arXiv subjects

Jiatong Liu

Publications and source records attributed to Jiatong Liu.

4 recordsLinked to original sources

arXiv2026

RepoTrace: Browser-Assisted Evidence Collection for GitHub Research Datasets

Empirical software engineering studies frequently build datasets from GitHub issues and pull requests. In many projects, researchers inspect pages in a browser, copy selected fields into spreadsheets, keep side notes in separate documents, and later run scripts to normalize or export the data. This workflow is flexible, but the page evidence, the research codes, and the rationale behind each decision end up spread across tabs and files, which leaves provenance, update tracking, and multi-reviewer labeling hard to audit. RepoTrace is a browser-assisted research tool that collects GitHub issue and pull-request evidence into a local SQLite-backed workspace. It combines a Chrome side-panel extension, an Express backend, and a React dashboard to capture page snapshots, comments, labels, notes, screening and labeling decisions, refresh history, and scoped exports, keeping the source evidence and the research interpretation linked together. A validation pass collected and checked 20 Matplotlib issues across two study projects. The resulting dataset preserves 22 snapshots, 38 comments, 20 research notes, 98 annotations, 20 screening reviews, 20 fix-evidence entries, and 4 simulated unresolved consensus conflicts. The results show that RepoTrace can support a complete local evidence-collection workflow for manually constructed GitHub issue and pull-request datasets.

cs.SE↗

arXiv2026

DebugTracker: Lightweight Process Evidence for Classroom Debugging

Debugging exercises are often assessed from final code and test outcomes, yet these artifacts hide how students reproduced failures, formed hypotheses, inspected evidence, edited code, and verified fixes. We present DebugTracker, a Visual Studio Code extension that records lightweight debugging-process evidence for classroom tasks. DebugTracker separates uncoached Evaluation Mode traces from coached Training Mode traces, stores append-only JSONL events, and exports timeline and Markdown reports for human review. The prototype records test commands, editor and debugger metadata, student checkpoints, source snapshots, optional image evidence, human labels, and optional AI-assisted practice feedback. DebugTracker is largely language-agnostic: it captures process evidence through standard VS Code mechanisms rather than language-specific tooling, although debugger evidence depends on the relevant VS Code language extension. We validate the prototype with debugging tasks in Python, TypeScript, and Java, 16 automated checks, and an 11-case manual trial matrix spanning packaged VSIX installation and three operating systems.

cs.SE↗

arXiv2026

Easier to Judge than to Find: Predicting In-Context Learning Success for Demonstration Selection

In-context learning (ICL) is highly sensitive to which demonstrations appear in the prompt, but selecting them is expensive because the space of possible demonstration contexts and combinations is enormous. We argue that demonstration selection is \emph{easier to judge than to find}: predicting whether a specific query--context pair $(q,D)$ will succeed is cheaper and more general than searching for an optimal $D^\star$. Based on this insight, we propose DiSP, a sample-and-judge framework that stratifies queries by difficulty. DiSP runs random demonstration trials to estimate success rate of each training query, trains a lightweight router to predict difficulty from the query, and trains level-specific judges for sampled demonstrations. At inference, DiSP performs stop-on-acceptance judging under an explicit budget, emitting diagnostic risk tags when no suitable context is found. Across five classification datasets with Llama~3--8B and Qwen~2.5--7B, DiSP achieves the best average accuracy, improving over strong learned selection baselines by up to 3.4\%, while achieving up to $23\times$ end-to-end wall-clock speedup.

cs.CL↗

arXiv2026

Orchestrating Intelligence: Confidence-Aware Routing for Efficient Multi-Agent Collaboration across Multi-Scale Models

While multi-agent systems (MAS) have demonstrated superior performance over single-agent approaches in complex reasoning tasks, they often suffer from significant computational inefficiencies. Existing frameworks typically deploy large language models (LLMs) uniformly across all agent roles, failing to account for the varying cognitive demands of different reasoning stages. We address this inefficiency by proposing OI-MAS framework, a novel multi-agent framework that implements an adaptive model-selection policy across a heterogeneous pool of multi-scale LLMs. Specifically, OI-MAS introduces a state-dependent routing mechanism that dynamically selects agent roles and model scales throughout the reasoning process. In addition, we introduce a confidence-aware mechanism that selects appropriate model scales conditioned on task complexity, thus reducing unnecessary reliance on large-scale models. Experimental results show that OI-MAS consistently outperforms baseline multi-agent systems, improving accuracy by up to 12.88\% while reducing cost by up to 79.78\%.

cs.AI↗