arXiv AI
Jun 2
Two-Fidelity Best-Action Identification for Stochastic Minimax Tree
arXiv:2606. 01708v1 Announce Type: cross Abstract: We study fixed-confidence best-action identification (BAI) in stochastic minimax trees.
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 AI
Jun 2
PBT-Bench: Benchmarking AI Agents on Property-Based Testing
arXiv:2605. 15229v3 Announce Type: replace-cross Abstract: Existing code benchmarks measure whether an agent can produce any test that reproduces a known bug, or whether it can produce a patch that fixes a described issue.
arXiv AI
Jun 2
Latent Reward Steering: An Adaptive Inference-Time Framework that Implicitly Promotes Cognitive Behaviors in Reasoning LLMs
arXiv:2606. 00726v1 Announce Type: new Abstract: Strong reasoning depends not only on model knowledge but also on how effectively cognitive behaviors are deployed during generation.
arXiv AI
Jun 2
Limits of Spatial Imagery Reasoning in Frontier LLM Models
arXiv:2603. 26779v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, yet they struggle with spatial tasks that require mental simulation, such as mental rotation.
arXiv AI
Jun 2
An NLP-Driven Framework for Curriculum-Labor Market Alignment: Schema-Constrained LLM Extraction, ESCO-Anchored Semantic Matching, and Multi-Dimensional Gap Quantification
arXiv:2606. 01982v1 Announce Type: new Abstract: Schema-constrained information extraction from diverse educational and labor-market corpora remains an open challenge in natural language processing because existing pipelines rely primarily on lexical-surface methods that cannot recover implicit competencies, lack grounding in shared taxonomies, and provide no formal measures of extraction reliability or document-level completeness.
arXiv AI
Jun 2
SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision
arXiv:2606. 01139v1 Announce Type: new Abstract: Agent skills are procedural artifacts that enable LLM agents to execute workflows, verify constraints, and recover from failures.
arXiv AI
Jun 2
Understand and Accelerate Memory Processing Pipeline for Large Language Model Inference
arXiv:2603. 29002v3 Announce Type: replace-cross Abstract: Modern large language models (LLMs) increasingly depends on efficient long-context processing and generation mechanisms, including sparse attention, retrieval-augmented generation (RAG), and compressed contextual memory, to support complex reasoning.
arXiv Machine Learning
Jun 2
ReFLEX: Length-Generalizable CSI Denoising for MIMO-OFDM via Relative-Frequency Bias
arXiv:2606. 00263v1 Announce Type: cross Abstract: This letter studies CSI denoising for MIMO--OFDM with variable NR resource block (RB) allocations.
arXiv AI
Jun 2
From "Weak" Signals to Strong Models: Preference Delta Aggregation with LoRA Merging
arXiv:2606. 00357v1 Announce Type: new Abstract: Training strong large language models (LLMs) requires high-quality supervision, which is often scarce.
arXiv Machine Learning
Jun 2
Interpreto: An Explainability Library for Transformers
arXiv:2512. 09730v3 Announce Type: replace-cross Abstract: Interpreto is an open-source Python library for interpreting HuggingFace language models, from early BERT variants to LLMs.
arXiv AI
Jun 2
Scaling Behavior of Single LLM-Driven Multi-Agent Systems
arXiv:2606. 00655v1 Announce Type: cross Abstract: The burgeoning field of LLM-based Multi-Agent Systems (MAS) promises to tackle complex tasks through collaborative intelligence, yet fundamental questions regarding their scaling behavior and intrinsic collective dynamics remain underexplored.
arXiv Machine Learning
Jun 2
Task Structure Reverses Layerwise State Encoding in Sequence Models
arXiv:2606. 00926v1 Announce Type: new Abstract: Mechanistic studies of sequence models often treat layerwise state encodings as architectural traits: recurrent models concentrate readable state, attention-based models distribute it.
arXiv Machine Learning
Jun 2
From Scaling to Structured Expressivity: Rethinking Transformers for CTR Prediction
arXiv:2511. 12081v2 Announce Type: replace-cross Abstract: Despite massive investments in scale, deep models for click-through rate (CTR) prediction often exhibit rapidly diminishing returns -- a stark contrast to the {predictable scaling laws} seen in large language models (LLMs).
arXiv Machine Learning
Jun 2
FAiT: Frequency-Aware Inverted Transformer for Multivariate Time Series Forecasting
arXiv:2606. 01306v1 Announce Type: new Abstract: While Transformer-based architectures have established themselves as a dominant paradigm in Multivariate Time Series Forecasting (MTSF), their core self-attention mechanism inherently functions as a low-pass filter, systematically smoothing out high-frequency signals vital for sharp local changes.
arXiv AI
Jun 2
Beyond One-shot: AI Agents for Learning in Field Experiments
arXiv:2606. 02458v1 Announce Type: new Abstract: Organizations routinely run experiments for A/B testing, yet the data generated from one experiment is underutilized to inform subsequent intervention design.
arXiv AI
Jun 2
Food Noise & False Safety: A Systematic Evaluation of How LLMs Fail to Adapt to Eating Disorder Queries with Clinician Feedback
arXiv:2606. 02444v1 Announce Type: new Abstract: Recent evidence shows that people with eating disorders (EDs) are increasingly seeking guidance, advice, and emotional support from Large Language Model (LLM)-based chat systems.
arXiv AI
Jun 2
Language Model Networks: Supervision-Efficient Learning through Dense Communication
arXiv:2505. 12741v3 Announce Type: replace Abstract: Language models are increasingly used not only as standalone predictors but also as components in larger inference systems, from test-time scaling to multi-agent collaboration.
arXiv AI
Jun 2
CA-BED: Conversation-Aware Bayesian Experimental Design
arXiv:2606. 01182v1 Announce Type: cross Abstract: Large Language Models (LLMs) excel at static reasoning tasks, yet their performance often degrades in interactive scenarios where information must be actively acquired through questioning.
arXiv Machine Learning
Jun 2
CRMA: A Spectrally-Bounded Backbone for Modular Continual Fine-Tuning of LLMs
arXiv:2606. 00382v1 Announce Type: new Abstract: Sequential fine-tuning of large language models forces a choice: let the shared substrate keep learning and accept catastrophic forgetting, or freeze it after task one and foreclose cross-task refinement.
arXiv Machine Learning
Jun 2
Adversarially Robust Control of Conditional Value-at-Risk via Rockafellar-Uryasev Conformal Inference
arXiv:2606. 00320v1 Announce Type: new Abstract: We present an online, distribution-free framework for controlling the Conditional Value-at-Risk (CVaR), extending conformal tail risk control to non-stationary and adversarial environments.