Search: “reasoning”

Compiling VGDL into Causal Models

arXiv:2609.05459v1 Announce Type: new Abstract: Reinforcement learning and large language models often struggle to accurately capture the causal mechanics of game environments. Standard reinforcement

Cyber-Financial Contagion: Modeling the Propagation of an AI Vendor Compromise Through the Banking System

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DGX agent

arXiv:2609.10350v1 Announce Type: cross Abstract: The banking system now depends on a small set of shared artificial intelligence vendors for fraud screening, credit decisioning, anti-money-laundering

researcharxiv-cs-lg

10 Sept 2026

DI-Bench: Systematically Generating In-Domain Data Intelligence Benchmarks for Enterprise Agents

arXiv:2609.05776v1 Announce Type: new Abstract: Evaluating enterprise agents on domain-specific benchmarks is critical, yet public benchmarks rarely evaluate whether agents can integrate business know

Diamond Agent: Agentic Control of Federated HPC Resources as a Service

arXiv:2609.06181v1 Announce Type: cross Abstract: Efficiently aggregating and orchestrating computing power across heterogeneous clusters for HPC workflows faces four practical challenges: preserving

Do Dynamic Routers Need Memory? HeRo: History-Aware Routing for Efficient LLM Inference

arXiv:2609.08189v1 Announce Type: new Abstract: Dynamic layer routing reduces the inference cost of Large Language Models (LLMs) by learning to skip layers for individual tokens. Existing methods, how

Tutorials

Do speech foundation models really learn words?

arXiv:2609.10434v1 Announce Type: new Abstract: Self-supervised speech foundation models are now used in a wide array of downstream applications, including traditional speech recognition and as the ba

tutorialsarxiv-cs-cl

DSAEval: Evaluating Data Science Agents on a Wide Range of Real-World Data Science Problems

arXiv:2601.13591v3 Announce Type: replace Abstract: Recent LLM-based data agents aim to automate data science tasks ranging from data analysis to deep learning. However, the open-ended nature of real-

EdgeMem: LLM-Free Agent Memory Construction and Retrieval via Evidence-Preserving Multi-Anchor Hypergraph

arXiv:2609.05553v1 Announce Type: new Abstract: Agent memory allows LLM agents to use earlier interactions when answering new queries. Existing methods often compress interaction histories into summar

local-aiarxiv-cs-ai

Elastic Horizon: Discovering the Effective Interaction Frontier in Agentic Reinforcement Learning

arXiv:2609.07247v1 Announce Type: new Abstract: Scaling the interaction horizon-the maximum number of environment interactions per episode-improves LLM agents on long-horizon tasks, and curriculum-bas

ESSA: Evolutionary Strategies for Scalable Alignment

model-releasesarxiv-cs-lg

arXiv:2507.04453v4 Announce Type: replace Abstract: Online alignment of large language models (LLMs) is dominated by reinforcement learning from human feedback (RLHF) with gradient-based optimizers su

Everything in Moderation: Per-Domain Coverage Optima and Alignment-Resistant Domain Gaps in Multi-Domain Mid-Training

arXiv:2609.09081v1 Announce Type: new Abstract: Mid-training, the stage between pre-training and alignment, is where a model's per-domain data composition is typically set by data availability rather

safetyarxiv-cs-ai

Explaining AI Agents Through Execution Traces

arXiv:2609.06063v1 Announce Type: new Abstract: AI Agents are increasingly deployed in real-world settings, where they interact with external tools and make sequential decisions with limited human ove

From Scores to Evidence: Auditable Decisions Can Improve Speech Deepfake Detection

arXiv:2609.08899v2 Announce Type: replace-cross Abstract: Speech deepfakes can mimic a speaker's voice convincingly enough to deceive listeners and automated systems. This has driven strong progress i

researcharxiv-cs-cl

From Simulated Citizens to Simulated Deliberation: Challenges in Representation and Interaction

arXiv:2609.07573v1 Announce Type: new Abstract: Multi-agent LLM deliberation has been explored as a scalable way to simulate public deliberation. For such simulations to be informative, persona agents

safetyarxiv-cs-ai

GANDR: Claim Auditing for Verifiable Legal Answer Generation

arXiv:2609.10293v1 Announce Type: new Abstract: In high-stakes domains such as legal practice, a language-model answer is only useful to the extent that a reader can verify each claim against the sour

