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New Complexity-Theoretic Frontiers of Tractability for Neural Network Training

arXiv:2607.20811v1 Announce Type: new Abstract: In spite of the fundamental role of neural networks in contemporary machine learning research, our understanding of the computational complexity of opti

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Research

news-crawler-LM: A Small Long-Context Model For High-Quality News Crawling

DGX agent

arXiv:2607.21284v1 Announce Type: new Abstract: Extracting structured content from news pages remains challenging due to heterogeneous HTML layouts, inconsistent markup, and substantial boilerplate su

researcharxiv-cs-cl

24 Jul 2026

Nipping the Butterfly Effect in the Bud: Self-Output Fine-Tuning for Autoregressive Weather Prediction

arXiv:2607.21080v1 Announce Type: new Abstract: Long-horizon weather forecasting is a fundamental challenge in atmospheric science, for which autoregressive Deep Learning Weather Prediction (DLWP) has

Non-Stationary Functional Bilevel Optimization

model-releasesarxiv-cs-lg

arXiv:2601.15363v2 Announce Type: replace-cross Abstract: Functional bilevel optimization (FBO) provides a powerful framework for hierarchical learning in function spaces, yet current methods are limi

Non-Zipfian Distribution of Stopwords or Function Words and Subset Selection Models

arXiv:2603.04691v2 Announce Type: replace Abstract: Stopwords and function words are relatively less informative for the content of a language and more often play a structural role in a sentence. Stop

researcharxiv-cs-cl

Hardware

NVIDIA-labs OO Agents: Native Python Object-Oriented Agents

arXiv:2607.20709v1 Announce Type: new Abstract: Traditional agent development is split across prompt templates, tool schemas, callback code, and workflow graphs. We present NVIDIA Object-Oriented Agen

hardwarearxiv-cs-ai

Ocular Verification for Virtual Reality

arXiv:2607.20790v1 Announce Type: new Abstract: Virtual reality (VR) headsets (e.g., Meta Quest, Apple Vision Pro) provide a seamless user experience due to their fast, frictionless interaction with t

ODeform: Learning Continuous 4D Motion for Shape Deformation with Neural ODEs

model-releasesarxiv-cs-cv

arXiv:2607.20670v1 Announce Type: new Abstract: Modeling continuous object deformation is important for many computer vision and robotics tasks, such as manipulation and simulation. Existing approache

Offline RL with Hierarchical Action Chunking

arXiv:2607.20834v1 Announce Type: new Abstract: Offline goal-conditioned reinforcement learning (RL) holds the promise of learning general-purpose policies from static datasets. However, scaling these

On the Granularity of Causal Effect Identifiability

arXiv:2510.16703v3 Announce Type: replace-cross Abstract: The classical notion of causal effect identifiability is defined in terms of treatment and outcome variables. In this paper, we consider the i

One More Turn, Less Regret: A Regret-Based Multi-Turn Benchmark for LLMs' Clarification Policies

arXiv:2607.21143v1 Announce Type: cross Abstract: Ambiguous user requests make clarification a sequential decision problem for conversational LLM assistants: they must decide whether to ask, what to a

One Round Is All You Need: Analytic Federated Learning for Task-Heterogeneous Multi-Label Medical Image Classification

arXiv:2607.20641v1 Announce Type: new Abstract: Federated learning (FL) enables multiple clinical institutions to collaboratively train a shared disease classifier without centralizing patient data. I

safetyarxiv-cs-lg

OpenForgeRL: Train Harness-native Agents in Any Environment

arXiv:2607.21557v1 Announce Type: new Abstract: Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, and access to e

Operational Identity: A Finite Audit of Declared and Implemented Rules of Sameness

arXiv:2607.20729v1 Announce Type: cross Abstract: A record system declares when two records refer to the same entity, occurrence, scope, or rule. Its disclosed implementation mechanisms induce a corre

OPOD: On-Policy Omni Distillation

arXiv:2607.20918v1 Announce Type: new Abstract: Omni-modal models can handle text, images, and audio in one system, but improving all of these abilities together remains difficult. Training a single m

Optimizing Hypergraph-Based RAG: Toward Better Fact Extraction and Chunk Retrieval

arXiv:2607.20506v1 Announce Type: new Abstract: GraphRAG enables deeper reasoning by structuring knowledge as graphs but struggles with n-ary facts. HyperGraphRAG uses hypergraphs for richer semantics

