:2512.13374v2 Announce Type: replace Abstract: Recent advances in Large Language Models (LLMs) open new perspectives for automation in optimization, yet little is known about whether their intern

BEST-KAG: Enhancing Question Answering of Building Engineering Standards with Multimodal Knowledge Graph Modeling and Large Language Model

arXiv:2608.11244v1 Announce Type: new Abstract: Construction standards are critical for building safety and sustainability. Existing standard application workflows rely on keyword-based document retri

Chemically Meaningful Textualization Enables Explainable Validation of Metal-Organic Frameworks by Large Language Models

arXiv:2608.11283v1 Announce Type: cross Abstract: Computation-ready metal-organic framework (MOF) databases are essential for high-throughput screening, yet many reported crystal structures remain che

Commonsense on Demand: Generating and Selectively Integrating Commonsense Knowledge for Natural Language Inference

arXiv:2507.15100v3 Announce Type: replace-cross Abstract: Natural Language Inference (NLI) determines whether a premise entails, contradicts, or is neutral with respect to a hypothesis. The task is of

Conflict and Congruency Effects in Large Language Models: In-Weight and In-Context Competition in a Verbal Conflict Task

arXiv:2608.11510v1 Announce Type: cross Abstract: Congruency effects, observed in conflict tasks such as Stroop and flanker tasks, have been investigated for nearly a century in psychology and neurosc

Confucius4-TTS: Transcript-Free Cross-Lingual Zero-Shot TTS with a Learnable Speaker Encoder

arXiv:2608.11650v1 Announce Type: cross Abstract: Recent advances in zero-shot text-to-speech (TTS) have substantially improved speech quality and voice cloning fidelity. However, many zero-shot TTS s

Designing Agentic AI-Based Screening for Portfolio Investment

arXiv:2603.23300v2 Announce Type: replace-cross Abstract: We introduce a new agentic artificial intelligence (AI) platform for portfolio management. Our architecture consists of three layers. First, t

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

Do Not Forget the Obvious - RISC: A Risk-Informed Slice-Coverage Protocol for Safe Autonomous Driving

arXiv:2608.12051v1 Announce Type: new Abstract: Aggregate metrics may not fully reflect performance in insufficiently examined high-risk driving conditions. We propose RISC (Risk-Informed Slice Covera

safetyarxiv-cs-cv

From Monolithic to Modular: Segment-level Automatic Prompt Optimization

arXiv:2608.11219v1 Announce Type: new Abstract: Automatic Prompt Optimization (APO) often rewrites prompts monolithically, which can improve one behavior while degrading others. We present SAPO, a seg

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

Glance, Scrutinize, and Think: Advancing Video Anomaly Detection from Training-Free to Agentic Reasoning

arXiv:2608.11260v1 Announce Type: new Abstract: Video Anomaly Detection (VAD) aims to identify anomalous events and localize their temporal intervals. Existing approaches exhibit a 'when-what' dissoci

How China-Origin Vision-Language Models Move from Refusal to Reframing in State Alignment

arXiv:2608.11816v1 Announce Type: cross Abstract: State-aligned distortion has been documented in China-origin text-based large language models (LLMs), but whether, and in what form, it arises in mult

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

LoRAQuant: Mixed-Precision Quantization of LoRA to Ultra-Low Bits

model-releasesarxiv-cs-lg

arXiv:2510.26690v3 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has become a popular technique for parameter-efficient fine-tuning of large language models (LLMs). In many real-world sc

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

arXiv:2608.12004v1 Announce Type: cross Abstract: In modern AI frameworks, GPU kernels are key to overall system performance. Combining usability, portability, and near-handwritten CUDA performance, T

SAG: SQL-Retrieval Augmented Generation with Query-Time Dynamic Hyperedges

arXiv:2608.12129v1 Announce Type: new Abstract: While retrieval-augmented generation (RAG) has proven effective at giving LLMs access to external knowledge, mainstream dense-retrieval implementations

Social Chain of Thought: A Multi-Agent Architecture Grounded in Medical Differential Diagnosis Methodology

arXiv:2608.11420v1 Announce Type: new Abstract: Medical diagnostic reasoning is a high-impact use case for LLMs that carries significant implications for the health and wellbeing of users. When OpenAI

TD-VAD: Breaking Visual Dependence in Video Anomaly Detection with Text-Driven Learning

arXiv:2608.11820v1 Announce Type: new Abstract: Visual data is typically a prerequisite for training existing video anomaly detection (VAD) methods. However, obtaining sufficient annotated anomaly dat

researcharxiv-cs-cv

Uncertainty as a Planning Signal: Multi-Turn Decision Making for Goal-Oriented Conversation

arXiv:2604.03924v2 Announce Type: replace-cross Abstract: Goal-oriented conversational systems require making sequential decisions under uncertainty about the user's intent, where the algorithm must b

Applications

Assessing Reliability of BERT-Based Models on Question Answering Tasks

arXiv:2608.10806v1 Announce Type: new Abstract: Reliability estimation of large language models is in many cases as crucial as their accuracy, as reliable models are more trustworthy, robust, and suit

applicationsarxiv-cs-cl

Attention-Path Fragility as an Uncertainty Signal in Large Language Models

arXiv:2608.11138v1 Announce Type: cross Abstract: We propose that a model's uncertainty about a token is reflected not only in the breadth of its output distribution but also in whether a confident pr

Beyond Forecasting: Recasting Volatility Control as a Routing Problem

arXiv:2608.10375v1 Announce Type: cross Abstract: Volatility control converts risk estimates into portfolio exposure, yet existing approaches often rely on a fixed volatility estimator or a pre-define

CapProbe: Evaluating Detailed Image Captions via Full-Scene Dense Question Answering

model-releasesarxiv-cs-cv

arXiv:2608.11074v1 Announce Type: new Abstract: Evaluating detailed image captions from Vision-Language Models (VLMs) requires going beyond surface-level semantic similarity. Reference-based metrics (

ConRub-Med: Reinforcement Learning with Consensus Rubrics for Open-Ended Medical Question Answering

arXiv:2608.10996v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards has been especially effective in mathematics and coding, where answers can be checked automatically. Many

safetyarxiv-cs-cl

Applications

ConVAWG: A Retrieval-Grounded Framework for Controlled Synthetic Dialogue Generation in Violence Against Women and Girls

arXiv:2608.11200v1 Announce Type: cross Abstract: Synthetic dialogue generation offers a way to study conversational dynamics in sensitive domains where real data are difficult to access, release, or

applicationsarxiv-cs-ai

Conversational versus Dashboard Explainable AI for UAV Intrusion Detection: An Empirical Study of Operator Trust and Reliance

arXiv:2608.10434v1 Announce Type: new Abstract: Machine learning-based Intrusion Detection Systems (IDS) have demonstrated superior performance in securing Unmanned Aerial Vehicle (UAV) networks. Howe