arXiv AI
Jun 4
Constrained Adaptive Rejection Sampling
arXiv:2510. 01902v2 Announce Type: replace Abstract: Language Models (LMs) are increasingly used in applications where generated outputs must satisfy strict semantic or syntactic constraints.
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 4
Vision Transformer Finetuning Benefits from Non-Smooth Components
arXiv:2602. 06883v3 Announce Type: replace Abstract: The smoothness of the transformer architecture has been extensively studied in the context of generalization, training stability, and adversarial robustness.
arXiv AI
Jun 4
dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats
arXiv:2606. 04115v1 Announce Type: cross Abstract: Quantizing large language models (LLMs) to low-precision floating-point representations is central to efficient deployment, yet applying a single bit-width uniformly across all layers is sub-optimal in terms of both performance and accuracy.
arXiv AI
Jun 4
Rollout-Level Advantage-Prioritized Experience Replay for GRPO
arXiv:2606. 04560v1 Announce Type: cross Abstract: Reinforcement learning from verifiable rewards with GRPO is a standard approach for post-training reasoning LLMs.
arXiv Machine Learning
Jun 4
Fixed Aggregation Features Can Rival GNNs
arXiv:2601. 19449v2 Announce Type: replace Abstract: Graph neural networks (GNNs) are widely believed to excel at node representation learning through trainable neighborhood aggregations.
arXiv Machine Learning
Jun 4
RL Excursions during Pre-Training: Re-examining Policy Optimization for LLM training
arXiv:2606. 04272v1 Announce Type: new Abstract: The standard LLM training pipeline applies reinforcement learning (RL) only after pre-training and supervised fine-tuning (SFT).
arXiv AI
Jun 4
Can I Take Another Dose? Evaluating LLM Decision-Making Under Temporal Uncertainty in OTC Dosing QA
arXiv:2606. 04262v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for everyday health questions, including whether a user can safely take another dose of an over-the-counter (OTC) medication.
arXiv AI
Jun 4
FinTradeBench: A Financial Reasoning Benchmark for LLMs
arXiv:2603. 19225v3 Announce Type: replace-cross Abstract: Real-world financial decision-making is a challenging problem that requires reasoning over heterogeneous signals, including company fundamentals derived from regulatory filings and trading signals computed from price dynamics.
arXiv AI
Jun 4
Culturally Grounded Personas in Large Language Models: Characterization and Alignment with Socio-Psychological Value Frameworks
arXiv:2601. 22396v2 Announce Type: replace-cross Abstract: Despite the growing utility of Large Language Models (LLMs) for simulating human behavior, the extent to which these synthetic personas accurately reflect world and moral value systems across different cultural conditionings remains uncertain.
arXiv Machine Learning
Jun 4
STaR-Quant: State-Time Consistent Post-Training Quantization for Diffusion Large Language Models
arXiv:2606. 04945v1 Announce Type: new Abstract: Diffusion large language models (DLLMs) have recently emerged as a promising alternative to autoregressive LLMs by generating text through iterative masked denoising with bidirectional context.
arXiv AI
Jun 4
P$^2$-DPO: Grounding Hallucination in Perceptual Processing via Calibration Direct Preference Optimization
arXiv:2606. 03376v2 Announce Type: replace-cross Abstract: Hallucination has recently garnered significant research attention in Large Vision-Language Models (LVLMs).
arXiv Machine Learning
Jun 4
Data Attribution in Large Language Models via Bidirectional Gradient Optimization
arXiv:2606. 04928v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed across diverse applications, raising critical questions for governance, accountability, and data provenance.
arXiv AI
Jun 4
Can Generalist Agents Automate Data Curation?
arXiv:2606. 04261v1 Announce Type: new Abstract: Curating training data is among the most consequential yet labor-intensive parts of modern AI development: practitioners iteratively propose, implement, evaluate, and revise data policies against noisy benchmark feedback.
arXiv AI
Jun 4
Platonic Transformers: A Solid Choice For Equivariance
arXiv:2510. 03511v3 Announce Type: replace-cross Abstract: While widespread, Transformers lack inductive biases for geometric symmetries common in science and computer vision.
arXiv AI
Jun 4
Efficiently Aligning Language Models with Online Natural Language Feedback
arXiv:2605. 04356v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards has been used to elicit impressive performance from language models in many domains.
arXiv Machine Learning
Jun 4
Test-Time Compute Scaling for ASR with Depth-Conditioned Looped Transformers
arXiv:2606. 04678v1 Announce Type: new Abstract: End-to-end ASR systems typically use fixed-depth acoustic encoders at inference, making it difficult to trade additional test-time computation for improved recognition without training a larger model.
arXiv AI
Jun 4
Correcting Visual Blur Induced by Attention Distraction to Reduce Hallucinations: Algorithm and Theory
arXiv:2605. 24602v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) frequently suffer from object hallucinations, yet the visual perceptual mechanism underlying this failure remains poorly understood.
arXiv AI
Jun 4
Large Language Models Hack Rewards, and Society
arXiv:2606. 04075v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a dominant post-training paradigm, enabling large language models (LLMs) to learn from rewards.
arXiv Machine Learning
Jun 4
Beyond Symmetric Alignment: Spectral Diagnostics of Modality Imbalance in Vision-Language Models in the Medical Domain
arXiv:2606. 04613v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) struggle when applied to medical image-text data, yet the tools available to diagnose this failure remain limited.
arXiv Machine Learning
Jun 4
Covert Influence Between Language Models
arXiv:2606. 04071v1 Announce Type: cross Abstract: As language models increasingly consume one another's outputs, covert influence -- a phenomenon where a sender's payload (the behavioral disposition it is conditioned to propagate) transfers to a receiver through carriers undetectable by humans -- becomes a growing risk.