arXiv AI
Jun 10
The Role of Feedback Alignment in Self-Distillation
arXiv:2606. 11173v1 Announce Type: new Abstract: Conditioning a language model on additional context, such as feedback on a previous attempt, typically improves its response.
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 10
Can Multi-Agent LLMs Identify Their Peers? Stylometric Fingerprinting in Role-Constrained Political Analysis
arXiv:2606. 09854v1 Announce Type: cross Abstract: Multi-agent large language model (LLM) pipelines for political statement analysis are vulnerable to peer-preservation bias: models tend to protect peer models from deactivation and show identity-dependent scoring distortions.
arXiv AI
Jun 10
Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA
arXiv:2606. 11117v1 Announce Type: cross Abstract: Designing FPGA-based accelerators for modern artificial intelligence workloads requires exploring a large and complex hardware design space that involves architectural parameters, data flow strategies, and memory hierarchies, making the process very time consuming.
arXiv AI
Jun 10
Making Time Editable in Video Diffusion Transformers
arXiv:2606. 10183v1 Announce Type: cross Abstract: Modern Diffusion Transformers for video generation provide limited control over the progression of time and the editing of temporal dynamics.
arXiv AI
Jun 10
Supervised Fine-tuning with Synthetic Rationale Data Hurts Real-World Disease Prediction
arXiv:2606. 10279v1 Announce Type: new Abstract: Supervised fine-tuning with synthetic rationale data is widely assumed to improve language model performance on clinical prediction tasks by teaching models not just what to predict but why.
arXiv AI
Jun 10
K-Forcing: Joint Next-K-Token Decoding via Push-Forward Language Modeling
arXiv:2606. 10820v1 Announce Type: cross Abstract: Autoregressive (AR) language modeling is the dominant paradigm for text generation, yet its sequential token-by-token decoding makes inference memory-bound and inefficient.
arXiv AI
Jun 10
PreAct-Bench: Benchmarking Predictive Monitoring in LLMs
arXiv:2606. 09890v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents capable of executing multi-step action trajectories toward a given objective.
arXiv AI
Jun 10
Dynamics of Adversarial Attacks on Large Language Model-Based Search Engines
arXiv:2501. 00745v3 Announce Type: replace-cross Abstract: The increasing integration of Large Language Model (LLM) based search engines has transformed the landscape of information retrieval.
arXiv AI
Jun 10
TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning
arXiv:2606. 11119v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models.
arXiv AI
Jun 10
CANVAS: Captioning Art with Narrative Visual-Audio AI Systems
arXiv:2606. 09846v1 Announce Type: cross Abstract: Visual art remains largely inaccessible to blind and low-vision (BLV) audiences due to brief or absent alt-text, which rarely conveys the sensory, spatial, or emotional qualities of an artwork.
arXiv Machine Learning
Jun 10
One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data
arXiv:2606. 10678v1 Announce Type: new Abstract: Transformer-based models have emerged as leading paradigms in time-series forecasting in recent years, employing self-attention mechanisms to capture long-range dependencies.
arXiv AI
Jun 10
Trace2Policy: From Expert Behavior Traces to Self-Evolving Decision Agents
arXiv:2606. 10457v1 Announce Type: new Abstract: Decision rules that enterprise experts apply tacitly -- in auditing, compliance, and contract review -- can be systematically recovered and improved through iterative error analysis.
arXiv AI
Jun 10
Attacks on Machine-Text Detectors Retain Stylistic Fingerprints
arXiv:2505. 14608v3 Announce Type: replace-cross Abstract: Despite considerable progress in the development of machine-text detectors, the ease with which machine-text can be manipulated to evade detection has led to suggestions that the problem is inherently intractable.
arXiv AI
Jun 10
Trainable Smooth-Rotation Transforms with Learned Channel Scales for LLM Quantization
arXiv:2606. 09927v1 Announce Type: cross Abstract: Post-training quantization (PTQ) is one of the most practical ways to reduce the serving cost of Large Language Models (LLMs), but activation quantization remains difficult because outlier-dominated channels lead to large quantization errors.
arXiv Machine Learning
Jun 10
DUET -- Dual User Embedding Transformers for Offsite Conversion Prediction
arXiv:2606. 10243v1 Announce Type: new Abstract: Offsite conversion rate (OCVR) prediction is an important ranking problem in computational recommendation systems.
arXiv AI
Jun 10
Integrating Local and Global Entropy for Uncertainty Quantification in LLMs
arXiv:2606. 09875v1 Announce Type: cross Abstract: Large language models hallucinate confidently, making uncertainty quantification (UQ) essential for reliable deployment.
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.
arXiv AI
Jun 10
A Constrained Natural-Language Interface for Variational Multi-Physics Finite Element Simulations in FEniCS
arXiv:2606. 10928v1 Announce Type: cross Abstract: Large language models can reduce the manual effort required to set up finite element simulations, but they introduce reliability risks when generated solver code lies on the critical path.
arXiv Machine Learning
Jun 10
LMT: A Bayesian Framework for Causal Discovery from Textual Alarm Records in Manufacturing Systems
arXiv:2606. 09892v1 Announce Type: new Abstract: Textual event records, such as alarm logs, have become an increasingly common data source in engineering and manufacturing systems.
arXiv Machine Learning
Jun 10
Pushing the limits of one-dimensional NMR spectroscopy for automated structure elucidation using artificial intelligence
arXiv:2512. 18531v2 Announce Type: replace-cross Abstract: One-dimensional NMR spectroscopy is one of the most widely used techniques for the characterization of organic compounds and natural products.