arXiv AI
Jun 10
Flaws in the LLM Automation Narrative
arXiv:2606. 11166v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly described as performing at the level of human experts on knowledge economy tasks.
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 10
GRAFT: Gain-Recalibrated Adapters for Transformer-Based Neural Population Activity Modeling
arXiv:2606. 11066v1 Announce Type: new Abstract: Neural population activity models can recover rich temporal structure from binned spikes, but their read-in and readout layers often remain tied to a fixed set of recorded neurons.
arXiv AI
Jun 10
LakeQA: An Exploratory QA Benchmark over a Million-Scale Data Lake
arXiv:2606. 10460v1 Announce Type: cross Abstract: Recent large language models (LLMs) have shown rapid progress in reading-based question answering (QA), where evidence is explicitly provided or can be trivially retrieved.
arXiv Machine Learning
Jun 10
How Does Reasoning Flow? Tracing Attention-Induced Information Flow for Targeted RL in LLMs
arXiv:2606. 10646v1 Announce Type: new Abstract: Token-level credit assignment remains a key obstacle for reinforcement learning (RL) in large language models (LLMs), where RL recipes typically treat all tokens equally, failing to distinguish decisive reasoning steps from routine formatting or fluent filler.
arXiv AI
Jun 10
KG-SoftMAP: Soft Knowledge-Graph Priors for Bayesian Network Structure Learning from Sparse Discrete Data
arXiv:2606. 10358v1 Announce Type: cross Abstract: Learning Bayesian network (BN) structure from sparse discrete data is hard: when each instance records only a few variables, most variable pairs lack the joint observations needed for reliable scoring, and data-only methods recover little structure.
arXiv AI
Jun 10
ASA: Backbone-Training-Free Representation Engineering for Tool-Calling Agents
arXiv:2602. 04935v3 Announce Type: replace-cross Abstract: Adapting LLM agents to domain-specific tool calling remains notably brittle under evolving interfaces.
arXiv AI
Jun 10
SocraticPO: Policy Optimization via Interactive Guidance
arXiv:2606. 09887v1 Announce Type: cross Abstract: Reinforcement learning (RL) for large language models usually supervises reasoning with scalar outcome rewards, such as binary correctness.
arXiv Machine Learning
Jun 10
A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models
arXiv:2606. 10277v1 Announce Type: new Abstract: Though wireless foundation models (WFMs) have shown strong potential in learning universal channel representations, their adaptation to various downstream tasks remains constrained by existing paradigms.
arXiv AI
Jun 10
CITRAS: Covariate-Informed Transformer for Time Series Forecasting
arXiv:2503. 24007v4 Announce Type: replace-cross Abstract: In time series forecasting, covariates represent external factors that influence target variables.
arXiv AI
Jun 10
Effective Reinforcement Learning for Agentic Search by Recycling Zero-Variance Queries During Training
arXiv:2606. 10709v1 Announce Type: cross Abstract: The use of GRPO-style algorithms has become the standard strategy for training LLM search agents under outcome-only rewards.
arXiv AI
Jun 10
How can we assess human-agent interactions? Case studies in software agent design
arXiv:2510. 09801v3 Announce Type: replace Abstract: While benchmarks measure the accuracy of LLM-powered agents, they mostly assume full automation, failing to represent the collaborative nature of real-world use cases.
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
Structure from Reasoning, Numbers from Search: On-Premise Open LLMs as Structural Priors for Coupled MIMO Controller Tuning
arXiv:2606. 11015v1 Announce Type: new Abstract: Tuning controllers for strongly coupled multi-input multi-output (MIMO) industrial processes is hard: decentralized classical auto-tuning ignores loop interaction, and local numerical optimization from natural initializations stalls in the resulting non-convex cost landscape.
arXiv Machine Learning
Jun 10
Generative Archetype-Grounded Item Representations for Sequential Recommendation
arXiv:2606. 11023v1 Announce Type: cross Abstract: Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior.
arXiv AI
Jun 10
A Unified Multi-Modal Framework for Intelligent Financial Systems: Integrating Reinforcement Learning, High-Frequency Trading, and Game-Theoretic Approaches with Cross-Modal Sentiment Analysis
arXiv:2606. 10412v1 Announce Type: new Abstract: The rapid evolution of financial technology demands sophisticated artificial intelligence systems capable of handling diverse challenges across multiple domains simultaneously.
arXiv AI
Jun 10
Decentralized Multi-Agent Systems with Shared Context
arXiv:2606. 10662v1 Announce Type: cross Abstract: Multi-agent systems (MAS) can scale large language model reasoning at test time by decomposing complex problems into parallel subtasks.
arXiv AI
Jun 10
CIAware-Bench: Benchmarking Control Intervention Awareness Across Frontier LLMs
arXiv:2606. 11063v1 Announce Type: new Abstract: AI control protocols oversee untrusted models by monitoring their actions and modifying potentially unsafe steps, often using a trusted model.
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 Machine Learning
Jun 10
Finer is Better (with the Right Scaling)
arXiv:2605. 08565v2 Announce Type: replace Abstract: Microscaling is a critical technique for preserving the quality of Large Language Models (LLMs) quantized to ultra-low precision formats.
arXiv AI
Jun 10
Dynamic Linear Attention
arXiv:2606. 10650v1 Announce Type: cross Abstract: The scalability of Large Language Models (LLMs) to long contexts is fundamentally constrained by the quadratic complexity of standard attention, motivating the adoption of linear attention mechanisms with sub-quadratic cost.