arXiv AI
Aug 3
Autonomous Repair for Multi-Agent Systems via Monte-Carlo Tree Search
arXiv:2607. 29055v1 Announce Type: cross Abstract: Multi-agent systems (MAS) are increasingly deployed to solve complex 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 AI
Aug 3
AMTFV: Agentic Mathematical Tool-Flow Verification for LLM Self-Correction
arXiv:2607. 29549v1 Announce Type: new Abstract: Large language models have demonstrated strong mathematical problem-solving capabilities, yet reliably verifying their candidate answers remains challenging.
arXiv AI
Aug 3
Tool Specifications Matter: Uncovering and Mitigating Safety Risks in AI Agents
arXiv:2607. 29254v1 Announce Type: new Abstract: AI agents extend large language models (LLMs) with external tools, enabling them to perform complex tasks and translate model outputs into consequential real-world actions.
arXiv AI
Aug 3
Memory Provenance Laundering in LLM Agents: A Non-Amplification Firewall for Persistent Memory
arXiv:2607. 29167v1 Announce Type: cross Abstract: Long-term memory lets large language model(LLM) agents reuse prior preferences and work flows, but it also turns untrusted observations into persistent action context.
arXiv AI
Aug 3
AREA3D: Active Reconstruction Agent with Unified Feed-Forward 3D Perception and Vision-Language Guidance
arXiv:2512. 05131v2 Announce Type: replace-cross Abstract: Active 3D reconstruction enables an agent to autonomously select viewpoints to efficiently obtain accurate and complete scene geometry, rather than passively reconstructing scenes from pre-collected images.
arXiv AI
Aug 3
The Self-Correction Illusion: Role Relabeling Gates Explicit Error Flagging in Large Language Models
arXiv:2606. 05976v2 Announce Type: replace Abstract: Recent works show that LLM agents struggle to correct errors in their own reasoning traces, despite their ability to correct errors from external sources.
arXiv AI
Aug 3
Don't Mix Rewards, Mix Policies: Policy Decomposition and Optimization for Multi-Reward RL
arXiv:2607. 29246v1 Announce Type: new Abstract: Modern large language models (LLMs) are expected not just to answer correctly, but to adapt their behavior to different human values and use cases.
arXiv AI
Aug 3
CAGE: Certified Authorization under Typed-Return Uncertainty for Tool-Using Agents
arXiv:2607. 29190v1 Announce Type: new Abstract: Tool-using LLM agents act on typed tool returns, records pairing provenance and categorical fields with numerical values.
arXiv AI
Aug 3
Shall We Play a Game? Language Models for Open-ended Wargames
arXiv:2509. 17192v3 Announce Type: replace Abstract: LLM-based social simulations can make a generated transcript look like a single behavioral signal, but the model behind that transcript may be doing several different jobs: choosing what an actor says or does, deciding what happens after an action, or both.
arXiv AI
Aug 3
Stem: Rethinking Causal Information Flow in Sparse Attention
arXiv:2603. 06274v2 Announce Type: replace-cross Abstract: The quadratic computational complexity of self-attention remains a fundamental bottleneck for scaling Large Language Models (LLMs) to long contexts, particularly during the pre-filling phase.
arXiv AI
Aug 3
AI4BayesCode: From Natural Language Descriptions to Validated Modular Stateful Bayesian Samplers
arXiv:2605. 18476v2 Announce Type: replace-cross Abstract: Coding and computation remain major bottlenecks in Markov chain Monte Carlo (MCMC) workflows, especially as modern sampling algorithms have become increasingly complex and existing probabilistic programming systems remain limited in model support, extensibility, and composability.
arXiv AI
Aug 3
Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation
arXiv:2601. 22546v2 Announce Type: replace-cross Abstract: The recent advancements in Large Language Models (LLMs) have attracted interest in exploring their in-context learning abilities and chain-of-thought capabilities.
arXiv AI
Aug 3
An Ontology-Guided, Deduplication-Aware Extraction Layer for Knowledge Graph Construction from Heterogeneous Documents
arXiv:2607. 28662v1 Announce Type: new Abstract: Large language models extract entities and relationships from unstructured documents fluently but inconsistently: type vocabularies fracture across documents, the same person surfaces under several name variants, relationships duplicate, and distinct individuals who share a name risk silent conflation.
arXiv Machine Learning
Aug 3
Exploring Block Anomaly Detection In HDFS Log Data Analysis
arXiv:2607. 29383v1 Announce Type: new Abstract: In recent years, with the development of big data technology, increasingly more companies use HDFS for data processing and storage.
arXiv Machine Learning
Aug 3
Simulation Code Generation for Fluid Systems using Large Language Models: Benchmarking Models and Prompting Strategies
arXiv:2607. 29389v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated a strong ability to generate syntactically correct code from natural-language specifications.
arXiv AI
Aug 3
Epistemic-aware Vision-Language Foundation Model for Fetal Ultrasound Interpretation
arXiv:2510. 12953v4 Announce Type: replace-cross Abstract: Recent medical vision-language models have shown promise on tasks such as VQA, report generation, and anomaly detection.
arXiv Machine Learning
Aug 3
RTLCurator: Label-Efficient Data Curation for RTL Generation
arXiv:2607. 29283v1 Announce Type: cross Abstract: Training large language models (LLMs) to write register-transfer level (RTL) requires large corpora of paired specifications and code, and such data is scarce enough that most public corpora are now synthesized.
arXiv Machine Learning
Aug 3
DeltaServe: Host-Agnostic Co-Serving of Inference and Fine-Tuning for LLMs
arXiv:2607. 28848v1 Announce Type: cross Abstract: LLM serving systems are provisioned for peak load to meet strict latency targets, leaving substantial GPU compute idle whenever traffic falls below peak.
arXiv AI
Aug 3
LLM Framework for Discovering Major Mathematical Conjectures: AI's Quest for the Next Riemann Hypothesis
arXiv:2607. 28632v1 Announce Type: new Abstract: Major mathematical conjectures still depend heavily on expert intuition, so a unified method for the systematic generation and validation of conjectures with substantial mathematical potential remains unavailable.
arXiv AI
Aug 3
Predict-then-Diffuse: Adaptive Response Length for Compute-Budgeted Inference in Diffusion LLMs
arXiv:2605. 04215v3 Announce Type: replace-cross Abstract: Diffusion-based Large Language Models (D-LLMs) represent a promising frontier in generative AI, offering fully parallel token generation that can lead to significant throughput advantages and superior GPU utilization over the traditional autoregressive paradigm.