Agents

OPTScientist: Multi-Agent Discovery of Typed Optimizer Programs for Transformer Pretraining

arXiv:2607.20486v1 Announce Type: new Abstract: Designing optimizers for modern deep learning remains a challenging scientific problem, requiring the joint consideration of optimization geometry, stat

agentsarxiv-cs-ai

Out of Sight, Still in Mind: Token Compression for Omni-LLMs

arXiv:2607.21179v1 Announce Type: new Abstract: The goal of this paper is to reduce the input token cost of Omni-modal large language models (Omni-LLMs) at inference time. Omni-LLMs reason jointly ove

Local Ai

Overcoming the Communication-Performance Tradeoff in LLM Pretraining

arXiv:2508.15706v3 Announce Type: replace Abstract: Communication-efficient distributed training algorithms (e.g., DiLoCo) have received considerable interest due to their benefits for training large

local-aiarxiv-cs-lg

pAI-Econ-claude: A Gated Human-in-the-Loop Multi-Agent Architecture for AI-Assisted Economic Theory Development

arXiv:2607.21268v1 Announce Type: cross Abstract: In many social-science research tasks, such as economics, LLM-based agents must produce outputs for which no cheap, task-complete, machine-readable co

PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning

arXiv:2607.21419v1 Announce Type: new Abstract: In long-horizon LLM agent reinforcement learning, weak policies often repeat similar failures, producing uninformative rollout trajectories and limiting

PC-Edit: Prompt-Contrastive Region Discovery and Region-Guided Editing

arXiv:2607.21318v1 Announce Type: cross Abstract: Replacing an object with one that differs in category or shape requires complete source removal, natural target formation unconstrained by the source

PersonaGesture: Single-Reference Co-Speech Gesture Personalization for Unseen Speakers

arXiv:2605.06064v2 Announce Type: replace Abstract: We propose PersonaGesture, a diffusion-based pipeline for single-reference co-speech gesture personalization of unseen speakers. Given target speech

PersonaTrail: Benchmarking Personalized Web Agents through Browsing Trails

arXiv:2607.20482v1 Announce Type: new Abstract: Recent advances in large language models have enabled web agents to autonomously execute complex tasks. In practice, users frequently provide underspeci

Perspective Latents as an Architectural Condition for Causal Emergence in Active Inference Agents

arXiv:2607.20708v1 Announce Type: new Abstract: A recent line of work measures causal emergence in reinforcement learning agents through Integrated Information Decomposition, reporting that Phi_r grow

safetyarxiv-cs-lg

PhantomFill: When the Form Demands an Answer, Language Models Invent One

arXiv:2607.20492v1 Announce Type: cross Abstract: Language models in production do not write prose. They fill forms: JSON fields, function arguments, extraction templates. We show that the form itself

Phonetic forced alignment for low-resource language varieties: Model training and evaluation on Chengdu Mandarin

arXiv:2607.21332v1 Announce Type: cross Abstract: Phonetic forced alignment is a key technique in phonetic research, yet existing alignment systems lack specialized models for low-resource language va

PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics

arXiv:2607.20653v1 Announce Type: cross Abstract: Predicting how deformable objects evolve under robotic manipulation is a longstanding challenge. Existing approaches typically rely on per-object opti

Physics-Informed Deep Learning Model for Cross-Modality Super-Resolution in Fluorescence Microscopy

arXiv:2607.21190v1 Announce Type: new Abstract: Cross-modality image translation offers a route to super-resolution fluorescence microscopy from low-resolution images while reducing phototoxicity and

PILD: Physics-Informed Learning via Diffusion

arXiv:2601.21284v2 Announce Type: replace-cross Abstract: Diffusion models have emerged as powerful generative tools for modeling complex data distributions, yet their purely data-driven nature limits

Pipelined Gradient Coding

arXiv:2607.20739v1 Announce Type: cross Abstract: In large-scale machine learning, distributed training commonly involves multiple workers evaluating the gradients of the model on different dataset pa

PISmith: Reinforcement Learning-based Red Teaming for Prompt Injection Defenses

model-releasesarxiv-cs-lg

arXiv:2603.13026v2 Announce Type: replace Abstract: Prompt injection poses serious security risks to real-world LLM applications, particularly autonomous agents. Although many defenses have been propo

