arXiv AI
Jun 10
Baseline-Free Policy Optimization for Neural Combinatorial Optimization
arXiv:2606. 10321v1 Announce Type: cross Abstract: Neural combinatorial optimization (NCO) trains autoregressive policies to solve routing problems.
Benchmarks and evaluation
Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv AI
Jun 10
Alignment Collapse Under KV Cache Quantization: Diagnosis and Mitigation
arXiv:2606. 09864v1 Announce Type: cross Abstract: Key-value (KV) cache quantization is widely used to reduce Large Language Model (LLM) inference memory, yet existing evaluations solely focus on measuring perplexity and accuracy without assessing the safety impact.
arXiv AI
Jun 10
NOVA: Symbolic Regression Discovery of Interpretable Car-Following and Lane-Change Models with Driver Heterogeneity
arXiv:2606. 10583v1 Announce Type: cross Abstract: We present NOVA, an autonomous symbolic regression framework that identifies interpretable car-following and lane-change structures from raw trajectory data with minimal behavioral priors.
arXiv Machine Learning
Jun 10
What Demonstration Curation Metrics Do to Your Policy
arXiv:2606. 10229v1 Announce Type: cross Abstract: We study whether demonstration-curation metrics that detect defective training episodes also improve the downstream behavior-cloning policy that trains on the curated data.
arXiv AI
Jun 10
A complementary study on PlanGPT: Evaluation with defined Performance Metrics and comparison with a planner
arXiv:2606. 10489v1 Announce Type: new Abstract: Automated Planning is a subfield of Artificial Intelligence (AI) where the main objective is generating a sequence of actions, known as a plan, that helps us reach a goal state from an initial state.
arXiv AI
Jun 10
BadRobot: Jailbreaking Embodied LLM Agents in the Physical World
arXiv:2407. 20242v5 Announce Type: replace-cross Abstract: Embodied AI represents systems where AI is integrated into physical entities.
arXiv AI
Jun 10
Transformer Based Model for Spatiotemporal Feature Learning in EEG Emotion Recognition
arXiv:2606. 10718v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a widely adopted technique for monitoring brain activity, offering valuable insights into neurological states due to its high temporal resolution and cost-effectiveness.
arXiv AI
Jun 10
Sim2Schedule: A Simulator-Guided LLM Framework for Autonomous Open-Pit Mine Scheduling
arXiv:2606. 10286v1 Announce Type: new Abstract: Open-pit mine scheduling is a critical process for maximizing economic return under complex geotechnical and operational constraints.
arXiv Machine Learning
Jun 10
Multi-task LLMs for Bug Classification: Efficient Inference with Auxiliary Decoding Heads
arXiv:2606. 09956v1 Announce Type: cross Abstract: The rapid adoption of LLM-powered code generation has dramatically accelerated software development, yet effective verification methods remain severely underdeveloped.
arXiv AI
Jun 10
The Price of Agreement: Measuring LLM Sycophancy in Agentic Financial Applications
arXiv:2604. 24668v3 Announce Type: replace Abstract: Given the increased use of LLMs in financial systems today, it becomes important to evaluate the safety and robustness of such systems.
arXiv AI
Jun 10
Using Probabilistic Programs to Train Inductive Reasoning in Large Language Models
arXiv:2606. 09856v1 Announce Type: cross Abstract: Post-training Large Language Models (LLMs) for reasoning typically focuses on deductive tasks such as mathematics and coding where correctness is verifiable.
arXiv Machine Learning
Jun 10
Overcoming Rank Collapse in Feedback Alignment
arXiv:2606. 11123v1 Announce Type: new Abstract: Backpropagation (BP) is widely viewed as biologically implausible, in part because it requires feedback weights to be the transpose of forward weights for error propagation.
arXiv AI
Jun 10
TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning
arXiv:2509. 25760v2 Announce Type: replace-cross Abstract: While large language models (LLMs) have demonstrated strong performance on factoid question answering, they are still prone to hallucination and untruthful responses, particularly when tasks demand information outside their parametric knowledge.
arXiv Machine Learning
Jun 10
FreshRetailNet-LT: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail
arXiv:2505. 16319v3 Announce Type: replace Abstract: Accurate demand estimation is critical for the retail business in guiding the inventory and pricing policies of perishable products.
arXiv AI
Jun 10
A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AI
arXiv:2505. 01458v2 Announce Type: replace-cross Abstract: Navigation and manipulation are core capabilities in Embodied AI, but training agents to perform them directly in the real world is costly, time-consuming, and unsafe.
arXiv Machine Learning
Jun 10
ClusBench: The Clustering Benchmark Data Resource You've All Been Waiting For (?)
arXiv:2606. 10673v1 Announce Type: cross Abstract: Although some very common test beds exist for assessing the performance of clustering methods, large scale benchmarking is typically limited to relatively simplistic simulation set-ups.
arXiv AI
Jun 10
Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings
arXiv:2606. 10716v1 Announce Type: cross Abstract: Pre-trained language models (PLMs) have achieved strong performance in keyphrase extraction (KPE), largely due to their ability to generate rich contextualized representations.
arXiv Machine Learning
Jun 10
Latent Guided Sampling for Combinatorial Optimization
arXiv:2506. 03672v2 Announce Type: replace-cross Abstract: Combinatorial Optimization problems are widespread in domains such as logistics, manufacturing, and drug discovery, yet their NP-hard nature makes them computationally challenging.
arXiv Machine Learning
Jun 10
Data-Driven Dynamic Assortment in Online Platforms: Learning about Two Sides
arXiv:2606. 11118v1 Announce Type: new Abstract: We study a dynamic assortment problem on a two-sided service platform with incomplete information and heterogeneous customers in a discrete-time setting.
arXiv AI
Jun 10
Recalling Too Well: Sycophancy Evaluation and Mitigation in Memory-Augmented Models
arXiv:2606. 10949v1 Announce Type: new Abstract: Persistent memory systems promise to make LLMs more helpful by storing user beliefs over time.