Shi Feng, Jiaqi Chen, Ming Wang, Yongkang Liu, Tingna Xie
We lifted 22 functions out of this paper's own repositories and ran 22 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 |
|---|---|---|
| cjia7/DPR | canonical | 22 of 22 |
| Function | Status | Where it lives |
|---|---|---|
| build_messages | Ran | cjia7/DPR/src/npti/neuron/apply_neuron_steering.py pointer only (licence: NONE) · get_code("d3120aa76158afc9") |
| build_prompt | Ran | cjia7/DPR/src/npti/eval/gpt4_score.py pointer only (licence: NONE) · get_code("16529904d2d5be9f") |
| calculate_quantiles | Ran | cjia7/DPR/src/npti/neuron/process_neuron.py pointer only (licence: NONE) · get_code("75aa9b42ee1374ef") |
| extract_again | Ran | cjia7/DPR/src/npti/eval/eval_mmlu.py pointer only (licence: NONE) · get_code("2e2c1555659e043f") |
| extract_answer | Ran | cjia7/DPR/src/npti/eval/eval_mmlu.py pointer only (licence: NONE) · get_code("0ad16da4e4f52ed7") |
| extract_final | Ran | cjia7/DPR/src/npti/eval/eval_mmlu.py pointer only (licence: NONE) · get_code("55653ca966a845f6") |
| extract_number | Ran | cjia7/DPR/src/npti/eval/eval_gsm8k.py pointer only (licence: NONE) · get_code("59c6ffbd98640ae2") |
| extract_predicted_answer | Ran | cjia7/DPR/src/npti/eval/eval_bbh.py pointer only (licence: NONE) · get_code("929072a79cd1c73f") |
| extract_predicted_index | Ran | cjia7/DPR/src/npti/eval/eval_musr.py pointer only (licence: NONE) · get_code("1a763a2dbd070c96") |
| last_boxed_only_string | Ran | cjia7/DPR/src/npti/eval/eval_math.py pointer only (licence: NONE) · get_code("0975c53e2a5fc2ab") |
| load_jsonl | Ran | cjia7/DPR/src/npti/eval/eval_bbh.py pointer only (licence: NONE) · get_code("8fe69f136a96c214") |
| load_jsonl | Ran | cjia7/DPR/src/npti/eval/gpt4_score.py pointer only (licence: NONE) · get_code("c7a248507261f132") |
| load_personality_descriptions | Ran | cjia7/DPR/src/npti/neuron/search_neuron.py pointer only (licence: NONE) · get_code("d9495551b5d8cd4f") |
| load_questions | Ran | cjia7/DPR/src/npti/neuron/apply_neuron_steering.py pointer only (licence: NONE) · get_code("e2da9069e83fab5e") |
| load_sorted_tuples | Ran | cjia7/DPR/src/npti/neuron/process_neuron.py pointer only (licence: NONE) · get_code("2e02b59ac05c3c0f") |
| make_mlp_forward | Ran | cjia7/DPR/src/npti/neuron/search_neuron.py pointer only (licence: NONE) · get_code("5594b19b20f7e8b2") |
| normalize_answer | Ran | cjia7/DPR/src/npti/eval/eval_bbh.py pointer only (licence: NONE) · get_code("caa05d10c796877f") |
| normalize_final_answer | Ran | cjia7/DPR/src/npti/eval/eval_math.py pointer only (licence: NONE) · get_code("4f5267c667058423") |
| parse_sampled_answer | Ran | cjia7/DPR/src/npti/eval/eval_gpqa.py pointer only (licence: NONE) · get_code("91da82f1ebb2518b") |
| parse_strengths | Ran | cjia7/DPR/src/npti/neuron/apply_neuron_steering.py pointer only (licence: NONE) · get_code("cbbf00812e6992fd") |
| process_differences | Ran | cjia7/DPR/src/npti/neuron/process_neuron.py pointer only (licence: NONE) · get_code("dbd00e000f9c65dd") |
| remove_boxed | Ran | cjia7/DPR/src/npti/eval/eval_math.py pointer only (licence: NONE) · get_code("ed70d70cd67847d0") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Imbuing Large Language Models (LLMs) with specific personas is prevalent for tailoring interaction styles, yet the impact on underlying cognitive capabilities remains unexplored. We employ the Neuron-based Personality Trait Induction (NPTI) framework to induce Big Five personality traits in LLMs and evaluate performance across six cognitive benchmarks. Our findings reveal that persona induction produces stable, reproducible shifts in cognitive task performance beyond surfacelevel stylistic changes. These effects exhibit strong task dependence: certain personalities yield consistent gains on instruction-following, while others impair complex reasoning. Effect magnitude varies systematically by trait dimension, with Openness and Extraversion exerting the most robust influence. Furthermore, LLM effects show 73.68% directional consistency with human personality-cognition relationships. Capitalizing on these regularities, we propose Dynamic Persona Routing (DPR), a lightweight query-adaptive strategy that outperforms the best static persona without additional training. Code and data are available at: https://github.com/cjia7/DPR.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2604.11048")
get_code_for_paper("2604.11048")
have("2604.11048")
Connect an agent — have() is free.