Towards Data Science
Jun 17
You Probably Don’t Need an Agent Framework
Most LLM applications need a clear workflow, not an autonomous agent. Here's how to build one in plain Python.
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
Hugging Face Trending Papers
Jun 17
G-IdiomAlign: A Gloss-Pivoted Benchmark for Cross-Lingual Idiom Alignment
Idioms are difficult to transfer across languages due to their non-compositionality and weak surface-form grounding, making literal mappings unreliable. We present G-IdiomAlign, a gloss-pivoted benchmark where each idiom is anchored by an English gloss from Wiktionary.
Hugging Face Trending Papers
Jun 17
Be Your Own Teacher: Steering Protein Language Models via Unsupervised Reward Optimization
Protein language models (PLMs) have emerged as powerful tools for controllable biomolecular design, yet their post-training adaptation typically relies on costly wet-lab validation or curated preference datasets. To overcome this supervision bottleneck, we introduce unsupervised reward optimization of PLMs, a comprehensive framework for steerable protein generation without ground-truth labels.
Hugging Face Trending Papers
Jun 17
SenFlow: Inter-Sentence Flow Modeling for AI-Generated Text Detection in Hybrid Documents
Sentence-level AI-generated text detection (S-AGTD) for hybrid documents, where humans and LLMs co-author one text, faces two gaps: existing methods classify each sentence in isolation, discarding inter-sentence dependencies, and existing benchmarks omit the newest generation of generators. We construct MOSAIC, a benchmark of 16,000 hybrid documents over PubMed and XSum, generated by DeepSeek-V3.
Hugging Face Trending Papers
Jun 17
SciRisk-Bench: A Risk-Dimension-Aware Benchmark for AI4Science Safety
Large language models (LLMs) are increasingly embedded in AI for Science (AI4Science) workflows, from scientific question answering and literature analysis to laboratory planning and autonomous discovery. This progress creates an urgent need for safety benchmarks that evaluate not only scientific competence, but also whether models recognize and avoid risks in high-stakes scientific contexts.
OpenAI Blog
Jun 17
A near-autonomous AI chemist improves a challenging reaction in medicinal chemistry
OpenAI and Molecule. one show how a near-autonomous AI chemist using GPT-5.
Hugging Face Trending Papers
Jun 17
Learning from Your Own Mistakes: Constructing Learnable Micro-Reflective Trajectories for Self-Distillation
Self-distillation improves reasoning in large language models by using the model's own rollouts as training signal, typically through implicit logit-level alignment that minimizes KL divergence toward a privileged target distribution. However, because this supervision is generated via uncontrolled sampling, it provides no diagnostic insight into the model's specific errors or corrective guidance for its individual failure patterns.
Hugging Face Trending Papers
Jun 17
Reinforcement Learning Foundation Models Should Already Be A Thing
Foundation models for language and vision are powered by internet-scale data, while structured domains (tabular prediction, time-series forecasting, graph learning, reinforcement learning) are not. The substitute is synthetic data, which shifts the burden from collection to prior design.
Hugging Face Trending Papers
Jun 17
Learning from Own Solutions: Self-Conditioned Credit Assignment for Reinforcement Learning with Verifiable Rewards
Reinforcement learning with verifiable rewards (RLVR) has driven substantial progress in training LLMs for reasoning tasks, but representative methods such as GRPO assign uniform credit across all tokens, wasting gradient on routine tokens while under-crediting pivotal reasoning steps. Existing token-level credit assignment methods require resources beyond the model's own rollouts.
Hugging Face Trending Papers
Jun 17
ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatch
Bringing Large Language Models (LLMs) into industrial ride-hailing dispatch as semantic feature extractors over platform-scale behavioral logs is a compelling but under-explored data systems problem. Production matching pipelines remain dominated by structured numerical features, yet decisive behavioral signals (e.
Hugging Face Trending Papers
Jun 17
SpectralDiT: Timestep-Conditioned Spectral Residual Correction for Flow-Matching DiTs
We propose SpectralDiT, a lightweight modification to flow-matching Diffusion Transformers that adds timestep-conditioned spectral correction to the MLP residual branch. The module decomposes each residual update into low- and high-frequency components on the patch-token grid, then learns a zero-initialized additive gate so the model initially matches the baseline DiT.
Hugging Face Trending Papers
Jun 17
LLMs Struggle to Measure What Distinguishes Students of Different Proficiency Levels: A Study of Item Discrimination in Reading Comprehension Assessment
Item discrimination is a fundamental psychometric property of educational assessment, which measures whether an item meaningfully distinguishes students with higher proficiency from students with lower proficiency. While various existing works have explored whether large language models (LLMs) can estimate item difficulty, it remains unclear whether they can capture item discrimination.
Hugging Face Trending Papers
Jun 17
EARS: Explanatory Abstention for Reliable Sub-Agent Modeling in Large-scale Multi-Agent Systems
In large-scale enterprise settings, centralized multi-agent systems (MAS) are increasingly adopted, in which a coordinator delegates user requests to lightweight, domain-specialized sub-agents. While this architecture improves modularity, scalability, and cost efficiency, its reliability depends not only on accurate routing but also on sub-agents' ability to calibrate their responses to capability constraints.
Hugging Face Trending Papers
Jun 17
RegMix-D: Dynamic Data Mixing via Proxy Training Trajectories
Data mixture selection is critical for Large Language Model pretraining. Existing methods such as RegMix select a single static mixture by fitting a regression model on small-scale proxy runs.
Hugging Face Trending Papers
Jun 17
LandslideAgent with Multimodal LandslideBench: A Domain-Rule-Augmented Agent for Autonomous Landslide Identification and Analysis
Intelligent landslide hazard interpretation is critical for disaster prevention, yet current paradigms struggle to simultaneously extract visual features and high-level geoscientific semantics, while general-purpose vision-language models (VLMs) suffer from perceptual limitations and domain hallucinations in complex geological scenarios. To address these challenges, we propose an instruction-driven agentic framework comprising three components.
arXiv AI
Jun 17
WEQA: Wearable hEalth Question Answering with Query-Adaptive Agentic Reasoning
arXiv:2606. 18147v1 Announce Type: new Abstract: Language models are remarkably capable at medical question answering, in some cases surpassing the accuracy of general physicians.
arXiv AI
Jun 17
ReproRepo: Scaling Reproducibility Audits with GitHub Repository Issues
arXiv:2606. 18237v1 Announce Type: cross Abstract: Reproducing research results from papers and released code is central to scientific progress.
arXiv Machine Learning
Jun 17
CheckMIABench: Firm Foundations For Membership Inference Attacks on Language Models
arXiv:2606. 17464v1 Announce Type: new Abstract: Membership inference attacks (MIAs) are a canonical way to assess a machine learning model's privacy properties.
arXiv AI
Jun 17
The Discrete-Log Clock: How a Transformer Learns Modular Multiplication
arXiv:2606. 17399v1 Announce Type: cross Abstract: When small transformers grok modular multiplication, prior work reports that the learned embedding has a "dense" Fourier spectrum requiring all frequencies.
arXiv AI
Jun 17
MapSatisfyBench: Benchmarking Satisfaction-Aware Map Agents through Behavior-Grounded Implicit Decision Factors
arXiv:2606. 17453v1 Announce Type: new Abstract: Large language model agents are increasingly integrated into map services.