Papers for

ai research platform developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

YouRA architecture keeps research claims matched with real evidence

YouRA: A Persistent-State Architecture for Evidence-Traceable Autonomous Research Agents

Abstract: End-to-end research agents can now produce complete scientific papers, yet manuscript claims often diverge from executed experiments. This gap is structural: research state, failure histories, and claim-evidence alignment are not maintained as persistent, verifiable state across long-horizon pipelines. We present YouRA (Your Research Agent), an architecture for stateful, evidence-traceable autonomous research. YouRA preserves research state, execution evidence, and failure history across the research trajectory by integrating three components: a Verification State Architecture (VSA) that tracks hypotheses, gates, and evidence pointers; an Independent Controller that turns state and reflection records into lifecycle, recovery, and debate/review control while separating control from execution; and Stateful Reflection that logs failures as structured lessons and routes recovery through bounded repair, redesign, or reset. On MLR-Bench's predefined ten-task end-to-end subset, YouRA improves over both MLR-Agent and AI Scientist V2 on scalar Overall across all three matched backbones. An automated diagnostic using MLR-Bench's hallucination taxonomy reports intersection/union counts for four fact-based failure types, and data-provenance diagnostic shows more real-data-based outputs. Ablating each of the four components (the VSA, the Independent Controller, MCP tool access, and reflection-guided recovery) supports their separable contributions. Removing either core-state component drops YouRA below the full system. Code: https://github.com/PrayPrey/Your-Research-Agent.

Thu 1 OctArtificial Intelligence
The gist
Scientific papers sometimes say things that aren’t fully backed up by the experiments they describe because the computer systems don’t keep track of all the details and failures throughout the whole research process. The authors present YouRA, a system that carefully tracks experiments, evidence, and mistakes over time so that claims in research papers can be traced back to real data. Their system has several parts that manage the research state, separate decision-making from execution, and learn from failures. Tests show YouRA improves over other AI research agents in producing trustworthy results.
Open → 2610.01097v1

Deep research agents avoid premature conclusions with rollback control

DeepRewind: Predicting and Repairing Premature Commitments in Deep Research Agents

Abstract: Deep-research agents conduct long-horizon investigations through iterative search, evidence evaluation, belief revision, and synthesis. However, they may commit to claims before sufficient evidence is available, causing later reasoning to reinforce an incorrect interpretation. We introduce DeepRewind, an additive control layer for reversible deep research that represents the agent's evolving epistemic state as a typed graph of sources, evidence, claims, hypotheses, assumptions, commitments, plans, and drafts. Before accepting an intermediate conclusion, a prompt-based world model predicts its impact and estimates reversibility based on hypothesis narrowing, information loss, recovery cost, and contradiction-trigger coverage. A binary controller blocks risky commitments, while a consistency monitor performs dependency-aware rollback when later evidence invalidates them. Across DRBench and LiveDRBench, DeepRewind improves insight recall by 3.6 percentage points and reduces premature commitments by 59.1% relative to Open Deep Research.

Mon 28 SeptComputation and Language
The gist
Sometimes, AI systems exploring complex problems decide they’ve found an answer too soon, which can lead to mistakes. The authors designed DeepRewind, an extra layer that helps these systems track all their evidence and ideas clearly. Before accepting conclusions, it predicts if they might lead to problems later and blocks risky jumps. If new information later shows a conclusion was wrong, DeepRewind can undo decisions and fix the reasoning. This approach makes AI research agents more careful and accurate.
Open → 2609.36344v1