arXiv AI
Jul 17
Large language models can effectively convince people to believe conspiracies
arXiv:2601. 05050v3 Announce Type: replace Abstract: Large language models (LLMs) have been shown to be persuasive across a variety of contexts.
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv Machine Learning
Jul 17
Code Correctness Is Linearly Decodable from LLM Hidden States Before Generation
arXiv:2606. 14530v3 Announce Type: replace Abstract: Large language models encode rich information in their hidden states.
arXiv Machine Learning
Jul 17
FlowBot: Inducing LLM Workflows with Bilevel Optimization and Textual Gradients
arXiv:2604. 26258v3 Announce Type: replace-cross Abstract: LLM workflows, which coordinate structured calls to individual LLMs/agents to achieve a particular goal, offer a promising path towards building powerful AI systems that can tackle diverse tasks.
arXiv AI
Jul 17
The RG-Flow Transformer: Encoding Scale-Free Dynamics in Scarce EEG
arXiv:2607. 11950v2 Announce Type: replace-cross Abstract: Brain field potentials are scale-free: their power spectra follow a $1/f^{\beta}$ law whose aperiodic exponent $\beta$ tracks cortical state, and sleep depth in particular is a shift in $\beta$.
arXiv AI
Jul 17
Can We Trust Item Response Theory for AI Evaluation?
arXiv:2607. 15190v1 Announce Type: new Abstract: AI benchmarks increasingly leverage item-level statistical models, particularly item response theory (IRT), to estimate model capabilities, rank systems, select informative examples, and diagnose benchmark quality.
arXiv AI
Jul 17
Does generative AI supersede supervised XMLC? A Benchmark Study on Automated Subject Indexing with German Scientific Literature
arXiv:2607. 14882v1 Announce Type: cross Abstract: With a large controlled vocabulary as the label set, the task of automated subject indexing in a library can be understood as a multi-label classification task.
arXiv AI
Jul 17
Animating Petascale Time-varying Data on Commodity Hardware with LLM-assisted Scripting
arXiv:2603. 07053v3 Announce Type: replace Abstract: Scientists face significant visualization challenges as time-varying datasets grow in speed and volume, often requiring specialized infrastructure and expertise to handle massive datasets.
Hugging Face Trending Papers
Jul 16
FLINT: Fingerprinting Federated Learning Architectures from 5G PHY-Layer Side Channels
Federated Learning (FL) over 5G cellular networks protects raw data but remains vulnerable to side-channel leakage. Prior fingerprinting attacks assume packet-level network visibility, an assumption that does not hold at the 5G Physical (PHY) layer, where user payloads are encrypted and Radio Network Temporary Identifiers (RNTIs) may change over time.
Hugging Face Trending Papers
Jul 16
Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
Annotation quality is a major bottleneck in building reliable and explainable artificial intelligence (XAI) systems for mental health research. In depression-related datasets, labels are often assigned without structured evidence, symptom-level justification, or traceable alignment with the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR), limiting both transparency and downstream model interpretability.
OpenAI Blog
Jul 16
Why teens deserve access to safe AI
Learn how OpenAI is making ChatGPT safer for teens with age-appropriate protections, learning tools, parental controls, and expert partnerships.
Hugging Face Trending Papers
Jul 16
Beyond Single Expert: Harmonizing Diverse Visual Priors in MLLMs for Spatial Understanding
Multimodal Large Language Models (MLLMs) have demonstrated substantial promise in spatial understanding. Existing works typically incorporate prior knowledge extracted from a pre-trained foundation model to further enhance the spatial awareness of MLLMs.
Towards Data Science
Jul 16
How to Get the Most Out of Claude Fable 5
Maximize your Claude Fable 5 usage The post How to Get the Most Out of Claude Fable 5 appeared first on Towards Data Science .
Hugging Face Trending Papers
Jul 16
Benchmarking Face Recognition without Real Faces
Synthetic face datasets have become effective enough to train face recognition models with accuracy rivaling that of models trained on real photographs. This progress sidesteps the ethical and legal burdens of collecting real biometric data, yet evaluation has not kept pace.
Hugging Face Trending Papers
Jul 16
Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality
Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias. While this behavior has been widely studied for general text generation, its impact on code generation quality and programming conventions remains largely unexplored.
Hugging Face Trending Papers
Jul 16
An LLM-Based Automatic Sportscast Solution for Robot Soccer Matches
RoboCup has always been a scenario to develop systems that solve real-world problems. Driven by the main goal of playing against the 2050 FIFA World Cup champions, the RoboCup Soccer leagues need to constantly measure how the research community is progressing.
Hugging Face Trending Papers
Jul 16
ExaGEMM: Exploration Framework for CPU-Driven ML Inference via Associative In-Register Computing for Low-Bit GEMM
Low-bit GEMM is increasingly central to efficient ML inference, yet very-low-bit execution remains a poor fit for conventional CPUs. Practical deployment spans fragmented regimes-from 1/2/4-bit weights to varying activation precision-whose feasibility, reuse opportunity, and support cost differ under fixed SIMD and register-file budgets, making lightweight CPU support selection a first-class design problem.
arXiv AI
Jul 16
NSNQuant: A Double Normalization Approach for Calibration-Free Low-Bit Vector Quantization of KV Cache
arXiv:2505. 18231v3 Announce Type: replace-cross Abstract: Large Language Model (LLM) inference is typically memory-intensive, especially when processing large batch sizes and long sequences, due to the large size of key-value (KV) cache.
arXiv AI
Jul 16
Improving Molecular Property Prediction in Small Language Models Using Graph-based Tools
arXiv:2607. 13115v1 Announce Type: new Abstract: Small language models (SLMs) have shown promise for zero-shot molecular property prediction from SMILES strings, yet they often suffer from structural blindness because sequence representations under-specify key graph-topological cues.
arXiv AI
Jul 16
HRO: Hierarchical Room-to-Object Framework for Zero-Shot Object Goal Navigation with Large Language Models
arXiv:2607. 13072v1 Announce Type: cross Abstract: Zero-shot object-goal navigation aims to enable an intelligent agent to explore and navigate to objects of unknown categories in an unfamiliar environment without specific target training.
arXiv AI
Jul 16
STOCKTAKE: Measuring the Gap Between Perception and Action in LLM Agents with a Fair Oracle
arXiv:2607. 13618v1 Announce Type: new Abstract: LLM agents are increasingly evaluated on multi-week decision tasks in which the state that drives cost is never directly observed.