Answer Engineering: Local Trajectory Editing for Protocol-Constrained Decision Making in Large Language Models

• URL: http://arxiv.org/abs/2606.21121v1
• Date: Fri, 19 Jun 2026 05:47:44 GMT
• Title: Answer Engineering: Local Trajectory Editing for Protocol-Constrained Decision Making in Large Language Models
• Abstract summary: Large language models can produce confident but protocol-invalid answers in domains where procedural compliance is critical.<n>This paper presents Answer Engineering, a deterministic runtime and authoring layer that applies localized rule-guided interventions to the visible reasoning trajectory.<n>The method is evaluated on a controlled clinical benchmark for sudden sensorineural hearing loss.
• Score: 0.00304396936267654
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models can produce confident but protocol-invalid answers in domains where procedural compliance is critical. This paper presents Answer Engineering, a deterministic runtime and authoring layer that applies localized rule-guided interventions to the visible reasoning trajectory during standard autoregressive generation, without retraining, modifying model weights, or performing global search. The method is evaluated on a controlled clinical benchmark for sudden sensorineural hearing loss (SSNHL), where correct management depends on protocol-consistent interpretation of symptom timing, Weber/Rinne tuning-fork findings, and otoscopic findings. In the benchmark, step-by-step reasoning shifted rather than eliminated errors: compliant outcomes for SSNHL decreased from 54.5% under unguided generation to 25.1%, while acceptance on the conductive contrast condition increased from 1.6% to 58.9%. Local trajectory editing increased SSNHL compliance to 83.5% and conductive-case adherence to 77.9%, raising balanced accuracy from 42.0% under reasoning-only generation to 80.7%. The results support a systems-level view in which protocol adherence can be improved through auditable runtime control of reasoning trajectories, while also identifying limitations caused by rule coverage, trigger reliability, and persistent diagnosis-first generation dynamics.