Generating Instance Generators in PDDL Planning

arXiv:2609.06071v1 Announce Type: new Abstract: PDDL, the de-facto standard language in the AI Planning community, is designed to specify planning domains: sets of instances that share the same predic

researcharxiv-cs-ai

Knowing Your Uncertainty -- On the application of LLM in social sciences

arXiv:2512.05461v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are rapidly being integrated into computational social science research, yet their blackboxed training and design

researcharxiv-cs-ai

LiteRAG: Cost-Efficient Graph-Based Retrieval-Augmented Generation

arXiv:2609.10239v1 Announce Type: cross Abstract: Graph-based retrieval can improve multi-hop question answering, but existing approaches often incur high query-time costs and produce diffuse, oversiz

LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole-Slide Images

arXiv:2601.03163v2 Announce Type: replace Abstract: Background and Objective: Precise and scalable instance segmentation of cell nuclei is a fundamental prerequisite for computational pathology, yet g

local-aiarxiv-cs-cv

Mapping the Emerging Social Science of Large Language Models

arXiv:2609.07598v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly shape communication, learning, work, creativity, and decision-making, yet social-science research on these d

safetyarxiv-cs-ai

Monadic Second-Order Logic in HOL: Deep and Shallow with Automated Faithfulness (Extended Preprint)

arXiv:2609.07345v1 Announce Type: cross Abstract: In Isabelle/HOL, we apply the deep-and-shallow embedding methodology of our prior work to monadic second-order logic (MSO). Three embeddings are devel

local-aiarxiv-cs-ai

MVFA: A Multi-View Text-Guided Multimodal Fusion LLM Adapter for Sentiment Analysis and Emotion Recognition

arXiv:2609.06188v1 Announce Type: new Abstract: Multimodal sentiment analysis and emotion recognition in conversations demand effective modeling of heterogeneous interactions across textual, acoustic,

OntoKG-EQ: A provenance-grounded, competency-question-governed knowledge graph for auditable analyst querying

arXiv:2609.08869v1 Announce Type: cross Abstract: Analysts in emerging equity markets keep answering the same questions. Did fundamentals match the market's response? How does the local currency co-mo

OntologyBench: Can Dense Retrieval Satisfy Structured Biomedical Constraints?

arXiv:2609.08174v1 Announce Type: new Abstract: We introduce OntologyBench, a tiered biomedical retrieval benchmark comprising 471,854 training and 125,744 evaluation query-document relevance pairs ac

Personalized Execution Time Optimization for Billion-Scale Scheduled Jobs

arXiv:2203.06158v3 Announce Type: replace Abstract: Scheduled batch jobs are widely used on asynchronous computing platforms to execute enterprise applications such as promotional notifications and ca

safetyarxiv-cs-lg

PhysMAS: Physics-Grounded Multi-Agent Synthesis of Compositional 4D Gaussians

arXiv:2609.07174v1 Announce Type: new Abstract: Efficient, fully automatic, and physically plausible 4D Gaussian synthesis is an important goal for dynamic scene generation. Recent physics-based metho

Applications

RealSimLoop: Online Real-to-Sim Adaptation via Differentiable Reduced-Order Simulation with Vision Feedback

arXiv:2609.09828v1 Announce Type: cross Abstract: Real-world observations of deformable objects are often sparse or surface-level, while downstream tasks require hidden physical quantities such as int

applicationsarxiv-cs-cv

Applications

SequenceO1: End-to-End Ultra-Long (100K) Sequence Modeling in Recommendation with Low-Rank Caching

arXiv:2609.08443v1 Announce Type: cross Abstract: Modeling long-term user behavior is central to sequential recommendation and billion-scale industrial recommender systems, yet production ranking mode

applicationsarxiv-cs-ai

SerenAI: State-transition system inspired by text-based world AI models

arXiv:2609.06647v1 Announce Type: new Abstract: Although professional workflows leverage large language models widely, the interpretation for auditing unconstrained free-text generation is usually int

Show-Harness: Just a VLM Agent Can Play Robots

arXiv:2609.10522v1 Announce Type: cross Abstract: Foundation vision-language models (VLMs) exhibit broad intelligence about the world, yet translating this intelligence into robot control remains chal

local-aiarxiv-cs-cv

Skynet: Workflow-Level Anomaly Detection for Agentic AI via Semantic and Structural Modeling

arXiv:2609.06835v1 Announce Type: cross Abstract: Agentic AI systems execute complex tasks through long-horizon workflows of planning, tool use, and multi-agent coordination. Task failures in these sy

local-aiarxiv-cs-ai

Staying on the Attack Path: Structured State for Long-Horizon Automated Penetration Testing

arXiv:2609.07344v1 Announce Type: cross Abstract: Large language model (LLM) based agents are increasingly applied to cybersecurity tasks such as vulnerability discovery and automated penetration test

