Search: “models”

Argus: A General-Purpose Agentic Runtime for Long-Horizon Reasoning

arXiv:2608.05144v1 Announce Type: new Abstract: Long-horizon reasoning requires an agentic runtime that can persist when evidence supports its current approach and pivot when measurements reveal failu

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Beyond the Dirac Delta: Mitigating Diversity Collapse in Reinforcement Fine-Tuning for Versatile Image Generation

DGX agent

arXiv:2601.12401v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning large-scale generative models, such as diffusion and flow model

safetyarxiv-cs-ai

6 Aug 2026

ContextWeave: A Real-World Workflow Benchmark

arXiv:2608.04830v1 Announce Type: new Abstract: Memory is essential as language agents move from isolated tasks to long-horizon, stateful workflows, yet existing evaluations often reduce it to retriev

Safety

COSMO: Consensus-Driven Shift Modulation for Source-Free Domain Adaptation

arXiv:2608.04604v1 Announce Type: new Abstract: Source-free domain adaptation (SFDA) adapts a source-trained model to an unlabeled target domain without source data, a practical setting under privacy

safetyarxiv-cs-cv

Diverse and Plausible Algorithmic Recourse via Tractable Recourse Distributions

model-releasesarxiv-cs-lg

arXiv:2608.04677v1 Announce Type: new Abstract: Algorithmic recourse seeks to help individuals reverse unfavorable automated decisions by recommending actionable changes that achieve a desired outcome

Does Out-of-Sight Equal Out-of-Mind in CoT Monitorability?

arXiv:2608.04928v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning offers a window into the decision-making of large language models (LLMs), which can be monitored for target behaviors b

FinProBench: Evaluating Financial AI Agents with Role-Grounded Rubrics Derived from Professional Deliverables

arXiv:2608.04077v1 Announce Type: new Abstract: Evaluating financial AI agents requires criteria aligned with real professional work. Existing rubric methods typically derive criteria from task prompt

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From Brute Force to Semantic Insight: Performance-Guided Data Transformation Design with LLMs

arXiv:2601.03808v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved notable performance in code synthesis; however, data-aware augmentation remains a limiting factor, handle

safetyarxiv-cs-cv

InsightEmb: Learning Action-Intent Embeddings for Agentic Insight Retrieval

arXiv:2608.04761v1 Announce Type: cross Abstract: Self-improving agents accumulate reusable insights from prior trajectories, making retrieval increasingly important for turning accumulated experience

Monte Carlo Tree Search for Table-to-Multimodal Report Generation

arXiv:2608.04071v1 Announce Type: new Abstract: Automatically generating professional multimodal reports comprising both textual analysis and visual charts from structured tabular data is a critical c

NOLLI: A Difficulty-Calibrated Puzzle Benchmark for Diagnosing the English-Korean Performance Gap

arXiv:2608.04397v1 Announce Type: new Abstract: We introduce NOLLI, a procedurally generated English-Korean puzzle benchmark designed to diagnose where Korean performance gaps arise. It comprises 15 p

PICopilot: An LLM-based Agentic Framework for Assisting Photonic Integrated Circuit Design via Script Generation

arXiv:2608.01791v2 Announce Type: replace-cross Abstract: The rapid development of photonic integrated circuits (PICs) is shifting the design flow from traditional graphical user interface (GUI)-based

Privacy-Preserving Action Recognition: Taxonomy, Methods, and Privacy-Utility Trade-offs

arXiv:2608.04501v1 Announce Type: new Abstract: Video surveillance in public safety, healthcare, and smart environments has made continuous human monitoring routine, raising real risks to personal ide

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality

arXiv:2608.03487v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG), which augments large language model (LLM) generation with information retrieved from databases, has become a wid

researcharxiv-cs-ai

Robust Control under Stationary Ambiguity

model-releasesarxiv-cs-lg

arXiv:2608.04832v1 Announce Type: new Abstract: Control policies optimized in simulation can perform poorly in the real system when the parameters x of the simulator are estimated from limited data bu

SpecDrop: Parameter-Free Category-Conditioned Routing for Modular Specialization

arXiv:2608.04084v1 Announce Type: cross Abstract: Mixture-of-experts (MoE) networks pursue specialization through learned routers, gates, and load-balancing losses, yet at matched total-parameter budg

Towards a satellite image manipulation and deepfake localization benchmark dataset

arXiv:2608.04840v1 Announce Type: cross Abstract: Verifying the authenticity of satellite imagery has become increasingly critical given advances in generative artificial intelligence. Highly realisti