Related papers

• State of Thought Enables Endogenous Reasoning [48.902117018115256]
  Test-time reasoning has emerged as a major approach to improving the capabilities of Large Language Models (LLMs)<n>We propose State of Thought, a new reasoning paradigm that enables endogenous reasoning in LLMs.<n>We show that SoT consistently improves mean-baseline accuracy while reducing generated tokens by 62.6% and end-to-end latency by 44.6%.
  arXiv  Detail & Related papers  (2026-09-13T05:55:03Z)
• Adaptive Conformal Redistribution for Inter-class Transitional Uncertainty in Medical Image Classification [9.578682349231789]
  adaptive conformal redistribution (AdaConRed) is a label-free post-conformal decision rule that converts ambiguous prediction sets into refined class assignments.<n>AdaConRed outperforms LAC, APS and RAPS under an identical backbone and redistribution rule.<n>Conformal prediction can be extended beyond uncertainty toward actionable decision support where transitional disease categories are present.
  arXiv  Detail & Related papers  (2026-09-10T06:21:30Z)
• Grounded Normative Rule Generation with Structured Search [47.28595196084396]
  We formalize the problem as Grounded Normative Rule Synthesis (GNRS)<n>We introduce GNRS-Search, a framework that utilizes Markov Chain Monte Carlo (MCMC) sampling to optimize a discrete, five-slot And-Or Graph (AOG)<n>We evaluate our approach on GNRS-Bench, a benchmark spanning 116 controlled goals across eight scene families, and RealCharterBench, which evaluates transfer to 53 real-derived policy tasks with hidden source clauses.
  arXiv  Detail & Related papers  (2026-08-23T05:43:42Z)
• SPARK: Susceptibility-Guided Profiling and Steering of Latent Reasoning States in Large Language Models [26.849417177804302]
  We introduce SPARK, which uses hidden-state response to diagnose whether a model enters an effective reasoning state.<n>Steering can serve not only as a diagnostic signal for reasoning failures, but also as a practical guide for targeted test-time intervention.
  arXiv  Detail & Related papers  (2026-07-11T12:47:27Z)
• Reason, Reward, Refine: Step-Level Errors Corrections with Structured Feedback for Physics Reasoning in Small Language Models [32.7643564019866]
  We propose a step-level reward framework that identifies the first reasoning error, generates targeted structured feedback, and trains the model to revise its solution via policy gradient with KL regularization.<n>Across five physics benchmarks, our framework delivers accuracy gains of 17-20% over CoT prompting and 10-16% over the strongest baseline, reduces calculation errors from 56.9% to 23.5%, and reduces miscomprehension errors from 22.3% to 12.0% in the best observed cases.
  arXiv  Detail & Related papers  (2026-07-06T15:16:10Z)
• OptiLoop: Coordination-in-the-Loop Verification and Repair for LLM-Generated Optimization Agents [0.9543827270223156]
  Decentralized decision problems require multiple parties to coordinate on shared decisions.<n>Large language models (LLMs) offer a promising way to lower the barrier to participation by generating local optimization agents.<n>We propose coordination-in-the-loop verification and repair for LLM-generated optimization agents.
  arXiv  Detail & Related papers  (2026-05-26T19:49:41Z)
• ReacTOD: Bounded Neuro-Symbolic Agentic NLU for Zero-Shot Dialogue State Tracking [2.2573512203799626]
  We propose ReacTOD, a bounded neuro-symbolic architecture that reformulates NLU as discrete tool calls within a self-correcting ReAct loop governed by deterministic validation.<n>A bounded ReAct loop enables iterative self-correction, improving accuracy by up to 9.3 percentage points over single-pass inference on MultiWOZ.
  arXiv  Detail & Related papers  (2026-05-18T20:06:04Z)
• Correct Answers from Sound Reasoning: Verifiable Process Supervision for Language Models [94.68358825189738]
  We propose a post-training framework for verifiable domains that jointly optimize prediction accuracy and reasoning quality.<n>We evaluate VPS on chess, a controlled testbed where reasoning steps can be deterministically verified against engine signals.<n>VPS preserves accuracy while significantly improving reasoning quality, reducing win-rate error by up to 30% and restoring consistency to near saturation.
  arXiv  Detail & Related papers  (2026-04-03T15:19:46Z)
• TRIAGE: Type-Routed Interventions via Aleatoric-Epistemic Gated Estimation in Robotic Manipulation and Adaptive Perception -- Don't Treat All Uncertainty the Same [2.755751829139168]
  Most uncertainty-aware robotic systems collapse prediction uncertainty into a single scalar score and use it to trigger uniform corrective responses.<n>This aggregation obscures whether uncertainty arises from corrupted observations or from mismatch between the learned model and the true system dynamics.<n>We introduce a lightweight post hoc framework that decomposes uncertainty into aleatoric and epistemic components and uses these signals to regulate system responses at inference time.
  arXiv  Detail & Related papers  (2026-03-09T09:07:43Z)
• Agentic retrieval-augmented reasoning reshapes collective reliability under model variability in radiology question answering [1.295312759062166]
  Agentic retrieval-augmented reasoning pipelines are increasingly used to structure how large language models incorporate external evidence in clinical decision support.<n>These systems iteratively retrieve curated domain knowledge and synthesize it into structured reports before answer selection.<n>Although such pipelines can improve performance, their impact on reliability under model variability remains unclear.
  arXiv  Detail & Related papers  (2026-03-06T13:31:54Z)
• From Black Box to Glass Box: Cross-Model ASR Disagreement to Prioto Review in Ambient AI Scribe Documentation [43.148402136307716]
  Cross-model disagreement among heterogeneous ASR systems can act as a reference-free uncertainty signal.<n>We transcribed 50 publicly available medical audio clips with eight ASR systems spanning commercial APIs and open-source engines.<n>Low-agreement regions were enriched for content disagreements, with the content fraction increasing from 53.9% to 73.9% across quintiles of high-risk mass.
  arXiv  Detail & Related papers  (2026-03-02T13:02:13Z)
• ODAR: Principled Adaptive Routing for LLM Reasoning via Active Inference [60.958331943869126]
  ODAR-Expert is an adaptive routing framework that optimize the accuracy-efficiency trade-off via principled resource allocation.<n>We show strong and consistent gains, including 98.2% accuracy on MATH and 54.8% on Humanity's Last Exam.
  arXiv  Detail & Related papers  (2026-02-27T05:22:01Z)
• Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward Models [108.26461635308796]
  We introduce Rationale Consistency, a fine-grained metric that quantifies the alignment between the model's reasoning process and human judgment.<n>Our evaluation of frontier models reveals that rationale consistency effectively discriminates among state-of-the-art models.<n>We introduce a hybrid signal that combines rationale consistency with outcome accuracy for GenRM training.
  arXiv  Detail & Related papers  (2026-02-04T15:24:52Z)
• A DeepSeek-Powered AI System for Automated Chest Radiograph Interpretation in Clinical Practice [83.11942224668127]
  Janus-Pro-CXR (1B) is a chest X-ray interpretation system based on DeepSeek Janus-Pro model.<n>Our system outperforms state-of-the-art X-ray report generation models in automated report generation.
  arXiv  Detail & Related papers  (2025-12-23T13:26:13Z)
• From Scores to Steps: Diagnosing and Improving LLM Performance in Evidence-Based Medical Calculations [45.414878840652115]
  Large language models (LLMs) have demonstrated promising performance on medical benchmarks.<n>However, their ability to perform medical calculations remains underexplored and poorly evaluated.<n>In this work, we revisit medical calculation evaluation with a stronger focus on clinical trustworthiness.
  arXiv  Detail & Related papers  (2025-09-20T09:10:26Z)
• Evaluating Spoken Language as a Biomarker for Automated Screening of Cognitive Impairment [37.40606157690235]
  Alterations in speech and language can be early predictors of Alzheimer's disease and related dementias.<n>We evaluated machine learning techniques for ADRD screening and severity prediction from spoken language.<n>Risk stratification and linguistic feature importance analysis enhanced the interpretability and clinical utility of predictions.
  arXiv  Detail & Related papers  (2025-01-30T20:17:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.