David Guecha, Victor Gong
We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| dsgt-arc/erisk-task1-2026 | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| collect_samples | Ran | dsgt-arc/erisk-task1-2026/user/victor/experiments/002-multi-agent/select_runs.py code served (permissive licence) · get_code("fb0f36a1b11f8929") |
| collect_samples | Ran | dsgt-arc/erisk-task1-2026/user/victor/experiments/003-open-source/select_runs.py code served (permissive licence) · get_code("53e66997c7ca8438") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We describe DS@GT's submission to the eRisk 2026 Task 1 challenge on conversational depression screening, in which systems interview LLM personas that simulate individuals with varying depression profiles and produce a Beck Depression Inventory II (BDI-II) score plus four key symptoms per persona, without directly asking sensitive mental health questions. Our pipeline evolved through three stages: a monolithic single-model prototype to start off, a baseline multi-agent architecture that separates conversational interviewing from BDI-II scoring under a coordinating orchestration layer, and a final hybrid configuration that replaces the paid GPT-5-nano interviewer with the open-source Gemma 27B. To offset the model's weaker reasoning and instruction-following, the hybrid adds three algorithmic components: a precomputed dialogue tree that standardizes interview openers and follow-ups, a reliability-weighted consensus aggregation inspired by the Weaver framework, and a clusterbased imputation step for unprobed symptoms. We submitted three fully automated runs across all 20 personas, with Run 1 from the paid baseline and Runs 2 and 3 from the hybrid. Hybrid Run 3 achieved an ADODL of 0.9063, ranking 3rd among all complete-submission runs and placing DS@GT 2nd among the 21 teams overall, while outperforming our paid baseline Run 1 (0.8841) at roughly one-quarter of the per-persona API cost. These results support our central hypothesis that with sufficient algorithmic supervision, a weaker open-source model can compete with a stronger proprietary model in the conversational interviewer role. Our source code is available at https://github.com/dsgt-arc/erisk-task1-2026.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2607.16712")
get_code_for_paper("2607.16712")
have("2607.16712")
Connect an agent — have() is free.