PlanE: Meta Planning of Data, Tuning, and Inference for Extractive-based LLMs

arXiv:2607.20470v1 Announce Type: new Abstract: Enhancing the task-specific capabilities of Large Language Models (LLMs) primarily requires substantial instruction-tuning datasets. However, the sheer

Position Bias is Hidden Behind Ceiling Effects: A Permutation Diagnostic for LLM Benchmarks

model-releasesarxiv-cs-cl

arXiv:2607.20864v1 Announce Type: cross Abstract: Position bias in multiple-choice LLM evaluation is widely cited as a confound in capability comparisons, but published measurements rely on single ans

Applications

Position: Natural Language Should Not Fully Replace Formal Languages

arXiv:2607.20432v1 Announce Type: new Abstract: Recent advances in large language models and their widespread adoption have prompted claims that natural language could entirely replace formal language

applicationsarxiv-cs-cl

Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development

arXiv:2607.20532v1 Announce Type: cross Abstract: Many modern AI systems are designed to operate under diverse, open-ended, use-cases. To help generalize deployed systems, many deployed-system mainten

Post-Hoc Reasoning in Chain of Thought: Decoding and Steering Pre-Committed Answers

arXiv:2603.01437v2 Announce Type: replace Abstract: As chain of thought (CoT) has become central to scaling reasoning capabilities in large language models (LLMs), it has also emerged as a promising t

Preference Tuning as Spectral Update Reorganization

arXiv:2607.20438v1 Announce Type: cross Abstract: Preference-based post-training is usually understood through endpoint behavior, yet the learned update that produces this behavior remains largely opa

Tutorials

PrefReward: Learning User Preference Matrix for Personalized Text Generation

arXiv:2607.21067v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable ability in generating personalized content by leveraging user histories and contextual cues. H

tutorialsarxiv-cs-cl

Tutorials

Probabilistic Physics-Aware Machine Learning Predictions of Electric Truck Energy Consumption with Field Data

arXiv:2607.19054v2 Announce Type: replace Abstract: In this work, we incorporate first principle physics into the construction of data-driven methods by considering a model that accounts for the diffe

tutorialsarxiv-cs-lg

Probabilistic Residual Learning for Online Recommendations

arXiv:2607.20863v1 Announce Type: cross Abstract: Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items. As a res

ProCap: Prominence-guided Object Rectification for Faithful and Comprehensive Video Captioning

model-releasesarxiv-cs-cv

arXiv:2607.21022v1 Announce Type: new Abstract: Improving video captioning quality typically demands retraining large vision-language models, an expensive and often impractical requirement. Existing t

Profiling Lightweight Large Language Models

arXiv:2607.20806v1 Announce Type: new Abstract: Lightweight large language models (LLMs) are increasingly being deployed locally on personal computers and are expected to play a growing role in resour

Progressive Cramming: Reliable Token Compression and What It Reveals

arXiv:2607.21231v1 Announce Type: new Abstract: Token cramming compresses sequences into learned embeddings with near-perfect reconstruction, but fixed token budgets and 99% accuracy thresholds leave

researcharxiv-cs-cl

PromptPack: Scaling LLM Annotation Agents for Online Recommendation

arXiv:2607.20528v1 Announce Type: new Abstract: Online recommendation platforms increasingly use Large Language Models (LLMs) to extract structured features from ad creatives. While deploying a single

QATMA: Quantization-Aware Training with Multimodal Alignment for Open-Vocabulary Object Detection

arXiv:2603.05964v3 Announce Type: replace Abstract: Quantizing open-vocabulary object detection (OVOD) models reduces their memory and computational costs, but extremely low-bit quantization severely

safetyarxiv-cs-cv

Tutorials

Quality-Aware Multimodal Fusion Reveals Implicit Identity in Valence-Arousal Features

arXiv:2607.21347v1 Announce Type: new Abstract: Conventional face recognition relies on static appearance cues and degrades in unconstrained settings with expression variation, occlusion, and poor lig

tutorialsarxiv-cs-cv

QuantiBias: Benchmarking Quantization-Induced Bias in LLMs

model-releasesarxiv-cs-cl

arXiv:2607.21063v1 Announce Type: new Abstract: Almost every large language model that reaches a broad audience is quantized: trained in full precision, then compressed for efficiency. This step is as