ICACT20260248 Question.1
Questioner: qga0809@chungbuk.ac.kr 2026-02-11 오전 7:34:56 |
ICACT20260248 Answer.1
Answer by Auhor mrajhans@paloaltonetworks.com 2026-02-11 오전 7:34:56
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| What is the average number of retrieval iterations your adaptive controller takes before stopping, and does it differ significantly between NVD and MITRE queries? |
On average, the adaptive controller stops after 1–2 retrieval iterations.
For both NVD and MITRE ATT&CK, the controller typically evaluates the initial retrieval at k = 2 and terminates immediately because the marginal GECS gain falls below ε. There is no significant difference in stopping depth between the two datasets; the main difference lies in factuality levels, not iteration count |
ICACT20260248 Question.10
Questioner: li.yuanz3@yeah.com 2026-02-11 오전 11:52:49 |
ICACT20260248 Answer.10
Answer by Auhor mrajhans@paloaltonetworks.com 2026-02-11 오전 11:52:49
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| Why was Qwen2.5–7B–Instruct chosen for evaluation? |
It offers strong factual precision, efficient CPU inference, and open-weight reproducibility. This ensures results are not dependent on proprietary APIs or GPU-only setups. |
ICACT20260248 Question.2
Questioner: zhaozx@dsp.ac.cn 2026-02-11 오전 10:35:26 |
ICACT20260248 Answer.2
Answer by Auhor mrajhans@paloaltonetworks.com 2026-02-11 오전 10:35:26
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| Why is no retraining or model modification required? |
The controller operates externally to the LLM, treating it as a black box. This makes the approach model-agnostic and immediately deployable with any RAG-capable LLM. |
ICACT20260248 Question.3
Questioner: hripsime.hovhannisyan@rau.am 2026-02-11 오전 10:35:58 |
ICACT20260248 Answer.3
Answer by Auhor mrajhans@paloaltonetworks.com 2026-02-11 오전 10:35:58
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| How does this differ from heuristic adaptive context methods? |
Prior methods rely on relevance scores or static thresholds. Our approach uses factual entailment feedback, grounding decisions in whether retrieved evidence actually supports generated claims. |
ICACT20260248 Question.4
Questioner: md02anas02@gmail.co 2026-02-11 오전 10:39:02 |
ICACT20260248 Answer.4
Answer by Auhor mrajhans@paloaltonetworks.com 2026-02-11 오전 10:39:02
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| Why does MITRE ATT&CK show decreasing GECS at higher retrieval depths? |
MITRE text is conceptual and narrative-driven; additional retrieval introduces loosely related context that increases unsupported claims. GECS captures this dilution effect, correctly penalizing excessive context. |
ICACT20260248 Question.5
Questioner: shil@dsp.ac.cn 2026-02-11 오전 10:39:32 |
ICACT20260248 Answer.5
Answer by Auhor mrajhans@paloaltonetworks.com 2026-02-11 오전 10:39:32
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| Since GECS relies on an NLI model (DeBERTa-v3), How sensitive are results to NLI calibration errors or domain mismatch with CTI text? |
GECS mitigates calibration noise by using a difference score (entailment minus contradiction) rather than raw probabilities. While domain mismatch can affect absolute scores, relative marginal gains—which drive stopping decisions—remain stable across retrieval depths. |
ICACT20260248 Question.6
Questioner: izeljkov@gmail.com 2026-02-11 오전 10:39:56 |
ICACT20260248 Answer.6
Answer by Auhor mrajhans@paloaltonetworks.com 2026-02-11 오전 10:39:56
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| Could the adaptive policy oscillate (e.g., benefit at k=4 after stagnation at k=2)? |
In practice, GECS curves are monotonic or flattening for both datasets. Because CTI facts are redundant rather than complementary, later retrieval rarely introduces new entailed claims after early saturation. |
ICACT20260248 Question.7
Questioner: jepj352@drexel.edu 2026-02-11 오전 10:58:55 |
ICACT20260248 Answer.7
Answer by Auhor mrajhans@paloaltonetworks.com 2026-02-11 오전 10:58:55
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| Why not directly optimize hallucination rate H instead of GECS? |
Hallucination rate is binary and coarse, whereas GECS provides a graded signal per claim. This enables fine-grained marginal analysis required for adaptive control. |
ICACT20260248 Question.8
Questioner: pstarn711@csulb.edu 2026-02-11 오전 11:50:59 |
ICACT20260248 Answer.8
Answer by Auhor mrajhans@paloaltonetworks.com 2026-02-11 오전 11:50:59
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| Does the controller ever choose different k values for different queries within the same dataset? |
Occasionally, but variance is small. Most NVD queries stop at k=2, while some MITRE queries briefly probe k=4 before reverting, reflecting higher semantic dispersion rather than factual density. |
ICACT20260248 Question.9
Questioner: sam.rhys334@gmail.com 2026-02-11 오전 11:52:00 |
ICACT20260248 Answer.9
Answer by Auhor mrajhans@paloaltonetworks.com 2026-02-11 오전 11:52:00
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| How does the method behave on structured vs. semi-structured CTI text? |
On structured NVD data, factual gains saturate early and the agent converges to small k, matching optimal fixed-depth performance. On MITRE ATT&CK narratives, it behaves conservatively, avoiding costly retrieval when factual gains are limited. |
