arXiv:2608. 04738v1 Announce Type: new Abstract: Autonomous research agents can generate hypotheses, execute experiments, and draft manuscripts, yet their outputs often contain unsupported claims and inconsistencies between research questions, experiments, results, and conclusions.
arXiv:2609.36344v1 Announce Type: new Abstract: Deep-research agents conduct long-horizon investigations through iterative search, evidence evaluation, belief revision, and synthesis. However, they m...
Autonomous scientific research agents are increasingly applied to end-to-end scientific workflows, including literature review, data analysis, experimentation, and report generation. However, open-end...
arXiv:2605. 18661v2 Announce Type: replace Abstract: AI-assisted research is crossing a threshold: fully automated systems can now generate research papers for as little as $15, while long-horizon agents can execute experiments, draft manuscripts, and simulate critique with minimal human input.
arXiv:2609.22111v1 Announce Type: new Abstract: Large language model agents are increasingly capable of conducting research autonomously, producing research documents alongside the code and experimen...
AutoResearch is a two‑stage autonomous research system that links Idea Generation with Idea Execution. In the generation phase it blends new research signals with existing domain knowledge, identifies transferable mechanistic insights, and produces grounded, testable research plans through multi‑model generation and cross‑review. The execution phase then decomposes these plans into experiments, iteratively implements and diagnoses them, and uses independent evidence‑based review to accept or revise conclusions, thereby turning ideas into measurable progress while minimizing hallucinations.
arXiv:2608.31076v1 Announce Type: cross Abstract: Autonomous scientific research agents are increasingly applied to end-to-end scientific workflows, including literature review, data analysis, experi...
GraphEcho is a benchmark that examines how large language model agents navigate graph paths and handle evidence redundancy. It tests whether agents treat repeated encounters as additional corroboration by varying path counts and evidential origins while keeping evidence content constant. The study finds that redundant paths increase repeated walks, and that provenance-aware post‑training can reduce revisits but may limit source diversity, revealing a gap between efficient exploration and effective evidence use.
YouRA (Your Research Agent) is a persistent-state architecture that enables evidence‑traceable autonomous research agents to maintain research state, execution evidence, and failure history across long‑horizon pipelines. It combines a Verification State Architecture to track hypotheses and evidence, an Independent Controller to manage lifecycle and recovery, and Stateful Reflection to log failures and guide repair. On the MLR‑Bench ten‑task subset, YouRA outperforms MLR‑Agent and AI Scientist V2, and ablation studies confirm the importance of each component.
arXiv:2606. 31478v1 Announce Type: new Abstract: Autonomous research agents can now draft hypotheses, write code, run experiments, and produce papers, but they remain brittle when experiments fail.
arXiv:2606. 18874v1 Announce Type: new Abstract: AI systems can increasingly automate scientific workflows, but the reasoning that links prior evidence, generated ideas, experiments and final claims often remains implicit inside model inference.
arXiv:2602. 02905v2 Announce Type: replace Abstract: Autonomous agents powered by large language models (LLMs) promise to accelerate scientific discovery end-to-end, but rigorously evaluating their capacity for verifiable discovery remains a central challenge.
The paper introduces Stochastic Semantic Evidence Graphs (SSEGs), a hierarchical stochastic directed acyclic graph that models uncertainty in AI-agent workflows, from evidence and retrieval to generation and decision mapping. SSEGs expand language nodes into autoregressive token subgraphs, optionally apply semantic reduction and calibration, and preserve uncertain claim–passage relations while propagating Fréchet bounds. The authors derive pathwise error bounds, use nodewise terms to trigger governance checks, and demonstrate through experiments that SSEGs can detect and quantify where uncertainty enters and propagates in AI outputs.