Towards Valid B-Rep Generation: Training-Free Wireframe Anomaly Detection and Repair

arXiv:2608.04955v1 Announce Type: new Abstract: Multi-stage boundary representation (B-Rep) generation leverages intermediate wireframes to synthesize CAD models. However, geometric and topological ri

researcharxiv-cs-cv

Tutorials

Agogic: Performance-Timed Music Tokens for LLM-Native Text-to-Symbolic-Music Generation

arXiv:2608.03999v1 Announce Type: cross Abstract: Text-to-music language models begin with a choice usually made by default: how to tokenize music. Normally entangled with backbone, data, and recipe,

tutorialsarxiv-cs-cl

ANCHOR-RE: An Agentic Neuro-Symbolic Framework for Grounded Biomedical Relation Extraction

arXiv:2608.03154v1 Announce Type: new Abstract: Biomedical relation extraction (BioRE) extracts structured knowledge from biomedical literature for applications such as knowledge base construction and

ANNOTARES: A Dataset for Extracting Logical Structures from German Statutory Texts

arXiv:2608.03898v1 Announce Type: new Abstract: The automatic structural analysis of legal texts is a cornerstone of legal technology, yet the extraction of their logical components remains a signific

Attention is Case-Sensitive

arXiv:2608.03711v1 Announce Type: cross Abstract: In human visual perception, uppercase lettering serves as a natural salience cue that captures attention within lowercase text. In this paper, we pres

BulkPR-Bench: Benchmarking Queue-Level Governance of Interacting Pull Requests

arXiv:2608.02685v1 Announce Type: cross Abstract: Coding-agent benchmarks increasingly cover long-horizon, end-to-end, and interactive development, but typically retain one requested outcome or a fixe

Can LLMs Test Terminal User Interfaces?

arXiv:2608.03743v1 Announce Type: cross Abstract: Terminal User Interfaces (TUIs) combine the stateful, screen-oriented behaviour of GUIs with terminal deployment and are now common in developer tools

CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification

arXiv:2403.09281v3 Announce Type: cross Abstract: We propose CLIP-EBC, the first fully CLIP-based model for accurate crowd density estimation. While the CLIP model has demonstrated remarkable success

researcharxiv-cs-ai

Safety

Deferred Exposure of Future Trajectories for Verifiable Reasoning in Autonomous Driving VLMs

arXiv:2608.01755v2 Announce Type: replace Abstract: Recent Vision-Language-Action (VLA) models for autonomous driving (AD) increasingly utilize chain-of-thought (CoT) supervision to enhance the reason

safetyarxiv-cs-ai

Local Ai

DiagLoop: A Counterfactual Data Flywheel with Stage-Localized Reinforcement for Diagnostic LLMs

arXiv:2608.03674v1 Announce Type: new Abstract: Causal diagnostic models must explain how conclusions follow from evidence because diagnoses guide repairs and treatments. Yet serious cases are scarce,

local-aiarxiv-cs-lg

Don't Let Me Ask for It: LLMs Show Deficiencies in Active Multi-Turn Information Acquisition for Abductive Inference

arXiv:2608.03388v1 Announce Type: new Abstract: Abductive reasoning requires forming hypotheses that explain observed evidence and revising them as new evidence becomes available. While large language

Efficient unsupervised domain adaptation via self-supervised vision transformer and synergistic cross-domain alignment

arXiv:2407.21311v2 Announce Type: replace-cross Abstract: Unsupervised domain adaptation (UDA) aims to mitigate domain shift, where the distribution of labeled source data differs from that of unlabel

Enhancing Tabular Learners with Context-Aware Semantic Embeddings

arXiv:2608.03565v1 Announce Type: new Abstract: While modern tabular learners excel at capturing statistical patterns, they frequently operate in a semantic vacuum, treating textual features as discre

GoT-CD: Graph-of-Thoughts Causal Discovery and the Fragility of Post-hoc Path-Specific Fairness Audits

model-releasesarxiv-cs-lg

arXiv:2608.02877v1 Announce Type: new Abstract: Causal discovery recovers directed structure from observational data and is increasingly used in clinical settings to support mechanism reasoning and fa

In-Context Pure Exploration in Continuous Decision Spaces

arXiv:2602.17976v2 Announce Type: replace-cross Abstract: In active sequential testing, also termed pure exploration, a learner is tasked with the goal to adaptively acquire information so as to ident

