arXiv AI
Jun 18
CEO-Bench: Can Agents Play the Long Game?
arXiv:2606. 18543v1 Announce Type: new Abstract: Language model agents are becoming proficient executors at isolated, short-horizon tasks such as software engineering and customer service.
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
Jun 18
BLADE: Scalable Bi-level Adaptive Data Selection for LLM Training
arXiv:2606. 18650v1 Announce Type: new Abstract: As Large Language Model (LLM) datasets scale to trillions of tokens, data selection has emerged as a critical frontier to filter out uninformative noise and construct adaptive learning trajectories.
arXiv AI
Jun 18
HeRo-Q: A General Framework for Stable Low Bit Quantization via Hessian Conditioning
arXiv:2601. 21626v2 Announce Type: replace-cross Abstract: Post Training Quantization (PTQ), a mainstream model compression technique, often leads to the paradoxical 'low error, high loss' phenomenon because it focuses solely on minimizing quantization error.
arXiv AI
Jun 18
Spotlight: Synergizing Seed Exploration and Spot GPUs for DiT RL Post-Training
arXiv:2606. 19004v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training of Diffusion Transformers (DiTs) is prohibitively expensive, requiring thousands of high-end GPUs.
arXiv AI
Jun 18
TxBench-PP: Analyzing AI Agent Performance on Small-Molecule Preclinical Pharmacology
arXiv:2606. 19245v1 Announce Type: new Abstract: Artificial intelligence (AI) agents promise to accelerate drug discovery by compressing interpretation and decision-making loops, but practical deployment requires trusted evaluation on realistic program decisions.
arXiv AI
Jun 18
Engagement Intensity as a Learner-Modeling Signal for Adaptive AI Ethics Instruction
arXiv:2606. 18548v1 Announce Type: cross Abstract: Adaptive AI ethics instruction in graduate research training benefits from intake measures that reflect differences in prior LLM experience.
arXiv AI
Jun 18
STARE: Surprisal-Guided Token-Level Advantage Reweighting for Policy Entropy Stability
arXiv:2606. 19236v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards algorithms like GRPO have emerged as the dominant post-training paradigm for complex reasoning in LLMs, yet commonly suffer from policy entropy collapse during training.
arXiv AI
Jun 18
Skill-MAS: Evolving Meta-Skill for Automatic Multi-Agent Systems
arXiv:2606. 18837v1 Announce Type: cross Abstract: Large Language Model (LLM)-based automatic Multi-Agent Systems (MAS) generation has become a crucial frontier for tackling complex tasks.
arXiv AI
Jun 18
Beyond Safe Data: Pretraining-Stage Alignment with Regular Safety Reflection
arXiv:2606. 19168v1 Announce Type: new Abstract: To achieve deeper safety alignment for large language models (LLMs), recent efforts have studied how to push safety interventions earlier into the pretraining stage, primarily by filtering unsafe data or rewriting it into safer forms.
arXiv AI
Jun 18
Language Models as Interfaces, Not Oracles: A Hybrid LLM-ML System for Pediatric Appendicitis
arXiv:2606. 19183v1 Announce Type: cross Abstract: Large language models (LLMs) can make clinical decision support more accessible by interpreting free-text documentation, but their direct use as diagnostic engines is limited by sensitivity to prompts, information order, and plausible but incorrect outputs.
arXiv Machine Learning
Jun 18
Investigating Inductive Biases for Machine Learning Emulation of Sudden Stratospheric Warmings in Idealised Isca Simulations
arXiv:2606. 18857v1 Announce Type: new Abstract: Machine-learning emulators are increasingly used for weather prediction and have the potential to extend skill on subseasonal-to-seasonal timescales by learning dynamically important sources of predictability.
arXiv Machine Learning
Jun 18
Self-Driving Datasets: From 20 Million Papers to Nuanced Biomedical Knowledge at Scale
arXiv:2605. 07022v3 Announce Type: replace Abstract: Manually curated biomedical repositories -- spanning bioactivity, genomics, and chemistry -- are expensive to maintain, lag behind primary literature, and discard experimental context, obscuring nuances needed to assess data correctness and coverage.
arXiv AI
Jun 18
Rethinking Reward Supervision: Rubric-Conditioned Self-Distillation
arXiv:2606. 19327v1 Announce Type: new Abstract: Post-training of reasoning language models is commonly driven by supervised distillation and reinforcement learning with verifiable rewards.
arXiv Machine Learning
Jun 18
OpenAnt: LLM-Powered Vulnerability Discovery Through Code Decomposition, Adversarial Verification, and Dynamic Testing
arXiv:2606. 19149v1 Announce Type: cross Abstract: Automated vulnerability discovery in large codebases remains challenging: traditional static analysis produces high false-positive rates, while dynamic approaches such as fuzzing require substantial infrastructure and often target narrow classes of bugs.
arXiv AI
Jun 18
PatchWorld: Gradient-Free Optimization of Executable World Models
arXiv:2605. 30880v2 Announce Type: replace-cross Abstract: Text-agent environments are typically modeled as partially observable Markov decision processes (POMDPs), assuming that the simulator's latent state and transition dynamics are hidden from the agent.
arXiv AI
Jun 18
Enhancing CVRP Solver through LLM-driven Automatic Heuristic Design
arXiv:2602. 23092v2 Announce Type: replace Abstract: The Capacitated Vehicle Routing Problem (CVRP), a fundamental combinatorial optimization challenge, focuses on optimizing fleet operations under vehicle capacity constraints.
arXiv AI
Jun 18
DeFAb: A Verifiable Benchmark for Defeasible Abduction in Foundation Models
arXiv:2606. 18557v1 Announce Type: new Abstract: A rule-based logic solver resolves every instance in our benchmark in under 50 microseconds with 100% accuracy; the best frontier language model reaches 65% at best and drops to 23.
arXiv AI
Jun 18
Trade-offs in Medical LLM Adaptation: An Empirical Study in French QA
arXiv:2606. 19266v1 Announce Type: cross Abstract: The development of large language models (LLMs) has led to an increased focus on their adaptation to specialized domains and languages, yet the effectiveness of domain adaptation strategies remains unclear.
arXiv AI
Jun 18
A Multi-Domain Benchmark for Detecting AI-Generated Text-Rich Images from GPT-Image-2
arXiv:2606. 19259v1 Announce Type: cross Abstract: Text-rich images often contain privacy-sensitive, transactional, or decision-relevant information.
arXiv AI
Jun 18
Leadership as Coordination Control: Behavioral Signatures and the Recovery-Advantage Boundary in Multi-Agent LLM Teams
arXiv:2606. 19111v1 Announce Type: cross Abstract: Team science holds that leadership is contingent: it helps only under specific conditions, and capable, autonomous teams may need none at all.