StreamAlign: Streaming Text-Aligned Speech Tokenization

arXiv:2609.09719v1 Announce Type: new Abstract: Text-aligned speech tokenization methods have emerged to better align speech tokens with LLM token spaces, enabling more effective utilization of pretra

safetyarxiv-cs-cl

StreetDiff: Multi-view Street Scenes Generation via Cross-view Consistent Multi-view Stable Diffusion with Structure Prompts

arXiv:2609.09890v1 Announce Type: new Abstract: Multi-view diffusion models have shown strong performance in scenes with strong geometric priors and sparse semantics, such as indoor rooms or simple ou

safetyarxiv-cs-cv

Style Over Substance: Content-Invariant Wrappers Flip LLM Safety-Judge Verdicts

arXiv:2609.08236v1 Announce Type: new Abstract: Automatic safety judges -- systems such as Llama Guard or a GPT-4o grading prompt that decide whether a model's reply is harmful -- produce the numbers

Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks

arXiv:2609.09233v1 Announce Type: cross Abstract: How can language model agents effectively leverage libraries of reusable knowledge to solve long-horizon tasks? Recent work has increasingly focused o

SWORD: Wikidata-based Distortions Reveal Hidden Cross-Lingual Inconsistencies in LLM Factual Error Rejection

arXiv:2609.09349v1 Announce Type: new Abstract: Modern LLMs demonstrate impressive multilingual performance, yet standard benchmarks primarily reward selecting correct answers rather than evaluating g

Symbolic Informalization: Fluent, Productive, Multilingual

arXiv:2606.16893v2 Announce Type: replace Abstract: Symbolic informalization enables a reliable conversion of formal mathematics to natural language. It has the potential to make machine-checked conte

researcharxiv-cs-ai

The Living Library: Transforming Archival Collections into Conversational Knowledge Systems -- Lessons from the Theodore Roosevelt Presidential Library

model-releasesarxiv-cs-cv

arXiv:2609.09368v1 Announce Type: new Abstract: We present the Living Library, an end-to-end framework for transforming fragmented digital archives into governed, conversational, in-person exhibit exp

Applications

Train Overcomplete, Deploy Compact: Scaling Recovery Capacity for Structured LLM Pruning

arXiv:2609.06974v1 Announce Type: cross Abstract: Large language models achieve strong performance across diverse tasks, but deployment remains costly because of memory, latency, and energy demands. S

applicationsarxiv-cs-ai

VANTAGE-Bench: Evaluating the Infrastructure AI Gap in Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2609.09396v1 Announce Type: new Abstract: As Vision-Language Models (VLMs) advance toward physical deployment, the focus has remained on action-oriented Embodied AI evaluated on subject-centric

When Does Defendant Statement Matter? A Study of Bias and Persuasion in LLM-Simulated Jurors

arXiv:2609.09887v1 Announce Type: new Abstract: LLMs have been used to simulate human decision-making in professional settings, yet their behaviors in common-law jury trials remain unexplored. We stud

World-Time Compute with Verified Code World Models

arXiv:2609.09163v1 Announce Type: new Abstract: LLMs generalize across a domain only after seeing many real, labeled examples, which most domains lack. We study a way to manufacture it cheaply. When a

researcharxiv-cs-lg

X2-NativeCursor: Native-Token Text Progress Tracking for Incremental-Text Streaming Codec TTS

arXiv:2609.09677v1 Announce Type: new Abstract: Incremental-text streaming text-to-speech (TTS) needs online text progress tracking for synchronized highlighting, interruption handling, and dialogue-h

safetyarxiv-cs-cl

A Two-Stage Framework for Ego-Centric Key Object Identification via Object State Prediction

arXiv:2609.07125v1 Announce Type: new Abstract: This paper presents a novel framework designed to enhance key object identification in autonomous driving. Existing methods primarily focus on either de

agentsarxiv-cs-cv

Accuracy is Not Enough: A Divergence-Based Approach to Evaluate Fidelity Loss in Quantized LLMs

model-releasesarxiv-cs-lg

arXiv:2609.07664v1 Announce Type: new Abstract: Deployment of Large Language Models (LLMs) on memory-constrained edge devices relies heavily on aggressive post-training quantization. However, evaluati

Adapting Technical-Service LLM Agents with Latent Logic Augmentation, Robust Noise Reduction, and Hybrid Reward Modeling

arXiv:2603.18074v2 Announce Type: replace Abstract: Technical-service LLM agents are entering production workflows, where value depends on whether engineers adopt generated replies. Service tickets hi

safetyarxiv-cs-lg

AnomalyCraft-700K: Component-Level Controllable and Verifiable Synthetic Anomalies for Fine-Grained Video Anomaly Understanding

arXiv:2609.06978v1 Announce Type: new Abstract: Progress in video anomaly understanding (VAU) has long been limited by inherent deficiencies of real-world anomaly videos, which are hard to collect and

safetyarxiv-cs-cv