LiteMVS: Efficient Multi-View Stereo with Foundation Distillation and Expert Aggregation

arXiv:2608.03851v1 Announce Type: new Abstract: Real-time 3D perception is crucial for robotics, augmented reality, and embodied intelligence applications. Existing multi-view stereo (MVS) methods pri

researcharxiv-cs-cv

LocAnyMed: Vision-Language Grounding for Multimodal Medical Images

arXiv:2608.03322v1 Announce Type: new Abstract: Medical visual grounding connects free-form clinical queries to spatial evidence in medical images and is an important component of interpretable medica

LoopMTP: A looped transformer guided by latent multi-token prediction

arXiv:2608.03624v1 Announce Type: new Abstract: Looped transformers have emerged as a parameter-efficient alternative to scaling depth for strong reasoning. By reusing one stack of layers across T ite

Low-Dimensional High-Leverage Subspace Optimization: Beyond Full-Parameter Coupled Training for Neural Network Quantization

arXiv:2608.03919v1 Announce Type: new Abstract: Low-bit quantization suffers severe accuracy degradation on compact networks, rooted in the dominant full-parameter coupled training paradigm that ignor

Morphology-Aware Implicit Super-Resolution Network for Pathological Images

arXiv:2608.03664v1 Announce Type: new Abstract: Accurate diagnosis in Digital Pathology (DP) relies on high-resolution whole-slide images, yet clinical deployment is often limited by hardware costs. S

Local Ai

Moving the Safety Barrier: Dynamic Routing Adaptive Alignment Against White-Box Attacks

arXiv:2608.02674v1 Announce Type: cross Abstract: With the widespread deployment of large foundation models (LFMs) in open environments, safety threats are shifting from black-box jailbreaks toward wh

local-aiarxiv-cs-ai

MultiGlobeQA: A Multilingual and Globally Diverse Benchmark for Geospatial Reasoning

arXiv:2608.03882v1 Announce Type: cross Abstract: Geospatial reasoning, i.e., computing distances, containment, and other spatial relations over real-world entities, is central to navigation and logis

PAST-Bench: Benchmarking the Foundations of Recursive Self-Improvement in Personal Agents

arXiv:2608.04003v1 Announce Type: new Abstract: Recursive self-improvement requires agents to turn accumulated experience into better future behavior. Personal AI agents offer a concrete setting for s

Pingala: Prosody-Aware Decoding for Sanskrit Poetry Generation

arXiv:2603.24413v2 Announce Type: replace Abstract: Poetry generation in Sanskrit typically requires the verse to be semantically coherent and adhere to strict prosodic rules. In Sanskrit prosody, eve

PLAN: Parallel Liquid-Inspired Approximation Network for Efficient Representation Learning in Flexible Job Shop Scheduling

arXiv:2608.03041v1 Announce Type: cross Abstract: Deep reinforcement learning (DRL) approaches for flexible job shop scheduling (FJSP) heavily rely on attention-centric architectures to achieve state-

Should We Type or Talk to LLM Agents? A Comprehensive Study of Voice and Keyboard Input Perturbations

arXiv:2608.03970v1 Announce Type: new Abstract: Human input reaches language models by typing or speaking, and each channel leaves a distinct signature: orthographic noise for keyboards; for voice, di

researcharxiv-cs-ai

States Hidden in Hidden States: Implicit Discrete State Representations Emerge in LLMs' Hidden States

arXiv:2407.11421v2 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit emergent abilities that may reveal aspects of their internal mechanisms. We study one such capability: directly

TARL: Transaction-Aware Reliable Ledgers for Executable Memory Management in Long-Term Agents

arXiv:2608.03699v1 Announce Type: new Abstract: Persistent memory helps long-term agents retain knowledge, yet a single update error can repeatedly distort future retrieval and reasoning. Most existin

Test-Time Augmentation for Tabular-to-Image Classifiers under Distribution Shifts

arXiv:2608.03557v1 Announce Type: new Abstract: Tabular-to-image methods that convert tabular data into visual representations have emerged as a novel paradigm for leveraging the high performance of d

The production of meaning in the processing of natural language

arXiv:2603.20381v2 Announce Type: replace-cross Abstract: Understanding the fundamental mechanisms governing the production of meaning in the processing of natural language is critical for designing s

Agents

Towards Improving Sequential Decision-Making in LLM Agents via Experience Memory

arXiv:2608.03420v1 Announce Type: new Abstract: Large language models have improved substantially on single-shot reasoning tasks, but their performance in sequential decision-making is less well under

agentsarxiv-cs-ai