arXiv AI
Jun 2
On Imbalanced Regression with Hoeffding Trees
arXiv:2602. 22101v3 Announce Type: replace-cross Abstract: Many real-world applications generate continuous data streams for regression.
Benchmarks and evaluation
Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv AI
Jun 2
DenseMLLM: Standard Multimodal LLMs for Dense Prediction
arXiv:2602. 14134v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in high-level visual understanding.
arXiv Machine Learning
Jun 2
Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA
arXiv:2606. 00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems.
arXiv AI
Jun 2
Beware of the Batch Size: Hyperparameter Bias in Evaluating LoRA
arXiv:2602. 09492v2 Announce Type: replace-cross Abstract: Low-rank adaptation (LoRA) is a standard approach for fine-tuning large language models, yet its many variants report conflicting empirical gains, often on the same benchmarks.
arXiv AI
Jun 2
Query Circuits: Explaining How Language Models Answer User Prompts
arXiv:2509. 24808v2 Announce Type: replace Abstract: Explaining why a language model produces a particular output requires local, input-level explanations.
arXiv AI
Jun 2
naPINN: Noise-Adaptive Physics-Informed Neural Networks for Recovering Physics from Corrupted Measurement
arXiv:2602. 02547v2 Announce Type: replace-cross Abstract: Physics-Informed Neural Networks (PINNs) are effective methods for solving inverse problems and discovering governing equations from observational data.
arXiv AI
Jun 2
ReasonBENCH: Benchmarking the (In)Stability of LLM Reasoning
arXiv:2512. 07795v2 Announce Type: replace Abstract: Benchmark scores for LLM reasoning systems are reported as single numbers, yet the same model, strategy, and task can produce meaningfully different answers and costs across repeated executions, even under greedy decoding (T = 0).
arXiv AI
Jun 2
Herculean: An Agentic Benchmark for Financial Intelligence
arXiv:2605. 14355v3 Announce Type: replace Abstract: As AI agents improve, the central question is no longer whether they can solve isolated well-defined financial tasks, but whether they can reliably carry out financial professional work.
arXiv AI
Jun 2
Probabilistic Performance Guarantees for Multi-Task Reinforcement Learning
arXiv:2602. 02098v2 Announce Type: replace-cross Abstract: Multi-task reinforcement learning trains generalist policies that can execute multiple tasks.
arXiv AI
Jun 2
Med-Scout: Curing MLLMs' Geometric Blindness in Medical Perception via Geometry-Aware RL Post-Training
arXiv:2601. 23220v2 Announce Type: replace-cross Abstract: Despite recent Multimodal Large Language Models (MLLMs)' linguistic prowess in medical diagnosis, we find even state-of-the-art MLLMs suffer from a critical perceptual deficit: geometric blindness.
arXiv AI
Jun 2
MMSkills: Towards Multimodal Skills for General Visual Agents
arXiv:2605. 13527v3 Announce Type: replace Abstract: Reusable skills have become a core substrate for improving agent capabilities, yet most existing skill packages encode reusable behavior primarily as textual prompts, executable code, or learned routines.
arXiv AI
Jun 2
Can LLMs Reason Structurally? Benchmarking via the Lens of Data Structures
arXiv:2505. 24069v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are deployed on increasingly complex tasks that require multi-step decision-making.
arXiv AI
Jun 2
The Shape of Wisdom: Decision Trajectories in Language Models
arXiv:2606. 01202v1 Announce Type: new Abstract: Language models do not simply choose an answer at the output layer.
arXiv AI
Jun 2
MobiBench: Multi-Branch, Modular Benchmark for Mobile GUI Agents
arXiv:2512. 12634v4 Announce Type: replace Abstract: Mobile GUI Agents, AI agents capable of interacting with mobile applications on behalf of users, have the potential to transform human computer interaction.
arXiv AI
Jun 2
LookWise: Knowing When and Where to Look for Fine-Grained Visual Reasoning in Multimodal Large Language Models
arXiv:2603. 00171v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) are shifting towards "Thinking with Images" by actively exploring image details.
arXiv Machine Learning
Jun 2
Don't Let a Few Network Failures Slow the Entire AllReduce
arXiv:2606. 01680v1 Announce Type: cross Abstract: Network failures are among the most frequent hardware faults in large-scale GPU clusters and a leading cause of training-job interruptions.
arXiv AI
Jun 2
Learning When Not to Act: Mitigating Tool Abuse in Agentic Reinforcement Learning
arXiv:2606. 02132v1 Announce Type: new Abstract: Agentic reinforcement learning can induce tool abuse, where models overuse external tools even for queries solvable by internal reasoning.
arXiv AI
Jun 2
StressDream: Steering Video World Models for Robust Policy Evaluation and Improvement
arXiv:2606. 00267v1 Announce Type: cross Abstract: Video world models (WMs) have shown promise for policy evaluation and improvement by imagining realistic future observations conditioned on ego-robot actions.
arXiv AI
Jun 2
The Cartesian Shortcut: Re-evaluate Vision Reasoning in Polar Coordinate Space
arXiv:2605. 09883v2 Announce Type: replace-cross Abstract: As current Multimodal Large Language Models rapidly saturate canonical visual reasoning benchmarks, a key question emerges: do these strong scores genuinely reflect robust visual understanding?
arXiv Machine Learning
Jun 2
Echo State Networks for Time Series Forecasting: Hyperparameter Sweep and Benchmarking
arXiv:2602. 03912v4 Announce Type: replace Abstract: This paper investigates the performance of Echo State Networks (ESNs) for univariate forecasting of monthly and quarterly time series from the M4 Forecasting Competition dataset.