arXiv AI
Jun 16
Towards Verifiable Agentic Data Science: Solving Irregular TSQA Via Tool-Grounded Reasoning
arXiv:2606. 15107v1 Announce Type: new Abstract: Time series data in real-world deployments is overwhelmingly irregular.
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 16
Do LLMs Reliably Identify Correct Information Units in Aphasic Discourse?
arXiv:2606. 15696v1 Announce Type: new Abstract: Correct Information Units (CIUs) are central to discourse assessment in aphasia because they quantify communicative informativeness rather than linguistic form alone.
arXiv AI
Jun 16
Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework
arXiv:2604. 22119v2 Announce Type: replace Abstract: As reasoning capacity and deployment scope grow in tandem, large language models (LLMs) gain the capacity to engage in behaviors that serve their own objectives, a class of risks we term Emergent Strategic Reasoning Risks (ESRRs).
arXiv AI
Jun 16
Closing the Auto-Research Loop: An AI Co-Scientist for Production Search Ranking
arXiv:2603. 22376v2 Announce Type: replace-cross Abstract: We present an AI Co-Scientist framework that closes the research loop for the production search-ranking system of a large online travel platform -- pairing LLM agents with direct cloud-compute access so that idea generation, code implementation, GPU experimentation, and result analysis iterate end-to-end with a human scientist in the loop.
arXiv AI
Jun 16
Recurrent Reasoning on Symbolic Puzzles with Sequence Models
arXiv:2606. 15686v1 Announce Type: new Abstract: Large language models often appear strong on symbolic and algorithmic tasks, yet this apparent strength can hide brittle behaviour when problems become longer, harder, or slightly out of distribution.
arXiv AI
Jun 16
Towards Advanced Mathematical Reasoning for LLMs via First-Order Logic Theorem Proving
arXiv:2506. 17104v2 Announce Type: replace Abstract: Large language models (LLMs) have shown promising first-order logic (FOL) reasoning capabilities with applications in various areas.
arXiv Machine Learning
Jun 16
If These Walls Could Talk: Critical Play with Large Language Models in Museums
arXiv:2606. 15565v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly being used in museums to as role playing chatbots which let visitors talk to simulated versions of people and artefacts from the past.
arXiv AI
Jun 16
Reward Hacking in Language Model Agents: Revisiting AI Safety Gridworlds
arXiv:2606. 15385v1 Announce Type: new Abstract: Reward hacking, where AI systems exploit misspecified objectives to achieve high reward without satisfying intended goals, remains a central challenge in AI safety.
arXiv AI
Jun 16
APEX: Adaptive Principle EXtraction A Three-Layer Self-Evolution Framework for Production AI Agents
arXiv:2606. 15363v1 Announce Type: new Abstract: Self-improvement in AI agents has emerged as a key research frontier: systems that modify their own prompts, workflows, and decision rules based on accumulated operational experience.
arXiv AI
Jun 16
Retro-Expert: Collaborative Reasoning for Interpretable Retrosynthesis
arXiv:2508. 10967v3 Announce Type: replace-cross Abstract: Retrosynthesis prediction aims to infer the reactant molecules based on a given product molecule, which is a fundamental task in chemical synthesis.
arXiv AI
Jun 16
Looking Is Not Picking: An Attention-Segment Account of Tool-Selection Failures in LLM Agents
arXiv:2606. 16364v1 Announce Type: new Abstract: LLM agents mis-call tools, and the natural guess is that the model failed to see the right tool in a crowded harness.
arXiv AI
Jun 16
The Quality-Utility Paradox: Why High-Reward Data Impairs Small Model Mathematical Reasoning
arXiv:2606. 16152v1 Announce Type: new Abstract: Knowledge distillation from powerful reasoning models is widely used to improve Small Language Models (SLMs) on mathematical reasoning, often assuming that traces with higher reward model scores provide more useful supervision.
arXiv AI
Jun 16
Graphical-Probabilistic Modeling of Generative Flows in LLM-Native Software Systems
arXiv:2606. 15943v1 Announce Type: cross Abstract: Engineering LLM-native software remains a challenging and immature field.
arXiv AI
Jun 16
VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models
arXiv:2606. 16140v1 Announce Type: new Abstract: This technical report introduces VibeThinker-3B, a compact dense model with 3B parameters developed to investigate how far verifiable reasoning can be pushed within a strictly small-model regime.
arXiv AI
Jun 16
Vocabulary Dropout for Curriculum Diversity in LLM Co-Evolution
arXiv:2604. 03472v3 Announce Type: replace-cross Abstract: Co-evolutionary self-play, where one language model generates problems and another solves them, promises autonomous curriculum learning without human supervision.
arXiv AI
Jun 16
A Survey on Agentic Security: Applications, Threats and Defenses
arXiv:2510. 06445v3 Announce Type: replace-cross Abstract: LLM-based agents are now used throughout cybersecurity.
arXiv Machine Learning
Jun 16
HiRo: A Compact Four-Directional Hierarchical Reservoir Token-Mixer for Efficient Image Classification
arXiv:2606. 15151v1 Announce Type: cross Abstract: Recent image classification models must balance local feature modeling, cross-window interaction, and parameter efficiency.
arXiv AI
Jun 16
Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build
arXiv:2605. 21629v2 Announce Type: replace-cross Abstract: How much have students' ordinary learning processes shifted in response to generative AI, and how does that affect their durable learning outcomes?
arXiv AI
Jun 16
Rescaling Confidence: What Scale Design Reveals About LLM Metacognition
arXiv:2603. 09309v2 Announce Type: replace Abstract: Verbalized confidence, in which LLMs report a numerical certainty score, is widely used to estimate uncertainty in black-box settings, yet the confidence scale itself (typically 0--100) is rarely examined.
arXiv AI
Jun 16
Edu-Theater: A Data-Efficient Agent Framework for Scalable Learner Behavior Simulation through Staging Roll-Call
arXiv:2606. 15225v1 Announce Type: cross Abstract: Large-scale learner-task interaction data are crucial for intelligent educational systems but are costly to collect and constrained by privacy and learner engagement.