arXiv AI
Aug 7
StepReflect: Structured UI Transition Reflection for Mobile GUI Agents
arXiv:2608. 05587v1 Announce Type: new Abstract: Autonomous mobile GUI agents require accurate action reflection for reliable long-horizon execution.
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
Aug 7
Signal or Spurious Cue? A Randomized Audit of Survey-Country Metadata in LLM Social Inference
arXiv:2608. 06085v1 Announce Type: new Abstract: Survey-country metadata can improve an LLM's forecast of an individual response when informative, yet the same cue may redirect the forecast when assigned at random.
arXiv AI
Aug 7
Mean-Field Dynamics of Chain-of-Thought Reasoning in Large Language Models
arXiv:2608. 05152v1 Announce Type: cross Abstract: Large language models (LLMs) with chain-of-thought reasoning have been widely applied in recent years, and theoretical explanations of their behavior may help deepen our understanding and guide model optimization.
arXiv AI
Aug 7
BioAgent Bench: An AI Agent Evaluation Suite for Bioinformatics
arXiv:2601. 21800v4 Announce Type: replace Abstract: We introduce BioAgent Bench, an evaluation suite designed for measuring the performance and robustness of AI agents in common bioinformatics tasks.
arXiv AI
Aug 7
TS-RAG: Retrieval Augmented Generation for Time Series Forecasting
arXiv:2608. 06223v1 Announce Type: new Abstract: While deep learning models, particularly transformer-based architectures, have shown impressive performance in time series forecasting, the application of retrieval-augmented generation (RAG) in this domain remains limited.
arXiv AI
Aug 7
Reducing belief in conspiracy theories as they unfold using large language models
arXiv:2608. 06151v1 Announce Type: cross Abstract: The emergence of conspiracy theories in the wake of major events is a significant societal challenge.
arXiv AI
Aug 7
Studying People to Study AI: Expert Perspectives on the Epistemic Fit and Barriers of Human Research in AI Safety & Ethics
arXiv:2608. 05656v1 Announce Type: cross Abstract: Safety risks of AI are becoming increasingly evident in human interactions with AI technologies.
arXiv AI
Aug 7
Hierarchical Latent Prediction for Language Models
arXiv:2608. 05806v1 Announce Type: cross Abstract: While standard Next-Token Prediction (NTP) lays the foundation of language model pre- training, its teacher-forced training paradigm may not be optimal for long-horizon reasoning and planning.
arXiv AI
Aug 7
iARCS: Iterative Agentic RL for Controllable 3D Scene Generation
arXiv:2608. 06161v1 Announce Type: new Abstract: Synthetic 3D scene generation is increasingly used as a data source for computer vision and embodied AI, but existing generators often optimize perceptual realism without reliably satisfying task-critical functional constraints.
arXiv AI
Aug 7
Breaking the Curse of Multilinguality in Many-to-Many Speech-to-Text Translation via a Resource-Aware Mixture of Speech Encoders
arXiv:2608. 04586v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved significant success in speech-to-text translation (S2TT).
arXiv Machine Learning
Aug 7
Transformers with RL or SFT Provably Learn Sparse Boolean Functions, But Differently
arXiv:2511. 17852v3 Announce Type: replace Abstract: Transformers can acquire Chain-of-Thought (CoT) capabilities to solve reasoning tasks via fine-tuning.
arXiv AI
Aug 7
Coherence-Oriented Dream Scene Visualisation
arXiv:2608. 05233v1 Announce Type: new Abstract: Dreams can be emotionally intense but difficult to communicate.
arXiv AI
Aug 7
An Axiomatic Benchmark for Evaluation of Scientific Novelty Metrics
arXiv:2604. 15145v2 Announce Type: replace Abstract: The rigorous evaluation of the novelty of a scientific paper is, even for human scientists, a challenging task.
arXiv AI
Aug 7
MermaidSeqBench: An Evaluation Benchmark for NL-to-Mermaid Sequence Diagram Generation
arXiv:2511. 14967v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown great promise in generating structured diagrams from natural language descriptions, particularly Mermaid sequence diagrams for software engineering.
arXiv AI
Aug 7
Agentic Software Issue Resolution with Large Language Models: A Survey
arXiv:2512. 22256v2 Announce Type: replace-cross Abstract: Software issue resolution aims to address real-world issues in software repositories based on natural language descriptions provided by users, and represents a key aspect of software maintenance.
arXiv AI
Aug 7
The Impossibility Triangle of Long-Context Modeling
arXiv:2605. 05066v2 Announce Type: replace-cross Abstract: We identify and prove a fundamental trade-off governing long-sequence models: no model can simultaneously achieve (i) per-step computation independent of sequence length (Efficiency), (ii) state size independent of sequence length (Compactness), and (iii) the ability to recall a number of historical facts proportional to sequence length (Recall).
arXiv AI
Aug 7
Correct Answers from Sound Reasoning: Verifiable Process Supervision for Language Models
arXiv:2605. 12519v2 Announce Type: replace-cross Abstract: Training language models to produce both correct answers and sound reasoning remains an open challenge.
arXiv Machine Learning
Aug 7
EvoHarness-RL: Learning Self-Evolving Runtime Harness for Long-Horizon LLM Agents
arXiv:2608. 05446v1 Announce Type: new Abstract: Long-horizon LLM agents increasingly rely on external execution support to maintain state, track progress, invoke tools, verify outcomes, and reuse experience across interactions.
arXiv AI
Aug 7
MicroEvo: Knowledge-Guided LLM Sampling for Efficient Microarchitecture Design Space Exploration
arXiv:2608. 06183v1 Announce Type: new Abstract: Microarchitecture design space exploration suffers from expansive search spaces and expensive PPA evaluation, leaving only a small simulation budget for design decision-making.
Hugging Face Trending Papers
Aug 7
CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights
Weight quantization for large-language-model inference must balance adaptive reconstruction levels with representations regular enough for efficient GPU execution. Uniform integers constrain each group to a linear grid.